Journal articles on the topic 'Adaptive Decision Table'

This study presents the first attempt to link the multi-algorithm genetically adaptive search method (AMALGAM) with a groundwater model to define pumping rates within a well distributed set of Pareto solutions. The pumping rates along with three minimization objectives, i.e. minimizing shortage affected by the failure to supply, modified shortage index and minimization of extent of drawdown within prespecified regions, were chosen to define an optimal solution for groundwater drawdown and subsidence. Hydraulic conductivity, specific yield parameters of a modular three-dimensional finite-difference (MODFLOW) groundwater model were first optimized using Cuckoo optimization algorithm (COA) by minimizing the sum of absolute deviation between the observed and simulated water table depths. These parameters were then applied in AMALGAM to optimize the pumping rate variables for an arid groundwater system in Iran. The Pareto parameter sets yielded satisfactory results when maximum and minimum drawdowns of the aquifer were defined in a range of −40 to +40 cm/year. Overall, ‘Modelling – Optimization – Simulation’ procedure was capable to compute a set of optimal solutions displayed on a Pareto front. The proposed optimal solution provides sustainable groundwater management alternatives to decision makers in arid region.

Abstract Background Compared with those born at term gestation, infants with complex congenital heart defects (CCHD) who were delivered before 37 weeks of gestational age and received neonatal open cardiac surgery (OHS) have poorer neurodevelopmental outcomes in early childhood. Specific details related to the neurodevelopmental outcome of these infants remain unpublished. Objectives To describe the growth, disability, functional, and neurodevelopmental outcome in early childhood of preterm infants (born at <37+0 weeks gestation) with CCHD and neonatal OHS. Design/Methods We studied all infants with CCHD who received OHS within 6 weeks of corrected age between 1996 and 2016. In the Western Canadian Complex Pediatric Therapies Follow-up Program, comprehensive neurodevelopmental assessments at a corrected age of 18-24 months were done by multidisciplinary teams at the original referral sites. In addition to demographic and clinical data, standardized age-appropriate outcome measures included physical growth with calculated Z-scores, disabilities including cerebral palsy, visual impairment, sensorineural hearing loss; adaptive function (Adaptive Behavioural Assessment System-II); and cognitive, language, and motor skills (Bayley Scales of Infant and Toddler Development-III). Results From 1996 to 2016, 115 preterm infants (34±2 weeks gestation, 2339±637g, 64% males) with CCHD had OHS with 11(10%) deaths before first discharge and 21 (18%) by 2 years. Prior to the first surgery, 7 (6%) neonates had cerebral injuries. Overall, 7 had necrotizing enterocolitis; none had retinopathy of prematurity. All 94 surviving infants received comprehensive evaluation at 2 years corrected age; Eighteen (19%) had congenital syndromes who had worse functional and neurodevelopmental outcomes compared to those (n=76) without syndromal abnormalities (SA) (Table). Conclusion For preterm neonates with CCHD and early OHS, the mortality was significant, but the short-term neonatal morbidity was not increased. Compared with published preterm outcomes, the early outcome suggests more cerebral palsy but not sensorineural hearing loss, and greater neurodevelopmental delay. This information is important for management care of the infants, parental counselling and the decision-making process.

e15651 Background: This presentation will describe a Bayesian flexible design for an ongoing randomized, placebo controlled phase II trial in newly diagnosed, resected pancreas cancer comparing gemcitabine plus therapeutic vaccine (GI-4000) vs. gemcitabine alone. The design allows for infinitely many looks at the data, possible study expansion, and conversion to adaptive allocation. Unlike frequentist approaches, the Bayesian procedure does not require penalizing the final analysis for earlier analyses nor does it require down-weighting of data collected before the decision to expand the study. The same analysis procedure is used for final and interim analyses, whereas there is no consensus in the frequentist approach for how to analyze an expanded study. Methods: Our primary analysis is based on a Bayesian Cox proportional hazards model using a skeptical prior distribution. The endpoint is time to recurrence or death. Evidence for efficacy is taken to be a posterior probability of efficacy ≥ 0.95 at any analysis time, where ‘efficacy’ means a true hazard ratio < 1.0. The planned rule for expanding the study is a probability of efficacy ≥ 0.7 prior to the last pre-planned analysis. Results: This ongoing study remains blinded. Results will include statistical methods (including simulations to assess study properties of the design) and experience in collaborating on the design with the FDA. Conclusions: In the frequentist approach, computing the probability of getting a result as or more impressive than that observed if there is truly no treatment effect (the P-value) requires contemplating experiments that were never carried out and analyses that were never done. The Bayesian approach offers the advantage of computing only ‘forward’ probabilities (Bayesian posterior probabilities) and conditioning only on what has been observed up to the time of analysis. There is no requirement to assume unknowable information such as the true population treatment effect. The adaptability afforded by Bayesian trial design can greatly enhance the likelihood of gaining useful clinical data from phase II trials. [Table: see text]

Introduction The recent approvals of several effective targeted therapies have shifted treatment paradigms for chronic lymphocytic leukemia (CLL). The rapid pace of approvals and expanded indications may challenge oncology healthcare providers (HCPs) to make optimal treatment decisions. We developed an online treatment decision support tool designed to provide HCPs with case-specific treatment recommendations from five CLL experts; this tool has been regularly updated since 2017. In this analysis, we examined the CLL treatment patterns of HCPs and experts from three tool iterations-spanning early 2017 to August 2020-and evaluated comparative trends over time. Methods For each version of the CLL tool, five experts provided treatment recommendations for hundreds of different case scenarios in the newly diagnosed and relapsed/refractory CLL settings. These unique case scenarios were defined by patient and disease factors that the experts considered critical to making treatment decisions, including patient age and fitness, cytogenetic abnormalities, and previous treatment. To use the tool, HCPs entered patient information and their intended treatment plan; expert recommendations for that specific patient scenario were then provided, followed by a survey to determine whether the recommendations impacted the HCP's intended treatment. This analysis compared the intended treatment plans for cases entered into the tool by HCPs with expert recommendations in three versions of the tool (March 2017, October 2018, and October 2019). HCP responses to the post-recommendation survey also were assessed. Results HCPs sought recommendations for 543 cases from March to July 2017 for the 2017 CLL tool, for 656 cases from October 2018 to July 2019 for the 2018 tool, and for 1015 cases from October 2019 to August 2020 for the 2019 tool. HCPs were generally less experienced with CLL; for example, in the 2019 tool, 48% of HCPs who sought treatment recommendations treated ≤ 10 patients with CLL per year. Clear shifts in expert treatment patterns were observed over time (Table). For example, in assessing first-line therapy for patients with CLL with del(17p) or TP53 mutations, the expert panel recommended ibrutinib as first-line therapy regardless of any other characteristic in both the 2017 and 2018 tool iterations; however, in the 2019 tool, experts recommended acalabrutinib plus obinutuzumab, venetoclax plus obinutuzumab, and ibrutinib with similar frequency, dependent upon specific patient characteristics. Similarly, in younger (< 65 years of age), fitter patients with treatment-naive CLL and no del(17p) or TP53 mutations, IGHV mutation status was important for expert treatment recommendations in the 2017 and 2018 tools, as experts recommended fludarabine/cyclophosphamide/rituximab (FCR) for the majority of cases with IGHV mutations and ibrutinib for those without these mutations in both tool iterations. In the 2019 tool, however, experts had shifted toward the use of venetoclax plus obinutuzumab for 50% of cases with IGHV mutations, with FCR still recommended for 40%; for CLL with unmutated IGHV, experts also shifted to venetoclax plus obinutuzumab (70% of cases). Substantial variance was observed between expert recommendations and the planned treatment of HCPs for a variety of case scenarios across tool iterations. For example, in the 2019 tool, experts selected venetoclax plus obinutuzumab for 50% of cases of younger patients with treatment-naive CLL with no del(17p) or TP53 mutations but mutated IGHV; HCPs selected this regimen for 6% of these cases. Overall, after reviewing expert recommendations for their cases, 56% of HCPs whose planned treatment differed from the experts indicated that they would change their treatment based on panel recommendations. Conclusions Analysis of data from progressive iterations of an online treatment decision support tool suggest evolution in best practices in CLL treatment and differences in how experts and community providers manage patients with CLL. Expert recommendations in the tool changed the intended treatment plan of many HCPs, suggesting that online treatment decision tools providing patient-specific expert guidance may increase implementation of optimal therapeutic decisions for advanced CLL. A full analysis of cases entered into the 2019 tool and comparison with previous tools will be presented. Disclosures Awan: Dava Oncology: Consultancy; Kite Pharma: Consultancy; Sunesis: Consultancy; Gilead Sciences: Consultancy; MEI Pharma: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Abbvie: Consultancy; Astrazeneca: Consultancy; Genentech: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Blueprint medicines: Consultancy. Brown:Gilead, Loxo, Sun, Verastem: Research Funding; Janssen, Teva: Speakers Bureau; Abbvie, Acerta, AstraZeneca, Beigene, Invectys, Juno/Celgene, Kite, Morphosys, Novartis, Octapharma, Pharmacyclics, Sunesis, TG Therapeutics, Verastem: Consultancy. Lamanna:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncternal, Verastem, TG Therapeutics: Other: Institutional research grants, Research Funding; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; MingSight: Other: Institutional research grants, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Juno: Other: Institutional research grants, Research Funding; Octapharma: Research Funding; Loxo: Research Funding; Columbia University Medical Center: Current Employment; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; Bei-Gene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding. Sharman:Roche: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Acerta: Consultancy, Research Funding; BeiGene: Research Funding; Bristol Meyers Squibb: Consultancy, Research Funding. Zelenetz:MEI Pharma: Research Funding; MorphoSys: Research Funding; Sandoz: Research Funding; BeiGene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnology: Consultancy; Novartis: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Celgene: Research Funding; Roche: Research Funding; Gilead: Research Funding; Genentech/Roche: Consultancy.

Abstract Risk stratification tools in multiple myeloma (MM), such as the International Staging System (ISS) and the revised-ISS (R-ISS), have improved understanding of survival expectations using the strongest known predictors at time of diagnosis. Given their value at diagnosis, these have been used to define risk after first relapse in clinical trials and standard practice. Although these tools have not been validated in this setting, their use arises because of the need to better characterize patients in order to define survival expectations and treatment decisions. Once the patient has relapsed, there are additional variables that may need to be considered in order to systematically assess patient risk, understand drivers of disease progression and ensure that treatment strategies are aligned with patient risk. Using data from the Czech Registry of Monoclonal Gammopathies (RMG), this study assessed predictors of overall survival (OS) and developed a new Risk Stratification Tool (RST) to predict OS at time of treatment decision after first relapse (TTD1). The RST was developed by estimating the strongest predictors of OS at both diagnosis and TTD1 to define the final parameters for inclusion. The cut-offs for each parameter reflect conventional cut-offs used in clinical practice and some were supported by evidence using a K-adaptive Partitioning for Survival (KAPS) approach, which stratified data based on distinct survival expectations. The hazard ratio (HR) of the selected predictors was used to assign a score per parameter at a patient level where missing data were entered with a contribution equal to 1. Using the full RMG data set at TTD1 (N=1418) the (KAPS) method was run to define 4 distinct group of patients based on survival expectations. The RST consists of 4 dimensions and 12 item questions based on the strongest predictors of survival at TTD1, "Patient Factors" (age and Eastern Cooperative Oncology Group (ECOG) performance status), "Existing Stratification Factors (R-ISS at diagnosis and ISS at TTD1), "Disease Factors" (calcium level, number of bone lesions, extramedullary disease, thrombocyte count, clonal cells in bone marrow aspiration cytology, lactate dehydrogenase [LDH]) and "Treatment history" (refractory to prior therapy, time-to-next-treatment [from initiation of treatment of first anti-myeloma drug to initiation of treatment at first relapse]) (Table 1). Subsequently, we explored each group based on distribution of frailty-driven measures (age and ECOG) and aggressiveness of the disease (rest of parameters) to understand what is driving stratification. Figure 1 shows the KM curve of survival after TTD1 for each of the 4 groups estimated by KAPS. The new analysis shows strong differentiation in survival expectations between the 4 groups (Table 2), showing significantly different OS for all groups compared with reference. The median OS and Confidence Intervals per group did not overlap, supported by the positive association of HR across groups. The distribution of the Total Score (Figure 2) is between 1 and 2, which shows sufficient sensitivity to differentiate these groups by survival expectations. The RST can then be split into Frailty Score and Aggressiveness Score (Figure 3a & b) to understand what is driving disease severity. The distribution of these two scores shows that group 1 consists of low patient frailty and low disease aggressiveness, whereas group 4 shows high on both elements. Group 2 has an increased score for frailty and marginal increase in aggressiveness compared with group 1, and group 3 stratification is driven by an increase in aggressiveness over group 2. The analysis showed that predictors, patient's experience of prior treatment and level of disease impact at the point of treatment decision after first relapse provided an initial framework to demonstrate strong differentiation between groups based on patient severity and what is driving patient risk (patient frailty vs aggressiveness of disease). The RST has shown promising results when applied to the RMG, however further validation of this work is required using other real-world and clinical trials data. Nevertheless, this analysis is a first step in systematically assessing patient risk to improve the selection of treatments based on improved understanding of patient profiles. Disclosures Hájek: Amgen: Consultancy, Honoraria, Research Funding; Janssen: Honoraria; BMS: Honoraria; Takeda: Consultancy; Celgene: Consultancy, Research Funding. Bouwmeester:Amgen: Consultancy. Treur:Amgen: Consultancy. DeCosta:Amgen: Employment, Other: Holds Amgen Stock. Campioni:Amgen: Employment, Other: Holds Amgen Stock. Delforge:Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Raab:BMS: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Research Funding; Amgen: Consultancy, Research Funding. Schoen:Amgen: Employment, Other: Holds Amgen Stock. Szabo:Amgen: Employment, Other: Holds Amgen Stock. Lucie:Amgen: Consultancy. Gonzalez-McQuire:Amgen: Employment, Other: Holds Amgen Stock.

The article considers the road monitoring weather-stations which collects raw observations that are processed to be able to make the necessary forecasting for future decisions. For the road maintainers those predictions are crucial to make decisions daily. When it comes to the winter season when road safety is very important; however, the road condition is also affected by the snow and icing. In order to improve safety on the road network the road maintainers are trying to use every possible way to be able to provide it. A number of methods have been studied and compared to clarify the parameter required by Kalman filter, which can be improved by making forecasting more accurate. Several road monitoring weather-stations are merged into one region because they are relatively close to each other and it is assumed that there are common conditions in one region that may indicate changes in road conditions. The corresponding algorithms are applied for each region and then compared to each other. Adaptive Kalman filter is generalized in the relevant article in order to have a general understanding of how to correctly apply the approach. The main result of this article is a comparison with the different methods, which are finally compiled in a single table.

A Computerized Adaptive Test proposes items according to the student's knowledge level. Therefore, the number of items, which are given to students, is reduced. Besides, the ending of such test is determined by the student's knowledge level, which allows an instructor to reduce testing time. As usual, construction of such tests is based on the Item Response Theory (IRT). This theory gives models which use statistical data about the student's knowledge level and difficulty of items. We do not have such statistics for new tests. In such cases, this paper proposes to estimate the complexity of items on the basis of the experts' conclusions. These conclusions are based on the analytic hierarchy process (AHP) which was modified. The modification allows experts to estimate the complexity of items with the help of the collection of the items characteristics. This modification can remove the expert's inadequate estimates of items or their characteristics. This method allows experts to classify all items in clusters according to their complexity in the first stage of the testing when statistics of items use is absent. A test constructor, on the basis of a decision tables network, realizes the algorithm of the items' selection from different clusters. In the future, tutors will have tested a sufficient number of students' groups. They record statistics of the test using. A test constructor receives such statistics, which will allow them to use the models of the Item Response Theory for estimation of the test items' complexity. The assessment of the knowledge level of students is made with the help of an adaptive test, which is based on a network of decision tables. This network determines the algorithm of using items from different clusters for the testing. The adaptive test is built on the basis of the network of decision tables as a computer system. This system is constructed on the Java platform with the help of the programming environment Android Studio. It has the interface suitable for students as well as for a constructor, which allows the constructor to change the algorithm of using items if received statistics of items use shows such necessity.

Рассматривается вопрос формирования у бакалавров педагогического образования универсальных компетенций (критического мышления, кооперации, коммуникации, креативности, компьютерной грамотности). В условиях современной трансформации образования сформированные универсальные компетенции позволят молодым специалистам быть адаптивными, мобильными, соответствующими вызовам современного общества, а также помогут организовывать свою профессиональную деятельность с учетом отличительных особенностей представителей поколения Z. Представлена характеристика SMART-формы, таксономии Б. Блума и технологии «обратного дизайна». Описан собственный опыт проектирования результатов обучения по учебной дисциплине «Внеурочная деятельность в начальной школе» для бакалавров педагогического образования по следующему алгоритму: формулирование результатов обучения по дисциплине, декомпозиция результатов обучения по дисциплине на результаты обучения по модулю, разработка оценочных мероприятий, определение вида деятельности (микрообучение, дифференцированное обучение, Аgile-технология, перевернутый класс, групповая работа, взаимное комментирование), выбор интернет-инструментов и сервисов (Google-таблица, конструктор Mentimeter, конструктор socrative.com, платформа canva.com). Представленный педагогический опыт можно использовать при проектировании результатов обучения по всем дисциплинам с целью формирования у бакалавров педагогического образования универсальных компетенций. The article deals with the formation of universal competencies among the bachelors of pedagogical education (critical thinking, cooperation, communication, creativity, computer literacy). In the context of the modern transformation of education, the formed universal competencies will allow young professionals to be adaptive, mobile, corresponding to the challenges of modern society, and will help to organize their professional activities taking into account the characteristics of generation Z. A distinctive feature of the representatives of this generation in educational activities is the rapid finding of information on the Internet while simultaneously performing several tasks, the ability to assimilate large amounts of information grouped by a specific topic, multimedia, joint (group) decision-making, etc. An approach to the design of the educational process based on the technology of «reverse design», SMART-form (smart planning: concrete, measurable, achievable, coordination, time) and B. Bloom’s taxonomy is presented. The requirements for the formulation of learning outcomes, which should be measurable and motivating for learning, are outlined; the assessment activity should allow the student to confirm the achievement of the planned result; learning outcomes should be correlated with each other and correlated with the context of the discipline; the achievement of the planned result should be carried out in a specific time interval (during the period of studying the discipline). The author describes his own experience in designing of education outcomes for the academic discipline «Extracurricular activities in elementary school» for bachelors of pedagogical education. The design has the following algorithm: formulating of education outcomes in the discipline, decomposition of education outcomes in the discipline into education outcomes by module, development of assessment activities, determining the type of activity (microlearning, differentiated learning, agile-technology, inverted class, group work, mutual commenting), selection of Internet tools and services (Google-table, Mentimeter constructor, socrative.com, canva.com platform). The presented pedagogical experience can be used in the design of education outcomes in all disciplines, in order to form universal competencies for bachelors of pedagogical education.

INTRODUCTION: The survival of patients with chronic myeloid leukemia in chronic phase (CML-CP) is approaching that of general population after the approval of tyrosine-kinase inhibitors (TKI), particularly in younger patients who achieve remission. The optimal frontline TKI therapy in older patients in the context of comorbidity remains unknown. The aim of this study is to develop the LEukemia Artificial intelligence Program (LEAP) for treatment recommendations for patients with CML-CP. METHODS: From July 30, 2000 to November 25, 2014, 630 consecutive patients with newly diagnosed CML-CP were enrolled in frontline TKI therapy (imatinib 400 mg/day, imatinib 800 mg/day, nilotinib, dasatinib, and ponatinib). We included 101 social, demographic, clinical, chromosomal, and molecular variables such as the distance from home address to our institution, primary language, the European Treatment and Outcome Study (EUTOS) risk, the EUTOS long-term survival (ELTS) risk, and the severity of comorbidities by Adult Comorbidity Evaluation 27 (ACE-27). We developed an extreme gradient boosting decision tree model through ensemble learning after hyperparameter tuning. Hyperparameter optimization was calculated with Stampede2, a supercomputer located at Texas Advanced Computing Center, which was ranked as the 15th fastest supercomputer in June 2018. The extreme gradient boosting decision tree model was developed for the prediction of overall survival using only the training/validation cohort. We evaluated the final performance with the independent test cohort. A difference in hazard ratios of less than 0.005 between the best treatment option and alternative TKI therapy was considered as the LEAP recommendation. The test cohort was divided into the LEAP recommendation and the LEAP non-recommendation cohort by the LEAP recommendation. To confirm the association and causation of the LEAP recommendation with survival, we performed backward multivariate Cox regression, and inverse probability of treatment weighing (IPTW) to balance baseline difference of covariates. We calculated SHapley Additive exPlanations1 values to interpret the black box of the LEAP recommendation for the evaluation of the significance of variable for prediction. RESULTS: The whole cohort was randomly divided into a training/validation (N=504) cohort and a test cohort (N=126) at a 4:1 ratio (Figure 1). The training/validation cohort was used for 3-fold cross validation to develop the LEAP CML-CP model. The number of decision trees was 8417, 14659, and 14190 in the first, second, and third cross validation cohort, respectively (Figure 2). The accuracy of prediction at each iteration is shown in Supplemental Figure 1. The area under the curve (AUC) of the training in the first, second, and third cross validation cohort was 0.966, 0.978, and 0.977, respectively; the AUC of the validation in the first, second, and third cross validation cohort was 0.815, 0.832, and 0.742, respectively. The AUC in the test cohort was 0.819. We divided the test cohort (N=126) into the LEAP recommendation (N=94, 75%) and LEAP non-recommendation cohort (N=32, 25%) (Table 1). The LEAP did not recommend one particular TKI (P=0.128). Overall survival did not differ significantly by the type and dose of TKI (P=0.472) (Supplemental Figure 2). Patients in the LEAP recommendation cohort achieved higher rates of overall deep response (Table 2). In the test cohort, treatment consistent with the LEAP recommendation was associated with improved failure-free survival, transformation-free survival, event-free survival, and overall survival (P<0.001; P=0.002; P<0.001; P<0.001) (Figure 3). The median overall survival was 139 months (range, 3.7-216.1), and the median overall survival was 127 months and 148 months in the LEAP recommendation and LEAP non-recommendation cohorts, respectively (P=0.902). Backward multivariate Cox regression analysis and IPTW analysis confirmed the association and causation of improved OS with the LEAP recommendation, respectively (Supplemental Table 1; Supplemental Table 2). The SHAP values identified the presence and degrees of comorbidities and ELTS scores as top three importance for the prediction (Figure 4). Conclusion: The LEAP CML-CP recommendation improves overall survival in patients with CML-CP through higher tolerance, lower rates of progression, and higher rates of deep response. Disclosures Sasaki: Pfizer: Consultancy; Otsuka: Honoraria. Kantarjian:Jazz Pharma: Research Funding; Amgen: Honoraria, Research Funding; Cyclacel: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Takeda: Honoraria; BMS: Research Funding; Daiichi-Sankyo: Research Funding; Astex: Research Funding; Novartis: Research Funding; Immunogen: Research Funding. Jabbour:Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding. Ravandi:Cyclacel LTD: Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Macrogenix: Consultancy, Research Funding; Menarini Ricerche: Research Funding; Xencor: Consultancy, Research Funding; Selvita: Research Funding. Konopleva:Agios: Research Funding; Kisoji: Consultancy, Honoraria; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Astra Zeneca: Research Funding; Ablynx: Research Funding; Amgen: Consultancy, Honoraria; Cellectis: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Eli Lilly: Research Funding; Forty-Seven: Consultancy, Honoraria; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Ascentage: Research Funding; Genentech: Honoraria, Research Funding; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding. Borthakur:AstraZeneca: Research Funding; Polaris: Research Funding; AbbVie: Research Funding; Incyte: Research Funding; Argenx: Membership on an entity's Board of Directors or advisory committees; NKarta: Consultancy; PTC Therapeutics: Consultancy; Oncoceutics, Inc.: Research Funding; Cyclacel: Research Funding; GSK: Research Funding; Merck: Research Funding; Eli Lilly and Co.: Research Funding; BioLine Rx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Strategia Therapeutics: Research Funding; Arvinas: Research Funding; Janssen: Research Funding; BioTheryX: Membership on an entity's Board of Directors or advisory committees; Eisai: Research Funding; Xbiotech USA: Research Funding; Oncoceutics: Research Funding; Novartis: Research Funding; Bayer Healthcare AG: Research Funding; Agensys: Research Funding; FTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Cantargia AB: Research Funding; BMS: Research Funding; Tetralogic Pharmaceuticals: Research Funding. Wierda:Oncternal Therapeutics Inc.: Research Funding; Xencor: Research Funding; Cyclcel: Research Funding; Sunesis: Research Funding; GSK/Novartis: Research Funding; Miragen: Research Funding; KITE pharma: Research Funding; Loxo Oncology Inc.: Research Funding; Janssen: Research Funding; Juno Therapeutics: Research Funding; AbbVie: Research Funding; Genentech: Research Funding; Pharmacyclics LLC: Research Funding; Acerta Pharma Inc: Research Funding; Gilead Sciences: Research Funding. Takahashi:Symbio Pharmaceuticals: Consultancy. DiNardo:celgene: Consultancy, Honoraria; jazz: Honoraria; abbvie: Consultancy, Honoraria; agios: Consultancy, Honoraria; syros: Honoraria; medimmune: Honoraria; daiichi sankyo: Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees. Pemmaraju:plexxikon: Research Funding; novartis: Consultancy, Research Funding; celgene: Consultancy, Honoraria; cellectis: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; sagerstrong: Research Funding; incyte: Consultancy, Research Funding; affymetrix: Research Funding; mustangbio: Consultancy, Research Funding; samus: Research Funding; abbvie: Consultancy, Honoraria, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Cortes:Bristol-Myers Squibb: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Biopath Holdings: Consultancy, Honoraria; BiolineRx: Consultancy.

The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks. This approach is a natural generalization of several standard resource management problems, such as scheduling and transportation problems. First, reactive solutions are considered and defined as control policies of suitably reformulated Markov decision processes (MDPs). We argue that this reformulation has several favorable properties, such as it has finite state and action spaces, it is aperiodic, hence all policies are proper and the space of control policies can be safely restricted. Next, approximate dynamic programming (ADP) methods, such as fitted Q-learning, are suggested for computing an efficient control policy. In order to compactly maintain the cost-to-go function, two representations are studied: hash tables and support vector regression (SVR), particularly, nu-SVRs. Several additional improvements, such as the application of limited-lookahead rollout algorithms in the initial phases, action space decomposition, task clustering and distributed sampling are investigated, too. Finally, experimental results on both benchmark and industry-related data are presented.

Background Epstein-Barr virus infection is associated with a variety of life-threatening malignant and non-malignant diseases. In immunocompromised individuals where T-cell function is impaired, loss of immune control of Epstein-Barr virus (EBV) can lead to proliferation of EBV-transformed B cells and result in immunodeficiency-associated lymphoproliferative diseases (IA-LPD). EBV-driven IA-LPDs are a heterogenous group of diseases with variable clinical and pathologic features, including primary immunodeficiency-associated LPD (EBV+ PID-LPD), acquired immunodeficiency-associated LPD (EBV+ AID-LPD), and post-transplant lymphoproliferative disorder (EBV+ PTLD). In addition, EBV-infection of mesenchymal cells can cause sarcomas, including leiomyosarcoma (EBV+ sarcoma). In rare instances, EBV viremia (ie, persistent EBV infection) can lead to non-malignant yet life-threatening complications such as chronic active EBV (CAEBV) or hemophagocytic lymphohistiocytosis (HLH). During the transition from primary EBV infection to latent state, only a subset of viral genes is expressed. The resulting latency proteins are a target of CD8+ T cells, making EBV+ malignancies good candidates for treatment with EBV-targeted cytotoxic lymphocytes. Tabelecleucel (tab-cel) is an off-the-shelf, partially human leukocyte antigen (HLA)-matched, allogeneic EBV-specific T-cell immunotherapy generated from screened EBV+ donors. Tabelecleucel is characterized by both HLA genotype and HLA restriction, the HLA allele(s) through which tab-cel exerts cytotoxic activity. Tab-cel is selected for each patient from an inventory based on the HLA restriction(s) and partial HLA matching. Initial safety and efficacy data support that tab-cel was well tolerated and showed clinical activity in patients with each of the EBV-associated diseases being proposed in study 205 [Nikiforow S et al. ESMO 2020; Kurlander LS et al. Ann Oncol 2018; Prockop S et al.JCI 2020]. These data encourage investigation to further evaluate the safety and efficacy of tab-cel in these populations. Study design and objectives This is a multicenter, multicohort, open-label, single-arm per cohort, phase II study to assess the efficacy and safety of tab-cel in patients with EBV-associated diseases (ATA129-EBV-205). The primary objective of the study is to assess the clinical benefit of tab-cel as measured by objective response rate. This study will also evaluate clinically relevant disease-specific outcomes and will characterize the safety profile of tab-cel in these patient populations. This study will enroll patients who are relapsed/refractory or newly diagnosed and ineligible for first-line therapy in the following cohorts: EBV+ AID-LPD, EBV+ PID-LPD, EBV+ sarcoma, including LMS, CAEBV/HLH and EBV+ PTLD with central nervous system [CNS]-involvement. In addition, a cohort of patients with previously untreated EBV+ PTLD where first-line therapy is not appropriate is planned to start enrollment in 2021. A Phase III study of tab-cel for solid organ or allogeneic hematopoietic cell transplant patients with EBV+ PTLD after failure of rituximab or rituximab plus chemotherapy (NCT03394365) is ongoing. Patients in each cohort will receive tab-cel in 35-day cycles. During each cycle, patients will receive intravenous tab-cel at a dose of 2 × 106 cells/kg on days 1, 8, and 15, followed by an observation period through day 35 (Figure 1). An adaptive two-stage design will be used for each cohort in this study, and for each, a maximum of eight patients will be enrolled in stage 1. The decision to move to stage 2 enrollment for any given cohort will be based on an analysis of the first eight evaluable patients in the cohort using investigator's assessment (per defined radiologic, clinical, and/or laboratory response criteria). If at least two among the first eight evaluable patients in stage 1 are responders W(ie, complete response or partial response), additional patients may be enrolled in stage 2 of that cohort. The number of patients enrolled in stage 2 will depend on the number of observed responders in stage 1 (Table 1). This design will allow for minimization of the expected number of patients required to evaluate the efficacy of tab-cel in each disease cohort independently. Figure 1. Study design schema Disclosures Prockop: Jasper Pharmaceuticals: Research Funding; Mesoblast: Consultancy, Research Funding; Atara Biotherapeutics: Research Funding; Memorial Sloan Kettering: Patents & Royalties: IP related to the development of third party viral specific T cells with all of my interests assigned to MSK. Dinavahi:Atara Biotherapeutics: Current Employment, Current equity holder in publicly-traded company. Navarro:Atara Biotherapeutics: Current Employment, Current equity holder in publicly-traded company. Guzman-Becerra:Atara Biotherapeutics: Current Employment, Current equity holder in publicly-traded company. Sun:Atara Biotherapeutics: Current Employment, Current equity holder in publicly-traded company. Gamelin:Atara Biotherapeutics: Current Employment, Current equity holder in publicly-traded company.

Abstract Introduction: The randomized phase 3 ENDEAVOR trial demonstrated superior progression-free survival (PFS), overall survival (OS), and health-related quality of life in patients with relapsed or refractory multiple myeloma (RRMM) for patients treated with carfilzomib (56 mg/m2) and dexamethasone (Kd56) compared with bortezomib and dexamethasone (Vd). In patients with relapsed MM, the time of therapy initiation might impact treatment outcome. Prior studies have demonstrated a median of 5 months between the presence of biochemical and symptomatic relapse (Lopez. Leuk Res Rep. 2015;4:64-69). Herein, we report a post hoc subgroup analysis from the ENDEAVOR study to evaluate the impact of initiating Kd56 therapy upon biochemical relapse. Methods: Adults with RRMM who previously received 1-3 lines of therapy and had measurable disease were eligible to participate in the ENDEAVOR trial. Symptomatic disease was not required for eligibility. Kd56 patients received carfilzomib on days 1, 2, 8, 9, 15, and 16 as a 30-minute intravenous infusion and dexamethasone (20 mg) on days 1, 2, 8, 9, 15, 16, 22, and 23 of a 28-day cycle. Vd patients received bortezomib (1.3 mg/m2; intravenous bolus or subcutaneous injection) on days 1, 4, 8 and 11 and dexamethasone (20 mg) on days 1, 2, 4, 5, 8, 9, 11, and 12 of a 21-day cycle. Treatment continued until disease progression, physician decision, unacceptable toxicity, withdrawal of consent, or death. This post hoc subgroup analysis evaluated PFS, OS, and safety in subgroups defined according to the presence of symptoms at the time of enrollment. Patients with RRMM who experienced biochemical progression without CRAB symptoms (hypercalcemia, renal impairment, anemia, or bone lesions) upon relapse were considered asymptomatic, whereas symptomatic patients were those who had CRAB symptoms upon relapse. In each subgroup, PFS and OS were compared between treatment arms using an unstratified Cox proportional hazards model. Results: Of the 929 patients enrolled and randomized in ENDEAVOR, 117 (12.6%) were asymptomatic (Kd56, n=60; Vd, n=57) and 812 (87.4%) were symptomatic (Kd56, n=404; Vd, n=408). In the asymptomatic group, the median PFS was not estimable (NE) for Kd56 vs 13.7 months for Vd (hazard ratio [HR]: 0.462; 95% confidence interval [CI]: 0.232-0.922), and the median OS was NE for either treatment arm (HR: 0.768; 95% CI: 0.350-1.683) (Table). In the symptomatic group, median PFS was 17.7 months for Kd56 vs 8.8 months for Vd (HR: 0.539; 95% CI: 0.439-0.662), and median OS was 44.0 months for Kd56 vs. 36.8 months for Vd (HR: 0.801; 95% CI: 0.653-0.982) (Table). Kaplan-Meier PFS and OS curves are shown in the Figure. The rate of grade ≥3 treatment-emergent adverse events (Kd56 vs Vd) was 78.3% vs 58.9% in the asymptomatic group and 81.9% vs 72.8% in the symptomatic group (Table). Conclusions: Kd56 demonstrated superior survival outcomes compared with Vd in patients with RRMM, regardless of presence of CRAB symptoms at study randomization. As expected, outcomes were improved when Kd56 was initiated early in the disease course, before CRAB symptoms occurred. The small size of the subgroups in this study is a limitation. However, the findings warrant further investigation. The safety profile of Kd56 in both subgroups was consistent with that in the overall population as previously reported (Dimopoulos. Lancet Oncol. 2016;17:27-38; Siegel, Clin Lymphoma Myeloma Leuk. 2017;17:e142). Overall, Kd56 had a favorable benefit-risk profile in both patients with biochemical and symptomatic relapse. Disclosures Moreau: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Siegel:Merck: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau. Goldschmidt:Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; ArtTempi: Honoraria; Novartis: Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Chugai: Honoraria, Research Funding; Mundipharma: Research Funding; Adaptive Biotechnology: Consultancy; Amgen: Consultancy, Research Funding. Niesvizky:Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Amgen Inc.: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Bringhen:Takeda: Consultancy; Janssen: Honoraria, Other: Advisory Board; Amgen: Honoraria, Other: Advisory Board; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria. Orlowski:Spectrum Pharma: Research Funding; BioTheryX: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Sanofi-Aventis: Consultancy; Janssen: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Kite Pharma: Consultancy. Blaedel:Amgen: Employment, Equity Ownership. Yang:Amgen Inc.: Employment, Equity Ownership. Dimopoulos:Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria; Amgen: Honoraria.

Background: Immune thrombocytopenia (ITP) is characterized by immune-mediated platelet destruction and impairment of platelet production, leading to downstream thrombocytopenia, a predisposition to bleeding, and adverse impact on patient quality of life. Unmet needs in relapsed or refractory ITP are to improve remission rates and durability through targeting underlying disease mechanisms. PRN1008 is an oral, reversible, covalent inhibitor of Bruton tyrosine kinase (BTK) that modulates immune-mediated processes in ITP. Preclinical PRN1008 data showed inhibition of B-cell receptor-mediated activation of human B cells, Fc receptor (Fc-gamma and Fc-epsilon)-mediated activation of immune cells, and dose-dependent reduction in platelet loss in a mouse ITP model. In platelets from normal healthy volunteer and ITP patients, clinically-relevant concentrations of PRN1008 showed no platelet aggregation or interference with other platelet agonists, in contrast to ibrutinib (Langrish et al. ASH 2017:1052). Methods: This is an ongoing open-label, adaptive, intra-patient dose-escalation, phase I/II study of PRN1008 in adult patients with relapsed or refractory ITP (primary or secondary) who previously responded to ≥ 1 prior ITP therapy and have no available therapeutic options (NCT03395210). Eligible patients have two platelet counts < 30,000/µL within 15 days prior to treatment. Oral PRN1008 starting doses were 200 mg QD, 400 mg QD, 300 mg BID (total 600 mg daily), and 400 mg BID (total 800 mg daily), with intra-patient dose escalation allowed every 4 weeks (maximum 400 mg BID) as needed for efficacy. Stable doses of concomitant corticosteroids and thrombopoietin-receptor agonists (TPO-RA) are permitted. The primary end point is the proportion of patients with ≥ 2 consecutive platelet counts (separated by ≥ 5 days) of ≥ 50,000/µL and increased by ≥ 20,000/µL from baseline without requiring rescue medication. Results: A total of 21 patients have been enrolled to date at starting doses of 200 mg QD (n=9), 400 mg QD (n=1), 300 mg BID (n=5), and 400 mg BID (n=6). As of 15 July 2019 data cut-off, 11 patients were receiving ongoing treatment, 4 completed the study, and 6 patients withdrew (2 due to patient decision, 2 from non-treatment-related adverse events [AEs], 1 erroneously enrolled, and 1 because of rescue medication use). Patients had a median age of 54 y (range, 30-65), 4 (19%) had a prior splenectomy, 19 (90%) were diagnosed with primary ITP, and 2 (10%) with secondary ITP. Patients had ITP for a median of 8.3 years (range, 0.5-42.4) and had received a median of 4 prior ITP therapies. Median platelet count at study entry was 14,173/µL (range, 2,670-27,000/µL). During the study, 6 (29%) patients received PRN1008 monotherapy; 15 (71%) patients were on ≥ 1 concomitant ITP medication. Related treatment-emergent AEs (TEAEs) were reported by 4 (19%) patients; all were grade 1 or 2. The most frequent related TEAEs were nausea, diarrhea, and abdominal distension. There were no treatment-related bleeding or thrombotic events, and no significant changes in the ITP-BAT bleeding scale between baseline and the last visit. There were no dose limiting toxicities (DLT). Patients had received treatment for a median of 10.1 weeks (range, 0.1-31.0). Overall, 7 (33%) patients achieved the primary endpoint across all doses (Table). Patient responses were improved at the 2 higher doses. In 10 patients who had reached ≥ 12 weeks of treatment, ≥ 50% of patients had platelet counts of ≥ 50,000/µL and ≥ 20,000/µL increases from baseline. Conclusion: Overall, PRN1008 was active in 33% of ITP patients who were refractory to multiple treatments with no alternative therapeutic options. This result was demonstrated despite the limited duration of treatment and including patients at all dose levels. In addition, patients treated for longer periods of time have substantially improved response rates that support continued interest in this ongoing study. The safety profile was tolerable at all studied doses whether given as a monotherapy or with allowed concomitant ITP therapy. Importantly, TEAEs were grade 1 or 2 with no thrombotic events. The dose-escalation portion of the study is complete; enrollment is expanding at the 400 mg BID starting dose for a duration of 24 weeks to further characterize treatment benefit and for continued treatment beyond 24 weeks in patients who have responded. Disclosures Kuter: Dova: Consultancy, Honoraria; Kyowa-Kirin: Consultancy, Honoraria; Caremark: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Kezar: Research Funding; Argenx: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Platelet Disorder Support Association: Consultancy, Honoraria; Principia: Consultancy, Honoraria, Research Funding; Alnylam: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb (BMS): Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Genzyme: Consultancy, Honoraria; Shinogi: Consultancy, Honoraria; Shire: Consultancy, Honoraria; Merck Sharp Dohme: Consultancy, Honoraria; Momenta: Consultancy, Honoraria; Protalex: Consultancy, Honoraria, Research Funding; Protalix: Consultancy, Honoraria; Rigel: Consultancy, Honoraria, Research Funding; Takeda (Bioverativ): Consultancy, Honoraria, Research Funding; UCB: Consultancy, Honoraria; Up-to-Date: Consultancy, Honoraria, Patents & Royalties: 3 Up-to-Date chapters; Zafgen: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Actelion (Syntimmune): Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Boccia:AstraZeneca: Speakers Bureau; Celgene: Speakers Bureau; Amgen: Speakers Bureau; AMAG: Consultancy; Genentech: Speakers Bureau; DSI: Speakers Bureau. Lee:Weill Cornell Medical College: Employment. Tzvetkov:UMHAT Georgi Stranski: Employment; DCC Pleven: Consultancy. Mayer:AOP Orphan Pharmaceuticals AG: Research Funding. Trněný:Abbvie: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy; F. Hoffmann-La Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Kostal:Novartis: Honoraria; AOP: Honoraria; University Hospital in Hradec Kralove, Czech Republic: Employment. Hajek:Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Research Funding; PharmaMar: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau. McDonald:Bayer: Honoraria; Amgen: Honoraria; Novartis: Honoraria. Bandman:Principia Biopharma: Employment, Equity Ownership, Patents & Royalties: Institutional with Incyte and Portola, no royalties. Burns:Principia BioPharma: Employment. Neale:Principia BioPharma: Employment, Equity Ownership. Thomas:Principia Biopharma: Employment, Equity Ownership; BMS: Equity Ownership; Pfizer: Equity Ownership. Cooper:Principia: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Rigel: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Off Label Disclosure: Yes, this was an investigational clinical phase I/II study of PRN1008 in patients with relapsed/refractory ITP. Phase I dose escalation phase is now complete and expanded phase II studies ongoing.

The application of ecosystem considerations, and in particular ecosystem report cards, in federal groundfish fisheries management in Alaska can be described as an ecosystem approach to fisheries management (EAFM). Ecosystem information is provided to managers to establish an ecosystem context within which deliberations of fisheries quota occur. Our goal is to make the case for the need for qualitative ecosystem assessments in EAFM, specifically that qualitative synthesis has advantages worthy to keep a permanent place at the fisheries management table. These advantages include flexibility and speed in responding to and synthesizing new information from a variety of sources. First, we use the development of indicator-based ecosystem report cards as an example of adapting ecosystem information to management needs. Second, we review lessons learned and provide suggestions for best practices for applying EAFM to large and diverse fisheries in multiple marine ecosystems. Adapting ecosystem indicator information to better suit the needs of fisheries managers resulted in succinct report cards that summarize ecosystem trends, complementing more detailed ecosystem information to provide context for EAFM. There were several lessons learned in the process of developing the ecosystem report cards. The selection of indicators for each region was influenced by geography, the extent of scientific knowledge/data, and the particular expertise of the selection teams. Optimizing the opportunity to qualitatively incorporate ecosystem information into management decisions requires a good understanding of the management system in question. We found that frequent dialogue with managers and other stakeholders leads to adaptive products. We believe that there will always be a need for qualitative ecosystem assessment because it allows for rapid incorporation of new ideas and data and unexpected events. As we build modelling and predictive capacity, we will still need qualitative synthesis to capture events outside the bounds of current models and to detect impacts of the unexpected.

Background Anemia is common in patients (pts) with myeloproliferative neoplasm (MPN)-associated myelofibrosis (MF). Furthermore, anemia is an on-target effect of therapeutic Janus kinase 2 (JAK2) inhibition, and may be the most frequent cause of ruxolitinib (rux) discontinuation (d/c) in clinical practice (Kuykendall, Ann Hematol 2018). Current therapies for anemia of MF (erythropoietin and analogs, danazol, IMiDs®) are unsatisfactory. Sotatercept (ACE-011) is a first-in-class, activin receptor type IIA ligand trap that may improve anemia by sequestering stromal transforming growth factor beta superfamily ligands that suppress terminal erythropoiesis (Iancu-Rubin, Exp Hematol 2013). Methods This is a phase 2, investigator-initiated, open-label, single institution study of sotatercept, administered subcutaneously every 3 weeks, in 2 cohorts of anemic pts (Hgb <10 g/dl on every determination for 12 weeks (wks) or transfusion-dependent (TD) per IWG-MRT criteria (Tefferi, Blood 2013)) with MF: as a single agent, and in combination with a stable dose of rux. Pts on rux must have been on rux for ≥6 months with a stable dose for ≥8 weeks, and receive sotatercept at a dose of 0.75 mg/kg. Monotherapy pts receive either 0.75 or 1 mg/kg of sotatercept. In both cohorts, anemia response is defined as achievement of transfusion independence (TI) in TD pts, and an increase in Hgb level from baseline of ≥1.5 g/dl sustained for ≥12 wks in non-TD pts (Gale, Leuk Res 2011). Pts must have received ≥5 cycles of sotatercept to be response-evaluable. Results A total of 53 pts have been treated; one pt received only 0.3 mg/kg of sotatercept and is not considered further. Thirty one pts received sotatercept alone and 21 in combination with rux. Baseline characteristics appear in Table 1, panel A. Sixteen TD and 15 non-TD pts received sotatercept alone for a median of 5 (1-67) cycles. Thirteen pts received 0.75 mg/kg and 18, 1 mg/kg. Seven of 24 (29%) evaluable pts responded. Of these, 4 were anemia responses; 3 TD pts achieved TI. Five responses occurred at the 0.75 mg/kg dose, and 2 at the 1 mg/kg dose. Median time to response (TTR) was 21 (1-22) days and median duration of response (DOR) 13 (3.9-56.4) months. Seven pts (22.6%) received <5 cycles and were not response-evaluable: 2 proceeded to stem cell transplant (SCT), 2 had logistical (travel) issues, and 1 each d/ced sotatercept because of hypertension (HTN), unrelated medical problems and pt decision. Three pts continue on study. Reasons for d/c included no response (11), progressive MF (6), SCT (3), travel logistics (3), patient decision (2), hypertension (1), unrelated medical complications (1) and transformation to AML (1). The combination cohort comprised 15 non-TD pts and 6 TD pts. The median number of cycles was 8 (2-43). Five of 17 (29%) evaluable pts in the combination cohort responded, all non-TD pts. Median TTR was 14 (7-147) days and median DOR 34.6 (3.1-47.9) months. Four pts (19%) received <5 cycles and were not response-evaluable, 1 each due to MF progression, loss of insurance, SCT and pt decision. Five pts remain on study. Reasons for d/c included no response (6), SCT (4), progressive MF (2), travel logistics (2), loss of insurance (1) and pt decision (1). Several non-response-evaluable pts in both cohorts achieved ≥1.5 g/dl increments in Hgb from baseline that were not sustained for ≥12 wks because of early d/c of sotatercept. An additional pt in the combination cohort required a rux dose increase, leading to failure to sustain a ≥1.5 g/dl Hgb improvement. Across both cohorts, several responding pts required multiple protocol-specified drug holidays because of Hgb levels ≥11.5 g/dl, with resumption of sotatercept once Hgb was <11 g/dl. Sotatercept was well-tolerated (Table 1, panel B). Grade 3 adverse events possibly related to sotatercept were HTN (n=7), limb (bone/muscle/joint) pain (n=3) and headache (1). Conclusions Sotatercept is safe and effective against anemia of MPN-associated MF, both in non-TD and TD pts, with a response rate of 29% both when used alone and in conjunction with a stable dose of rux. A total of 60 pts are planned to be treated on this trial (NCT01712308). Disclosures Bose: Blueprint Medicines Corporation: Honoraria, Research Funding; NS Pharma: Research Funding; Constellation Pharmaceuticals: Research Funding; Astellas Pharmaceuticals: Research Funding; Pfizer, Inc.: Research Funding; Incyte Corporation: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene Corporation: Honoraria, Research Funding; CTI BioPharma: Honoraria, Research Funding; Promedior, Inc.: Research Funding; Kartos Therapeutics: Honoraria, Research Funding. Pemmaraju:Samus Therapeutics: Research Funding; AbbVie: Honoraria, Research Funding; MustangBio: Honoraria; SagerStrong Foundation: Other: Grant Support; Roche Diagnostics: Honoraria; Pacylex Pharmaceuticals: Consultancy; Plexxikon: Research Funding; Daiichi Sankyo: Research Funding; Stemline Therapeutics: Honoraria, Research Funding; Celgene: Honoraria; Incyte Corporation: Honoraria; Cellectis: Research Funding; Blueprint Medicines: Honoraria; Affymetrix: Other: Grant Support, Research Funding; Novartis: Honoraria, Research Funding; LFB Biotechnologies: Honoraria; DAVA Oncology: Honoraria. Daver:Fate Therapeutics: Research Funding; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; Servier: Research Funding; Genentech: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novimmune: Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Trovagene: Research Funding; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; ImmunoGen: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Trillium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Syndax: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees. Jabbour:BMS: Other: Advisory role, Research Funding; Amgen: Other: Advisory role, Research Funding; Pfizer: Other: Advisory role, Research Funding; AbbVie: Other: Advisory role, Research Funding; Takeda: Other: Advisory role, Research Funding; Adaptive Biotechnologies: Other: Advisory role, Research Funding; Genentech: Other: Advisory role, Research Funding. Kadia:Astellas: Research Funding; Pulmotec: Research Funding; Incyte: Research Funding; Ascentage: Research Funding; JAZZ: Honoraria, Research Funding; Cyclacel: Research Funding; Amgen: Research Funding; Celgene: Research Funding; Cellenkos: Research Funding; Genentech: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria; Abbvie: Honoraria, Research Funding; Astra Zeneca: Research Funding. Andreeff:Daiichi-Sankyo; Jazz Pharmaceuticals; Celgene; Amgen; AstraZeneca; 6 Dimensions Capital: Consultancy; Amgen: Research Funding; Daiichi-Sankyo; Breast Cancer Research Foundation; CPRIT; NIH/NCI; Amgen; AstraZeneca: Research Funding; Centre for Drug Research & Development; Cancer UK; NCI-CTEP; German Research Council; Leukemia Lymphoma Foundation (LLS); NCI-RDCRN (Rare Disease Clin Network); CLL Founcdation; BioLineRx; SentiBio; Aptose Biosciences, Inc: Membership on an entity's Board of Directors or advisory committees. Cortes:Bristol-Myers Squibb: Research Funding; Pfizer: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sun Pharma: Research Funding; BioPath Holdings: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Telios: Research Funding; Astellas: Research Funding; Amphivena Therapeutics: Research Funding; Arog: Research Funding; BiolineRx: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Merus: Research Funding; Immunogen: Research Funding; Novartis: Consultancy, Research Funding. Jain:BMS: Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; TG Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Aprea Therapeutics: Research Funding; Precision Bioscienes: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Fate Therapeutics: Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; BeiGene: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding. Borthakur:Nkarta Therapeutics: Consultancy; Treadwell Therapeutics: Consultancy; PTC Therapeutics: Research Funding; Jannsen: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Incyte: Research Funding; Polaris: Research Funding; Xbiotech USA: Research Funding; Oncoceutics: Research Funding; Curio Science LLC: Consultancy; FTC Therapeutics: Consultancy; Argenx: Consultancy; PTC Therapeutics: Consultancy; BioLine Rx: Consultancy; BioTherix: Consultancy; Cyclacel: Research Funding; GSK: Research Funding; BioLine Rx: Research Funding; BMS: Research Funding; AstraZeneca: Research Funding. Alvarado:Sun Pharma: Research Funding; Astex Pharmaceuticals: Research Funding; MEI Pharma: Research Funding; Daiichi-Sankyo: Research Funding; Tolero Pharmaceuticals: Research Funding; FibroGen: Research Funding; Jazz Pharmaceuticals: Research Funding; BerGenBio ASA: Research Funding. Huynh-Lu:Incyte Corporation: Speakers Bureau. Nguyen-Cao:Incyte Corporation: Speakers Bureau. Garcia-Manero:Acceleron Pharmaceuticals: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy; Novartis: Research Funding; Onconova: Research Funding; Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; H3 Biomedicine: Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amphivena Therapeutics: Research Funding. Kantarjian:Oxford Biomedical: Honoraria; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Delta Fly: Honoraria; Janssen: Honoraria; Ascentage: Research Funding; BioAscend: Honoraria; Amgen: Honoraria, Research Funding; Aptitute Health: Honoraria; Immunogen: Research Funding; Jazz: Research Funding; Novartis: Honoraria, Research Funding; Sanofi: Research Funding; Pfizer: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria, Research Funding; BMS: Research Funding; Adaptive biotechnologies: Honoraria; Abbvie: Honoraria, Research Funding. Verstovsek:Sierra Oncology: Consultancy, Research Funding; Gilead: Research Funding; Celgene: Consultancy, Research Funding; CTI Biopharma Corp: Research Funding; Roche: Research Funding; NS Pharma: Research Funding; Promedior: Research Funding; Novartis: Consultancy, Research Funding; AstraZeneca: Research Funding; ItalPharma: Research Funding; Protagonist Therapeutics: Research Funding; PharmaEssentia: Research Funding; Incyte Corporation: Consultancy, Research Funding; Blueprint Medicines Corp: Research Funding; Genentech: Research Funding.

e13092 Background: Longitudinal analysis of serial tumor biopsies is an under-utilized approach to studying adaptive mechanisms of resistance. We have established a comprehensive analytic platform to evaluate real-time trial sample analysis to inform precision oncology combinations in mTNBC. The primary endpoint of the study is feasibility of completing all CLIA assays within 28 days of biopsy. Methods: Following a pre-treatment biopsy and 4 weeks of olaparib monotherapy mTNBC patients underwent an on-treatment (tx) biopsy and durvalumab was added to their therapy. Pre- and on-tx biopsies underwent comparative analysis using CLIA assays (immunohistochemistry-IHC, whole exome seq, RNAseq and phospho-proteomics) as well as research assays (multiplex IHC-mIHC, cyclic immunofluorescence-IF, and reverse phase protein array-RPPA). Results: Serial biopsies were obtained from all 3 enrolled patients, and the primary endpoint was achieved for all patients (Table). Treatment was well tolerated, and 2 patients achieved clinical benefit > 6 months. In one patient with a prolonged CR ( > 18 months), the on-tx sample exhibited dramatic changes in protein network rewiring by protein data analysis (RPPA, cyclic-IF), and an increase in immune infiltrate by mIHC. Conclusions: This pilot confirmed the feasibility of rapid real-time analysis to inform treatment decisions. This led to the development and initiation of biomarker driven olaparib combination trials in mTNBC at our institution. Clinical trial information: NCT03544125 . [Table: see text]

Abstract Introduction: Venetoclax (Ven), an oral agent that targets the antiapoptotic protein, BCL-2, has demonstrated efficacy, as monotherapy and combined with proteasome inhibitor (PI) bortezomib, in relapsed/refractory (R/R) multiple myeloma (MM). We report preliminary safety and efficacy data for Ven combined with the second generation PI carfilzomib (K) and dexamethasone (VenKd) in R/R MM. Methods: In this ongoing phase 2, dose escalation study (NCT02899052), patients with R/R MM and no prior K exposure received VenKd on 28-d cycles in 4 dose finding and one expansion cohorts: Ven 400 mg/day + K 27 mg/m2 Day 1, 2, 8, 9, 15, 16 + dex 40 mg Day 1, 8, 15, 22 (Cohort 1), same regimen but with Ven 800 mg/day (Cohort 2), Ven 800 mg/day + K 70 mg/m2 Day 1, 8, 15 + dex 40 mg Day 1, 8, 15, 22 (Cohort 3/expansion cohort), or Ven 800 mg + K 56 mg/m2 Day 1, 2, 8, 9, 15, 16 + dex 40 mg Day 1, 2, 8, 9, 15, 16, 22, 23 (Cohort 4). Treatment continued until progressive disease (PD) or unacceptable toxicity. Results: As of June 11, 2018, 42 patients were enrolled. The median age was 66.5 years (min, max: 37, 79), 63% had ISS II/III disease, and 8 patients (19%) had t(11;14). Patients received a median of 2 prior therapies (range: 1 - 3), 93% had received prior PI (50% refractory), 62% were refractory to immunomodulatory therapies, and 33% double refractory. At the data cut off, 29 patients were still active and had completed ≥2 cycles and 13 patients discontinued with the primary reason being disease progression (n=4), death (n=3), physician decision (n=2), withdrawal of consent (n=2), lack of efficacy (n=1), and AE (n=1). All patients experienced at least one AE, and grade 3/4 AEs experienced by >10% of subjects included: decreased lymphocyte count (26%), decreased neutrophil count (14%), and hypertension (12%). Thirteen subjects experienced at least one serious AE. Maximum tolerated dose was not reached and Ven 800 mg/day + K 70 mg/m2 was selected for expansion. Ven mean (% coefficient of variation) maximum plasma concentration (Cmax) and area under the plasma concentration-time curve over 24 hours (AUC24) on Cycle 1 Day 15 were 2.7 (57) mg/mL and 33.1 (54) mg×h/mL, respectively, at 400 mg venetoclax (n=4); and were 2.42 (53) mg/mL and 38.7 (51) mg×h/mL, respectively, at 800 mg venetoclax (n=13) in the dose escalation cohorts. The overall response rate (ORR) was 78% and the very good partial response (VGPR) or better rate was 56% (Table). Median time from first dose to the data cut or discontinuation was 5.7 months (range: 0.9 - 16.3) and the median time to first response was 1.9 months (95% CI: 0.9, 9.2). ORRs for subgroups of interest are reported in the Table. Conclusions: The combination of VenKd appears tolerable with no new safety signals or changes in Ven pharmacokinetics. VenKd shows promising preliminary efficacy in R/R MM patient subgroups. Response rates were comparable in all high risk subgroups and overall population. However, the subset of patients with t(11;14) had the highest response. Overall, these results demonstrate that VenKd is a safe and efficacious regimen in R/R MM and support the continued study of VenKd. Disclosures Costa: Abbvie: Research Funding; BMS: Research Funding; Karyopharm: Research Funding; Amgen: Honoraria, Research Funding; Sanofi: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Research Funding. Stadtmauer:Celgene: Consultancy; AbbVie, Inc: Research Funding; Janssen: Consultancy; Takeda: Consultancy; Amgen: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Kovacsovics:Amgen: Honoraria, Research Funding; Abbvie: Research Funding. Jakubowiak:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; SkylineDx: Consultancy, Honoraria; Adaptive Biotechnologies: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Kaufman:Roche: Consultancy; BMS: Consultancy; Karyopharm: Other: data monitoring committee; Janssen: Consultancy; Abbvie: Consultancy. Mobasher:Genentech Inc: Employment; F. Hoffmann-La Roche Ltd: Other: Ownership interests non-PLC. Freise:AbbVie, Inc: Employment, Equity Ownership. Ross:AbbVie, Inc: Employment, Equity Ownership. Pesko:AbbVie, Inc: Employment, Equity Ownership. Munasinghe:AbbVie, Inc: Employment, Equity Ownership. Gudipati:AbbVie, Inc: Employment, Equity Ownership. Mudd:AbbVie, Inc: Employment, Equity Ownership. Bueno:AbbVie, Inc: Employment, Equity Ownership. Kumar:Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Introduction The combination of tyrosine kinase inhibitors (TKI) with intensive therapy has improved survival in patients with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL). The survival with the combination of hyper-CVAD (HCVAD) with ponatinib has now reached 3-year overall survival of 75%. Allogeneic stem cell transplant (ASCT) is indicated for patients with Ph+ALL in first complete response (CR) as standard recommendation. Given observed improved survival with effective frontline therapy and the risk of treatment-related mortality associated with ASCT, personalized assessment of the indication of ASCT is needed with the consideration of longitudinal data of BCR-ABL1 transcript levels after therapy. Conventional statistical models cannot incorporate dependencies between longitudinal and time-to-event data. Therefore, joint models can combine these two types of data to assess the effect of treatment as well as the impact of longitudinal biomarkers such as BCR-ABL1 transcript levels. The aim of this study is to develop a multivariate joint model for dynamic personalized assessment of outcome in patients with Ph+ALL with or without ASCT. Methods From April 2001 to June 2017, 223 patients enrolled in frontline trials of the combination of intensive therapy with TKI were analyzed (HCVAD [hyper-fractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone] + imatinib, 54 patients; HCVAD + dasatinib, 72 patients; HCMAD [hyper-fractionated cyclophosphamide, liposomal vincristine, doxorubicin, and dexamethasone] + TKI, 21 patients; HCVAD + ponatinib, 68 patients), including 2445 measurements of BCR-ABL1 transcripts by reverse transcriptase polymerase chain reaction. Multivariate joint modeling with multiple longitudinal measurements was performed for dynamic personalized assessment with the combination of multivariate Cox proportional hazard model with generalized linear mixed models. For the estimation of parameters of the joint model, a Bayesian approach was used with Markov Chain Monte Carlo methods. The BCR-ABL1/ABL1 ratio and time from diagnosis to ASCT were considered as time-dependent covariates in the generalized linear mixed model. Results Overall, the median follow-up was 72 months (range, 0.2-199.4); HCVAD + imatinib, 168 months; HCVAD + dasatinib, 97 months; HCVAD + ponatinib, 45 months; H (Table 1). The median overall survival (OS) duration was 27.7 months, 67.0 months, not reached, not reached, and 87.2 months in the HCVAD + imatinib, HCVAD + dasatinib, HCVAD + ponatinib, and HCMAD + TKI, respectively (p= 0.027). Of the 48 patients (22%) who received ASCT, the median time to SCT was 5 months (range, 0.89-21.26). The trajectories of BCR-ABL1 transcript levels of P190 and P210 were shown in Figure 1 and Figure 2, respectively. Multivariate joint model identified age at the start of therapy (p <0.001; post-mean, 0.0167; 95% credible interval [CI], 0.0150 - 0.0175), type of transcript (P210 or P190) (p=0.030; post-mean, 2.5375; 95% CI, 1.3568 - 3.9252), BCR-ABL1 transcripts levels at diagnosis (p< 0.001; post-mean, 0.0083; 95% CI, 0.0072 - 0.0097), TKI therapy (imatinib, dasatinib, or ponatinib) (p <0.001; post-mean, -0.4929; 95% CI, -0.5543 - -0.4532), time-dependent logarithmic P190 levels (p= 0.006; post-mean, 0.0407; 95% CI, 0.0300 - 0.0605), time-dependent logarithmic P210 levels (p<0.001; post-mean, 0.0599; 95% CI, 0.0423 - 0.0812), and the use of ASCT (p= 0.020; post-mean, 0.1276; 95% CI, 0.0869 - 0.2371) as prognostic factors for OS. An example of dynamic personalized assessment for the guidance of the ASCT indication was shown in Figure 3. Patient #1 was a 47-year-old male with newly diagnosed Ph+ALL who was treated with front-line HCVAD + ponatinib. At diagnosis, the patient had P190 transcript type with a BCR-ABL level of 100% by reverse transcriptase polymerase chain reaction. At 2.89 months of therapy, his BCR-ABL level was undetectable. Dynamic personalized assessment by multivariate joint model estimated 5-year OS rates were 84.6% (95% CI, 69.4-94.1) without ASCT, and 81.9% (95% CI, 49.8-93.4) with ASCT. Conclusion Dynamic personalized assessment of outcome in patients with Ph+ALL is feasible to optimize treatment decision in patients with Ph+ALL. Through the personalized recommendation, the assessment can identify patients who may benefit from ASCT. Disclosures Sasaki: Otsuka Pharmaceutical: Honoraria. Jabbour:Abbvie: Research Funding; Takeda: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Research Funding; Pfizer: Consultancy, Research Funding. Ravandi:Orsenix: Honoraria; Abbvie: Research Funding; Seattle Genetics: Research Funding; Sunesis: Honoraria; Bristol-Myers Squibb: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Jazz: Honoraria; Sunesis: Honoraria; Xencor: Research Funding; Xencor: Research Funding; Jazz: Honoraria; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Abbvie: Research Funding; Macrogenix: Honoraria, Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Amgen: Honoraria, Research Funding, Speakers Bureau; Macrogenix: Honoraria, Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Orsenix: Honoraria. Short:Takeda Oncology: Consultancy. Daver:Daiichi-Sankyo: Research Funding; ImmunoGen: Consultancy; Novartis: Research Funding; Karyopharm: Research Funding; ARIAD: Research Funding; Pfizer: Research Funding; Sunesis: Research Funding; BMS: Research Funding; Incyte: Research Funding; Kiromic: Research Funding; Otsuka: Consultancy; Novartis: Consultancy; Incyte: Consultancy; Karyopharm: Consultancy; Sunesis: Consultancy; Pfizer: Consultancy; Alexion: Consultancy. Kadia:Abbvie: Consultancy; BMS: Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Abbvie: Consultancy; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Research Funding; Novartis: Consultancy; Pfizer: Consultancy, Research Funding; Celgene: Research Funding; Takeda: Consultancy; BMS: Research Funding; Jazz: Consultancy, Research Funding. Konopleva:Stemline Therapeutics: Research Funding. Jain:Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Verastem: Research Funding; Celgene: Research Funding; Adaptive Biotechnologioes: Research Funding; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Genentech: Research Funding; Infinity: Research Funding; Pfizer: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cellectis: Research Funding; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologioes: Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; Cellectis: Research Funding; ADC Therapeutics: Research Funding; Verastem: Research Funding; Pfizer: Research Funding; Servier: Research Funding; Astra Zeneca: Research Funding; BMS: Research Funding; Celgene: Research Funding; Infinity: Research Funding; Incyte: Research Funding; Seattle Genetics: Research Funding; Genentech: Research Funding; ADC Therapeutics: Research Funding; BMS: Research Funding. Wierda:AbbVie, Inc: Research Funding; Genentech: Research Funding. Thompson:Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Research Funding; Gilead Sciences: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Research Funding; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees. Pemmaraju:celgene: Consultancy, Honoraria; cellectis: Research Funding; samus: Research Funding; novartis: Research Funding; plexxikon: Research Funding; stemline: Consultancy, Honoraria, Research Funding; Affymetrix: Research Funding; abbvie: Research Funding; daiichi sankyo: Research Funding; SagerStrong Foundation: Research Funding. Cortes:Daiichi Sankyo: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding. O'Brien:Janssen: Consultancy; Pharmacyclics: Consultancy, Research Funding; Astellas: Consultancy; Alexion: Consultancy; Sunesis: Consultancy, Research Funding; Celgene: Consultancy; Regeneron: Research Funding; Gilead: Consultancy, Research Funding; Vaniam Group LLC: Consultancy; GlaxoSmithKline: Consultancy; Pfizer: Consultancy, Research Funding; Abbvie: Consultancy; Amgen: Consultancy; Aptose Biosciences Inc.: Consultancy; Acerta: Research Funding; TG Therapeutics: Consultancy, Research Funding; Kite Pharma: Research Funding.

Today, in an era of integration of artificial intelligence systems in almost every industry, very demand are studies of classification methods that, prior to their introduction into decision support systems. Compare analysis of the secant planes method, method of the potentials and potential method in the field of receptors are shown in the paper. At first, in introduction, authors shew needs of autonomic systems of adaptive perception on visible diapason of specter. As particularly aim, these methods are compared by criteria of speed, accuracy and amount of storage used after training. As general idea we are looking for we are looking for methodic of the best combination of method for different condition on observe field of visual spectral diapason. Theories of the every method are presented, and then tables of compare analysis of results are shown. Step-by-step comparative experiments are described in detail. Changes at each step are shown in detail in the tables of the corresponding signs. Moreover, at the end of the paper, comparative characteristics of each method with the same learning time in same type of experiments for each method are presented. As a result, in the first group of tables , we see a difference in the recognition time and the amount of memory required for correct operation. Those are truth tables for two points, three points, two points and two planes, three points and two planes, three points and three planes, three points and seven planes. The conclusion gives a thorough explanation of where to use the best method. The needs of the system for computing resources in the application of each mode are presented and corresponding dependencies are derived. Next, If you train several times on the same object (ie, train several times), you can expect that the errors in the breakdown of the receptor space will be different. In this case, you can improve the performance of the algorithm by parallelizing its process into several threads. Using this method simultaneously and independently of each other on the same image is multi-threaded learning on multiple computer kernels. When recognizing new objects, they will refer to some image, not necessarily the same. The final decision is made by "vote" - the object refers to the image to which it was attributed to a greater number of parallel streams.

Abstract Background: The combination of low intensity therapy with inotuzumab ozogamicin improved survival compared to intensive chemotherapy and to single agent inotuzumab ozogamicin in first salvage (Jabbour et al. Cancer. 2018 (in press)). The incidence of veno-occlusive disease (VOD) is minimized with weekly divided dosage and reduced dose of inotuzumab ozogamicin per cycle. Blinatumomab single agent improves survival in relapsed / refractory ALL compared to that of standard chemotherapy. The sequential addition of blinatumomab to mini-hyper-CVD + inotuzumab ozogamicin might further improve survival and minimize the risk of veno-occlusive disease (VOD) by allowing a reduction of inotuzumab dose and spacing allogeneic stem cell transplant (ASCT) from the last dose of inotuzumab. Methods: Patients with relapsed / refractory Philadelphia chromosome negative ALL were eligible. The mini-hyper-CVD (cycles 1, 3, 5, 7) comprised cyclophosphamide (150 mg/m2 every 12 h on days 1-3), vincristine (2 mg flat dose on days 1 and 8), and dexamethasone (20 mg on days 1-4 and days 11-14) without anthracycline. Even cycles (cycles 2, 4, 6, 8) comprised methotrexate (250 mg/m2 on day 1) and cytarabine (0.5 g/m2 given every 12 h on days 2 and 3). Rituximab and intrathecal chemotherapy were given for first 4 courses. Inotuzumab ozogamicin was originally given on day 3 of the first four cycles at the dose of 1.3-1.8 mg/m2 at cycle 1, followed by 1.0-1.3 mg/m2 in subsequent cycles. After 67 pts were treated, an amendment was made to incorporate 4 cycles of blinatumomab after 4 cycles of mini-hyper-CVD + inotuzumab ozogamicin. Inotuzumab ozogamicin was given on days 2 and 8 at the dose of 0.6 and 0.3 mg/m2 at cycle 1, respectively, followed by days 2 and 8 at the dose of 0.3 and 0.3 mg/m2 at subsequent cycles; blinatumomab was continuously infused over 28 days every 42-day cycle for 4 cycles. The decision to proceed with ASCT was based on the discretion of the treating physician after discussion with the patient. Results: From 2/2013 to 5/2018, 84 patients were enrolled and treated including 17 patients with mini-hyper-CVD + inotuzumab + blinatumomab. The median follow-up is 31 months (range, 0.1-64.1). Patient characteristics and outcome are summarized in Table 1. The median age was 35 years (range, 9-87), and 23% of patients had received prior ASCT. The overall response rate was 80% (CR, 58%, CRp/CRi, 21%). These rates were 92% in S1 (primary refractory, 100%; CR1 duration <12 months, 82%; CR1 duration >12 months, 100%) and 56% in S2, and 60% in S3 or higher. Among 64 evaluable patients for minimal residual disease (MRD) assessment, 51 patients (80%) achieved negative MRD by 6-color flow cytometry with higher rates of negative MRD at 85% in salvage 1. Thirty four patients (40%) received ASCT. Three-year CR duration and overall survival (OS) rates were 49% and 33%, respectively (Figure 1). The median OS was 25 months, 6 months, and 7 months in salvage 1, salvage 2, and salvage 3 or more, respectively (p=0.001). Historical comparison showed median OS of 14 months and 6 months in hyper-CVD + inotuzumab ozogamicin +/- blinatumomab and inotuzumab ozogamicin single agent, respectively (p=0.001) (Figure 2). Among the 79 evaluable patients, VOD was observed in 9 (11%). The incidence of VOD was reduced from 9/61 (15%) with single dose of inotuzumab ozogamicin to 0/18 (0%) with weekly divided dose schedule. Of the 17 patients treated with mini-hyper-CVD + inotuzumab ozogamicin + blinatumomab, 3 patients underwent ASCT (2, haploidentical transplant; 1, cord blood transplant). Conclusion: The combination of inotuzumab ozogamicin plus/minus blinatumomab with low-intensity mini-hyper-CVD chemotherapy is effective and shows encouraging results in patients with relapsed/refractory ALL. The risk of VOD can be minimized with fractionated inotuzumab ozogamicin dosing. Disclosures Sasaki: Otsuka Pharmaceutical: Honoraria. Ravandi:Xencor: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Jazz: Honoraria; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Abbvie: Research Funding; Sunesis: Honoraria; Astellas Pharmaceuticals: Consultancy, Honoraria; Sunesis: Honoraria; Bristol-Myers Squibb: Research Funding; Jazz: Honoraria; Abbvie: Research Funding; Orsenix: Honoraria; Xencor: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Macrogenix: Honoraria, Research Funding; Macrogenix: Honoraria, Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Orsenix: Honoraria; Bristol-Myers Squibb: Research Funding. Short:Takeda Oncology: Consultancy. Jain:Verastem: Research Funding; Abbvie: Research Funding; Abbvie: Research Funding; BMS: Research Funding; Seattle Genetics: Research Funding; Genentech: Research Funding; Cellectis: Research Funding; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Pfizer: Research Funding; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Infinity: Research Funding; Genentech: Research Funding; Infinity: Research Funding; Astra Zeneca: Research Funding; Celgene: Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Servier: Research Funding; Pharmacyclics: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Incyte: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Research Funding; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cellectis: Research Funding; Adaptive Biotechnologioes: Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Verastem: Research Funding; Servier: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Research Funding; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologioes: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Konopleva:Stemline Therapeutics: Research Funding. Champlin:Otsuka: Research Funding; Sanofi: Research Funding. Kadia:Pfizer: Consultancy, Research Funding; BMS: Research Funding; Jazz: Consultancy, Research Funding; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Research Funding; Abbvie: Consultancy; Abbvie: Consultancy; Takeda: Consultancy; Novartis: Consultancy; Celgene: Research Funding; BMS: Research Funding; Novartis: Consultancy. Cortes:Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding. Jabbour:Takeda: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: Research Funding; Pfizer: Consultancy, Research Funding.

205 Background: NEOCOPING study analyzes the influence of clinicopathological, personal and social variables on coping strategies and quality of life (QoL) in patients with resected tumors at the time of starting adjuvant chemotherapy. Methods: NEOCOPING is a prospective, multicenter, observational study that involves 19 centers and 34 researchers. Applied main questionnaires were: Mini-Mental Adjustment to Cancer (MAC), EORTC QLQ-C30, Brief Symptom Inventory (BSI-18), Shared Decision Making Questionnaire (SDM-Q) patient and doctor, and Multidimensional Scale of Perceived Social Support (MSPSS). Results: Table summarizes the characteristics of the first 71 patients enrolled. The most used coping strategies were fighting spirit (X=75.8, SD=25.9) and avoidance (X=64.6, SD=25); most patients were found to have good QoL (X=76.5, SD=16.6). Most did not have psychiatric symptoms, and were pleased with family and social support perceived. Patients were very satisfied with the information received (X=83, SD=19.9), and shared opinions with the doctor (X=90, SD=22.9). QoL was significantly negatively correlated with depression (r=-.688, r=.0001), anxiety (r=.-655, p=.0001), somatization (r=-.638, p=.0001) and hopelessness (r =-.287, p =.033). Depression and somatization predicted 54.8% of the QoL of this sample (F=23,636, p=.0001). Conclusions: Even though patients have a good QoL, adaptative coping strategies and no noticeable psychopathological symptoms at baseline, these symptoms may influence the well-being perception and modulate personal adaptations to the diagnosis and treatment of cancer at a curable stage. [Table: see text]

The international development sector is increasingly implementing collaborative approaches that facilitate a range of sectoral-level stakeholders to jointly address complex problems facing sustainable public service delivery, for which guidance does not explicitly exist. The literature on collaborative approaches has been built on experiences in high-income countries with vastly different governance capabilities, limiting their global relevance. A Delphi expert panel addressed this need by evaluating 58 factors hypothesized in the literature to contribute to the success of collaborative approaches. The panel rated factors according to their importance in low-income country contexts, on a scale from Not Important to Essential. Experts agreed on the importance of 49 factors, eight of which were essential for success. Rich qualitative data from open-ended responses revealed factors that may be unique to low-income country contexts and to service delivery applications, including how government capacity, politics, donor influence, and culture can influence decisions on structuring leadership and facilitation roles, appropriately engaging the government, and building legitimacy. Key considerations for future practice and research are summarized in a table in the appendix. This study contributes to both literature and practice by identifying the relative importance of factors to consider when designing collaborative approaches in low-income countries with limited governance capabilities.

We modeled and combined outputs for 125 tree species for the eastern United States, using habitat suitability and colonization potential models along with an evaluation of adaptation traits. These outputs allowed, for the first time, the compilation of tree species’ current and future potential for each unit of 55 national forests and grasslands and 469 1 × 1 degree grids across the eastern United States. A habitat suitability model, a migration simulation model, and an assessment based on biological and disturbance factors were used with United States Forest Service Forest Inventory and Analysis data to evaluate species potential to migrate or infill naturally into suitable habitats over the next 100 years. We describe a suite of variables, by species, for each unique geographic unit, packaged as summary tables describing current abundance, potential future change in suitable habitat, adaptability, and capability to cope with the changing climate, and colonization likelihood over 100 years. This resulting synthesis and summation effort, culminating over two decades of work, provides a detailed data set that incorporates habitat quality, land cover, and dispersal potential, spatially constrained, for nearly all the tree species of the eastern United States. These tables and maps provide an estimate of potential species trends out 100 years, intended to deliver managers and publics with practical tools to reduce the vast set of decisions before them as they proactively manage tree species in the face of climate change.

10022 Background: LOGIC2 evaluates the benefit of a 3rd agent added to encorafenib (enco)/binimetinib (bini), selected at progression based on the genetic tumor evolution. Methods: In part I/run-In, pts were treated with enco/bini until disease progression (as defined per RECIST v1.1). Foundation One NGS was applied on a baseline sample and on a PD sample. Based on the genetic evolution between the biopsy at inclusion (bxI) and at progression (bxPD) and clinical considerations, pts entered part II and received one of four 3rd agent additions to enco/bini combinations: A. LEE011 (CDK4/6 inhibitor), B. BKM120 (PI3K inhibitor), C. INC280 (c-Met inhibitor), or D. BGJ398 (FGFR inhibitor). An adaptive Bayesian logistic regression model (BLRM) guided by the escalation with overdose control (EWOC) principle was used to make dose escalation decisions. Assessments include objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and safety. Data cutoff for this analysis was May 12, 2019. Data is as is. Part 1 of study is ongoing. Part 2 of study is closed to enrollment. Results: 58 pts enrolled into part II (group A=38; B=6; C=13; D=1). 29 pts were assigned to treatment based on bxPD results (Table). In groups A, B, and C, the confirmed ORR was 5.3%, 0%, and 0%, and the DCR was 26.3%, 16.7%, and 15.4%, with median PFS of 2.1, 1.6, and 2.2 months, respectively. Safety was consistent with known profiles of the individual agents. Conclusions: Triple therapy is feasible when a 3rd agent is added to enco/bini at progression based on genetic alterations, although activity observed was low. Further exploration to identify patterns of resistance susceptible to the addition of a 3rd agent is needed. Gene alterations for enrollment into part 2. Clinical trial information: NCT02159066. [Table: see text]

219Animal Biodiversity and Conservation 35.2 (2012)© 2012 Museu de Ciències Naturals de BarcelonaISSN: 1578–665XRosell, C. & Llimona, F., 2012. Human–wildlife interactions. Animal Biodiversity and Conservation, 35.2: 219–220. The nature of wildlife management throughout the world is changing. The increase in the world’s human population has been accompanied by a rapid expansion of agricultural and urban areas and infrastructures, especially road and railway networks. Worldwide, wildlife habitats are being transformed and fragmented by human activities, and the behavior of several species has changed as a result of human activities. Some species have adapted easily to urban or peri–urban habitats and take advantage of the new resources available. These data provide the context for why human–wildlife interactions are increasing. At the 30th International Union of Game Biologists Congress held in Barcelona in early September 2011, in addition to two plenary presentations, 52 authors from 12 different countries and three continents presented 15 papers in the Interactions of Humans and Wildlife Session, three of which are included in this volume. To some extent, all the papers reflected the inherent difficulty in solving the complex problems caused either by rapidly increasing species that begin to inhabit urban and agricultural areas in numbers not seen previously (e.g. coyo-tes, Canis latrans, inhabiting big cities; wild boar, Sus scrofa, across western Europe; wood pigeons, Columba palumbus, in France), or species whose populations are threatened by human activities (e.g., Eurasian Lynx, Lynx lynx, in the Czech Republic). Some papers addressed the contentious issue of predator control (e.g., gamebirds in Great Britain), while others presented data regarding how human activities influenced animal behavior (e.g., pink footed geese, Anser brachyrhynchus; and red deer, Cervus elaphus, in Germany). The papers presented at the congress show how human activities affect the distributions and dynamics of wildlife populations and also change the behavior of some species. Wildlife causes social and economic con-flicts by damaging agricultural and forest resources, bringing about traffic collisions, and creating problems for residents in urban areas; while many are increasingly distant from nature and may not accept the presence of wildlife others may actively encourage the presence of wild animals. The first paper in this volume, by Cahill et al. (2012), analyzes the management challenges of the increasing abundance of wild boar in the peri–urban area of Barcelona. This conflict has arisen in other large cities in Europe and elsewhere. The presence of the species causes problems for many residents, to such an extent that it is considered a pest in these areas. Wild boar habituation has not only been facilitated by population expansion, but also by the attitudes of some citizens who encourage their presence by direct feeding. This leads to wild boar behavior modification and also promotes an increase in the fertility rate of habituated females, which are significantly heavier than non–habituated females. Public attitudes regarding the species and harvesting methods (at present most specimens are removed by live capture and subsequently sacrificed) are highlighted as one of the key factors in the management of the conflict. The second paper provides an example of how the distribution of irrigated croplands influences wild boar roadkills in NW Spain (Colino–Rabanal et al., 2012). By modeling the spatial distribution of wild boar collisions with vehicles and using generalized additive models based on GIS, the authors show that the number of roadkills is higher in maize croplands than in forested areas. This factor is the main explanatory variable in the model. The paper provides an excellent example of how the synergies of diverse human elements in the landscape (maize croplands and roads in this case) affect the location and dimensions of these types of conflicts. The third and final paper, by Belotti et al. (2012), addresses the effects of tourism on Eurasian lynx movements and prey usage at Šumava National Park in the Czech Republic. The monitoring of 5 GPS–collared lynxes and analyses of data regarding habitat features suggests that human disturbance (proximity of roads and tourist trails) can modify the presence of lynxes during the day close to the site where they have hidden a prey item, such as an ungulate, that can provide them with food for several days. In such cases, adequate management of tourism development must involve a commitment to species conservation. The analyses and understanding of all these phenomena and the design of successful wildlife management strategies and techniques used to mitigate the conflicts require a good knowledge base that considers informa-tion both about wildlife and human attitudes. The papers presented stress the importance of spatial analyses of the interactions and their relationship with landscape features and the location of human activities. Species distribution and abundance are related to important habitat variables such as provision of shelter, food, comfor-table spaces, and an appropriate climate. Therefore, it is essential to analyze these data adequately to predict where conflicts are most likely to arise and to design successful mitigation strategies. The second key factor for adequate management of human–wildlife interactions is to monitor system change. An analysis of the variety of data on population dynamics, hunting, wildlife collisions, and wildlife presence in urban areas would provide a basis for adaptive management. In this respect, in the plenary session, Steve Redpath mentioned the importance of the wildlife biologist’s attitude when interpreting and drawing conclusions from recorded data and stressed the importance of conducting clear, relevant, and transparent science for participants involved in the management decision process, which often involves a high number of stakeholders. All of the papers addressing the problems associated with human wildlife interactions were characterized by a common theme. Regardless of the specific nature of the problem, the public was generally divided on how the problem should be addressed. A particularly sensitive theme was that of population control methods, especially when conflicts are located in peri–urban areas. Several presenters acknowledged that public participation was necessary if a solution was to be reached. Some suggested, as have other authors (Heydon et al., 2010), that a legislative framework may be needed to reconcile human and wildlife interests. However, each problem that was presented appeared to involve multiple stakeholders with different opinions. Solving these kinds of problems is not trivial. Social factors strongly influence perceptions of human–wildlife conflicts but the methods used to mitigate these conflicts often take into account technical aspects but not people’s attitudes. A new, more innovative and interdisciplinary approach to mitigation is needed to allow us 'to move from conflict towards coexistence' (Dickman, 2010). Other authors also mentioned the importance of planning interventions that optimize the participation of experts, policy makers, and affected communities and include the explicit, systematic, and participatory evaluation of the costs and benefits of alternative interventions (Treves et al., 2009). One technique that has been used to solve problems like these is termed Structured Decision Making (SDM). This technique was developed by the U.S. Geological Survey and the U.S. Fish and Wildlife Service. As described by Runge et al. (2009), the process is 'a formal application of common sense for situations too complex for the informal use of common sense', and provides a rational framework and techniques to aid in prescriptive decision making. Fundamentally, the process entails defining a problem, deciding upon the objectives, considering the alternative actions and the consequences for each, using the available science to develop a model (the plan), and then making the decision how to implement (Runge et al., 2009). Although complex, SDM uses a facilitator to guide stakeholders through the process to reach a mutually agreed–upon plan of action. It is clear that human–wildlife interactions are inherently complex because many stakeholders are usually involved. A rational approach that incorporates all interested parties would seem to be a productive way of solving these kinds of problems

Abstract Introduction: Invasive fungal infections (IFIs) are important causes of morbidity and mortality among patients (pts) with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS), and consensus guidelines recommend the use of mold-active antifungal prophylaxis (ppx), e.g., voriconazole (vori) or posaconazole (posa). Isavuconazole (ISAV), the most recently introduced triazole antifungal, is currently approved for the treatment of invasive aspergillosis and invasive mucormycosis. ISAV is an appealing option for ppx against IFIs in neutropenic pts with AML/MDS because of its extended spectrum, superior tolerability (over vori), fewer drug-drug interactions (than posa), absence of need for therapeutic drug monitoring (TDM) and lack of prolongation of the QT interval (enabling easier administration in pts receiving certain anti-leukemic targeted therapies, 5-HT3 antagonists, quinolones, etc). NCT03019939 is an investigator-initiated, phase 2, single-arm, open-label trial of primary antifungal ppx with ISAV in pts with AML/MDS. Methods: Previously untreated adult pts with AML or MDS who are or are anticipated to become neutropenic as a result of their first therapy for AML/MDS are eligible to participate in this ongoing trial. Accrual of 100 pts is planned. Stopping rules exist for both futility and toxicity, assuming equivalence between ISAV and the current standard of care, posa, in preventing breakthrough IFIs (expected rate, 5%). In pts who have begun definitive anti-leukemic treatment, ISAV must be initiated within 4 days. Use of systemic antifungal therapy for >72 hours during the week prior to ISAV initiation is not allowed. ISAV is administered orally as the pro-drug, isavuconazonium sulfate, and dosed per the US label. ISAV ppx is administered until recovery from neutropenia (absolute neutrophil count (ANC) ≥0.5 x 109/L and attainment of complete remission (CR), with or without complete count recovery, occurrence of a proven or probable IFI (per 2008 EORTC/MSG criteria), development of unacceptable toxicity, pt withdrawal or death, or for a maximum of 12 weeks. The primary endpoint is the incidence of proven/probable IFI during the study period (up to 30 days from the last dose of ISAV). ISAV plasma concentrations are determined immediately pre-dose on days 8 and 15 using a validated assay. Results: A total of 50 pts were enrolled between April 28, 2017 and July 10, 2018. Four pts did not begin ppx with ISAV (1 each could not complete caspofungin washout and screening tests in time, 2 insurance denials). Of the remaining 46 pts (Table 1), 2 remain on study. Reasons for going off study (n = 44) included achievement of CR with neutrophil recovery (n = 26), completion of 12 weeks of therapy (n = 7), possible IFI (n = 5), investigator decision (n = 2), death (n = 2, 1 disease progression, 1 cardiac arrest), probable IFI (n = 1) and transaminitis (n = 1). In these 44 pts, the median duration of ISAV ppx was 30 (10-86) days. Only 1 case of proven breakthrough IFI occurred: a gluteal abscess that later grew Candida glabrata in a pt who had come off study upon achievement of CR. One case of probable IFI (focal mass-like opacity with ground-glass halo on CT; elevated Aspergillus antigen in broncho-alveolar lavage (BAL) fluid) occurred. All 5 cases of possible IFI were based on pulmonary radiologic findings alone: lower respiratory fungal cultures remained negative at 4 weeks in all 5 pts, and galactomannan was not detected in serum or BAL fluid in any pt. Tolerability of ISAV was excellent, with mild transaminitis attributed to ISAV reported in 1 pt (2%). No pt experienced QTc prolongation while on ISAV. Plasma ISAV levels were measured in 62 blood samples from 34 individual pts, including 28 paired samples. Median (range) ISAV concentrations were 3.74 (2.03-7.65) and 4.1 (2.17-9.25) mcg/ml on days 8 and 15, respectively. There was no correlation between plasma ISAV concentrations (available in 4 of the 7 pts) and the occurrence of confirmed, probable or possible IFI. Conclusions: These results demonstrate ISAV to be a safe and effective alternative for antifungal ppx in treatment-naïve pts with AML/MDS undergoing induction therapy with a variety of different regimens. ISAV's weak inhibition of P-glycoprotein and lack of risk of QT prolongation may make ISAV particularly attractive for antifungal ppx in the era of recently approved or emerging AML therapies such as enasidenib, ivosidenib, midostaurin and quizartinib. Disclosures Bose: CTI BioPharma: Research Funding; Constellation Pharmaceuticals: Research Funding; Blueprint Medicines Corporation: Research Funding; Celgene Corporation: Honoraria, Research Funding; Incyte Corporation: Honoraria, Research Funding; Astellas Pharmaceuticals: Research Funding; Pfizer, Inc.: Research Funding. Wiederhold:Astellas Pharmaceuticals: Research Funding. Kadia:Celgene: Research Funding; Amgen: Consultancy, Research Funding; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy; Abbvie: Consultancy; Abbvie: Consultancy; BMS: Research Funding; Pfizer: Consultancy, Research Funding; BMS: Research Funding; Novartis: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Research Funding; Takeda: Consultancy; Jazz: Consultancy, Research Funding. Ravandi:Abbvie: Research Funding; Xencor: Research Funding; Xencor: Research Funding; Jazz: Honoraria; Orsenix: Honoraria; Jazz: Honoraria; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Abbvie: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Sunesis: Honoraria; Bristol-Myers Squibb: Research Funding; Sunesis: Honoraria; Astellas Pharmaceuticals: Consultancy, Honoraria; Astellas Pharmaceuticals: Consultancy, Honoraria; Seattle Genetics: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Macrogenix: Honoraria, Research Funding; Macrogenix: Honoraria, Research Funding; Orsenix: Honoraria. Thompson:Gilead Sciences: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Research Funding; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Pemmaraju:celgene: Consultancy, Honoraria; abbvie: Research Funding; SagerStrong Foundation: Research Funding; cellectis: Research Funding; samus: Research Funding; stemline: Consultancy, Honoraria, Research Funding; daiichi sankyo: Research Funding; novartis: Research Funding; plexxikon: Research Funding; Affymetrix: Research Funding. Daver:Sunesis: Consultancy; Otsuka: Consultancy; Incyte: Research Funding; BMS: Research Funding; Karyopharm: Research Funding; Daiichi-Sankyo: Research Funding; Kiromic: Research Funding; ARIAD: Research Funding; Incyte: Consultancy; Karyopharm: Consultancy; Pfizer: Research Funding; Sunesis: Research Funding; ImmunoGen: Consultancy; Novartis: Consultancy; Alexion: Consultancy; Novartis: Research Funding; Pfizer: Consultancy. Cortes:Arog: Research Funding; Pfizer: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding. Verstovsek:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Italfarmaco: Membership on an entity's Board of Directors or advisory committees. Konopleva:Stemline Therapeutics: Research Funding. Jain:Verastem: Research Funding; Astra Zeneca: Research Funding; Celgene: Research Funding; Celgene: Research Funding; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Verastem: Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; Genentech: Research Funding; Seattle Genetics: Research Funding; Adaptive Biotechnologioes: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Research Funding; Astra Zeneca: Research Funding; Genentech: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Research Funding; Cellectis: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Incyte: Research Funding; Infinity: Research Funding; Pfizer: Research Funding; ADC Therapeutics: Research Funding; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Research Funding; Adaptive Biotechnologioes: Research Funding; Infinity: Research Funding; BMS: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cellectis: Research Funding; Abbvie: Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; BMS: Research Funding; Pfizer: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novimmune: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. DiNardo:Karyopharm: Other: Advisory role; Medimmune: Other: Advisory role; Celgene: Other: Advisory role; Bayer: Other: Advisory role; Agios: Consultancy, Other: Advisory role; AbbVie: Consultancy, Other: Advisory role. Wierda:Genentech: Research Funding; AbbVie, Inc: Research Funding. Sasaki:Otsuka Pharmaceutical: Honoraria. Kontoyiannis:Astellas Pharmaceuticals: Research Funding.

Abstract Background Relative to the study of diagnostic reasoning, less is known about how clinicians make therapeutic decisions. Past work has explored how physicians choose particular antimicrobials in specific cases (antimicrobial therapeutic reasoning - ATR), but how pharmacists make similar determinations has remained unexplored. Understanding ATR by pharmacists could inform pharmacist education and improve antimicrobial stewardship (AS). Methods We conducted individual interviews with a purposeful sample of 11 pharmacists (5 ID specialist pharmacists and 6 non-specialists), adapting a protocol for semi-structured interviews utilizing clinical vignettes based on a prior study in physicians. In addition, participants were asked to describe their ATR process generally using a novel notecard exercise. Interviews were transcribed and analyzed with Dedoose, using the prior study’s codebook as an initial framework and adding and adapting codes through an iterative process. Results We found that pharmacists generally engage in the same major ATR steps (Naming the Syndrome, Delineating Pathogens, Selecting the Antimicrobial) previously described in physicians (Figure 1). Pharmacists also seemed to incorporate similar patient- and system-factors and to utilize “therapy scripts”. However, specific factors and therapy script categories did not overlap completely, with some new factors and nuances emerging (Table 1). Overall, the antimicrobial reasoning framework described for physicians encompassed pharmacists’ AR, but some pharmacists described “Revisiting the Syndrome” in light of the clinical data and in some cases pharmacists appeared to filter script options (for example, due to allergies) before proceeding. Figure 1 - Antimicrobial Therapeutic Reasoning Framework Table 1 - Factors Involved in Pharmacists’ Antimicrobial Reasoning Process Conclusion The framework describing pharmacist antimicrobial ATR and is similar to that in a prior study of physicians, with some nuances that may be attributable to the pharmacist’s reviewer role in AS. Application of this framework has potential to aid in teaching learners, identifying where error or bias may occur, improving multidisciplinary AS efforts, and providing a common framework for communication. Disclosures All Authors: No reported disclosures

Global sea-level rise (SLR) is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging (LiDAR) digital elevation models (DEMs) in support of SLR decision-making. A significant shortcoming in the methodology is the lack of comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service (VDatum). Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’ returns are essential, yet LiDAR providers may not remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation (reduced drainage coupled with higher water tables). Spatial analysis can identify potentially vulnerable ecosystems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.

Background Evidence-based guidelines for hemophilia management support shared decision-making (SDM) as a method for personalizing treatment decisions and achieving hemophilia control. Using a validated collaborative learning model (Sapir 2017), we evaluated patient and healthcare provider (HCP) perceptions regarding SDM and hemophilia treatment. Methods From April to June 2020, 161 patients and caregivers of patients with hemophilia and 66 HCPs participated in 1 of 6 live, virtual collaborative learning sessions developed with the National Hemophilia Foundation (Table 1). Before and after the sessions, patients and their providers completed tethered surveys to assess alignments and discordances in preferences, experiences, and concerns around hemophilia treatment and SDM. Results Patients and HCPs differed in their estimates of how often providers engage their patients in components of SDM (Figure 1; all comparisons P < 0.01). Relative to patients' responses, HCPs were more likely to report that they usually or always: askhow hemophilia is affecting the patient's quality of life (50% vs 71%), ask about the patient's goals for treatment (48% vs 67%), explain their goals for hemophilia treatment (48% vs 67%), describe different treatment options (46% vs 67%), explain the pros/cons of each treatment option (45% vs 71%), and work with the patient to create a treatment plan that fits the patient's needs and goals (52% vs 74%). When asked why patients are not more involved in treatment decisions, HCPs were more likely than patients to select the following reasons: patients trust the care team to make decisions on their behalf (42% HCPs, 26% patients), patients lack knowledge about hemophilia and available therapies (30% HCPs, 15% patients), and patients feel too overwhelmed to make decisions (27% HCPs, 5% patients). Conversely, HCPs were less likely to report that patients are already fully involved in treatment decisions (15% HCPs, 46% patients). In addition, 10% of patients reported that they are not more involved in treatment decisions because their care team never asks about their treatment goals and priorities. Patients and HCPs held discordant beliefs about the degree of patients' progress toward treatment goals. While HCPs estimated that 79% of their patients are on track to meet their goals, only 49% of patients described themselves similarly; instead, 51% reported that they are only somewhat on track, not on track, or unsure about their degree of progress. Notably, during the past year, 49% of patients treated 2 or more bleeds at home and 22% had 2 or more bleeds treated at an ER. Regarding switching hemophilia treatments, HCPs overestimated patients' concerns about whether a new plan will work for the patient's type of hemophilia (39% HCPs, 25% patients) and fear of side effects (30% HCPs, 22% patients). By comparison, providers correctly estimated patients' low degree of concern about adapting to a new treatment schedule (9% HCPs, 11% patients) and knowing how to treat a bleed (9% HCPs, 7% patients), but underestimated patients' concern about affording different treatment (3% HCPs, 8% patients). In total, providers underestimated how many patients would not worry about switching treatment (8% HCPs, 19% patients). Following the collaborative learning sessions, patients set goals to talk to their care team about their treatment goals (45%), consider their treatment options more closely (40%), take a more active role in treatment decision-making (38%), and notify their care team with concerns about their treatment (31%). HCPs made commitments to engage their patients more frequently in SDM (52%), increase the variety of educational materials they provide to patients (52%), educate their patients about wellness strategies and self-care (33%), and conduct additional small-group education sessions with their patients (30%). Conclusions Patients with hemophilia and their HCPs differed in their experiences, perceptions, and beliefs related to SDM and other key aspects of patient-centered care. Collaborative education can support improved knowledge, communication, and understanding between patients and providers, leading to greater engagement in SDM around personalized hemophilia care. Study Sponsor Statement The study reported in this abstract was funded by an independent educational grant from Genentech. The grantor had no role in the study design, execution, analysis, or reporting. Disclosures No relevant conflicts of interest to declare.

PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

Abstract Systematic evidence mapping offers a robust and transparent methodology for facilitating evidence-based approaches to decision-making in chemicals policy and wider environmental health (EH). Interest in the methodology is growing; however, its application in EH is still novel. To facilitate the production of effective systematic evidence maps for EH use cases, we survey the successful application of evidence mapping in other fields where the methodology is more established. Focusing on issues of “data storage technology,” “data integrity,” “data accessibility,” and “transparency,” we characterize current evidence mapping practice and critically review its potential value for EH contexts. We note that rigid, flat data tables and schema-first approaches dominate current mapping methods and highlight how this practice is ill-suited to the highly connected, heterogeneous, and complex nature of EH data. We propose this challenge is overcome by storing and structuring data as “knowledge graphs.” Knowledge graphs offer a flexible, schemaless, and scalable model for systematically mapping the EH literature. Associated technologies, such as ontologies, are well-suited to the long-term goals of systematic mapping methodology in promoting resource-efficient access to the wider EH evidence base. Several graph storage implementations are readily available, with a variety of proven use cases in other fields. Thus, developing and adapting systematic evidence mapping for EH should utilize these graph-based resources to ensure the production of scalable, interoperable, and robust maps to aid decision-making processes in chemicals policy and wider EH.

Background: While most patients (pts) with follicular lymphoma (FL) have excellent outcomes with frontline chemoimmunotherapy (CIT), a subset of pts will experience early progression, which is associated with inferior survival. Earlier identification of high-risk FL pts could allow for intervention with novel treatments to forestall early progression. Current prognostic tools are imperfect, particularly for pts receiving bendamustine-based regimens, and novel biomarkers are needed. In Hodgkin lymphoma, interim positron emission tomography (iPET) evaluated based on Deauville score (DS) is highly prognostic and is used to guide response-adapted therapy. The prognostic value of iPET using DS has not yet been assessed in a large population of FL pts receiving frontline CIT. We hypothesized that iPET would predict progression-free survival (PFS) in this population which could support PET-guided treatment approaches. Methods: We retrospectively identified pts with a diagnosis of FL (grade 1-3B) who initiated frontline CIT at Dana-Farber Cancer Institute from 1/2005-3/2019 and underwent an iPET after 2-4 cycles of CIT. Pts who received radiation (XRT) prior to CIT were included. Baseline, interim, and (when available) end-of-treatment (EOT) PET scans were reviewed by a nuclear medicine radiologist in a blinded fashion and assigned a DS of 1-5. Results: 118 pts were identified. The median age was 55 (range 26-82). 73 pts (62%) had grade 1-2 FL, 17 pts (14%) grade 3A, 15 pts (13%) grade 3B, 12 pts (10%) grade 3 NOS, and 1 pt (1%) grade not reported. FLIPI score was low for 32%, intermediate for 42% and high for 26%. In total, 5 pts (4%) received XRT before CIT. The most common CIT regimens were RCHOP (54%) and BR (42%) (Table 1). 107 pts (91%) received 6 cycles of CIT and 4 pts (3%) received 8 cycles, while 7 pts (6%) discontinued CIT after 4-5 cycles due to cytopenias (4), heart failure (1), infection (1), or pt decision (1). 88% of iPETs were performed after 3 cycles. iPET DS was 1 for 18%, 2 for 57%, 3 for 13%, 4 for 9%, and 5 for 3%. EOT PET was available for review for 112 pts (95%) and demonstrated DS of 1 for 32%, 2 for 56%, 3 for 3%, 4 for 4%, and 5 for 5%. After CIT, 29 pts (25%) received a median of 9 doses (range 1-13) of rituximab maintenance (RM) and 2 pts (2%) received consolidative XRT. With a median follow-up of 54 months (range 5-186), the 4-year (yr) PFS and overall survival (OS) for the entire cohort were 69% (95% CI 58-77%) and 94% (95% CI 87-98%), respectively. iPET was a significant predictor of PFS (p=0.0011 for 5 categories). Compared to pts with an iPET DS of 1-2, pts with a DS of 3 (HR 3.0, p=0.006) or a DS of 4-5 (HR 3.4, p=0.004) had inferior PFS (Figure 1) and were grouped together in a +iPET group (n=30) for all analyses. The 4-yr PFS for DS 1-2 and DS 3-5 pts were 77% and 46%, respectively (HR 3.2, p<0.001). iPET had similar prognostic value among pts receiving BR (HR 3.3 p=0.033) or RCHOP (HR 3.6, p=0.005) and retained significance when pts with grade 3B FL were excluded (HR 2.6, p=0.007). iPET was not predictive of OS (HR 1.6, p=0.48). EOT PET was also a significant predictor of PFS (p<0.0001 for 5 categories). 3 pts with a DS of 3 on EOT PET had favorable outcomes and were grouped with DS 1-2 pts. A positive EOT PET (defined as DS 4-5) was observed more frequently among pts with an iPET DS of 3-5 (9/29 pts; 31%) compared to an iPET DS of 1-2 (1/83 pts; 1%) (p<0.001). To determine if iPET provides additional prognostic information beyond EOT PET, we sorted pts into 4 groups based on iPET/EOT PET status (i.e. -/-, +/-, -/+, and +/+). Compared to -/- pts, +/- pts (HR 2.4, p=0.039), -/+ pts (HR 3.6, p=0.045) and +/+ pts (HR 9.1, p=<0.001) all had inferior PFS (Figure 2). A multivariable analysis confirmed that iPET (HR 2.9, p=0.017), EOT PET (HR 7.6, P<0.001), high FLIPI (HR 2.5, p=0.011), and RM (HR 0.3, p=0.015) were significant predictors of PFS, while CIT regimen (p=0.94) and grade (p=0.21) were not. Conclusions: Our study suggests that iPET may be a useful prognostic marker in FL. Additionally, iPET interpretation may be different in FL compared to other lymphomas. In this cohort, pts with a DS of 3 on iPET had inferior PFS with outcomes similar to those of pts with a DS of 4-5. A DS of 3-5 on iPET appears to predict earlier progression independent of EOT PET while providing response-driven prognostic information earlier in a patient's treatment course. If validated, these results suggest that iPET could be investigated as a tool for response-adapted treatment strategies in FL. Disclosures Salles: BMS/Celgene: Honoraria, Other: consultancy or advisory role; Kite, a Gilead Company: Honoraria, Other: consultancy or advisory role ; Epizyme: Honoraria, Other: consultancy or advisory role; Janssen: Honoraria, Other: consultancy or advisory role; MorphoSys: Honoraria, Other: consultancy or advisory role; Novartis: Honoraria, Other: consultancy or advisory role; Roche: Honoraria, Other: consultancy or advisory role; Abbvie: Other: consultancy or advisory role; Autolos: Other: consultancy or advisory role; Debiopharm: Consultancy, Honoraria, Other: consultancy or advisory role; Genmab: Honoraria, Other; Karyopharm: Honoraria; Takeda: Honoraria. Zelenetz:MEI Pharma: Research Funding; Celgene: Research Funding; Sandoz: Research Funding; Novartis: Consultancy; Gilead: Research Funding; Celgene: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnology: Consultancy; MorphoSys: Research Funding; Gilead: Consultancy; Genentech/Roche: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Roche: Research Funding. Brown:Janssen, Teva: Speakers Bureau; Gilead, Loxo, Sun, Verastem: Research Funding; Abbvie, Acerta, AstraZeneca, Beigene, Invectys, Juno/Celgene, Kite, Morphosys, Novartis, Octapharma, Pharmacyclics, Sunesis, TG Therapeutics, Verastem: Consultancy. Crombie:AbbVie: Research Funding; Bayer: Research Funding. Davids:Ascentage Pharma: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; BeiGene: Consultancy; Celgene: Consultancy; Eli Lilly: Consultancy; AbbVie: Consultancy; Adaptive Biotechnologies: Consultancy; Genentech: Consultancy, Research Funding; Janssen: Consultancy; Bristol Myers Squibb: Research Funding; Merck: Consultancy; Research to Practice: Honoraria; Syros Pharmaceuticals: Consultancy; Zentalis: Consultancy; Sunesis: Consultancy; Gilead Sciences: Consultancy; Novartis: Consultancy, Research Funding; MEI Pharma: Consultancy, Research Funding; Surface Oncology: Research Funding; Pharmacyclics: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Verastem: Consultancy, Research Funding. Fisher:Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees. Jacobsen:Merck: Consultancy; Acerta: Consultancy; Astra-Zeneca: Consultancy; Pharmacyclics: Research Funding; F. Hoffmann-LaRoche: Research Funding; Novartis: Research Funding; Takeda: Honoraria. LaCasce:BMS: Consultancy; Research to Practice: Speakers Bureau; UptoDate: Patents & Royalties. Armand:Sigma Tau: Research Funding; Tensha: Research Funding; Pfizer: Consultancy; Affimed: Consultancy, Research Funding; IGM: Research Funding; Adaptive: Consultancy, Research Funding; Celgene: Consultancy; Merck & Co., Inc.: Consultancy, Honoraria, Research Funding; Otsuka: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Infinity: Consultancy; ADC Therapeutics: Consultancy; Genentech: Research Funding.

Prior work has shown that the brain represents memories within a cognitive map that supports inference about connections between individual related events. Real-world adaptive behavior is also supported by recognizing common structure among numerous distinct contexts; for example, based on prior experience with restaurants, when visiting a new restaurant one can expect to first get a table, then order, eat, and finally pay the bill. We used a neurocomputational approach to examine how the brain extracts and uses abstract representations of common structure to support novel decisions. Participants learned image pairs (AB, BC) drawn from distinct triads (ABC) that shared the same internal structure and were then tested on their ability to infer indirect (AC) associations. We found that hippocampal and frontoparietal regions formed abstract representations that coded cross-triad relationships with a common geometric structure. Critically, such common representational geometries were formed despite the lack of explicit reinforcement to do so. Furthermore, we found that representations in parahippocampal cortex are hierarchical, reflecting both cross-triad relationships and distinctions between triads. We propose that representations with common geometric structure provide a vector space that codes inferred item relationships with a direction vector that is consistent across triads, thus supporting faster inference. Using computational modeling of response time data, we found evidence for dissociable vector-based retrieval and pattern-completion processes that contribute to successful inference. Moreover, we found evidence that these processes are mediated by distinct regions, with pattern completion supported by hippocampus and vector-based retrieval supported by parahippocampal cortex and lateral parietal cortex.

Connected and automated vehicles (CAVs) will undoubtedly transform many aspects of transportation systems in the future. In the meantime, transportation agencies must make investment and policy decisions to address the future needs of the transportation system. This research provides much-needed guidance for agencies about planning-level capacities in a CAV future and quantify Highway Capacity Manual (HCM) capacities as a function of CAV penetration rates and vehicle behaviors such as car-following, lane change, and merge. As a result of numerous uncertainties on CAV implementation policies, the study considers many scenarios including variations in parameters (including CAV gap/headway settings), roadway geometry, and traffic characteristics. More specifically, this study considers basic freeway, freeway merge, and freeway weaving segments in which various simulation scenarios are evaluated using two major CAV applications: cooperative adaptive cruise control and advanced merging. Data from microscopic traffic simulation are collected to develop capacity adjustment factors for CAVs. Results show that the existence of CAVs in the traffic stream can significantly enhance the roadway capacity (by as much as 35% to 40% under certain cases), not only on basic freeways but also on merge and weaving segments, as the CAV market penetration rate increases. The human driver behavior of baseline traffic also affects the capacity benefits, particularly at lower CAV market penetration rates. Finally, tables of capacity adjustment factors and corresponding regression models are developed for HCM implementation of the results of this study.

Abstract Background Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare disease. In the absence of consensus guidelines, treatment is controversial. Here, we describe the characteristics and outcomes of consecutive NLPHL patients diagnosed at Memorial Sloan Kettering Cancer Center (MSKCC) with a focus on active surveillance (AS) as a first-line management strategy. Methods We included consecutive patients aged 16 years (y) or older who were diagnosed and followed at MSKCC between 1974 and 2016. All cases were confirmed by a MSKCC hematopathologist as NLPHL without evidence of transformation. We excluded composite lymphomas and patients with any prior hematological malignancy or concomitant active cancer. We compared outcomes between patients followed expectantly (AS) and those treated actively (AT), which included radiotherapy (RT) only, chemotherapy (+/- Rituximab) (CT), combined modality (+/- Rituximab) (CMT), and rituximab monotherapy (R). Progression-free survival (PFS) was defined as time from diagnosis to a biopsy-proven disease progression or relapse, initiation of further treatment or death. PFS2 was defined as time from diagnosis to second biopsy-proven disease progression or relapse, initiation of 3rd-line treatment or death. AS was considered a 1st-line treatment. We used univariable and multivariable survival analyses stratifying patients by disease stage. Results In total, 163 patients were included. The median follow-up was 5.7y (Min-Max: 0.3 - 42.7) with 24.5% (n=40) of patients followed for 10y or more. Patient characteristics for all, AS and AT patients are shown in Table 1. Patients were treated with RT only (n=75, 46.0%), AS (n=37, 22.7%), CT (n=26, 15.9%), CMT (n=19, 11.7%) or R alone (n=6, 3.7%). Overall, outcome of patients was excellent, with 10-year PFS, PFS2 and overall survival (OS) estimates of 71.2% [95%-CI: 59.3-80.1], 92.5% [95%-CI: 83.7-96.6] and 96.6% [95%-CI: 87.6-99.1], respectively. Seven patients died, 3 deaths were likely treatment related and only one lymphoma related death occurred after progression. Twelve transformations to aggressive lymphomas occurred after a median of 7.0y (Min-Max: 0.4 - 15.6). The transformation rate was 0.99% per patient year. Twelve secondary cancers occurred after a median of 7.8y (Min-Max: 1.1 - 24.8). Only bulky disease ≥ 5 cm (n=21, HR: 3.1 [95%-CI: 1.3-7.2], p=0.008) and extranodal disease (n=11, HR: 7.7 [95%-CI: 2.1-28.5], p=0.002] were risk factors for a shorter PFS in the final multivariate model after correcting for received treatment. In pairwise comparison, PFS with CMT (p=0.038) and RT (p=0.032) was superior to AS, but CT was not (p>0.999). PFS2 and OS were not significantly different between groups. Patients, who received AT (n=126) overall tended to have superior PFS than those in the AS group (n=37) (5-year PFS 97.2% [95%-CI: 79.2-92.2] vs. 76.5% [95%-CI: 55.7-88.5], p=0.068), though this benefit was mainly seen in early stage disease (Ann Arbor I/II) (5-year PFS 94.2% [95%-CI: 86.7-97.6] vs. 65.1% [95%-CI: 43.5-80.2], p<0.001). This did not translate into better PFS2 (p=0.568) or OS (p=0.303) (Figure 1) and was confirmed in a multivariate model controlling for potential confounders that influenced the initial treatment decision. Comparing results in the AS and AT group, death occurred in 0 vs. 7 (0.0% vs. 5.6%), transformation in 2 vs. 10 (5.4% vs. 7.9%) and secondary cancers in 2 vs. 10 (5.4% vs. 7.9%) patients. Only 24.3% (n=9) of the 37 AS patients required treatment after a median of 5.1y (Min-Max: 0.3 - 23.2). Treatments after initial AS were localized radiation (n=4), R-CHOP (n=4) and R monotherapy (n=1) given for local symptoms (n=3), transformation (n=2), progressing nodes (n=2), systemic symptoms (n=1), adrenal insufficiency due to adrenal mass (n=1) and change of management strategy in stable disease (n=1). Conclusion NLPHL has an excellent prognosis. Bulky and extranodal disease were identified as potential risk factors for progression. With the limitations of a retrospective analysis, it can be concluded that AS is a viable management strategy in NLHPL. The majority of AS patients require no treatment after multiple years of observation and those that do, can be adequately managed with established treatments. Additionally, no evidence was found for an increased rate of transformation or worse PFS2 or OS in AS patients. Treatment related deaths exceeded deaths from lymphoma. Figure 1 Figure 1. Disclosures Moskowitz: Celgene: Consultancy; Genentech BioOncology: Consultancy; Seattle Genetics: Consultancy, Other: Ad Board, Research Funding; Merck: Consultancy, Research Funding; Pharmacyclics: Research Funding. Zelenetz: Genentech/Roche, GSK, Gilead, Celgene, Janssen, Hospira, Amgen, Takeda Pharma, Novartis, Nanostring Technologies, Portola Pharmaceuticals, Adaptive Biotechnology: Consultancy; GSK, Janssen, Roche, Gilead, Bristol Myers: Research Funding; Boehringer Ingelheim: Other: DMC Membership. Kumar: Seattle Genetics: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees. Moskowitz: Incyte: Research Funding; Bristol Myers-Squibb: Consultancy, Research Funding; Seattle Genetics: Honoraria, Research Funding; Takeda: Honoraria; ADC Therapeutics: Research Funding.

This paper focuses on stability relations for the Romanian main financial markets: capital, ForEx and monetary markets, as well as the intensity of the link between them and how they are interconnected, because this represents the best indicator of the situation of an economy, which is seen as a complex, adaptive and dynamic system, that is continuously changing. This analysis examines their deviation from the state of equilibrium, and what are the factors that modify this state. The study incorporates the markets evolution, their estimated volatilities, it shows that the most sensitive to the impact of a financial shock are the currency and the stock market. All the obtained results are correlated with events, news and market information from those particular moments to find explanations and understand the behavior of investors and how their decisions affected the market. Because of instability on some markets, investors started moving their finances to other markets, where they had more confidence, causing imbalances. Behavior of investors, as they react to the emergence of a shock, is decisive and extremely important in anticipating the effects that such a financial shock can produce. The values of the estimated volatilities were embedded into a volatility table to be easier to track their evolution over the period under review (2007 – 2018). Besides the financial crisis, there have been other events that have translated into a higher degree of volatility: raising the minimum wage, the Brexit, protests against corruption, the raise of salaries for the public workers which has created instability in the monetary market. The analysis continues with an estimate of a spillover index that only confirms the significant vulnerability period in the markets: 2010-2012, period during which the phenomenon of contagion may have occurred.

Background: The current standard of care in MM, lenalidomide (LEN), is frequently used as part of first-line (1L) treatment (tx) or as maintenance tx following autologous stem cell transplantation; therefore, there is a growing need for appropriate tx options in patients (pts) with relapsed/refractory MM (RRMM) previously treated with LEN. OPTIMISMM is the first and only phase 3 trial in early RRMM (median 1-2 prior lines) to specify prior LEN tx as an inclusion criterion. The trial showed a significant improvement in progression-free survival (PFS) with pomalidomide + bortezomib (BORT) + dexamethasone (DEX) (PVd) vs BORT + DEX (Vd) (median PFS 11.2 months (mo) [95% confidence interval (CI): 9.66-13.73] vs 7.1 mo [5.88-8.48]; hazard ratio [HR] = 0.61, 95% CI: 0.49-0.77; P < 0.0001). How the PVd results from the OPTIMISMM trial compare with results achieved with other tx options for LEN-exposed pts with RRMM has not been established. Aim: This analysis aimed to put the phase 3 OPTIMISMM (PVd) trial results into perspective by comparing with results achieved for other tx options post LEN. Methods: A systematic literature review (SLR) was conducted in May 2018 and updated in Dec 2018, in line with National Institute for Health and Care Excellence (NICE) and Cochrane guidelines, to identify randomized controlled trial (RCT) data on efficacy outcomes in LEN-exposed pts with early RRMM. Electronic database searches were performed in Embase®, MEDLINE, and the Cochrane Library, and study eligibility criteria were defined using the PICOS framework. Searches were restricted to Jan 2004 onward. Descriptive statistics were used to assess between-trial heterogeneity in study design, baseline demographics, and clinical characteristics. Where the evidence network and heterogeneity assessment suggested an indirect treatment comparison (ITC) was feasible, the analysis was conducted in the Bayesian framework, according to the NICE Decision Support Unit guidelines. Results: ENDEAVOR and CASTOR were the only relevant trials that reported PFS in pts with RRMM previously treated with LEN. Comparator txs included carfilzomib + DEX (Kd) (ENDEAVOR) and daratumumab + BORT + DEX (DVd) (CASTOR). OPTIMISMM was designed to prospectively evaluate pts who had prior LEN, whereas ENDEAVOR and CASTOR reported only pt subgroups with prior LEN; all studies reported the number of pts who were refractory to LEN, with values varying from 18% (DVd) in CASTOR to 71% (PVd) in OPTIMISMM (Table). Differences in prognostic baseline characteristics were noted between the overall study populations in OPTIMISMM and ENDEAVOR; as the corresponding data were not available for CASTOR, a full assessment of heterogeneity was not possible. Although Vd initially seemed to link the network of evidence, the comparator arm of the CASTOR trial had a fixed 8-cycle Vd tx duration, whereas pts randomized to the comparator arm in OPTIMISMM and ENDEAVOR received Vd continuously until disease progression. As the Vd arms could not be considered comparable, DVd was excluded from the ITC. Based on data from ENDEAVOR and OPTIMISMM, PVd could only be compared with Kd and Vd. Based on the ITC, Vd was associated with a statistically significant shorter PFS vs PVd (HR PVd vs Vd = 0.62, 95% credible interval [Crl]: 0.50-0.76) in the prior-LEN RRMM population. No statistically significant difference was observed in PFS for Kd vs PVd (HR PVd vs Kd = 0.90, 95% Crl: 0.62-1.28). It is important to note that pt characteristics vary between these trials, particularly regarding prior LEN. Discussion: Due to increasing use of LEN in the 1L setting and as maintenance tx, a growing population of pts with early RRMM are treated with LEN. This SLR found that OPTIMISMM is the only study to date to prospectively investigate the efficacy of regimens in this population. Only 2 other RCTs were identified that reported data for pts with prior LEN, and in both cases, these were subgroups of the overall trial population, thus limiting the robustness of the comparator data. HRs for PFS from the ITC aligned with those from OPTIMISMM, confirming the superiority of PVd over Vd. The ITC between PVd and Kd found no statistically significant difference between these regimens. Comparison with DVd was not possible given the differences in design between CASTOR and OPTIMISMM. Further studies in pts previously treated with LEN are warranted, given the impact of prior tx on outcomes for pts with early RRMM. Disclosures Weisel: Janssen: Consultancy, Honoraria, Research Funding; GSK: Honoraria; Takeda: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Research Funding; Adaptive Biotech: Consultancy, Honoraria; Celgene Corporation: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Juno: Consultancy. Dhanasiri:Celgene Corporation: Employment, Equity Ownership. Gauthier:Amaris Consulting: Employment, Equity Ownership; Celgene Corporation: Consultancy. Padhiar:Amaris Consulting: Employment. Casal:Celgene Corporation: Employment. Richardson:Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees.

Background: The outcome of patients with relapsed-refractory (R-R) acute lymphoblastic leukemia (ALL) is poor. Inotuzumab ozogamicin and blinatumomab are highly active single agents in R-R ALL. The combination of inotuzumab with low-intensity mini-hyper-CVD chemotherapy showed encouraging results in this patient population. The sequential addition of blinatumomab might optimize the efficacy of the regimen, reduce its toxicities, and further improve outcomes in R-R ALL. The aim of the analysis is to evaluate the efficacy and safety of inotuzumab ozogamicin plus low-intensity chemotherapy with or without blinatumomab in R-R ALL. Methods: Patients with relapsed-refractory Philadelphia chromosome-negative ALL were eligible. The mini-hyper-CVD (cycles 1, 3, 5, 7) were comprised of cyclophosphamide (150 mg/m2 every 12 h on days 1-3), vincristine (2 mg flat dose on days 1 and 8), and dexamethasone (20 mg on days 1-4 and days 11-14) without anthracyclines. Even cycles (cycles 2, 4, 6, 8) were comprised of methotrexate (250 mg/m2 on day 1) and cytarabine (0.5 g/m2 given every 12 h on days 2 and 3). Rituximab and intrathecal chemotherapy were given for the first 4 courses. Inotuzumab ozogamicin was originally given on day 3 of the first four cycles at the dose of 1.3-1.8 mg/m2 at cycle 1, followed by 1.0-1.3 mg/m2 in subsequent cycles. After 67 pts were treated, an amendment was made to incorporate 4 cycles of blinatumomab after 4 cycles of mini-hyper-CVD + inotuzumab ozogamicin. Inotuzumab ozogamicin was given on days 2 and 8 at the dose of 0.6 and 0.3 mg/m2 at cycle 1, respectively, followed by days 2 and 8 at the dose of 0.3 and 0.3 mg/m2 at subsequent cycles; blinatumomab was continuously infused over 28 days every 42-day cycle for 4 cycles. The decision to proceed with allogeneic hematopoietic cell transplantation (HCT) was based on the discretion of the treating physician after discussion with the patient. Results: From 2/2013 to 9/2019, 96 patients were enrolled and treated including 29 patients with mini-hyper-CVD + inotuzumab + blinatumomab. The median follow-up is 36 months (range, 0.1-87.5). Patient characteristics and outcome are summarized in Table 1. The median age was 37 years (range, 17-87), and 20% of patients had received prior HCT. The overall response rate was 80% (CR, 57%, CRp/CRi, 20%). These rates were 91% in salvage(S) 1 (primary refractory, 100%; CR1 duration <12 months, 84%; CR1 duration >12 months, 94%) 61% inS2, and 57% in S3 or higher. Among 75 evaluable patients for minimal residual disease (MRD) assessment, 62 patients (83%) achieved negative MRD by multi-color flow cytometry with higher rates of MRD negativity in S1 (89%). Forty four patients (46%) received HCT. Three-year CR duration and overall survival (OS) rates were 32% and 33%, respectively (Figure 1). The 3-year OS rates for patients treated in S 1 and S 2+ were 42% and 13%, respectively (p=0.002). Historical comparison showed median OS of 13 months versus 6 months in hyper-CVD + inotuzumab ozogamicin +/- blinatumomab versus inotuzumab ozogamicin single agent, respectively (p<0.001) (Figure 2). Among the 96 evaluable patients, VOD was observed in 10 (10%). The incidence of VOD was reduced from 9/67 (13%) with single dose of inotuzumab ozogamicin to 1/29 (3%) with fractionated dose schedule. Of the 17 patients treated with mini-hyper-CVD + inotuzumab ozogamicin + blinatumomab, 15 patients underwent HCT; among 15 patients, 1 patient developed VOD. Conclusion: The long-term follow-up of the combination of inotuzumab ozogamicin plus/minus blinatumomab with low-intensity mini-hyper-CVD chemotherapy continues to show promising results in patients with relapsed/refractory ALL. The risk of VOD can be minimized with a fractionated inotuzumab ozogamicin followed by blinatumomab schedule. Disclosures Sasaki: Novartis: Consultancy, Research Funding; Otsuka: Honoraria; Pfizer Japan: Consultancy; Daiichi Sankyo: Consultancy. Kantarjian:Aptitute Health: Honoraria; Pfizer: Honoraria, Research Funding; Sanofi: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive biotechnologies: Honoraria; Oxford Biomedical: Honoraria; Novartis: Honoraria, Research Funding; BMS: Research Funding; Delta Fly: Honoraria; Janssen: Honoraria; Daiichi-Sankyo: Honoraria, Research Funding; Ascentage: Research Funding; Jazz: Research Funding; BioAscend: Honoraria; Abbvie: Honoraria, Research Funding; Immunogen: Research Funding; Amgen: Honoraria, Research Funding. Ravandi:Macrogenics: Research Funding; Celgene: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Orsenix: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Xencor: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria. Short:Astellas: Research Funding; Takeda Oncology: Consultancy, Honoraria, Research Funding; Amgen: Honoraria; AstraZeneca: Consultancy. Kebriaei:Jazz: Consultancy; Amgen: Other: Research Support; Ziopharm: Other: Research Support; Pfizer: Other: Served on advisory board; Kite: Other: Served on advisory board; Novartis: Other: Served on advisory board. Jain:TG Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cellectis: Research Funding; ADC Therapeutics: Research Funding; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Incyte: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Aprea Therapeutics: Research Funding; Fate Therapeutics: Research Funding; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Precision Bioscienes: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BeiGene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Konopleva:Ascentage: Research Funding; F. Hoffmann La-Roche: Consultancy, Research Funding; Stemline Therapeutics: Consultancy, Research Funding; Kisoji: Consultancy; Forty-Seven: Consultancy, Research Funding; Agios: Research Funding; Eli Lilly: Research Funding; Rafael Pharmaceutical: Research Funding; Calithera: Research Funding; Ablynx: Research Funding; AstraZeneca: Research Funding; Sanofi: Research Funding; Cellectis: Research Funding; Reata Pharmaceutical Inc.;: Patents & Royalties: patents and royalties with patent US 7,795,305 B2 on CDDO-compounds and combination therapies, licensed to Reata Pharmaceutical; AbbVie: Consultancy, Research Funding; Amgen: Consultancy; Genentech: Consultancy, Research Funding. Garcia-Manero:Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Acceleron Pharmaceuticals: Consultancy, Honoraria; Novartis: Research Funding; Amphivena Therapeutics: Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Champlin:Takeda: Patents & Royalties; Omeros: Consultancy; DKMS America: Membership on an entity's Board of Directors or advisory committees; Cytonus: Consultancy; Johnson and Johnson: Consultancy; Actinium: Consultancy; Genzyme: Speakers Bureau. Kadia:Genentech: Honoraria, Research Funding; Astellas: Research Funding; Cyclacel: Research Funding; Cellenkos: Research Funding; Astra Zeneca: Research Funding; JAZZ: Honoraria, Research Funding; Celgene: Research Funding; Pulmotec: Research Funding; Abbvie: Honoraria, Research Funding; Amgen: Research Funding; BMS: Honoraria, Research Funding; Incyte: Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria; Ascentage: Research Funding. Daver:Fate Therapeutics: Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Trovagene: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; ImmunoGen: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Trillium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Syndax: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Research Funding; Servier: Research Funding; Genentech: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novimmune: Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees. Rafei:United States Provisiona: Patents & Royalties: I have a filed patent. O'Brien:Pharmacyclics: Research Funding; TG Therapeutics: Research Funding; Acerta: Research Funding; Vida Ventures: Consultancy; Pfizer: Research Funding; Celgene: Consultancy; Eisai: Consultancy; KITE: Research Funding; Regeneron: Research Funding; Amgen: Consultancy; Vaniam Group LL: Consultancy; AbbVie: Consultancy; Verastem: Consultancy; Janssen Oncology: Consultancy; Gilead: Consultancy; Sunesis: Research Funding; Alexion: Consultancy; Aptose Biosciences: Consultancy; Juno Therapeutics: Consultancy; GlaxoSmithKline: Consultancy; Astellas: Consultancy. Jabbour:Takeda: Other: Advisory role, Research Funding; BMS: Other: Advisory role, Research Funding; Amgen: Other: Advisory role, Research Funding; Genentech: Other: Advisory role, Research Funding; Adaptive Biotechnologies: Other: Advisory role, Research Funding; Pfizer: Other: Advisory role, Research Funding; AbbVie: Other: Advisory role, Research Funding.

Introduction: Patients (pts) with RRMM have a poor prognosis, with limited treatment options and poor overall survival (OS). Despite the increasing use of lenalidomide (len) in frontline treatment of multiple myeloma, len-refractory pts are often excluded from randomized studies. In the few studies including len-refractory pts, median progression-free survival (PFS) of <11 months has been reported with carfilzomib (K)/dexamethasone (d), daratumumab (DARA)/bortezomib (V)/d, pomalidomide (pom)/Vd, or elotuzumab/pom-d treatment. Therefore, there is an unmet need for effective therapies to treat pts with len-refractory RRMM. DARA is a human IgGκ monoclonal antibody targeting CD38 approved as monotherapy in RRMM and in combination with standard-of-care regimens for newly diagnosed multiple myeloma and RRMM. K is a proteasome inhibitor approved as monotherapy and in combination with len and d for RRMM. Here, with a median follow-up of 23.7 months, we present the final analysis of the treatment arm from a phase 1b study that evaluated DARA in combination with K and d (D-Kd) in pts with RRMM, including len-refractory pts. Methods: MMY1001 (NCT01998971) is a multi-arm, phase 1b study evaluating DARA in combination with various backbone therapies. Eligible pts with RRMM in the D-Kd arm had an ECOG score of ≤2, were treated with >1 prior line of therapy, including V and an immunomodulatory drug, and were K naïve. All pts (N = 85) received 28-day cycles of D-Kd until disease progression. DARA was given weekly for Cycles 1-2, every 2 weeks for Cycles 3-6, and every 4 weeks thereafter. 10 pts received the first DARA dose as a single infusion (16 mg/kg Cycle 1 Day 1) and 75 pts received a split first dose (8 mg/kg Cycle 1 Days 1 and 2). Pts received K weekly on Days 1, 8, and 15 of each cycle (20 mg/m2 initial dose, escalated to 70 mg/m2 thereafter), and d (40 mg) weekly. The primary endpoint was safety and tolerability. Overall response rate was a secondary endpoint, and minimal residual disease (MRD) was an exploratory endpoint. Results: Pts received a median of 2 (range, 1-4) prior lines of therapy. All pts received prior V; 81 (95%) received prior len (51 [60%] were len-refractory), and 13 (15%) received prior pom. 50 (59%) pts discontinued study treatment: 36 (42%) due to progressive disease, 6 (7%) due to patient withdrawal, 5 (6%) due to adverse event (AE), 2 (2%) due to physician decision, and 1 (1%) due to death. In the safety-analysis set (N = 85), the most common (>10%) grade 3/4 treatment-emergent AEs were thrombocytopenia (32%), lymphopenia (25%), anemia (21%), neutropenia (21%), hypertension (20%), and asthenia (15%). Serious AEs occured in 41 (48%) pts; 9%, 19%, and 14% were related to DARA, K, and d, respectively. Median left ventricular ejection fraction did not notably change over time from baseline (64% at baseline [n = 84], 62% at Cycle 6 [n = 54], 61% at Cycle 12 [n = 47], 59% at Cycle 18 [n = 22], and 63% at Cycle 24 [n = 10]). 7 (8%) pts experienced grade 3/4 cardiac AEs of interest, 2 (2%) pts each with cardiac failure and sinus tachycardia, and 1 (1%) patient each with acute pulmonary edema, left ventricular failure, atrial fibrillation, and myocardial ischemia. Infusion-related reactions occured in 6 of 10 (60%) and 31 of 75 (41%) pts receiving single- and split-first doses of DARA, respectively; the vast majority were mild (grade 1/2) and occurred during the first infusion. After a median follow-up of 23.7 (range, 0.5-34.7) months, median PFS was 25.7 months (95% confidence interval [CI], 14.8- not estimable [NE]; Figure) in all-treated and 22.3 months (95% CI, 12.0-NE) in len-refractory pts. The 24-month PFS rate was 53% in all-treated and 47% in len-refractory pts. Response and MRD-negativity rates are summarized in the Table. The median duration of response among pts that responded to treatment was 27.5 months (95% CI, 20.5-NE). A total of 32 (38%) pts received subsequent anticancer therapy, with a median time to subsequent therapy of 29.2 (95% CI, 19.5-NE) months. Median PFS on next line of therapy (PFS2) and OS were NE; the 24-month PFS2 rate was 61% (95% CI, 49-71) and the 24-month OS rate was 71% (95% CI, 60-80). The DARA pharmacokinetic profile was consistent with previous studies, and no pts were positive for anti-DARA antibodies. Conclusions: D-Kd was well tolerated, with no new safety concerns identified. Patients treated with D-Kd demonstrated deep responses and encouraging PFS outcomes, including in patients refractory to len. Disclosures Chari: Janssen, Celgene, Novartis Pharmaceuticals, Amgen, Bristol Myers Squibb, Pharmacyclics, Karyopharm, Sanofi, Seattle Genetics, OncoPeptides, Millenium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lonial:Celgene Corporation: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy. Martinez-Lopez:F. Hoffmann-La Roche Ltd: Honoraria; Janssen: Honoraria, Other: Advisory boards and Non-Financial Support ; Celgene: Honoraria, Other: Advisory boards and Non-Financial Support ; VIVIA Biotech: Honoraria; BMS: Honoraria, Other: Advisory boards; Incyte: Honoraria, Other: Advisory boards; Novartis: Honoraria, Other: Advisory boards; Amgen: Honoraria, Other: Non-Financial Support . Mateos:Pharmamar: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; EDO: Membership on an entity's Board of Directors or advisory committees. Bladé:Irctures: Honoraria; Janssen, Celgene, Amgen, Takeda: Membership on an entity's Board of Directors or advisory committees. Oriol:Celgene, Amgen, Takeda, Jansse: Consultancy, Speakers Bureau. Rodriguez Otero:Kite Pharma: Consultancy; Takeda: Consultancy; BMS: Honoraria; Celgene Corporation: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria. Jakubowiak:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Juno: Consultancy, Honoraria; SkyLineDx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Honoraria; KaryoPharm Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Olyslager:Janssen: Employment, Equity Ownership. Wang:Janssen: Employment. Nnane:Janssen: Employment, Equity Ownership. Ukropec:Janssen: Employment, Equity Ownership. Shreeve:Johnson & Johnson: Equity Ownership; Janssen: Employment. Qi:Janssen: Employment. Moreau:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. OffLabel Disclosure: In the submitted abstract, we present data from a phase 1b clinical trial of a combination regimen that is not currently approved for the treatment of relapsed/refractory multiple myeloma. Components of the combination regimen including daratumumab and carfilzomib are, however, approved as monotherapy or in combination with standard-of-care regimens for treatment of patients with relapsed/refractory multiple myeloma.

Background Recently, treatment options for RRMM have increased substantially with multiple approvals of novel agents/combination, making the treatment algorithm increasingly complex, with changes driven chiefly by access to novel agents/regimens. Furthermore, patient (pt) and disease characteristics have a profound impact on treatment decisions. To understand the impact of recently approved novel regimens on real-world (RW) treatment patterns, we conducted a multi-national survey to investigate the management of RRMM across Europe. Methods Retrospective, anonymized data from RRMM pts, treated in academic or community hospitals/clinics in 8 countries were extracted from Jan 2016 to Dec 2018. Data were analyzed overall and for Germany, Austria, and Switzerland (DACH) vs other countries (Belgium, France, Greece, Spain and UK) due to differences in treatment access. Results The cumulative number of pts included was 2782 in 2016, 3902 in 2017, and 4658 in 2018. Of the pts enrolled in 2016, 2017 and 2018, 40%, 49% and 51%, respectively, were in 3rd+ line (≥3L), potentially reflecting the increasing availability of treatment options for RRMM and extended survival in MM. Median age at diagnosis in pts enrolling in 2016, 2017, and 2018, was 68, 69, and 70 years, respectively, with 23%, 24%, 26% aged >75 years, underlining the fact that MM remains a disease of the elderly. The data revealed a difficult-to-treat RW population: 31%-36% of pts had an ECOG PS ≥2 at 2nd line (2L) in 2016-2018; increasing to 44%-49% at 4th+ lines (≥4L). At 2L, 42%-45% of pts presented ≥1 treatment-dependent comorbidity in 2016-2018, including hypertension (23-27%) and renal impairment (9-10%). Cytogenetic risk, evaluated in 38%-42% of pts at initial diagnosis, was reported as high in 8%-10% of the total population. Treatment initiation due to biochemical relapse was reported in 33%/36% of pts at 2L/3L in 2016, and in 30%/28% in 2018, indicating that ~1/3 of pts manifested an asymptomatic rather than clinical relapse. The proportion of pts treated with triplet regimens increased from 26%, 26%, and 30% at 2L, 3L and ≥4L in 2016 to 43%, 40%, and 38% in 2018, reflecting the adoption of newly approved triplets in RRMM, particularly in DACH countries. Use of proteasome inhibitor (PI)-based regimens increased from 35%, 30% and 34% at 2L, 3L and ≥4L in 2016, to 43%, 37% and 37% in 2018, driven by increased/earlier use of novel PIs (carfilzomib and ixazomib). These trends were more obvious in DACH, highlighting the impact of earlier access to modern treatment in these countries. Similarly, the proportion of pts on daratumumab-based regimens increased from 0, 5%, and 20% at 2L, 3L and ≥4L in 2016, to 10%, 24% and 31% in 2018. From 2016 to 2018, prior IMiD exposure at 2L increased from 11% to 20% in DACH, but remained stable at 42% in other countries; at 3L, there was an increase from 77% to 82% in all countries reflecting the uptake of novel triplet combinations. Most pts were IMiD-exposed or IMiD-refractory at ≥4L. Regarding the treatment algorithm, the rate of PI-based treatment at 1L was 74%-75%. PI- to IMiD-based therapy was the commonest treatment sequence from 1L to 2L, at 64%-66%, while PI- to PI-based therapy at 1L to 2L increased from 22% in 2016 to 30% in 2018. Key disease/pt characteristics associated with the selection of regimens at 2L and 3L are summarized in the Table. Prior IMiD treatment limited the use of IMiD-based therapy in subsequent lines. The use of KRd, IRd and DRd was mostly associated with ISS stage III, while the use of KRd was less frequently reported in pts with cardiac comorbidities. In pts with prior PI treatment, KRd and IRd (but not Kd) were more common at 2L, while DRd was preferred at 3L. A higher proportion of fit, young, or prior-SCT pts were treated with KRd or DRd, while IRd was the preferred treatment in pts with biochemical relapse. Conclusions Multiple drug approvals for RRMM in Europe have resulted in marked changes in the treatment algorithm, with a more immediate impact in countries with earlier access to new treatment options. Multiple decision drivers such as age, fitness, comorbidities and prior treatment are associated with uptake of different novel regimens at 2L and 3L. The increasing range of treatment options has resulted in pts receiving more lines of therapy for RRMM, highlighting the need for cautious planning of treatment sequencing to optimize the use of available combinations according to pt characteristics and disease factors. Disclosures Merz: Janssen: Other: Travel grants; Amgen: Membership on an entity's Board of Directors or advisory committees, Other: Travel grants; Abbvie: Other: Travel grants; Celgene: Other: Travel grants; Takeda Vertrieb GmbH: Other: Travel grants, Research Funding. Pérez:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees. Kolb:Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Other: travel and registration for my participation to international medical congres (ASH). Symeonidis:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Tekeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees, Research Funding. Zomas:Takeda: Employment. Gonzalez:Takeda: Employment. Kellermann:Amgen: Research Funding; BMS: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Sanofi: Research Funding; Takeda: Research Funding. Goldschmidt:Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; John Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; ArtTempi: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; Mundipharma: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product.

Abstract Introduction: For transplant-ineligible patients (pts) with newly diagnosed MM, the efficacy of VMP was established in the phase 3 VISTA trial. To reduce toxicity of VMP, twice-weekly bortezomib (V) was limited to the first cycle or completely replaced with once-weekly V in subsequent VMP-based trials (GIMEMA MM-03-05, GEM2005MAS65, and ALCYONE), and recent guidelines have recommended a less-intensive VMP schedule (Moreau P, et al. Ann Oncol 2017. 28[suppl_4]:iv52-iv61). In the absence of clinical trials directly comparing the VISTA VMP dosage regimen with modified VMP regimens, a matching adjusted indirect comparison (MAIC) provides a means to compare absolute treatment effects across diverse populations while minimizing the risk of bias associated with a naive indirect comparison. The objective of the present analysis is to compare the efficacy and safety of a modified dosing schedule of V in VMP-based regimens vs the dosing schedule established in VISTA for transplant-ineligible MM. Methods: Primary analysis was a comparison of the VMP schedule of VISTA vs modified VMP schedules pooled from the ALCYONE (once-weekly V during Cycles 2-9) and GIMEMA MM-03-05 trials; only the once-weekly V schedule (Cycles 1-9) from GIMEMA MM-03-05 (GIMEMA-QW) was included in this analysis. GEM2005MAS65 was excluded from the primary analysis because there was no control arm without maintenance (all patients received V-based maintenance after VMP). The supplemental analysis was a comparison of VISTA vs pooled modified VMP schedules from all 3 trials. Individual pt-level data (IPD) were obtained from the sponsor for the VISTA and ALCYONE trials, and a published validated method was used to reconstruct IPD for both progression-free survival (PFS) and overall survival (OS) of the GIMEMA-QW and GEM2005MAS65 trials (Guyot P, et al. BMC Med Res Methodol 2012.12:9). For each analysis, two comparisons were performed, a naive comparison and MAIC. The naive comparison made no adjustments. MAIC comparison weighted individual pts in the VISTA VMP treatment arm to match the baseline characteristics to those in the pooled VMP-modified treatment arms. Available effect modifiers and prognostic factors included age, gender, International Staging System, β2-microglobulin, albumin, serum creatinine, creatinine clearance, and cytogenetic risk. For each pt in the VISTA VMP arm, a weight was attached based on propensity scores. These weights were then used to calculate weighted outcomes. Results: A total of 344 pts received VMP in the VISTA trial and 356, 191, and 130 pts received modified VMP in the ALCYONE, GIMEMA-QW, and GEM2005MAS65 trials, respectively. Baseline demographics and clinical characteristics are provided in Table 1. The primary analysis of VISTA vs pooled data from GIMEMA-QW and ALCYONE revealed that there were no significant differences in median PFS between VISTA (20.7 months) vs GIMEMA-QW and ALCYONE (19.6 months) in the naive comparison (HR: 0.96; 95% CI, 0.77-1.20; P = 0.74) and 23.1 vs 19.6 months in the MAIC (HR: 0.99; 95% CI, 0.76-1.30; P = 0.96; Figure 1A). Similarly, there were no significant differences between the observed overall response rates for the VISTA schedule and the modified VMP dosing schedule for both the naive (75% vs 76%; P = 0.63) and MAIC (77% vs 76%; P = 1.00) comparisons. For OS, survival data from ALCYONE have not yet matured, thus the median OS values for the pooled GIMEMA-QW and ALCYONE data were not reached. Median OS in VISTA was 56.4 months for the naive comparison and 58.1 months for MAIC (naive HR: 0.92; 95% CI, 0.70-1.21; P = 0.54; MAIC HR: 0.94; 95% CI, 0.68-1.28; P = 0.68; Figure 1B). For safety, incidences of peripheral neuropathy were significantly reduced with the modified VMP dosing schedule compared with the VISTA schedule for both the naive (all-grade: 32% vs 47%; P <0.0001; Gr. 3/4: 4% vs 13%; P <0.0001) and MAIC (all-grade: 32% vs 48%; P <0.0001; Gr. 3/4: 4% vs 11%; P = 0.001) comparisons. The supplemental analysis showed consistent results to the primary analysis for both efficacy and safety outcomes. Conclusions: As naive, indirect comparisons are prone to bias due to patient heterogeneity between studies, a MAIC can provide useful insights for clinicians and reimbursement decision-makers on the relative efficacy and safety of different treatments. Our MAIC analysis demonstrates similar efficacy of modified VMP with VISTA VMP and a potential reduction in rates of peripheral neuropathy. Disclosures San-Miguel: Sanofi: Honoraria; Novartis: Honoraria; BMS: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Roche: Honoraria. Mateos:Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees. Goldschmidt:Amgen: Consultancy, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Adaptive Biotechnology: Consultancy; Janssen: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Mundipharma: Research Funding; Novartis: Honoraria, Research Funding; ArtTempi: Honoraria. Sonneveld:Amgen: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding. Dimopoulos:Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria; Bristol-Myers Squibb: Honoraria. Heeg:Ingress-health Nederland BV: Employment, Equity Ownership, Research Funding. Hashim:Ingress Health: Employment. Deraedt:Janssen Research & Development, LLC: Employment. Hu:Janssen Research & Development, LLC: Employment. Lam:Janssen Global Services, LLC: Employment. He:Janssen global services: Employment.

Background: Ibrutinib (IBR), a BTK inhibitor, and venetoclax (VEN), a BCL-2 inhibitor are approved for patients (pts) with CLL. The rationale for combining IBR and VEN includes: 1) preclinical models showing synergism, 2) non-overlapping toxicities; 3) non-overlapping mechanisms of action. We recently reported results of the first-line cohort of an investigator-initiated phase II trial of combined IBR and VEN for pts with CLL (Jain N et al. NEJM 2019). Here we report results of the 80 pts from the R/R CLL cohort of the trial (NCT02756897). Methods: Pts with R/R CLL meeting 2008 IWCLL treatment criteria were enrolled. Pts received IBR monotherapy (420 mg daily) for 3 cycles followed by addition of VEN (weekly dose-escalation to the 400mg daily target dose). Combined therapy was administered for 24 cycles. Pts with bone marrow (BM) undetectable MRD (U-MRD) (multi-color flow cytometry; sensitivity 10-4) at 24 cycles of combined therapy stopped both VEN and IBR; MRD+ pts could continue IBR. Response assessments were performed using blood, BM and CT imaging (2008 IWCLL criteria) at the following time-points (after cycle 3 of IBR monotherapy, and then after cycles 3, 6, 9, 12, 18, and 24 of combined therapy). Progression-free survival (PFS) was assessed as the time from the start of study drug to CLL progression, Richter transformation, or death. Overall survival (OS) was assessed as the time from the start of study drug to death. Results: A total of 80 pts were enrolled. The median age was 61.5 yrs (32-79). The baseline characteristics are shown in Table 1. Overall, 30 (38%) pts had a TP53 aberration. The median follow-up for all pts is 22.3 months. Of the 80 pts, 1 pt was later reclassified as splenic marginal zone lymphoma and is excluded from further analyses. Five pts came off study during IBR monotherapy phase (reasons listed below). 74 pts initiated VEN. Serial BM MRD responses are shown in Figure 1. One pt with a prior allo-SCT had no marrow disease at screening and is therefore excluded from serial MRD analyses. After 3 cycles of IBR monotherapy, none of the 73 pts achieved BM U-MRD. After addition of VEN, increasing proportions of pts achieved BM U-MRD remission. After 3 cycles of the combination, 7/68 (10%) achieved BM U-MRD remission. After 6 cycles of the combination, 15/67 (22%) achieved BM U-MRD remission. After 12 cycles of the combination, 29/60 (48%) achieved BM U-MRD remission. After 24 cycles of the combination, 16/24 (67%) achieved BM U-MRD remission. To assess the incremental benefit of the combined IBR and VEN beyond the first 12 cycles, we analyzed the MRD for pts who had received 24 cycles of combined therapy (n=24). Among these 24 pts, 13 were MRD+ at end of 12 cycles; 4/13 (31%) became U-MRD at end of 18 cycles of combination. Similarly, among these 24 pts, 9 were MRD+ at end of 18 cycles of combination; only 1/9 (11%) achieved U-MRD after 24 cycles of combination. PFS and OS are shown in Figure 2. Two pts had CLL progression after completing 24 cycles of combined therapy. 1 pt was in U-MRD remission at 24 cycles and stopped both IBR and VEN per study design; he relapsed 3 months later and responded to IBR monotherapy. The second pt was MRD+ at 24 cycles of combined therapy and continued IBR monotherapy after 2 yrs per study design; he relapsed few months later and is now in remission post CD19 CAR-T. One pt developed Hodgkin's transformation. Two pts died; both due to infectious complications during the IBR monotherapy. One pt developed severe cytopenias during the VEN dose escalation, was non responsive to multiple therapies for worsening cytopenias, then underwent an allogeneic stem cell transplant. One pt, with prior FCR therapy developed MDS. A total of 15 (19%) pts have come off trial; 5 pts came off study during IBR monotherapy (death from infection, n=2; skin rash, n=1; insurance issue, n=1; pt decision, n=1). Three pts came off trial during VEN ramp up (cytopenias, n=2; noncompliance, n=1). Four pts came off trial during the combination phase of IBR and VEN (arthralgia, n=1; Hodgkin's transformation, n=1; renal cancer, n=1; MDS, n=1). Three pts came off trial after completing 24 cycles of combined therapy (CLL progression, n=2; pt decision to continue VEN beyond 24 cycles, n=1). Grade 3-4 neutropenia occurred in 29% pts. Grade 3-4 thrombocytopenia occurred in 3% pts. Atrial fibrillation occurred in 7 (9%) pts. Conclusions: Combined VEN and IBR is an effective well-tolerated chemotherapy-free oral regimen for pts with R/R CLL. Disclosures Jain: Janssen Pharmaceuticals, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Precision Biosciences: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics, an AbbVie company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; ADC Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding. Thompson:Gilead: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; Pharmacyclics: Research Funding; Pfizer: Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Research Funding. Burger:Gilead Sciences: Research Funding; Pharmacyclics, an AbbVie company: Research Funding; Janssen Pharmaceuticals: Consultancy, Honoraria; Aptose Biosciences, Inc: Research Funding; BeiGene: Research Funding; AstraZeneca: Honoraria. Borthakur:Cyclacel: Research Funding; Eli Lilly and Co.: Research Funding; PTC Therapeutics: Consultancy; AbbVie: Research Funding; Xbiotech USA: Research Funding; FTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Research Funding; Agensys: Research Funding; Bayer Healthcare AG: Research Funding; AstraZeneca: Research Funding; BMS: Research Funding; Oncoceutics, Inc.: Research Funding; Tetralogic Pharmaceuticals: Research Funding; Merck: Research Funding; Arvinas: Research Funding; Polaris: Research Funding; Strategia Therapeutics: Research Funding; Argenx: Membership on an entity's Board of Directors or advisory committees; Cantargia AB: Research Funding; NKarta: Consultancy; BioLine Rx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Eisai: Research Funding; Janssen: Research Funding; GSK: Research Funding; BioTheryX: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding. Takahashi:Symbio Pharmaceuticals: Consultancy. Bose:Constellation: Research Funding; Astellas: Research Funding; Pfizer: Research Funding; Promedior: Research Funding; NS Pharma: Research Funding; CTI BioPharma: Research Funding; Incyte Corporation: Consultancy, Research Funding, Speakers Bureau; Blueprint Medicine Corporation: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Kartos: Consultancy, Research Funding. Fowler:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Bioline RX: Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; AbbVie: Consultancy, Research Funding. Konopleva:Ablynx: Research Funding; Eli Lilly: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Agios: Research Funding; Astra Zeneca: Research Funding; Forty-Seven: Consultancy, Honoraria; Cellectis: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Ascentage: Research Funding; Genentech: Honoraria, Research Funding; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Kisoji: Consultancy, Honoraria. Alvarado:Jazz Pharmaceuticals: Research Funding; Abbott: Honoraria. DiNardo:medimmune: Honoraria; agios: Consultancy, Honoraria; abbvie: Consultancy, Honoraria; syros: Honoraria; daiichi sankyo: Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; celgene: Consultancy, Honoraria; jazz: Honoraria. Pemmaraju:samus: Research Funding; abbvie: Consultancy, Honoraria, Research Funding; mustangbio: Consultancy, Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; novartis: Consultancy, Research Funding; plexxikon: Research Funding; Daiichi-Sankyo: Research Funding; sagerstrong: Research Funding; affymetrix: Research Funding; incyte: Consultancy, Research Funding; cellectis: Research Funding; celgene: Consultancy, Honoraria. Jabbour:Cyclacel LTD: Research Funding; Pfizer: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; BMS: Consultancy, Research Funding. Sasaki:Pfizer: Consultancy; Otsuka: Honoraria. Garg:Garglet LLC: Other: Owner; Enlitic inc.: Other: Advisor. Plunkett:Cyclacel Ltd: Research Funding. Kantarjian:Daiichi-Sankyo: Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria; Immunogen: Research Funding; AbbVie: Honoraria, Research Funding; Ariad: Research Funding; Astex: Research Funding; Cyclacel: Research Funding; Novartis: Research Funding; Jazz Pharma: Research Funding; Pfizer: Honoraria, Research Funding; Agios: Honoraria, Research Funding; BMS: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees. Wierda:Oncternal Therapeutics Inc.: Research Funding; Miragen: Research Funding; Genentech: Research Funding; Sunesis: Research Funding; Pharmacyclics LLC: Research Funding; KITE pharma: Research Funding; Acerta Pharma Inc: Research Funding; Juno Therapeutics: Research Funding; Gilead Sciences: Research Funding; Xencor: Research Funding; Janssen: Research Funding; Loxo Oncology Inc.: Research Funding; Cyclcel: Research Funding; AbbVie: Research Funding; GSK/Novartis: Research Funding. OffLabel Disclosure: Combination of ibrutinib and venetoclax is not FDA approved

Background: The combination of low intensity therapy with InO improved survival compared to intensive chemotherapy and to single agent InO in Salvage 1 (Jabbour et al. Cancer. 2018). The sequential addition of blinatumomab (blina) may allow the administration of weekly lower dose of InO and distancing allogeneic stem cell transplant (ASCT) from the last dose of InO, while deepening the minimal residual disease response. This will lead to less veno-occlusive disease (VOD) and better long-term efficacy. The aim of this study is to evaluate the outcome of pts in first relapse treated with this combination. Methods: The mini-hyper-CVD (cycles 1, 3, 5, 7) comprised cyclophosphamide (150 mg/m2 every 12 h on days 1-3), vincristine (2 mg flat dose on days 1 and 8), and dexamethasone (20 mg on days 1-4 and days 11-14) without anthracycline. Even cycles (cycles 2, 4, 6, 8) comprised methotrexate (250 mg/m2 on day 1) and cytarabine (0.5 g/m2 given every 12 h on days 2 and 3). Rituximab and intrathecal chemotherapy were given for first 4 courses. InO was originally given on day 3 of the first four cycles at the dose of 1.3-1.8 mg/m2 at cycle 1, followed by 1.0-1.3 mg/m2 in subsequent cycles. After 38 pts were treated, an amendment was made to incorporate 4 cycles of blina after 4 cycles of mini-hyper-CVD + InO. InO was given on days 2 and 8 at the dose of 0.6 and 0.3 mg/m2 at cycle 1, respectively, followed by days 2 and 8 at the dose of 0.3 and 0.3 mg/m2 at subsequent cycles; blina was continuously infused over 28 days every 42-day cycle for 4 cycles. The decision to proceed with ASCT was based on the discretion of the treating physician after discussion with the pt. After amendment, all pts were placed on prophylactic ursodiol. Inverse probability of treatment weighting (IPTW) was performed after propensity score calculations using covariates at diagnosis. Results: From 2/2013 to 3/2019, 62 pts were treated with mini-hyper-CVD + InO with (n=38, 55%) and without blina (n=24, 45%) as the first salvage therapy. Pt characteristics and outcome are summarized in Table 1. The median age at diagnosis was 39 years (range, 19-87). Among 62 pts, 8 (13%) pts had primary refractory disease; 24 (38%), CR1 duration less than 12 months. 7 (11%) pts had prior history of ASCT. MLL rearrangement by FISH was observed in 11 (18%) pts; CRLF2 rearrangement was observed in 6 (10%). Overall, 57 (92%) pts achieved response including CR in 43 (69%), and CRp/CRi in 14 (23%). MRD negativity by 6-color flow cytometry was achieved in 31/52 pts (60%) after 1 cycle and 47/55 pts (85%) overall. Three pts with detectable MRD before blina achieved negative MRD after blina therapy. The 30-day and 60-day mortality rates were 2% and 3%, respectively. Among 57 who achieved remission, 11 (19%) relapsed, 31 (54%) underwent allogeneic SCT in CR2, and 4 (6%) died in CR/CRp. Causes of death for pts in CR/CRp included: unknown (n=1), VOD (n=1), sepsis (n=1), and pneumonia (n=1). Overall, 6 pts (10%) developed VOD; 5 after subsequent ASCT. The rate of VOD was 5/42 (12%) in pts who did not receive blina; all 5 had ASCT-related; 1 received 2 ASCT, 1 in CR1 and 1 in CR2; 1 received ASCT in CR1; and 3 received ASCT in CR2. In contrast, only 1 case of VOD was observed among the 20 pts (5%) who received the weekly lower dose of InO and sequential addition of blina; this pt had VOD post ASCT in CR2. With a median follow-up of 34 months (range, 2-75 months), 29 pts (47%) were alive, 21 of whom (34%) were in CR and MRD negative status. The 3-year EFS and OS rates were 31% and 42%, respectively (Figure 1). The median EFS and OS were 10 and 17 months, respectively. With the landmark analysis at the median time to ASCT of 4 months (range, 1.9-9.5), the median EFS was 13 months and 15 months in pts with and without ASCT, respectively (Figure 2A; P=0.92); the median OS was 38 months and 31 months, respectively (Figure 2B; P=0.910). Using the IPTW analysis, compared to a similar historical cohort of pts treated with standard salvage chemotherapy (n=39) or Ino monotherapy (n=29), mini-hyper-CVD + InO ± blina (n=62) resulted in significantly improved survival (P<0.001; P<0.001). Conclusion: The combination of InO with mini-hyper-CVD +/- blina is highly effective and shows improved outcome compared to standard single agent Ino and intensive chemotherapy in pts with relapsed/refractory ALL in first relapse, with 3-year OS rate of 42%. The risk of VOD can be minimized with fractionated low dose InO and sequential combination of blina. Figure 1 Disclosures Sasaki: Pfizer: Consultancy; Otsuka: Honoraria. Kantarjian:Astex: Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Research Funding; Amgen: Honoraria, Research Funding; Jazz Pharma: Research Funding; Cyclacel: Research Funding; BMS: Research Funding; Immunogen: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria; Daiichi-Sankyo: Research Funding; Agios: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Ariad: Research Funding. Ravandi:Xencor: Consultancy, Research Funding; Cyclacel LTD: Research Funding; Menarini Ricerche: Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Selvita: Research Funding; Macrogenix: Consultancy, Research Funding. Short:Amgen: Honoraria; Takeda Oncology: Consultancy, Research Funding; AstraZeneca: Consultancy. Kebriaei:Kite: Honoraria; Amgen: Research Funding; Jazz: Consultancy; Pfizer: Honoraria. Jain:Precision Biosciences: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics, an AbbVie company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; ADC Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; Incyte: Research Funding; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Pharmaceuticals, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Konopleva:Agios: Research Funding; Astra Zeneca: Research Funding; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Ablynx: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Eli Lilly: Research Funding; Forty-Seven: Consultancy, Honoraria; Calithera: Research Funding; Amgen: Consultancy, Honoraria; Cellectis: Research Funding. Garcia-Manero:Merck: Research Funding; Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding. Champlin:Sanofi-Genzyme: Research Funding; Actinium: Consultancy; Johnson and Johnson: Consultancy. Kadia:Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Bioline RX: Research Funding; Celgene: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding. Cortes:Jazz Pharmaceuticals: Consultancy, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; BiolineRx: Consultancy; Biopath Holdings: Consultancy, Honoraria; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Takahashi:Symbio Pharmaceuticals: Consultancy. Jabbour:Amgen: Consultancy, Research Funding; Cyclacel LTD: Research Funding; Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding.