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Phalak, Priti, and Rekha Sharma. "Optimization of Horizontal Aggregation in SQL by using C4. 5 Algorithm." International Journal of Computer Applications 93, no. 13 (2014): 31–37. http://dx.doi.org/10.5120/16277-6048.
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Sharma, Seema, Jitendra Agrawal, and Sanjeev Sharma. "Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies." International Journal of Computer Applications 82, no. 16 (2013): 28–32. http://dx.doi.org/10.5120/14249-2444.
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Redemann, J., Q. Zhang, J. Livingston, et al. "Testing aerosol properties in MODIS Collection 4 and 5 using airborne sunphotometer observations in INTEX-B/MILAGRO." Atmospheric Chemistry and Physics 9, no. 21 (2009): 8159–72. http://dx.doi.org/10.5194/acp-9-8159-2009.
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Abstract. The 14-channel Ames Airborne Tracking Sunphotometer (AATS) was operated on a Jetstream 31 (J31) aircraft in March 2006 during MILAGRO/INTEX-B (Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment). We compare AATS retrievals of spectral aerosol optical depth (AOD) and related aerosol properties with corresponding spatially coincident and temporally near-coincident measurements acquired by the MODIS-Aqua and MODIS-Terra satellite sensors. These comparisons are carried out for the older MODIS Collection 4 (C4) and the new Collection 5 (C5) data set, the latter representing a reprocessing of the entire MODIS data set completed during 2006 with updated calibration and aerosol retrieval algorithm. Our analysis yields a direct, validated assessment of the differences between select MODIS C4 and C5 aerosol retrievals. Our analyses of 37 coincident observations by AATS and MODIS-Terra and 18 coincident observations between AATS and MODIS-Aqua indicate notable differences between MODIS C4 and C5 and between the two sensors. For MODIS-Terra, we find an average increase in AOD of 0.02 at 553 nm and 0.01 or less at the shortwave infrared (SWIR) wavelengths. The change from C4 to C5 results in less good agreement with the AATS derived spectral AOD, with average differences at 553 nm increasing from 0.03 to 0.05. For MODIS-Aqua, we find an average increase in AOD of 0.008 at 553 nm, but an increase of nearly 0.02 at the SWIR wavelengths. The change from C4 to C5 results in slightly less good agreement to the AATS derived visible AOD, with average differences at 553 nm increasing from 0.03 to 0.04. However, at SWIR wavelengths, the changes from C4 to C5 result in improved agreement between MODIS-Aqua and AATS, with the average differences at 2119 nm decreasing from −0.02 to −0.003. Comparing the Angstrom exponents calculated from AOD at 553nm and 855nm, we find an increased rms difference from AATS derived Angstrom exponents in going from C4 to C5 for MODIS-Terra, and a decrease in rms difference, hence an improvement, for the transition from C4 to C5 in MODIS-Aqua. Combining the AATS retrievals with in situ measurements of size-dependent aerosol extinction, we derive a suborbital measure of the aerosol submicron fraction (SMF) of AOD and compare it to MODIS retrievals of aerosol fine mode fraction (FMF). Our analysis shows a significant rms-difference between the MODIS-Terra FMF and suborbitally-derived SMF of 0.17 for both C4 and C5. For MODIS-Aqua, there is a slight improvement in the transition from C4 to C5, with the rms-difference from AATS dropping from 0.23 to 0.16. The differences in MODIS C4 and C5 AOD in this limited data set can be traced to changes in the reflectances input to the aerosol retrievals. An extension of the C4-C5 comparisons from the area along the J31 flight track to a larger study region between 18–23° N and 93–100° W on each of the J31 flight days supports the finding of significant differences between MODIS C4 and C5.
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Redemann, J., Q. Zhang, J. Livingston, et al. "Testing aerosol properties in MODIS (MOD04/MYD04) Collection 4 and 5 using airborne sunphotometer observations in INTEX-B/MILAGRO." Atmospheric Chemistry and Physics Discussions 9, no. 3 (2009): 11753–81. http://dx.doi.org/10.5194/acpd-9-11753-2009.
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Abstract. The 14-channel Ames Airborne Tracking Sunphotometer (AATS) was operated on a Jetstream 31 (J31) aircraft in March 2006 during MILAGRO/INTEX-B (Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment). We compare AATS retrievals of spectral aerosol optical depth (AOD) and related aerosol properties with corresponding spatially coincident and temporally near-coincident measurements acquired by the MODIS-Aqua and MODIS-Terra satellite sensors. These comparisons are carried out for the older MODIS Collection 4 (C4) and the new Collection 5 (C5) data set, the latter representing a reprocessing of the entire MODIS data set completed during 2006 with updated calibration and aerosol retrieval algorithm. Our analysis yields a direct, validated assessment of the differences between select MODIS C4 and C5 aerosol retrievals. Our analyses of 37 coincident observations by AATS and MODIS-Terra and 18 coincident observations between AATS and MODIS-Aqua indicate notable differences between MODIS C4 and C5 and between the two sensors. For MODIS-Terra, we find an average increase in AOD of 0.02 at 553 nm and 0.01 or less at the shortwave infrared (SWIR) wavelengths. The change from C4 to C5 results in less good agreement with the AATS derived spectral AOD, with average differences at 553 nm increasing from 0.03 to 0.05. For MODIS-Aqua, we find an average increase in AOD of 0.008 at 553 nm, but an increase of nearly 0.02 at the SWIR wavelengths. The change from C4 to C5 results in slightly less good agreement to the AATS derived visible AOD, with average differences at 553 nm increasing from 0.03 to 0.04. However, at SWIR wavelengths, the changes from C4 to C5 result in improved agreement between MODIS-Aqua and AATS, with the average differences at 2119 nm decreasing from -0.02 to -0.003. Comparing the Angstrom exponents calculated from AOD at 553 nm and 855 nm, we find an increased rms difference from AATS derived Angstrom exponents in going from C4 to C5 for MODIS-Terra, and a decrease in rms difference, hence an improvement, for the transition from C4 to C5 in MODIS-Aqua. Combining the AATS retrievals with in situ measurements of size-dependent aerosol extinction, we derive a suborbital measure of the aerosol submicron fraction (SMF) of AOD and compare it to MODIS retrievals of aerosol fine mode fraction (FMF). Our analysis shows a significant rms-difference between the MODIS-Terra FMF and suborbitally-derived SMF of 0.17 for both C4 and C5. For MODIS-Aqua, there is a slight improvement in the transition from C4 to C5, with the rms-difference from AATS dropping from 0.23 to 0.16. The differences in MODIS C4 and C5 AOD in this limited data set can be traced to changes in the reflectances input to the aerosol retrievals. An extension of the C4-C5 comparisons from the area along the J31 flight track to a larger study region between 18–23° N and 93–100° W on each of the J31 flight days supports the finding of significant differences between MODIS C4 and C5.
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Suherman, Asep, DIDI KURNAEDI, Sofian Lusa, and Rizqi Darmawan. "Junior Class Preparedness Classification Faces A National Exam Using C.45 Algorithm with A Particle Swarm Optimization Approach." bit-Tech 2, no. 3 (2020): 101–9. http://dx.doi.org/10.32877/bt.v2i3.133.
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These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage.
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Siregar, Kristian. "TESTING THE C4.5 ALGORITHM WITH RAPID MINER TO DETERMINE DECISIONS FOR IMPLEMENTING SPORTS ACTIVITIES." INFOKUM 11, no. 04 (2023): 40–47. http://dx.doi.org/10.58471/infokum.v11i04.1790.
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In the current era of information technology, decision making is an important aspect in various fields, including in the world of sports. The decision to carry out sports activities can be influenced by many factors such as weather conditions, availability of facilities, and the physical condition of the participants. With advances in technology and data science, there are algorithms that can assist in making these decisions. One of them is the C4.5 algorithm. This study aims to test the effectiveness of the C4.5 algorithm in determining decisions to carry out sports activities using the Rapid Miner software. The data used in this study is historical data from sports activities which include variables such as weather, date, and condition of the participants. The test results show that the C4. 5 is able to provide decisions with fairly high accuracy. By using Rapid Miner software, the process of learning and testing data becomes faster and more efficient. The conclusion of this study is that the C4.5 algorithm, with the help of Rapid Miner software, can be used as an effective method to assist in making decisions about sports activities.
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Suherman, Asep, Didi Kurnaedi, and Rizqi Darmawan. "Junior Class Preparedness Classification Faces A National Exam Using A C.45 Algorithm With A Particle Swarm Optimization Approach." bit-Tech 3, no. 1 (2020): 11–20. http://dx.doi.org/10.32877/bt.v3i1.169.
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These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage
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Santoso, Trio, and Fitria Saftarina. "Clusterization Of Paddy Field Farmers Condition In Kota Metro Lampung Province Indonesia Using K-Means Clustering Algorithm." Journal of Agribusiness and Community Empowerment 3, no. 1 (2020): 37–43. http://dx.doi.org/10.32530/jace.v3i1.187.
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Metro City is one of the administrative cities in Lampung province, Indonesia and also one of important rice producing regions in Lampung. Application of intensive agriculture in trend of declining area, low quality of land and differences of farmers internal characteristics that requires special treatment as solutions. Clustering farmers using the K-Means Cluster algorithm and Elbow Method can be used to facilitate policy makers determine programs and activities must be taken. Results showed that farmers are ideally grouped into 5 clusters (C1, C2, C3, C4 and C5). C1 members having most family members (4,54 persons). C2 members are the oldest age (68 years old) with longest farming experience (52.00 years) but have lowest formal education (7.67 years), least family members (3.33 person) and lowest total area (0.37 hectare). C3 having highest formal education (14.60 years) and largest paddy fields (0.80 hectare) but don't use any pesticides in paddy cropping management. Whereas farmers in C4 have largest family members helped (2.00 people). Farmers in C5 are the youngest (45.50 years old) and having the shortest experience (29.50 years) but use the most types (4 brands) and amounts of pesticides (400.00 mm.hectare.rotation-1) in paddy field management practices in Metro City.
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Renaldi, Renaldi, and Yusuf Kurnia. "Alleged Bad Credit at Saving Cooperatives Borrow Flamboyant Assistance PPSW Jakarta With Comparasion the Algorithms Naive Bayes and C4.5." bit-Tech 2, no. 3 (2020): 141–47. http://dx.doi.org/10.32877/bt.v2i3.163.
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Data mining is often used in the financial sector, one of which is cooperatives. According to Law No. 25 of 1992, what is meant by cooperatives are business entities whose members are individual or cooperative legal entities based on activities based on the principles of cooperatives as well as as a people's economic movement based on the principle of kinship. One of the things that needs to be considered is the provision of credit or borrowing in the Flamboyan cooperative, which in this study there are many bad crediting occurrences that occur in the Flamboyan cooperative. By using a lot of data mining techniques, data can be utilized optimally. From the above problems, it can be overcome by utilizing data mining techniques, namely Predicting Bad Credit at the Flamboyant Savings and Loan Cooperative Fostered by PPSW Jakarta Using Comparative Algorithms Naive Bayes and C4.5. The algorithm used in the system is the best result of the Naive Bayes and C4.5 comparison based on data from the Flamboyan cooperative. The results obtained from the comparative data processing between the Naïve Bayes algorithm and the C4.5 using a dataset of 2282 transaction data obtained the results of the accuracy of the Naïve Bayes algorithm of 69.19% and the C4.5 algorithm of 71.87%, based on the accuracy results state that the C4 algorithm .5 is superior to the Naïve Bayes algorithm. Then the results from the C4.5 decision tree are translated into the bad credit prediction system in the Flamboyan cooperative.
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Sugianto, Castaka Agus, and Nadya Selvi Pujiyanita. "OPTIMALISASI ALGORITMA C4.5 MENGGUNAKAN ALGORITMA GENETIKA UNTUK PREDIKSI KELULUSAN SISWA SMKN 2 CIMAHI." Infotekmesin 10, no. 2 (2019): 9–14. http://dx.doi.org/10.35970/infotekmesin.v10i2.35.
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SMK 2 Cimahi is an educational institution Vocational high school in Cimahi. Vocational high schools play an important role in creating the best graduates for the needs of the workforce. The graduation process is the latest activity of student management. Student graduation criteria for national exams must meet minimum scores, complete all subjects, take school exams, national exams, and competency tests. In the 2015 class of 300 students, students who did not pass the national exam were 1 student, in the 2016 class of 299 students, students who did not pass the national exam were 1 student, whereas in the 2017 class totaling 302 students 100% students graduate on time. However in the coming year, it is still unknown whether students will be 100% graduated or not. Therefore this study was conducted to predict student graduation using the C4.5 algorithm + Genetic Algorithm. C4.5 algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall and 1 second execution time. While the C4.5 + genetic algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall AUC 0.500, and 36 second execution time and after T-test testing between the C4.5 Algorithm and C4 Algorithm .5 +, Genetic Algorithm is said to be significant if alpha = 0.050. The results of this study obtained alpha = 1,000.
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Prabowo, Handhy Nur, Resad Setyadi, and Wahyu Adi Prabowo. "Application of Data Mining for Clustering of Foreign Tourist Visits Based on Arrival Entrance." Sinkron 7, no. 1 (2022): 49–58. http://dx.doi.org/10.33395/sinkron.v7i1.11217.
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Indonesia is a country with unique tourist destinations from each region. The tourism sector has an impact on the Indonesian economy which can encourage economic growth and increase the country's foreign exchange from foreign tourist visits. Tourism growth in Indonesia was disrupted due to the Covid-19 pandemic with the imposition of major social restrictions which resulted in a decrease in tourist visits and the paralysis of the tourism sector. Based on the problems described above, the authors are interested in conducting research in order to classify data on foreign tourist arrivals based on the entrance of foreign tourist arrivals. This research uses data mining method and K-Means Algorithm to form 5 clusters. The 5 clusters are divided into groups of tourist entrances which are categorized as very high (C1), high (C2), moderate (C3), low (C4) and very low (C5). In forming the 5 clusters, the researchers used Ms. Excel and Rapidminer 10.1 to process data. The results of this study obtained that the tourist entrance group was categorized as very high (C1) with 1 data, high (C2) with 1 data, moderate (C3) with 1 data, low (C4) with 1 data and very low (C5). ) that is with 21 data. This study aims to provide suggestions and future considerations to the Ministry of Tourism and Creative Economy of the Republic of Indonesia (Kemenparekraf) to carry out policies so that the Indonesian tourism sector can return to normal.
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Aithmia, Dr Rashmi, Dr Monika Pangotra, and Dr Sindhu Sharma. "IAC Yokohama Reporting of Breast Cytology to Assess Risk of Malignancy and Predictive Values." Saudi Journal of Pathology and Microbiology 7, no. 7 (2022): 267–71. http://dx.doi.org/10.36348/sjpm.2022.v07i07.003.
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Background: IAC Yokohama system has 5 categories that may be stratified by their risk of malignancy (ROM) and supply guidance within a management algorithm for every category. The main objectives were to categorize the Breast FNAC samples according to new system of reporting and to assess the Risk of malignancy (ROM), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy for all categories. Material and Methods: A total of 174 cases were prospectively studied over a period of one year from 1st November 2019 to 31st October 2020 in GMC, Jammu. All the FNAC received was reported routinely according to the newly proposed Yokohama system of reporting breast cytology. The ROM, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and accuracy of Breast FNAC were calculated. Results: ROM is 0% for category 1, 2.27% for category 2, 50% for category 3, 50% for category 4 and 100% for category 5. Sensitivity, specificity, and diagnostic accuracy were, respectively, 100%, 100% and 99.11% for category A (only C5 category cases considered positive test results), 88.2%, 93.6%, and 93.6% for category B, (both C4 and C5 categories considered positive test results), and 94.1%, 91.48%, and 92.1% for category C (C3, C4, and C5 category cases grouped as positive test results). PPV and NPV were also calculated. Conclusion: Categorization of the Breast FNAB cytology according to IAC Yokohama system of reporting helps pathologist in the diagnostic clarity and guides clinician in the appropriate patient management.
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Cawrse, Brian M., Nia’mani M. Robinson, Nina C. Lee, Gerald M. Wilson, and Katherine L. Seley-Radtke. "Structural and Biological Investigations for a Series of N-5 Substituted Pyrrolo[3,2-d]pyrimidines as Potential Anti-Cancer Therapeutics." Molecules 24, no. 14 (2019): 2656. http://dx.doi.org/10.3390/molecules24142656.
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Pyrrolo[3,2-d]pyrimidines have been studied for many years as potential lead compounds for the development of antiproliferative agents. Much of the focus has been on modifications to the pyrimidine ring, with enzymatic recognition often modulated by C2 and C4 substituents. In contrast, this work focuses on the N5 of the pyrrole ring by means of a series of novel N5-substituted pyrrolo[3,2-d]pyrimidines. The compounds were screened against the NCI-60 Human Tumor Cell Line panel, and the results were analyzed using the COMPARE algorithm to elucidate potential mechanisms of action. COMPARE analysis returned strong correlation to known DNA alkylators and groove binders, corroborating the hypothesis that these pyrrolo[3,2-d]pyrimidines act as DNA or RNA alkylators. In addition, N5 substitution reduced the EC50 against CCRF-CEM leukemia cells by up to 7-fold, indicating that this position is of interest in the development of antiproliferative lead compounds based on the pyrrolo[3,2-d]pyrimidine scaffold.
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Hidayati, Wenika, and Paska Marto Hasugian. "Data Mining Power Determination Nurses Sultan Sulaiman Hospital With C4.5 Algorithm." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 1 (2020): 77–82. http://dx.doi.org/10.47709/cnapc.v2i1.360.
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The hospital is an agency engaged in health services in the which there are a number of special professions that can provide health services to the community items, namely doctors, Midwives and nurses and other professes. In this discussion, Arise and problems that can be raised into case studies to find out the results and information of each process in data mining Carried out with the C4.5 algorithm items, namely nurses. However, there are Several obstacles to Determine the nurses who will be declared passed or failed and accepted to work and can provide health services to the community, especially Patients who come for treatment. Therefore we need a method to identify nurses in a hospital. Data Mining with c4. 5 Algorithm can be used to the make predictions or classifications of nurses who are eligible to perform health services in hospitals by making decision trees based on existing data. This study aims to apply the data mining algorithm C4.5 in Determining nurses based on four attributes of used items, namely Accreditation, GPA, Age, and the value of each criterion has been determined in advance. The results of the study in the form of a decision tree Obtained from the data mining process with the C4.5 algorithm will provide information on the determination of nurses in the Sultan Sulaiman Regional Hospital.
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Ramos, Júlia, Mafalda Aguiar, and Miguel Pais-Vieira. "Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study." Applied Sciences 13, no. 16 (2023): 9356. http://dx.doi.org/10.3390/app13169356.
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This paper investigates the changes in sensory neural activity during exoskeleton control. Exoskeletons are becoming reliable tools for neurorehabilitation, as recent studies have shown that their use enhances neural plasticity. However, the specific neural correlates associated with exoskeleton control have not yet been described in detail. Therefore, in this pilot study, our aim was to investigate the effects of different pavement textures on the neural signals of participants (n = 5) while controlling a lower limb ExoAtlet®-powered exoskeleton. Subjects were instructed to walk on various types of pavements, including a flat surface, carpet, foam, and rubber circles, both with and without the exoskeleton. This setup resulted in eight different experimental conditions for classification (i.e., Exoskeleton/No Exoskeleton in one of four different pavements). Four-minute Electroencephalography (EEG) signals were recorded in each condition: (i) the power of the signals was compared for electrodes C3 and C4 across different conditions (Exoskeleton/No Exoskeleton on different pavements), and (ii) the signals were classified using four models: the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN). the results of power analysis showed increases and decreases in power within the delta frequency bands in electrodes C3 and C4 across the various conditions. The results of comparison between classifiers revealed that LDA exhibited the highest performance with an accuracy of 85.71%. These findings support the notion that the sensory processing of pavement textures during exoskeleton control is associated with changes in the delta band of the C3 and C4 electrodes. From the results, it is concluded that the use of classifiers, such as LDA, allow for a better offline classification of different textures in EEG signals, with and without exoskeleton control, than the analysis of power in different frequency bands.
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Schmider, Angela B., Nicholas C. Bauer, Hongjae Sunwoo, et al. "Two- and three-color STORM analysis reveals higher-order assembly of leukotriene synthetic complexes on the nuclear envelope of murine neutrophils." Journal of Biological Chemistry 295, no. 17 (2020): 5761–70. http://dx.doi.org/10.1074/jbc.ra119.012069.
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Over the last several years it has become clear that higher order assemblies on membranes, exemplified by signalosomes, are a paradigm for the regulation of many membrane signaling processes. We have recently combined two-color direct stochastic optical reconstruction microscopy (dSTORM) with the (Clus-DoC) algorithm that combines cluster detection and colocalization analysis to observe the organization of 5-lipoxygenase (5-LO) and 5-lipoxygenase–activating protein (FLAP) into higher order assemblies on the nuclear envelope of mast cells; these assemblies were linked to leukotriene (LT) C4 production. In this study we investigated whether higher order assemblies of 5-LO and FLAP included cytosolic phospholipase A2 (cPLA2) and were linked to LTB4 production in murine neutrophils. Using two- and three-color dSTORM supported by fluorescence lifetime imaging microscopy we identified higher order assemblies containing 40 molecules (median) (IQR: 23, 87) of 5-LO, and 53 molecules (62, 156) of FLAP monomer. 98 (18, 154) molecules of cPLA2 were clustered with 5-LO, and 77 (33, 114) molecules of cPLA2 were associated with FLAP. These assemblies were tightly linked to LTB4 formation. The activation-dependent close associations of cPLA2, FLAP, and 5-LO in higher order assemblies on the nuclear envelope support a model in which arachidonic acid is generated by cPLA2 in apposition to FLAP, facilitating its transfer to 5-LO to initiate LT synthesis.
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Maqfiroh, Fifi, and Sri Mujiyono. "PENERAPAN KLASIFIKASI ALGORITMA DATA MINING C4.5 UNTUK MEMPREDIKSI TINGKAT KELULUSAN SISWA DI LEMBAGA PELATIHAN KERJA SHINJU SEMARANG." Jurnal Mahasiswa Teknik Informatika 1, no. 2 (2022): 35–49. http://dx.doi.org/10.35473/.v1i2.1874.
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AbstrakLPK. SHINJU telah menyimpan data-datanya dalam database berupa hardcopy dan softfile. Data-data tersebut jika dimaksimalkan pemanfaatannya dapat memberikan informasi yang berguna, salah satunya adalah prediksi kelulusan siswa. Penelitian bertujuan menerapkan klasifikasi algoritma C4.5 dalam prediksi kelulusan siswa. Data yang digunakan yaitu data alumni siswa yang telah lulus tahun 2020. Atribut yang dipakai adalah Jenis Kelamin, Tempat Tinggal, Asal kelulusan, Status Bekerja, Ekonomi dan Nilai Akhir. Atribut Labelnya yaitu tepat waktu dan terlambat. Implementasi menggunakan aplikasi RapidMiner 5. Metode yang digunakan adalah data mining dengan algoritma C4.5. Metode pengumpulan data yang dipakai dalam penelitian yaitu observasi dan studi literatur. Berdasarkan uji coba diperoleh kesimpulan bahwa bidang ilmu data mining dengan menggunakan algoritma C4.5 dapat diimplementasikan untuk melakukan prediksi kelulusan siswa Lembaga Pelatihan Kerja, setelah melakukan rangkaian uji data set dengan rapid miner, diperoleh hasil accuracy sebagai nilai ketentuan seberapa besar keakuratan menggunakan algoritma C4.5 dalam memprediksi kelulusan siswa Lembaga Pelatihan Kerja SHINJU. Kata kunci : Algoritma C4.5, Prediksi Kelulusan, Data mining, Rapidminer Abstrack            LPK. SHINJU has stored is data in the database in the form of hardcopy and softfile. These data, if maximized, can provide useful information, one of which is the prediction of student graduation. This study aims to apply the classification algorithm C4.5 in predicting student graduation. The data used is the alumni data of students who have graduated in 2020. The attributes used are Gender, Place of Residence, Origin of Graduation, Work Status, Economy and Final Value. The Label attribute is on time and late. Implementation using the RapidMiner 5 application. The method used is data mining with the C4.5 algorithm. Data collection methods used in this research are observation and literature study. Based on the trial, it was concluded that the field of data mining science using the C4.5 algorithm can be implemented to predict the graduation of Job Training Institute students. After conducting a series of data set tests with rapid miners, accuracy results are obtained as the value of the provision of how much accuracy is using the C4 algorithm. 5 in predicting the graduation of SHINJU Job Training Institute students. Keywords: C4.5 Algorithm, Graduation Prediction, Data mining, Rapidminer
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Sari, Kiki Apni Puspita, Eka Irawan, and Fitri Rizky. "Implementasi Algoritma Weighted Product (WP) Dengan Model Fuzzy Multi Attribute Decission Making (FMADM) Dalam Penilaian Kinerja Karyawan." Brahmana : Jurnal Penerapan Kecerdasan Buatan 2, no. 1 (2020): 57–65. http://dx.doi.org/10.30645/brahmana.v2i1.49.
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Employee performance appraisal is a work evaluation activity that is used to determine the quality which will be used for the achievement of each individual employee. The purpose of this research is to appraise employee performance, especially for the security guard. In this study the authors used the Decision Support System technique using the Fuzzy Multi Attribute Decision Making algorithm, namely Weighted Product. The source of the research data used was to provide an assessment form to Krani Papam. In the employee performance appraisal, researchers used 5 assessment criteria, including: Attendance (C1), Responsibility (C2), Commitment (C3), Cooperation (C4), and Motivation (C5). In this study, the alternative used as a sample is employees in the security section (security guard). From the results of manual calculations and system testing, the highest value is 0.0286. It is expected that the results of this study can provide input to the office, especially the security section (security guard) in selecting employee performance so that the assessment is carried out objectively.
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Marchetti, M., C. Rognoni, R. Pastorino, et al. "Clinical Classification of Myelofibrosis with Myeloid Metaplasia (MMM): Cluster Analysis of 861 Patients Enrolled into a Nationwide Prospective Registry (RIMM)." Blood 106, no. 11 (2005): 2579. http://dx.doi.org/10.1182/blood.v106.11.2579.2579.
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Abstract BACKGROUND: MMM is a rare chronic myeloproliferative disease with heterogeneous clinical presentation, natural history and prognosis. Knowledge about MMM is burdened by limited sample size and selection bias of the available studies. AIM: To get a clinical classification of MMM for biological and clinical studies. METHODS: The database of the RIMM, a population-based prospective cohort started in 1999, was enquired and data of 861 patients at diagnosis were extracted. The EM-algorithm (WEKA software) was used for a non-supervised data-mining based on 11 variables: age, spleen size, constitutional symptoms, hemoglobin level, platelet and blood cell count, LDH and percentages of erythroblasts, immature myeloid cells, blasts, and basophils. Differences among clusters were first tested with ANOVA or n-way Chi-square and subsequently investigated with t-test or 2x2 Chi-square. Survival differences among clusters were tested through Cox univariate and multivariate regression analysis. RESULTS: Five clusters (C) were identified: C1 identified younger patients with thrombocytosis and scarce myeloproliferation. C2 also identified patients with a high platelet count, but older and anemic. C3 identified patients with intense myeloproliferation (marked leukocytosis and splenomegaly), while C4 (5% of the patients) collected patients with an even more severe myeloproliferation, associated with a high number of circulating immature myeloid cells and blasts. C4 was also characterized by frequent chromosome abnormalities, anemia and thrombocythopenia. Finally, C5 identified patients with bilinear/trilinear cytopenias. The clusters also differed for variables not incorporated by the data-mining process, i.e. CD34+ cell count (p=0.028); i.e. the rate of homozygous V617F mutation of JAK2 gene, which was significantly higher in C3 and C4 (32.4% vs 12.5%, p=0.019). The clusters also significantly differed for overall survival (p<0.0001), independently of patients’ age. Finally, referral patterns differed among clusters, with C5 and C1 patients being mostly referred to Internal Medicine and Hematology units (p<0.01), respectively. CONCLUSIONS: Thanks to a large and unbiased repository of cases, we identified 5 classes of MMM patients with specific clinical presentation. The biologic and prognostic value of the clusters deserves further investigations at longer follow-up periods. Clinical parameters in the different clusters (means) CLUSTER AGE (yrs) * HB (g/l) */PLT*10(9)* WBC *10(9) * SPLEEN (cm) * BLASTS*(%) CD34 %/μl SEVERITY SCORE§ * * p<0.0001 § The score considers HB, WBC, PLT and SPLEEN (Barosi, Leuk Lymph 2002). 1 56.7 13.3 /583 11.5 3.5 0.21 0.85 /63 1.25 2 72.1 10.6 /482 18.3 2.7 0.82 1.12 /297 2.04 3 65.1 10.8 /303 18.3 9.9 1.47 1.7 /313 2.54 4 67.6 8.4 /110 27.5 13.4 5.66 2.72 /275 4.30 5 68.2 8.4 /134 4.2 6.8 0.78 2.92 /52 3.36 All 65.5 10.7 /362 13.5 6.5 1.18 1.78 /200 2.36
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Montalban, X., J. Sastre-Garriga, M. Filippi, et al. "Primary progressive multiple sclerosis diagnostic criteria: a reappraisal." Multiple Sclerosis Journal 15, no. 12 (2009): 1459–65. http://dx.doi.org/10.1177/1352458509348422.
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The diagnostic criteria used in primary progressive (PP) and relapsing—remitting (RR) multiple sclerosis (MS) show substantial differences. This introduces complexity in the diagnosis of MS which could be resolved if these criteria could be unified in terms of the requirements for dissemination in space (DIS). The aim of this study was to assess whether a single algorithm may be used to demonstrate DIS in all forms of MS. Five sets of RRMS criteria for DIS were applied to a cohort of 145 patients with established PPMS (mean disease duration: 11 years — PPMS-1): C1: Barkhof—Tintoré (as in 2005 McDonald’s criteria); C2: Swanton et al. (as in JNNP 2006); C3: presence of oligoclonal bands plus two lesions (as in McDonald’s criteria); C4 and C5: a two-step approach was also followed (patients not fulfilling C1 or C2 were then assessed for C3). Two sets of PPMS criteria for DIS were applied: C6: Thompson et al. (as in 2001 McDonald’s criteria); C7: 2005 McDonald criteria. A second sample of 55 patients with less than 5 years of disease duration (PPMS-2) was also analysed using an identical approach. For PPMS-1/PPMS-2, fulfilment was: C1:73.8%/66.7%; C2:72.1%/59.3%; C3:89%/79.2%; C4:96%/92.3%; C5:96%/85.7%; C6:85.8%/78.7%; C7:91%/80.4%. Levels of fulfilment suggest that the use of a single set of criteria for DIS in RRMS and PPMS might be feasible, and reinforce the added value of cerebrospinal fluid (CSF) findings to increase fulfilment in PPMS. Unification of the DIS criteria for both RRMS and PPMS could be considered in further revisions of the MS diagnostic criteria.
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Sari, Desi Ratna, Dedy Hartama, Irfan Sudahri Damanik, and Anjar Wanto. "Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen." Prosiding Seminar Nasional Riset Information Science (SENARIS) 1 (September 30, 2019): 287. http://dx.doi.org/10.30645/senaris.v1i0.34.
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This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.
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Guarnier, Letícia, Fabricia Benda Oliveira, Carlos Henrique Rodrigues de Oliveira, and Vicente Sombra da Fonseca. "MULTI-SPATIOTEMPORAL SIMULATION OF EDGE EFFECT ON FOREST PATCHES IN THE BARRA SECA RIVER BASIN, ES." FLORESTA 50, no. 4 (2020): 1864. http://dx.doi.org/10.5380/rf.v50i4.66577.
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The Atlantic Forest is intensely fragmented and this fragmentation process has caused an expressive increase of forest remnants and, consequently, increased edge effect with different physical-biological intensities in the transition areas between the patch and the matrix. This study used landscape metrics to understand and analyze how different edge effect distances affect the structure of the forest landscape in the Barra Seca River basin (ES), in 1985, 1996, 2006 and 2016. Remote sensing images were processed and using the Bhattacharya algorithm with supervised classification, the forest patches of the study area were classified and isolated. Landscape ecology metrics were computed with Patch Analyst and V-Late 2 Beta extensions. The forest patches were divided into four size classes as follows smaller than 5 ha (C1); between 5 and 10 ha (C2); between 10 and 100 ha (C3); and over 100 ha (C4). The edge effect simulation using landscape metrics was performed using the edge effect distances of 20, 40, 60, 80, 100, 140, and 200 m. Forest fragmentation increased between 1985 and 2016 while the number of patches greater than 100 ha decreased. Currently, the basin landscape consists mainly of small patches, which have larger relative areas affected by edge effect while many patches smaller than 10 ha are completely dominated by edge effect for distances greater than 60 meters. The edge effect simulation for different distances allowed verifying the intensification of the edge effect on the forest patches of the Barra Seca River basin.
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Rambe, Diana Indriani, Marnis Nasution, and Rahma Muti Ah. "Penerapan Metode Algoritma C4.5 Untuk Memprediksi Loyalitas Karyawan Pada PT.Tolan Tiga Indonesia Perlabian Estate." INFORMATIKA 12, no. 2 (2024): 132–38. https://doi.org/10.36987/informatika.v12i2.5646.
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The capital market is a place where various financial instruments are sold and bought can be long-term or short-term financial instruments. The Indonesian stock market is regulated by law No. 8 of 1995 capital market. The law clarifies the role of capital markets in the country's economy. The capital market has two functions, namely as a container for corporate financing in collecting funds from investors and as a means of investment. Loyalty is a person's loyalty in serving in a company or institution where there is a dedication and trust given by the company or institution in carrying out tasks in accordance with its expertise. in which there is a sense of love and responsibility to strive to provide the best service and behavior to show good work performance. . Loyalty is needed by companies because if employees do not have loyalty at work, it can harm the company. Thus in order to determine employee loyalty,several assessments are needed such as : employee performance,neatness in work, behavior and frequency of cooperation. Sehinnga from the assessment the company can assess how employee loyalty and if the contract employees include employees who have loyalty to the work it will be adopted as permanent employees at PT. There Are Three Types Of Indonesian Real Estate. The need for information services is very important, in predicting employee loyalty .in predicting employee loyalty can still be done manually but requires a very long processing time can be days.therefore, the author provides convenience with the use of meaching learning method, namely C4. 5 algorithm . C4.5 algorithm can facilitate the company in the process of employee prediction with just a matter of hours. So that it can make it easier for companies to determine employees who have loyalty to PT. There are three types of Indonesian real Estate.
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Bagaswara, Faris, Muchlis Abd Muthalib, and Rini Meiyanti. "Clustering of Futsal Interest Level Among Students K-Means Method." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 41–50. https://doi.org/10.52088/ijesty.v5i3.879.
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Futsal is a small field sport with a time of 20 minutes per round. Malikussaleh University is one of the universities that initiated Futsal as a health sport for its students. To determine students' interest in Futsal, clustering was carried out using the K-Means method on 100 students of the Faculty of Engineering involved in this study. This research proposal uses five variables: time variables, field facilities, motivation, environment, and plans. This study aims to help students at Malikussaleh University of Engineering find out what level of interest students have in Futsal. Grouping is based on data mining to determine the pattern of each sequence. Data mining includes tracking patterns, classification, association, outlier detection, clustering, regression, and forecasting. This study also led to an innovative grouping system using the Python programming language and MySQL as a database. The K-Means Clustering algorithm used in this grouping system states that out of 100 Malikussaleh University students, 20 people are students who have a professional player futsal interest level (C1), 28 students have a regular player futsal interest level (C2), five students have a Beginner player futsal interest level (C3), 47 students have an amateur player futsal interest level (C4). The study results showed that 20% were professional, 28% were regular, 5% were beginner, and 47% were amateur players. These results indicate that the interest in Futsal for Malikussaleh University students is still minimal, so encouragement is needed for students to participate in futsal activities.
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Lang, Stefan, Philip Hoelter, Manuel Alexander Schmidt, et al. "Accuracy of Dose-Saving Artificial-Intelligence-Based 3D Angiography (3DA) for Grading of Intracranial Artery Stenoses: Preliminary Findings." Diagnostics 13, no. 4 (2023): 712. http://dx.doi.org/10.3390/diagnostics13040712.
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Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to cut the patient dose by 50%. The object was to evaluate 3DA’s diagnostic value for visualization of intracranial artery stenoses (IAS) compared to 3D-DSA. Materials and methods: 3D-DSA datasets of IAS (nIAS = 10) were postprocessed using conventional and prototype software (Siemens Healthineers AG, Erlangen, Germany). Matching reconstructions were assessed by two experienced neuroradiologists in consensus reading, considering image quality (IQ), vessel diameters (VD1/2), vessel-geometry index (VGI = VD1/VD2), and specific qualitative/quantitative parameters of IAS (e.g., location, visual IAS grading [low-/medium-/high-grade] and intra-/poststenotic diameters [dintra-/poststenotic in mm]). Using the NASCET criteria, the percentual degree of luminal restriction was calculated. Results: In total, 20 angiographic 3D volumes (n3DA = 10; n3D-DSA = 10) were successfully reconstructed with equivalent IQ. Assessment of the vessel geometry in 3DA datasets did not differ significantly from 3D-DSA (VD1: r = 0.994, p = 0.0001; VD2:r = 0.994, p = 0.0001; VGI: r = 0.899, p = 0.0001). Qualitative analysis of IAS location (3DA/3D-DSA:nICA/C4 = 1, nICA/C7 = 1, nMCA/M1 = 4, nVA/V4 = 2, nBA = 2) and the visual IAS grading (3DA/3D-DSA:nlow-grade = 3, nmedium-grade = 5, nhigh-grade = 2) revealed identical results for 3DA and 3D-DSA, respectively. Quantitative IAS assessment showed a strong correlation regarding intra-/poststenotic diameters (rdintrastenotic = 0.995, pdintrastenotic = 0.0001; rdpoststenotic = 0.995, pdpoststenotic = 0.0001) and the percentual degree of luminal restriction (rNASCET 3DA = 0.981; pNASCET 3DA = 0.0001). Conclusions: The AI-based 3DA is a resilient algorithm for the visualization of IAS and shows comparable results to 3D-DSA. Hence, 3DA is a promising new method that allows a considerable patient-dose reduction, and its clinical implementation would be highly desirable.
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Chernookov, A. I., S. I. Kandyba, A. A. Atayan, et al. "The use of echoscleroobliteration in patients with recurrent varicose veins." Ambulatornaya khirurgiya = Ambulatory Surgery (Russia) 22, no. 1 (2025): 50–58. https://doi.org/10.21518/akh2025-011.
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Introduction. Currently, surgeons are increasingly using endovasal treatment methods to treat patients with recurrent varicose veins. The most common method of eliminating recurrent varicose veins is echosclerobliteration. The question of the isolated use of echoscleroobliteration in patients with recurrent varicose veins remains unexplored.Aim. To develop an algorithm for the optimal use of echosclerobliteration of recurrent veins and to improve the results of treatment of patients with recurrent varicose veins.Materials and methods. Puncture echosclerobliteration was performed in 67 patients with relapses of varicose veins with clinical class C2, C3, C4 according to the CEAR classification. Among them there were 46 (68.7%) women and 21 (31.3%) men aged 21 to 79 years.Results. The feasibility of echosclerobliteration of recurrent veins was 100%, the average course of treatment consisted of 2.5 ± 0.3 sessions. The development of thrombotic complications was observed in 10 (14.9%) patients, neurological disorders – in 3 (4.5%), hyperpigmentation – in 16 (23.9%), secondary telangiectasia – in 11 (16.4%). After 2 years, a recurrence of the disease was detected in 5 (7.5%) patients. In the process of dynamic follow-up, all patients after echosclerotherapy showed a decrease in the clinical manifestations of varicose veins by 4.3–82.1%, an improvement in all quality oflife factors by 12.1–24.8%.Discussion. The use of echoscleroobliteration in patients with recurrent varicose veins allows achieving good clinical results, reliable obliteration of recurrent veins was achieved in 62 (92.5%) patients after 2 years, and the positive dynamics of all quality oflife factors 1 year after treatment was 12.1–24.8%.Conclusion. The use of echoscleroobliteration allows to provide a good clinical, cosmetic result of treatment, to improve the quality of life in patients with recurrent varicose veins.
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Leszczyńska, Katarzyna, Agnieszka Wincek, Wojciech Fortuna, et al. "Treatment of patients with cervical and upper thoracic incomplete spinal cord injury using repetitive transcranial magnetic stimulation." International Journal of Artificial Organs 43, no. 5 (2019): 323–31. http://dx.doi.org/10.1177/0391398819887754.
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Purpose: To evaluate the short- and long-term effectiveness of repetitive transcranial magnetic stimulation with parameters based on results of comparative neurophysiological studies in patients with incomplete spinal cord injury. Results may help to understand mechanisms responsible for regeneration of the incomplete spinal cord after injury. Methods: Repetitive transcranial magnetic stimulation sessions (three to five sessions per month for not less than 5 months) to 15 patients with C4-Th2 incomplete spinal cord injury were applied with individually designed parameters. One session consisted of bilateral stimulation of the primary motor cortex (for 10 min each with 800 stimuli in 2-s lasting trains and the inter-train intervals of 28 s) with frequency at 20–22 Hz and stimulus strength that was 70%–80% of the resting motor threshold (0.84–0.96 T). Recordings of surface electromyography at rest and during the attempt of maximal muscle contractions and motor evoked potentials were performed from abductor pollicis brevis and tibialis anterior muscles bilaterally. Amplitude parameters of surface electromyography and motor evoked potentials were used as outcomes. All neurophysiological tests were comparatively applied before and after treatment. Results: Decrease in surface electromyography amplitudes recorded at rest from abductor pollicis brevis (p = 0.009), increase in surface electromyography amplitudes during maximal contraction of abductor pollicis brevis (p = 0.03) and increase in motor evoked potential parameters recorded from abductor pollicis brevis (p = 0.04) were found. Conclusion: Proposed repetitive transcranial magnetic stimulation algorithm reduced the increased muscle tension in upper extremity muscles, improved the function of upper extremity muscle motor units and slightly improved the transmission of efferent neural impulses within spinal pathways. Besides functional recovery in descending spinal pathways, repetitive transcranial magnetic stimulation may also inhibit inevitable pathological changes in nerves.
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Pozzo, Federico, Francesca Cuturello, Edith Villegas Garcia, et al. "An Unsupervised Machine Learning Method Stratifies Chronic Lymphocytic Leukemia Patients into Novel Categories with Different Risk of Early Treatment." Blood 142, Supplement 1 (2023): 1892. http://dx.doi.org/10.1182/blood-2023-177884.
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Scoring systems designed to improve the accuracy of prognostication in chronic lymphocytic leukemia (CLL) usually rely on discretized/dichotomic values of the clinical and biological variables. Here we analyzed the immunophenotypic and (immuno)genetic profiles of 2,243 CLL patients with Rai stage 0-I-II, by applying unsupervised machine learning methods, elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional hierarchical models. The study included an internal training cohort (n=863) and two external validation cohorts provided from Cardiff University/Brighton Medical School (n=455) and the Mayo Clinic (Rochester, MN; n=925). Primary endpoint was Time to First Treatment (TTFT). Laboratory-based markers evaluated in the training cohort were: surface antigen expression by flow cytometry (as % of positive cells) of CD20, FMC7, CD49d, CD49c, CD38, CD23, CD43, CD22, ZAP-70; chromosomal aberrations (% of nuclei with abnormal signal) del13q, tris12, del11q and del17p,; mutational status of TP53 by NGS as % VAF; IGHV mutational status as % mutation. 420/863 cases received treatment (median 46 months). Univariate Cox regression identified FISH del11q, del17p and tris12, TP53 and IGHV mutations, CD38 and CD49d expression, as features associated with TTFT (p-value <0.001). An unsupervised k-means algorithm partitioned cases into 6 clusters (C1-C6), as selected by the elbow method, and centroid analysis evaluated which feature most contributed to each cluster (Panel A): C1 (n=275) was heavily IGHV-mutated (mutation range 4.5-22.0%) with low representation of all other features; C2 (n=208) was mostly IGHV-unmutated/intermediate (up to 4.5%) in the absence of other features; C3 (n=169) were CD49d-expressing cases (>50% expression), with low representation of other features, except CD38; C4 (n=127) were cases with trisomy 12 (>28%), also expressing CD49d and CD38; C5 (n=48) were cases with highly clonal del11q (44-98%) and IGHV-unmutated, mutually exclusive with TP53 disruption; C6 (n=34) contained TP53-disrupted cases with high mutation burden (VAF 36-97%) and/or del17p (range 40-96%), with low importance of all the other features. Of note, alternative use of either TP53 mutation or del17p as defining features of cluster 6 did not result in changes of the classification accuracy. Clinically, clusters 3-4-5-6 showed median TTFT of 26, 22, 9, 5 months respectively whereas median TTFT was not reached for clusters 1-2. Hierarchical agglomerative clustering aggregated the 6 clusters in 3 major risk tiers: high (C5-6), intermediate (C2-3-4) and low (C1), with median TTFT of 7, 45 and not reached, respectively (Panel B). Multivariate Cox analysis (MVA) with Rai staging demonstrated that the 3-tier risk score contributed significantly and independently to TTFT estimate (p<0.0001; Harrel's c-index=0.71). To classify novel patients, we designed a hierarchical algorithm, based on each clusters' estimated risk and according to calculated cut-off, i.e. C6, del17p/ TP53>36%; C5, del11q>43%; C4, tris12>28%; C3, CD49d>50%; C2, IGHV≤4.5%; C1, the remaining patients with IGHV>4.5%. This approach was highly robust by an internal training-validation bootstrap (cluster classification accuracy = 0.96±0.02). External samples from two independent cohorts were classified into the six clusters using the cut-offs defined by the hierarchical classifier, and then aggregated into the 3-risk tiers. The classifier performed robustly in both cohorts, again adding independent prognostic information to Rai stage in MVA, with p<0.0001 and c-index of 0.76 for Cardiff/Brighton cohort and p<0.0001 and c-index of 0.74 for Mayo cohort. In conclusion, our machine-learning-driven, laboratory-based classification identifies clusters of patients who at higher risk of requiring early treatment, independent of clinical staging, and may help to better identify patients who may benefit from early treatment or more frequent disease surveillance. For some features, TP53 or IGHV mutational status, our unsupervised approach selected cut-offs not recapitulating the canonical ones, envisioning different biological activities in the TTFT setting. For example, TP53 mutations were clinically relevant only if present in the vast majority of the clone , suggesting the involvement of specific activities of the TP53 mutant in this context.
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Pawlak-Bus, K., W. Schmidt, and P. Leszczynski. "FRI0184 ATTRIBUTION OF NEUROPSYCHIATRIC MANIFESTATIONS TO SYSTEMIC LUPUS ERYTHEMATOSUS IN POLISH COHORT OF PATIENTS WITH THE USE OF THE ITALIAN MODEL." Annals of the Rheumatic Diseases 79, Suppl 1 (2020): 675.1–676. http://dx.doi.org/10.1136/annrheumdis-2020-eular.2470.
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Background:Distinguishing primary NPSLE (neuropsychiatric systemic lupus erythematosus) from secondary causes remains challenging (1). Attribution models were developed in order to aim clinicians in correct classification of NPSLE cases (2).Objectives:To investigate the prevalence of primary NPSLE manifestations assigned with Italian model of attribution (2).Methods:We retrospectively assessed clinical details of 164 patients with SLE classified with 2012 SLICC (Systemic Lupus International Collaborating Clinics) classification criteria, 21 were excluded due to incomplete information. Data was gathered with a questionnaire comprising demographics, medical history, laboratory results (concentrations of antibodies against double stranded DNA – anti-dsDNA, complement components C3 and C4), disease activity measured with Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and Physician Global Assessment (PGA) and damage determined with SLICC/ACR (American College of Rheumatology) Damage Index (SDI). Neuropsychiatric manifestations were categorized in accordance with 1999 ACR glossary and attribution of manifestations was performed with the use of Italian model with the score ≥7 out of 10 points enabling assignment to primary NPSLE group (2). Statistical analysis was conducted with Statistica v.13.3 using Mann-Whitney U, chi-square and Fisher exact test.Results:We encountered 155 NP manifestations in our cohort and 52 (34%) were attributed to SLE. Characteristics of the study groups are presented in Table 1. Exact manifestations and their attribution rates are presented on Graph 1. Patients with attributable NPSLE were younger, had earlier disease onset, presented higher disease activity, lower damage accrual without taking NP damage into account and more often had increased anti-dsDNA serum concentration.Table 1.Demographic and laboratory characteristics with disease activity and damage of the study groups, N(%) or mean(±SD).CharacteristicPatients with attributed NPSLE manifestationsPatients without attributed NPSLE manifestationsPatients34 (23.8%)109 (76.2%)Sex, female30 (88.2%)102 (93.6%)Age (years)37.6 (±11.7)44.3 (±13.9)*Age of disease onset (years)32.5 (±11.4)37.6 (±12.6)*Disease duration (years)5.1 (±4.1)6.8 (±5.6)SLEDAI-2K29.2 (±10.7)12.2 (±8.1)*patients with clinically active disease (defined as SLEDAI-2K≥6 in clinical manifestations)34 (100%)93 (85.3%)*SLEDAI-2K without NP manifestations14.8 (±8.4)11.0 (±6.7)*PGA2.1 (±1.0)1.2 (±1.0)*SDI0.5 (±0.8)0.7 (±1.1)SDI without NP damage0.3 (±0.6)0.7 (±1.1)*low C3/C4 complement component concentration in serum21 (61.8%)55 (50.4%)elevated anti-dsDNA autoantibody concentration in serum27 (79.4%)55 (50.4%)*NPSLE – neuropsychiatric systemic lupus erythematosus, SLEDAI-2K – Systemic Lupus Erythematosus Disease Activity Index version 2000, PGA – physician global assessment, SDI – SLICC/ACR (Systemic Lupus International Collaborating Clinics/American College of Rheumatology) Damage Index*p<0,05, Mann-Whitney U, χ2or Fisher’s exact test, as appropriateConclusion:Primary NP manifestations in patients with SLE occur mainly in young patients with high disease activity. Cerebrovascular disease, seizures, psychosis and cranial neuropathy are most frequent primary NPSLE manifestations.References:[1]The American College of Rheumatology nomenclature and case definitions for neuropsychiatric lupus syndromes. Arthritis Rheum. 1999;42(4):599–608.[2]Bortoluzzi A, Scirè CA, Bombardieri S, Caniatti L, Conti F, De Vita S, et al. Development and validation of a new algorithm for attribution of neuropsychiatric events in systemic lupus erythematosus. Rheumatol Oxf Engl. 2015;54(5):891–8.Disclosure of Interests:None declared
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Chaltsev, B. D., V. I. Vasilyev, S. G. Palshina, et al. "Characteristics of clinical, laboratory, and immunological manifestations in patients with anticentromere antibody-associated Sjögren's disease." Modern Rheumatology Journal 14, no. 4 (2020): 50–59. http://dx.doi.org/10.14412/1996-7012-2020-4-50-59.
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Objective: to study clinical and laboratory features in patients with anticentromere antibody (ACA)-positive SjЪgren's disease (SD), as well as the sensitivity of different methods for determination of ACA, and to elaborate an algorithm for differential diagnosis in ACA-positive patients.Patients and methods. The V.A. Nasonova Research Institute of Rheumatology followed up 136 patients who were highly positive for ACA. The investigators used the 2001 Russian criteria for the diagnosis for SD; the 2013 ACR/European League Against Rheumatism (EULAR) criteria for that of scleroderma systematica (SDS); the guidelines of the American Association for the Study of Liver Diseases, the Russian Gastroenterological Association, and the Russian Society for the Study of the Liver for that of primary biliary cholangitis (PBC)/biliary duct epitheliitis in the presence of SD. Lymphomas were diagnosed by biopsies of affected organs according to the WHO classification. SD was diagnosed in 119 patients; SDS in 49 cases (37 with SDS concurrent with SD and 12 with isolated SDS), PBC/biliary duct epitheliitis in 23 (all cases with PBC/biliary duct epitheliitis concurrent with SD and/or SDS); 5 patients were excluded from the investigation. Further analysis included 131 ACA-positive patients. The patients were divided into three groups: SD (n=82 or 62.6%); SD+SDS (n=37 or 28.24%); SDS (n=12 or 9.16%).Results and discussion. Autoantibodies to centromere peptide (CENP) A and CENP-B in the same titers were detected in all ACA-positive patients, regardless of diagnosis. Comparative analysis of three patient groups revealed no statistically significant differences in the frequency of laboratory deviations. The signs characteristic of classical SD (rheumatoid factor (RF)), anti-Ro and anti-La antibodies, leukopenia, higher ESR values, hypergammaglobulinemia, and elevated IgG/IgA levels) were found in a small proportion of patients. The frequency and severity of glandular manifestations did not differ in SD and SD + SDS. PBC/biliary duct epitheliitis was present in 17.5% of ACA-positive patients (in most antimitochondrial antibody-positive cases); no statistically significant differences in its frequency were found between the groups. Other extraglandular manifestations in SD and SD + SDS were identified in a smaller number of patients. All sclerodermic spectrum manifestations were more common in SD and SD + SDS than in BS. Pulmonary arterial hypertension was not diagnosed in any patient from the SD group. MALT lymphomas were detected in 19 ACA-positive patients. Those were present only in BS patients and absent in the SDS group. MALT lymphomas developed in the first 10 years after the onset of SD. The transformation of MALT lymphoma into diffuse large B-cell lymphoma was observed in 2 patients. The main signs of lymphomas in SD patients were persistent parotid salivary gland enlargement, decreased levels of complement C4 and peripheral blood CD19+ cells, as well as cryoglobulinemic vasculitis, serum monoclonal secretion, lymphoid infiltration in the minor salivary glands (a focus score of >4), and severe damage to the salivary and lacrimal glands.Conclusion. ACA-associated SD is an independent disease subtype characterized by an increased risk for SDS, PBC, and MALT lymphomas and by a low frequency of the systemic manifestations and laboratory signs characteristic of classical SD. Regardless of the detected type of antibodies and the presence or absence of extraglandular manifestations, damage to the salivary and lacrimal glands progresses in SD, which often leads to lymphomas; therefore, the therapy that may prevent this complication should be initiated as soon as possible after SD diagnosis. The lymphoproliferation signs identified in this investigation should be taken into account in all ACA-positive patients with SD for the early diagnosis of lymphoid tumors before therapy is prescribed. An algorithm for differential diagnosis in seropositivity for ACA is presented. Determination of autoantibodies to CENP-A and CENP-B does not allow the differential diagnosis in ACA-positive patients.
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Demchuk, Olga Vladimirovna, and Irina Alexandrovna Sukmanova. "A PERSONALIZED APPROACH TO ASSESSING THE RISK OF REPEAT CARDIOVASCULAR EVENT AND CHRONIC KIDNEY DISEASE IN PATIENTS WITH MYOCARDIAL INFARCTION AND ACUTE KIDNEY INJURY." Baikal Medical Journal 2, no. 3 (2023): 51–53. http://dx.doi.org/10.57256/2949-0715-2023-3-51-53.
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Introduction: AKI (acute kidney injury) is a common comorbid condition in patients with AMI (acute myocardial infarction), complicating its course and treatment. In the future, the prognosis of these patients after discharge from the hospital, as well as the incidence of recurrent cardiovascular events (CVS) and chronic kidney disease (CKD) are not known. The purpose of the study: to study the predictors of the development of recurrent cardiovascular events (CVS) in patients with myocardial infarction (MI) with acute kidney injury (AKI). Develop a calculator - a risk meter for repeated CCC. Materials and methods: the study was performed in the Altai Regional Cardiological Dispensary. 193 patients with MI and PCI (percutaneous coronary intervention) were included, divided into 2 groups: the first - 123 patients aged 62.8±1.1 years with MI and AKI, the second - 70 patients without AKI, age 61.3± 1.6 years. Upon admission and before discharge, the level of the KIM-1 molecule (Kidney Injury Molecule-1) and IL-18 (interleukin-18) was studied by ELISA in the urine. All patients underwent coronary angiography using low-osmolar contrast. One year after discharge, the frequency of development of repeated CCC was assessed. Statistical calculations were carried out using statistical packages STATISTICA 12.0. The level of statistical significance was taken equal to 0.05. Results. The level of KIM-1 (at admission) in the first group was statistically higher than in the second: 1998.9±147.6 versus 1289.8±126.1 pg/ml p=0.001, and IL-18 in the AKI group exceeded the corresponding parameter of the group without AKI: 179.0±12.9 versus 114.9±11.5 pg/ml p=0.007, respectively. A year later, it was found that the frequency of recurrent cardiovascular events was higher in the group with AMI and AKI: unstable angina was diagnosed in 26 (21.1%) patients in the group with AKI and 5 (7.1%) patients without AKI in history, p=0.010; 19 (15.4%) and 2 (2.8%) patients, p=0.006, 20 (16.2%) and 3 (4.2%), p=0.013 patients, respectively, had repeated myocardial infarction and progression of CHF in a year . Progression of renal dysfunction during the year was observed 5 times more often in patients with MI and AKI: CKD C2 was diagnosed in 49 (39.8%) patients of the first group and 16 (22.8%) of the second, p = 0.01; CKD C3a and C3b stages - in 34 (27.6%), p˂0.001 and 19 (15.4%) patients, p=0.002, respectively, CKD stage C4 was detected in 4 (3.2%) patients of the group with AMI and AKI in the early postinfarction period. As a result of constructing a multifactorial logistic regression model, predictors were identified that have a multiplicative effect on the development of recurrent CV events: BMI over 25 kg/m2 increases the risk of recurrent CV events by 0.91 [0.83; 0.99] times, p=0.028, history of prior MI in 3.32 [1.24; 9.86] times, p=0.022. Increase in CRP by 1.01 [1; 1.03] times, p=0.045, troponin I, 0.97 [0.94;1] times, p=0.037, and IMT-1, 1 [1;1] times, p=0.030. On the development of CKD: an increase in age is associated with an increase in the chances of developing CKD by 1.18 [1.1; 1.29], p ˂0.001; troponin I by 0.96 times [0.92; 1], p=0.030; systolic blood pressure in 1.03 [1; 1.07] times, p = 0.029. A calculator has been developed for risk stratification of the development of recurrent CV events and an algorithm for managing this category of patients after discharge from the hospital. Conclusions: thus, the identified predictors make it possible to calculate the risk of developing recurrent CV events and CKD using the developed calculator and allocate these patients to a separate group for personalized observation, taking appropriate measures of secondary prevention.
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Bernal-Casas, David, Joan Serrano-Marín, Juan Sánchez-Navés, Josep M. Oller, and Rafael Franco. "Advancing Personalized Medicine by Analytical Means: Selection of Three Metabolites That Allows Discrimination between Glaucoma, Diabetes, and Controls." Metabolites 14, no. 3 (2024): 149. http://dx.doi.org/10.3390/metabo14030149.
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Background: This paper aimed at devising an intelligence-based method to select compounds that can distinguish between open-angle glaucoma patients, type 2 diabetes patients, and healthy controls. Methods: Taking the concentration of 188 compounds measured in the aqueous humour (AH) of patients and controls, linear discriminant analysis (LDA) was used to identify the right combination of compounds that could lead to accurate diagnosis. All possibilities, using the leave-one-out approach, were considered through ad hoc programming and in silico massive data production and statistical analysis. Results: Our proof of concept led to the selection of four molecules: acetyl-ornithine (Ac-Orn), C3 acyl-carnitine (C3), diacyl C42:6 phosphatidylcholine (PC aa C42:6), and C3-DC (C4-OH) acyl-carnitine (C3-DC (C4-OH)) that, taken in combination, would lead to a 95% discriminative success. 100% success was obtained with a non-linear combination of the concentration of three of these four compounds. By discarding younger controls to adjust by age, results were similar although one control was misclassified as a diabetes patient. Conclusions: Methods based on the consideration of individual clinical chemical parameters have limitations in the ability to make a reliable diagnosis, stratify patients, and assess disease progression. Leveraging human AH metabolomic data, we developed a procedure that selects a minimal number of metabolites (3–5) and designs algorithms that maximize the overall accuracy evaluating both positive predictive (PPV) and negative predictive (NPV) values. Our approach of simultaneously considering the levels of a few metabolites can be extended to any other body fluid and has potential to advance precision medicine. Artificial intelligence is expected to use algorithms that use the concentration of three to five molecules to correctly diagnose diseases, also allowing stratification of patients and evaluation of disease progression. In addition, this significant advance shifts focus from a single-molecule biomarker approach to that of an appropriate combination of metabolites.
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Stein, Eytan M., Abigail Shoben, Uma Borate, et al. "Enasidenib Is Highly Active in Previously Untreated IDH2 Mutant AML: Early Results from the Beat AML Master Trial." Blood 132, Supplement 1 (2018): 287. http://dx.doi.org/10.1182/blood-2018-99-118287.
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Abstract The majority of patients with acute myeloid leukemia (AML), including nearly all patients older than age 60, present with multiple, sequentially acquired, somatic mutations. The 5-year overall survival (OS) for AML patients ≥ 60 with the current standard-of-care is less than 10 percent and the median OS in most genetically defined subtypes is < 1 year. The Leukemia & Lymphoma Society (LLS) Beat AML Master Trial is a precision medicine trial for previously untreated AML pts age ≥ 60. Eligible patients are assigned to an interventional sub-study based upon an algorithm incorporating cytogenetic and mutational analysis, all within 7 days of enrollment. The primary objective of this phase 1B/II sub-study is to assess the efficacy of the oral IDH2 inhibitor enasidenib, as measured by overall response rate, in newly diagnosed AML patients, ≥ 60, with IDH2 mutant AML. Pt eligibility included ECOG performance status of 0-2, AST/ALT < 5 x the upper limit of normal (ULN), bilirubin ≤ 2.0 x ULN, creatinine ≤ 1.5 x ULN, cardiac ejection fraction of 40%, and no prior chemotherapy for AML or MDS. Exclusion criteria included symptomatic disseminated intravascular coagulation, leukostasis requiring urgent therapy, active hepatitis, or active second malignancy. Patients were treated with enasidenib 100 mg/day in continuous 28-day cycles. Azacitidine (75 mg/m2 days 1-7) was added to enasidenib for patients not achieving a complete remission or complete remission with incomplete hematologic recovery (CR/CRi) by cycle 5, or those with earlier progression as part of a delayed phase 1b study using a standard 3 + 3 design. At data cut off (April 30, 2018), 24 patients were consented to the trial and 23 received therapy. Of these, 18 (78%) had an IDH2 R140 and 5 (22%) had an IDH2 R172 mutation. The median age was 76 (range 62 -84) and 57% were female. Median WBC was 6.0 x 109/L (range .69-30.1), hemoglobin 8.2 g/dl (range 6.8-12.8) and platelets of 66 x 1012/L (range 6-517). 44% of patients had abnormal cytogenetics, including 17 % which were high risk according to CALGB criteria. Of the 23 patients enrolled, there were no deaths within the first 28 days of treatment. A total of 13 pts experienced one or more serious adverse events on enasidenib monotherapy, the most common being differentiation syndrome (n=4), sepsis (n=4), bleeding (n=3, 1 grade 5), elevated liver tests (n=3), and respiratory failure (n=2). Other common adverse events grade ≥3 occurring in 20% or more patients included fatigue, fever, edema, anorexia, low albumin, low electrolytes, nausea, diarrhea, constipation, insomnia and depression. For the 23 patients receiving enasidenib monotherapy, the median time on any treatment (including combination) is 138 days (min=43, max=312), and the median time on enasidenib monotherapy is 110 days (min=43, max=312). CR/CRi was achieved in 43% (7 CR/2CRi) of patients. Of the 6 pts with RAS or PTPN11 mutations, 1 responded. Reasons for discontinuing monotherapy include proceeding to allogeneic stem cell transplant after attaining CR (2), failure to respond to monotherapy (9), CR or CRi with discontinuation (2) or progression after CR/CRi (1). At the time of data cutoff, five pts have died. Five pts with inadequate response to monotherapy proceeded to the phase 1b portion of the study with enasidenib in combination with azacitidine. The combination was generally well tolerated with one DLT (nausea) and no other safety concerns. One patient attained a CRi and four pts have discontinued combined therapy without response. This trial demonstrates the significant clinical activity of enasidenib in previously untreated IDH2 mutated AML patients who do not choose to receive intensive chemotherapy. Enasidenib in this trial was associated with a low early death rate and an acceptable toxicity profile. These results justify further exploration of single agent enasidenib in newly diagnosed AML and of novel combination strategies building upon the efficacy of enasidenib in newly diagnosed AML patients ≥ 60. Disclosures Stein: Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Agios: Consultancy; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy. Borate:Novartis: Consultancy; Agios: Consultancy. Stock:Jazz Pharmaceuticals: Consultancy. Patel:France Foundation: Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Dava Oncology: Honoraria. Kovacsovics:Abbvie: Research Funding; Amgen: Honoraria, Research Funding. Blum:Pfizer: Consultancy; Boehringer Ingelheim: Research Funding; Forma: Research Funding; Tolero: Research Funding; Astellas: Consultancy; Xencor: Research Funding. Vergilio:Foundation Medicine Inc: Employment. Druker:Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; McGraw Hill: Patents & Royalties; Aileron Therapeutics: Consultancy; Novartis Pharmaceuticals: Research Funding; Fred Hutchinson Cancer Research Center: Research Funding; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Monojul: Consultancy; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Patient True Talk: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Research Funding; Oregon Health & Science University: Patents & Royalties; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millipore: Patents & Royalties; Celgene: Consultancy. Levine:Celgene: Consultancy, Research Funding; C4 Therapeutics: Equity Ownership; Isoplexis: Equity Ownership; Epizyme: Patents & Royalties; Gilead: Honoraria; Roche: Consultancy, Research Funding; Loxo: Consultancy, Equity Ownership; Imago: Equity Ownership; Qiagen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Prelude: Research Funding; Novartis: Consultancy; Janssen: Consultancy, Honoraria. Mims:Novartis: Consultancy; Abbvie Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees.
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Chapuy, Bjoern, Chip Stewart, Timothy Wood, et al. "Validation of the Genetically-Defined DLBCL Subtypes and Generation of a Parsimonious Probabilistic Classifier." Blood 134, Supplement_1 (2019): 920. http://dx.doi.org/10.1182/blood-2019-131250.
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Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous disease with recognized transcriptional subtypes associated with normal cells of origin, activated B-cell (ABC) and germinal center B-cell (GCB) tumors. Emerging data suggested that additional heterogeneity existed, prompting us to comprehensively characterize genomic signatures of 304 newly diagnosed DLBCLs from patients treated with state-of-the-art therapy. We integrated recurrent mutations, somatic copy number alterations (SCNAs) and structural variants (SVs) and identified 5 genetically distinct DLBCL clusters (C1- C5 DLBCLs; Chapuy, Stewart, Dunford, et al. Nat Med 2018). Specifically, we identified two genetically distinct ABC subtypes, including favorable-risk C1 DLBCLs with features of extrafollicular origin and alterations also seen in transformed marginal zone lymphomas (NOTCH2 and NF-κB pathway member mutations and BCL6 SVs). Unfavorable-risk C5 ABC DLBCLs harbored frequent 18q/BCL2 copy gain and co-occurring CD79B and MYD88L265P mutations. We also identified two genetically distinct GCB subtypes, including unfavorable-risk C3 DLBCLs with frequent BCL2 SVs, mutations in chromatin-modifying enzymes (CREBBP, MLL2, EZH2) and BCR/PI3K signaling pathway members (including inactivating PTEN mutations and copy loss). Favorable-risk C4 GCB DLBCLs had frequent mutations in core and linker histones and signaling intermediates (SGK1, BRAF and STAT3). Additionally, we identified an ABC/GCB-independent subtype, C2 DLBCLs, characterized by frequent bi-allelic TP53 inactivation, 9p21.23/CDKN2A copy loss and associated genomic instability reflected in recurrent SCNAs, increased genome doublings and a distinct outcome following induction therapy. A next step in utilizing the characterized genetic substructure was to confirm it in an independent series and develop a molecular classifier that allows prospective identification of C1-C5 DLBCLs. To this end, we accessed whole exome sequencing, copy number and SV data from a recent cohort of newly diagnosed DLBCLs (39 tumor-normal pairs, 462 tumor-only samples; Schmitz et al. NEJM 2018). All samples were re-analyzed using our mutational and SCNA pipelines and our newly generated tumor-only algorithm (Chapuy, Stewart, Dunford, et al. Nat Med 2018) to avoid batch effects and harmonize the datasets. SVs were used as reported. Purity and ploidy were inferred using ABSOLUTE and samples with missing data or low purity were removed. For the combined cohort (579 samples), we assessed our previously characterized 158 genetic drivers (Chapuy, Stewart, Dunford, et al. Nat Med 2018) and confirmed equal distribution of their marginal frequencies (R=0.88, p=1.5e-51), excluding batch effects. Next, we applied non-negative matrix factorization (NNF) consensus clustering to the combined dataset (158 genetic drivers vs. 579 tumors) and confirmed the C1-C5 DLBCL genetic clusters. Notably, tumors from both series contributed at comparable frequencies to the respective C1-C5 DLBCLs. We also noted an enrichment of the alternative genetic labels from Schmitz et al. in 3 of our C1-C5 DLBCL subtypes (B2N in C1 DLBCLs, p&lt;0.0001; EZB in C3 DLBCLs, p&lt;0.0001; MCD in C5 DLBCLs, p&lt;0.0001). These data confirmed the identity of the C1-C5 DLBCL clusters in an independent cohort. Next, we developed a molecular classifier that prospectively identified C1-C5 DLBCLs using a minimum number of easy-to-measure features. The NMF-defined classes of the combined cohort were used as gold-standard training and validation datasets. We tested different models for classification and selected an artificial neural network approach which provides accurate classification of individual samples and well-calibrated confidence metrics. To minimize potential overtraining, we developed a reduced input feature set of the 22 most discriminating features, constructed confidence metrics for each sample and trained an ensemble of Feed-Forward Neural Networks via 10-fold cross validation. With this approach, our classifier had 84% accuracy for the total set and 94% accuracy for the high-confidence samples (70% of all samples). The newly developed parsimonious classifier will allow prospective identification of the independently confirmed C1-C5 DLBCL subtypes in newly diagnosed patients, a necessity for clinical application. Disclosures Getz: Pharmacyclics: Research Funding; IBM: Research Funding; MuTect, ABSOLTUE, MutSig and POLYSOLVER: Patents & Royalties: MuTect, ABSOLTUE, MutSig and POLYSOLVER. Shipp:BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck & Co.: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Gilead Sciences: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees.
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Buege, Michael J., Phuong Dao, Raymond G. Dematteo, et al. "Impact of Choice of Platinum-Based Salvage Therapy on CNS Relapse in Patients with Relapsed or Refractory Diffuse Large B-Cell Lymphoma." Blood 138, Supplement 1 (2021): 2529. http://dx.doi.org/10.1182/blood-2021-147527.
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Abstract Introduction Risk of central nervous system (CNS) relapse and its management in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) have been extensively studied. However, similar data in patients with relapsed or refractory (R/R) disease are lacking. Specifically, it is unknown whether choice of treatment in this setting may influence the CNS risk. A number of commonly used platinum-based regimens do not contain agents that consistently penetrate the CNS at therapeutic concentrations. High-dose cytarabine, used in standard salvage therapy alongside rituximab, dexamethasone, and either cisplatin (R-DHAP) or oxaliplatin (R-DHAX), is known to cross the blood-brain barrier. Therefore, we asked whether the use of R-DHAP/R-DHAX may be associated with lower risk of CNS relapse than other platinum-based regimens in patients with R/R DLBCL. Methods We reviewed consecutive adult patients who initiated a standard platinum regimen (R-ICE, R-GDP, R-GemOx, R-DHAP, or R-DHAX) for the treatment of DLBCL in first relapse at MSKCC. Patients with evidence of CNS involvement prior to, or at initiation of salvage, history of human immunodeficiency virus, or whose front-line treatment included a platinum agent were excluded. The primary objective of the analysis was to compare the cumulative incidence of CNS relapse in patients receiving R-DHAP/R-DHAX versus other salvage regimens (OSR). Death without relapse was treated as a competing risk. Results Between April 1996 and September 2020, we identified 302 eligible patients (Table 1), of whom 51 received R-DHAP/R-DHAX and 251 received OSR. Median age at the time of R/R disease was 61 years (range, 19 to 92 years). Patients who received R-DHAP/R-DHAX were significantly more likely to have germinal center B-cell-like phenotype by Hans algorithm (61% vs 24%, p &lt; 0.001) and to have double- or triple-hit lymphoma (DHL/THL; 22% vs 2%, p &lt; 0.001). A higher proportion of patients who received R-DHAP/R-DHAX received CNS prophylaxis during front-line treatment (37% vs 21%, p = 0.01), possibly owing to the number of patients with DHL/THL. Intrathecal methotrexate was used in 68% of patients receiving R-DHAP/R-DHAX and 17% of patients receiving OSR; high-dose methotrexate was used in 5% of the R-DHAP/R-DHAX group and 2% of OSR. A higher proportion of patients who received R-DHAP/R-DHAX underwent consolidative chimeric antigen receptor T-cell therapy (CAR-T) for residual disease (33% vs 5%, p &lt; 0.001), but fewer underwent consolidative autologous hematopoietic cell transplantation (HCT; 33% vs 54%; p = 0.008). After median overall follow-up of 68 months (34 months for R-DHAP/R-DHAX vs 79 months for OSR, p &lt; 0.001), CNS relapse was observed in 20 patients: 2 in the R-DHAP/R-DHAX group and 18 in the OSR group (Table 2). CNS relapse occurred as leptomeningeal disease only in both patients who received R-DHAP/R-DHAX; all brain parenchyma relapses occurred in patients who received OSR. This numerical difference did not translate into a statistically significant difference in cumulative incidence of CNS relapse (p = 0.52, Figure 1). No independent predictors of CNS relapse were identified in multivariate analysis, and cumulative incidence of CNS relapse was similar when the OSR group was restricted to patients who received R-ICE (n = 198). Median progression-free survival (PFS) was shorter in patients who received R-DHAP/R-DHAX compared to those who received OSR (5.4 months vs 24 months; p = 0.008). However, in Cox regression analysis controlling for effects of salvage regimen (R-DHAP/R-DHAX vs OSR), international prognostic index, DHL/THL biology, and consolidative use of either CAR-T or autologous HCT, only autologous HCT appeared to be an independent predictor of longer PFS (p &lt; 0.001). There was no significant difference in median overall survival (38 months vs 58 months; p = 0.5). Conclusion In patients with R/R DLBCL receiving platinum-based salvage therapy, the cumulative incidence of CNS relapse after salvage initiation was not significantly different between patients who received R-DHAP/R-DHAX and those who received other salvage regimens. The lower number of CNS relapses and absence of brain parenchyma relapses in the R-DHAP/R-DHAX group, despite a greater proportion of patients with DHL/THL, suggests that parenchymal penetration by cytarabine may influence the pattern of relapse, warranting study in a larger cohort. Figure 1 Figure 1. Disclosures Batlevi: Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; Regeneron: Current holder of individual stocks in a privately-held company; Moderna: Current holder of individual stocks in a privately-held company; Bayer: Research Funding; Dava Oncology: Honoraria; Life Sciences: Consultancy; GLG Pharma: Consultancy; Juno/Celgene: Consultancy; Kite Pharma: Consultancy; TouchIME: Honoraria; BMS: Current holder of individual stocks in a privately-held company; Pfizer: Current holder of individual stocks in a privately-held company; ADC Therapeutics: Consultancy; Viatris: Current holder of individual stocks in a privately-held company; Memorial Sloan Kettering Cancer Center: Current Employment; Medscape: Honoraria; Karyopharm: Consultancy; Xynomic: Research Funding; Roche/Genentech: Research Funding; Novartis: Research Funding; Epizyme: Research Funding; Janssen: Research Funding; Autolus: Research Funding. Hamlin: Incyte, Janssen, Molecular Templates: Research Funding; Alexion, AstraZeneca Rare Disease (formerly Portola Pharmaceuticals): Other: Study investigator, Research Funding; Kite, Karyopharm, Celgene: Membership on an entity's Board of Directors or advisory committees. Horwitz: ADC Therapeutics, Affimed, Aileron, Celgene, Daiichi Sankyo, Forty Seven, Inc., Kyowa Hakko Kirin, Millennium /Takeda, Seattle Genetics, Trillium Therapeutics, and Verastem/SecuraBio.: Consultancy, Research Funding; Affimed: Research Funding; Aileron: Research Funding; Acrotech Biopharma, Affimed, ADC Therapeutics, Astex, Merck, Portola Pharma, C4 Therapeutics, Celgene, Janssen, Kura Oncology, Kyowa Hakko Kirin, Myeloid Therapeutics, ONO Pharmaceuticals, Seattle Genetics, Shoreline Biosciences, Inc, Takeda, Trillium Th: Consultancy; Celgene: Research Funding; C4 Therapeutics: Consultancy; Crispr Therapeutics: Research Funding; Daiichi Sankyo: Research Funding; Forty Seven, Inc.: Research Funding; Kura Oncology: Consultancy; Kyowa Hakko Kirin: Consultancy, Research Funding; Millennium/Takeda: Research Funding; Myeloid Therapeutics: Consultancy; ONO Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy, Research Funding; Secura Bio: Consultancy; Shoreline Biosciences, Inc.: Consultancy; Takeda: Consultancy; Trillium Therapeutics: Consultancy, Research Funding; Tubulis: Consultancy; Verastem/Securabio: Research Funding. Joffe: AstraZeneca: Consultancy; Epizyme: Consultancy. Khan: Seattle Genetics: Research Funding. Kumar: Adaptive Biotechnologies, Celgene, Abbvie Pharmaceticals, Pharmacyclics, Seattle Genetics: Research Funding; Kite Pharmaceuticals: Other: advisory board , Research Funding; Abbvie Pharmaceuticals: Research Funding; Celgene: Honoraria, Other: advisory board, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; Astra Zeneca: Honoraria, Other: Advisory Board, Research Funding. Lee: Intellisphere, LLC: Consultancy. Matasar: Takeda: Consultancy, Honoraria; GlaxoSmithKline: Honoraria, Research Funding; Teva: Consultancy; Juno Therapeutics: Consultancy; Genentech, Inc.: Consultancy, Honoraria, Research Funding; IGM Biosciences: Research Funding; Memorial Sloan Kettering Cancer Center: Current Employment; ImmunoVaccine Technologies: Consultancy, Honoraria, Research Funding; Rocket Medical: Consultancy, Research Funding; Pharmacyclics: Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Current holder of individual stocks in a privately-held company; TG Therapeutics: Consultancy, Honoraria; Janssen: Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy; Merck: Consultancy. Moskowitz: ADC Therapeutics: Research Funding; Imbrium Therapeutics L.P./Purdue: Consultancy; Seattle Genetics: Consultancy, Research Funding; Miragen: Research Funding; Janpix Ltd.: Consultancy; Takeda: Consultancy; Incyte: Research Funding; Beigene: Research Funding; Merck: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding. Noy: Epizyme: Consultancy; Rafael Parhma: Research Funding; Morphosys: Consultancy; Targeted Oncology: Consultancy; Medscape: Consultancy; Pharmacyclics: Consultancy, Research Funding; Janssen: Consultancy, Honoraria. Palomba: Notch: Honoraria, Other: Stock; PCYC: Consultancy; Pluto: Honoraria; Nektar: Honoraria; WindMIL: Honoraria; Lygenesis: Honoraria; Priothera: Honoraria; Ceramedix: Honoraria; Wolters Kluwer: Patents & Royalties; BeiGene: Consultancy; Seres: Honoraria, Other: Stock, Patents & Royalties, Research Funding; Rheos: Honoraria; Juno: Patents & Royalties; Magenta: Honoraria; Kite: Consultancy; Novartis: Consultancy. von Keudell: Incyte: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria; AbbVie: Research Funding; Merck: Research Funding; BMS: Research Funding; Janssen: Research Funding. Zelenetz: Abbvie: Honoraria, Research Funding; MethylGene: Research Funding; Beigene: Honoraria, Other, Research Funding; MorphoSys: Honoraria; LFR: Other; Gilead: Honoraria, Research Funding; Janssen: Honoraria; NCCN: Other; AstraZeneca: Honoraria; Pharmacyclics: Honoraria; BMS/Celgene/JUNO: Honoraria, Other; MEI Pharma: Honoraria, Research Funding; Genentech/Roche: Honoraria, Research Funding; Gilead: Honoraria; Amgen: Honoraria; Verastem: Honoraria; SecuraBio: Honoraria; Novartis: Honoraria. Sauter: Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding; GSK: Consultancy; Genmab: Consultancy; Kite/Gilead: Consultancy; Precision Biosciences: Consultancy; Gamida Cell: Consultancy; Spectrum Pharmaceuticals: Consultancy; Juno Therapeutics: Consultancy, Research Funding; Sanofi-Genzyme: Consultancy, Research Funding. Salles: Novartis: Consultancy; Kite/Gilead: Consultancy; Morphosys: Consultancy, Honoraria; Miltneiy: Consultancy; Regeneron: Consultancy, Honoraria; Incyte: Consultancy; Rapt: Consultancy; Takeda: Consultancy; Ipsen: Consultancy; Loxo: Consultancy; Genmab: Consultancy; Epizyme: Consultancy, Honoraria; Genentech/Roche: Consultancy; Janssen: Consultancy; Velosbio: Consultancy; Allogene: Consultancy; Debiopharm: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria. Falchi: Roche: Research Funding; Abbvie: Consultancy, Research Funding; Genmab: Consultancy, Research Funding; Genetech: Research Funding.
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Falchi, Lorenzo, Annie Qiu, Mark D. Ewalt, et al. "Clinical Outcomes and CNS Relapse Risk in Patients with Primary Cutaneous DLBCL, Leg Type Treated in the Rituximab Era: Long-Term Follow-up of a Single-Center Experience." Blood 138, Supplement 1 (2021): 2513. http://dx.doi.org/10.1182/blood-2021-151994.
Full textAbstract:
Abstract Introduction: Primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL,LT) is a rare subtype of lymphoma that has been reported to have aggressive clinical behavior and carry a poor prognosis. However, due to the extreme scarcity of data, optimal management of this entity remains poorly defined, particularly with respect to optimal induction therapy and the role for central nervous system (CNS) prophylaxis. We therefore reviewed our institutional experience with patients with PCDLBCL,LT to better delineate their clinical course and risk of CNS relapse. Methods: We searched our electronic database and individual electronic medical records for cases of skin-only PCDLBCL,LT and analyzed the presenting characteristics, treatment approaches, use of CNS prophylaxis, and clinical outcomes, including skin, systemic, and CNS relapse. Primary cutaneous follicle center lymphoma, primary cutaneous marginal zone lymphoma, and skin-only DLBCL arising in patients with a concurrent or antecedent indolent B-cell lymphoma were excluded. Additionally, cases with DLBCL involving both skin and nodal and/or extranodal sites at diagnosis were excluded. All patients underwent staging with positron emission tomography-computerized tomography (PET-CT). Diagnostic specimens were independently reviewed by hematopathologists with expertise in cutaneous lymphoma. Results: We identified 38 patients with PCDLBCL,LT followed at Memorial Sloan Kettering Cancer Center between July 2002 and October 2020. Fifteen (39%) were female and the median age was 76 years (range, 40-96). The disease was localized to the lower extremity in 15 cases (39%) and consisted of multiple lesions in 11 cases (29%). All but one patient had IPI 0-2. By Hans algorithm 79% of the samples had a non-GCB phenotype, 79% expressed MUM-1, 3% expressed CD10, and 82% exhibited Bcl2 expression. The median Ki-67 was 85% (range, 20%-100%). According to the CNS-IPI score, 25 out of 36 evaluable patients (69%) had low-risk disease and 11 (31%) intermediate-risk disease; no patient had high-risk disease. Induction therapy consisted of R-CHOP immunochemotherapy in 13 patients (33%), R-CHOP followed by radiation therapy (RT), at doses of 30-40 Gy, in 12 (31%), and RT alone, at doses of 30-45 Gy, in 10 (26%). One patient underwent excision alone, one received rituximab after excisional biopsy, and one best supportive care. Three patients (2 treated with chemotherapy + RT and one with chemotherapy only) received CNS prophylaxis with intrathecal methotrexate. Among 35 patients evaluable for response, the objective response rate was 86%, all of which were complete responses. One patient treated with rituximab had stable disease and 4 (2 treated with chemotherapy only, one RT, and one best supportive care) progressed. At a median follow-up of 8.9 years [range, 2.9-14.7], the 5-year progression-free survival for the cohort is 42% (Figure panel A). CNS relapse occurred in 5 patients (13%) involving leptomeninges in 1 case, parenchyma in 2, and both in 2. None of these patients received CNS prophylaxis. Of note, none of the three who did receive prophylaxis experienced CNS relapse. The 2-year and 5-year cumulative risk of CNS relapse are 7% and 19%, respectively (Figure panel B). At the time of this analysis, the 5-year overall survival rate is 66% (Figure Panel A), and in all, 16 patients (42%) have died. Among the 7 patients with known cause of death, 5 succumbed to progressive disease (including 3 with CNS disease), one to sepsis, and one to secondary acute myelogenous leukemia. Conclusions: To the best of our knowledge, this is the largest study analyzing the risk of CNS relapse in PCLBCL,LT patients treated with rituximab-containing immunochemotherapy. Our findings indicate that PCLBCL,LT is a highly aggressive disease associated with poor prognosis and high risk of CNS relapse. The 13% rate of CNS relapse in a population of patients with limited-stage disease, none of whom had high-risk CNS-IPI, suggests that alternate models of risk prediction are needed for patients with PCLBCL,LT and that clinical trials aimed at improving rates of cure and reducing the risk of CNS relapse for patients with PCLBCL,LT are needed. Figure 1 Figure 1. Disclosures Falchi: Abbvie: Consultancy, Research Funding; Genmab: Consultancy, Research Funding; Roche: Research Funding; Genetech: Research Funding. Ewalt: Loxo Oncology at Lilly: Membership on an entity's Board of Directors or advisory committees; Acceleron Pharma: Membership on an entity's Board of Directors or advisory committees. Batlevi: Memorial Sloan Kettering Cancer Center: Current Employment; Pfizer: Current holder of individual stocks in a privately-held company; Viatris: Current holder of individual stocks in a privately-held company; Life Sciences: Consultancy; Bayer: Research Funding; BMS: Current holder of individual stocks in a privately-held company; Seattle Genetics: Consultancy; Kite Pharma: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Dava Oncology: Honoraria; TouchIME: Honoraria; Moderna: Current holder of individual stocks in a privately-held company; TG Therapeutics: Consultancy; Medscape: Honoraria; Regeneron: Current holder of individual stocks in a privately-held company; Juno/Celgene: Consultancy; GLG Pharma: Consultancy; Xynomic: Research Funding; Roche/Genentech: Research Funding; Novartis: Research Funding; Epizyme: Research Funding; Janssen: Research Funding; Autolus: Research Funding. Caron: Astra-Zeneca: Current holder of individual stocks in a privately-held company; bristol myers: Current holder of individual stocks in a privately-held company; GlaxoSmithKlein: Current holder of individual stocks in a privately-held company; Johnson and Johnson: Current holder of individual stocks in a privately-held company; Novartis: Current holder of individual stocks in a privately-held company; pfizer: Current holder of individual stocks in a privately-held company; Teva: Current holder of individual stocks in a privately-held company. Hamlin: Alexion, AstraZeneca Rare Disease (formerly Portola Pharmaceuticals): Other: Study investigator, Research Funding; Incyte, Janssen, Molecular Templates: Research Funding; Kite, Karyopharm, Celgene: Membership on an entity's Board of Directors or advisory committees. Horwitz: ADC Therapeutics: Consultancy, Research Funding; Kyowa Hakko Kirin: Consultancy, Research Funding; Myeloid Therapeutics: Consultancy; Affimed: Research Funding; Vividion Therapeutics: Consultancy; Aileron: Research Funding; Verastem: Research Funding; C4 Therapeutics: Consultancy; Janssen: Consultancy; Seattle Genetics: Consultancy, Research Funding; Millennium /Takeda: Consultancy, Research Funding; Daiichi Sankyo: Research Funding; Forty Seven, Inc.: Research Funding; Trillium Therapeutics: Consultancy, Research Funding; Acrotech Biopharma: Consultancy; ONO Pharmaceuticals: Consultancy; Shoreline Biosciences, Inc.: Consultancy; Celgene: Research Funding; Tubulis: Consultancy; SecuraBio: Consultancy, Research Funding; Kura Oncology: Consultancy. Joffe: Epizyme: Consultancy; AstraZeneca: Consultancy. Khan: Seattle Genetics: Research Funding. Kumar: Seattle Genetics: Research Funding; Kite Pharmaceuticals: Other: advisory board , Research Funding; Pharmacyclics: Research Funding; Astra Zeneca: Honoraria, Other: Advisory Board, Research Funding; Celgene: Honoraria, Other: advisory board, Research Funding; Adaptive Biotechnologies, Celgene, Abbvie Pharmaceticals, Pharmacyclics, Seattle Genetics: Research Funding; Abbvie Pharmaceuticals: Research Funding. Moskowitz: Merck: Consultancy, Research Funding; Miragen: Research Funding; Janpix Ltd.: Consultancy; Bristol-Myers Squibb: Research Funding; Incyte: Research Funding; Beigene: Research Funding; ADC Therapeutics: Research Funding; Imbrium Therapeutics L.P./Purdue: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy, Research Funding. Noy: Pharmacyclics: Consultancy, Research Funding; Medscape: Consultancy; Targeted Oncology: Consultancy; Morphosys: Consultancy; Rafael Parhma: Research Funding; Epizyme: Consultancy; Janssen: Consultancy, Honoraria. Palomba: Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Kite Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. von Keudell: Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; AbbVie: Research Funding; Merck: Research Funding; BMS: Research Funding; Janssen: Research Funding. Zelenetz: MEI Pharma: Honoraria, Research Funding; Pharmacyclics: Honoraria; BMS/Celgene/JUNO: Honoraria, Other; Gilead: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria; Verastem: Honoraria; AstraZeneca: Honoraria; MethylGene: Research Funding; MorphoSys: Honoraria; Genentech/Roche: Honoraria, Research Funding; NCCN: Other; SecuraBio: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Beigene: Honoraria, Other, Research Funding; Abbvie: Honoraria, Research Funding; LFR: Other. Dogan: Takeda: Consultancy, Research Funding; Peer View: Honoraria; Physicians' Education Resource: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy, Research Funding; EUSA Pharma: Consultancy. Salles: Allogene: Consultancy; Miltneiy: Consultancy; Loxo: Consultancy; Velosbio: Consultancy; Takeda: Consultancy; Novartis: Consultancy; Rapt: Consultancy; Regeneron: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Genmab: Consultancy; Incyte: Consultancy; Genentech/Roche: Consultancy; Ipsen: Consultancy; Janssen: Consultancy; Kite/Gilead: Consultancy; Debiopharm: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria. Matasar: Janssen: Honoraria, Research Funding; TG Therapeutics: Consultancy, Honoraria; Teva: Consultancy; Daiichi Sankyo: Consultancy; Merck Sharp & Dohme: Current holder of individual stocks in a privately-held company; Genentech, Inc.: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; IGM Biosciences: Research Funding; Rocket Medical: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; ImmunoVaccine Technologies: Consultancy, Honoraria, Research Funding; Juno Therapeutics: Consultancy; Memorial Sloan Kettering Cancer Center: Current Employment; Pharmacyclics: Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Merck: Consultancy.
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Qualls, David, Connie Lee Batlevi, Philip C. Caron, et al. "Favorable Outcomes of Patients with Limited-Stage Ocular Adnexal DLBCL Treated in the Rituximab Era: Long-Term Follow-up of a Single Center Experience." Blood 138, Supplement 1 (2021): 4578. http://dx.doi.org/10.1182/blood-2021-153171.
Full textAbstract:
Abstract Introduction Diffuse large B-cell lymphoma of the ocular adnexa (OA-DLBCL) is extremely rare and historically was associated with poor prognosis and higher risk of central nervous system (CNS) relapse. In the rituximab era its prognosis may be more favorable, particularly in individuals with limited-stage disease. However, published series of patients with extranodal limited-stage DLBCL, including a study from our group (Bobillo et al. Blood 2021), rarely included OA-DLBCL. Studies specifically looking at OA-DLBCL reported diverse treatment approaches, including some that are no longer current, such as chemotherapy without rituximab or radiation alone. Thus, the optimal management of limited-stage OA-DLBCL remains poorly defined. To address this knowledge gap, we reviewed treatments and outcomes of OA-DLBCL patients treated at Memorial Sloan Kettering Cancer Center (MKSCC). Methods We retrospectively reviewed electronic medical records of consecutive patients with DLBCL managed at MSKCC who had ocular adnexal involvement and stage IE or IIE disease, and complete clinical information. Patients with stage III-IV disease, or evidence of intraocular or CNS involvement were excluded. Results Between 2000 and 2020 we identified 17 patients with limited-stage OA-DLBCL. The median age at diagnosis was 66 years [range: 24-84], all had ECOG PS 0-1, 94% had IPI 0-1 and 94% CNS-IPI 0-1. Primary sites of ocular adnexal involvement included: extraconal orbit (9), intraconal orbit (2), lacrimal gland (4), and eyelid (2). Median greatest diameter of lesions was 3.0 cm [range: 0-4.8 cm]. Among 11 evaluable patients, the cell of origin by Hans algorithm was GCB in 64%, non-GCB in 36% (Table). Staging procedures included brain and/or orbit imaging in 100% (MRI in 88%, CT in 12%), PET-CT in 94%, bone marrow evaluation in 69% (negative in all cases), and CSF evaluation in 47% (negative in all cases). Sixteen patients were treated with R-CHOP and one with CHOP. Eleven patients received short-course chemotherapy (3 cycles in 7 patients, 4 cycles in 4), and 6 received full-course therapy (i.e., 6 cycles). Thirteen patients (76%) received radiation therapy (RT) to the ocular adnexa, including 9/11 patients treated with short-course treatment and 3/6 receiving 6 cycles. One patient with stage IIE disease received RT to the left neck after 3 cycles R-CHOP, but did not receive RT to ocular adnexum. Six patients received CNS prophylaxis, all with intrathecal methotrexate (Table). First-line systemic therapy resulted in complete response (CR) in all patients. At a median follow-up of 64 months [range: 11-147], the 2- and 5-year PFS are 93% (95% CI 79-100%), and 84% (95% CI 64-100%), respectively, and 2- and 5-year OS are 100% and 91.7% (95% CI 76-100%), respectively (Figure). One of 17 patients had systemic relapse 18 months after first-line therapy and underwent salvage therapy followed by high-dose therapy and autologous stem cell transplantation, and remains in CR. There were no CNS relapses. Due to the paucity of events, we found no statistically significant associations between the number of chemotherapy cycles or the use of RT and PFS or OS. At the time of this analysis, four patients have died, none from lymphoma; one of the four died from acute myeloid leukemia 3 years after treatment. Discussion Our study demonstrates high response rates and highly favorable long-term outcomes in patients with limited stage OA-DLBCL treated with R-CHOP and RT. Our results are in line with those reported by Bobillo et al. in patients with stage 1 DLBCL involving other extranodal sites. With the limitation of a small sample size, short course R-CHOP was not associated with significantly worse response rates or increased risk of recurrence. Whether consolidation radiation therapy improves the results of chemotherapy alone remains to be determined in larger studies. Figure 1 Figure 1. Disclosures Batlevi: Dava Oncology: Honoraria; TG Therapeutics: Consultancy; Seattle Genetics: Consultancy; Epizyme: Research Funding; Novartis: Research Funding; Medscape: Honoraria; Viatris: Current holder of individual stocks in a privately-held company; Roche/Genentech: Research Funding; Regeneron: Current holder of individual stocks in a privately-held company; Pfizer: Current holder of individual stocks in a privately-held company; Memorial Sloan Kettering Cancer Center: Current Employment; Bayer: Research Funding; Kite Pharma: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; TouchIME: Honoraria; Life Sciences: Consultancy; BMS: Current holder of individual stocks in a privately-held company; Janssen: Research Funding; Autolus: Research Funding; Moderna: Current holder of individual stocks in a privately-held company; Xynomic: Research Funding; GLG Pharma: Consultancy; Juno/Celgene: Consultancy. Caron: Astra-Zeneca: Current holder of individual stocks in a privately-held company; bristol myers: Current holder of individual stocks in a privately-held company; GlaxoSmithKlein: Current holder of individual stocks in a privately-held company; Johnson and Johnson: Current holder of individual stocks in a privately-held company; Novartis: Current holder of individual stocks in a privately-held company; pfizer: Current holder of individual stocks in a privately-held company; Teva: Current holder of individual stocks in a privately-held company. Hamlin: Kite, Karyopharm, Celgene: Membership on an entity's Board of Directors or advisory committees; Incyte, Janssen, Molecular Templates: Research Funding; Alexion, AstraZeneca Rare Disease (formerly Portola Pharmaceuticals): Other: Study investigator, Research Funding. Horwitz: Tubulis: Consultancy; Affimed: Research Funding; ONO Pharmaceuticals: Consultancy; Trillium Therapeutics: Consultancy, Research Funding; Acrotech Biopharma: Consultancy; Aileron: Research Funding; Shoreline Biosciences, Inc.: Consultancy; Celgene: Research Funding; Forty Seven, Inc.: Research Funding; Kyowa Hakko Kirin: Consultancy, Research Funding; SecuraBio: Consultancy, Research Funding; Verastem: Research Funding; C4 Therapeutics: Consultancy; Janssen: Consultancy; Kura Oncology: Consultancy; Millennium /Takeda: Consultancy, Research Funding; Myeloid Therapeutics: Consultancy; Daiichi Sankyo: Research Funding; Seattle Genetics: Consultancy, Research Funding; Vividion Therapeutics: Consultancy; ADC Therapeutics: Consultancy, Research Funding. Joffe: AstraZeneca. Epizyme: Consultancy. Khan: Seattle Genetics: Research Funding. Kumar: Celgene: Honoraria, Other: advisory board, Research Funding; Kite Pharmaceuticals: Other: advisory board , Research Funding; Astra Zeneca: Honoraria, Other: Advisory Board, Research Funding; Abbvie Pharmaceuticals: Research Funding; Seattle Genetics: Research Funding; Adaptive Biotechnologies, Celgene, Abbvie Pharmaceticals, Pharmacyclics, Seattle Genetics: Research Funding; Pharmacyclics: Research Funding. Matasar: Janssen: Honoraria, Research Funding; Teva: Consultancy; Juno Therapeutics: Consultancy; Merck: Consultancy; IGM Biosciences: Research Funding; Genentech, Inc.: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Current holder of individual stocks in a privately-held company; TG Therapeutics: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Memorial Sloan Kettering Cancer Center: Current Employment; Rocket Medical: Consultancy, Research Funding; Pharmacyclics: Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy; Takeda: Consultancy, Honoraria; ImmunoVaccine Technologies: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Honoraria, Research Funding. Moskowitz: Miragen: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Janpix Ltd.: Consultancy; Imbrium Therapeutics L.P./Purdue: Consultancy; Bristol-Myers Squibb: Research Funding; Takeda: Consultancy; Incyte: Research Funding; Beigene: Research Funding; ADC Therapeutics: Research Funding. Noy: Medscape: Consultancy; Targeted Oncology: Consultancy; Morphosys: Consultancy; Rafael Parhma: Research Funding; Pharmacyclics: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Epizyme: Consultancy. Palomba: Wolters Kluwer: Patents & Royalties; BeiGene: Consultancy; Rheos: Honoraria; PCYC: Consultancy; Juno: Patents & Royalties; Kite: Consultancy; Pluto: Honoraria; Novartis: Consultancy; Notch: Honoraria, Other: Stock; Seres: Honoraria, Other: Stock, Patents & Royalties, Research Funding; Magenta: Honoraria; WindMIL: Honoraria; Lygenesis: Honoraria; Nektar: Honoraria; Ceramedix: Honoraria; Priothera: Honoraria. von Keudell: Merck: Consultancy, Honoraria; AbbVie: Research Funding; Pharmacyclics: Consultancy, Honoraria; BMS: Research Funding; Merck: Research Funding; Incyte: Consultancy, Honoraria; Janssen: Research Funding. Zelenetz: SecuraBio: Honoraria; Pharmacyclics: Honoraria; AstraZeneca: Honoraria; MEI Pharma: Honoraria, Research Funding; Verastem: Honoraria; NCCN: Other; Janssen: Honoraria; BMS/Celgene/JUNO: Honoraria, Other; Amgen: Honoraria; Gilead: Honoraria; Gilead: Honoraria, Research Funding; Genentech/Roche: Honoraria, Research Funding; MethylGene: Research Funding; MorphoSys: Honoraria; Abbvie: Honoraria, Research Funding; LFR: Other; Beigene: Honoraria, Other, Research Funding; Novartis: Honoraria. Dogan: Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Peer View: Honoraria; EUSA Pharma: Consultancy; Seattle Genetics: Consultancy; Physicians' Education Resource: Honoraria. Salles: Debiopharm: Consultancy; Takeda: Consultancy; Allogene: Consultancy; Velosbio: Consultancy; Genmab: Consultancy; Loxo: Consultancy; Miltneiy: Consultancy; Ipsen: Consultancy; Janssen: Consultancy; Kite/Gilead: Consultancy; Incyte: Consultancy; Morphosys: Consultancy, Honoraria; Novartis: Consultancy; Rapt: Consultancy; Regeneron: Consultancy, Honoraria; Genentech/Roche: Consultancy; Epizyme: Consultancy, Honoraria; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria.
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Borate, Uma, Fei Yang, Richard D. Press, et al. "Prevalence of Inherited Cancer Predisposition Mutations in a Cohort of Older AML Patients Enrolled on the Beat AML Master Trial." Blood 134, Supplement_1 (2019): 373. http://dx.doi.org/10.1182/blood-2019-131925.
Full textAbstract:
Introduction: Inherited predisposition to myeloid malignancies in adults may be more common than previously suggested with recent studies suggesting a prevalence of candidate predisposition alleles in 15-20% of patients. An inherited predisposition may not be considered in older AML patients despite significant clinical implications for family members as potential stem cell transplant donors. To better define the role of inherited genetic alterations in older AML patients , we analyzed a unique cohort of newly diagnosed older (&gt;60 years) patients enrolled in) Beat AML® Master Trial(BAMT) for candidate genes associated with a known or putative inherited cancer predisposition. Methods: We analyzed extracted DNA from skin and/or saliva samples compared to paired leukemia samples of 176 AML patients enrolled in the BAMT. All samples underwent genomic profiling using a modified FoundationOne®Heme platform (capture-based) and/or the Oregon Health Sciences University panel (amplicon-based), evaluating 477 and 220 genes respectively, with a known role in hematologic malignancies. Germline(GL) variants were identified by the haplotype-based Bayesian genetic variant detector FreeBayes and using variant allele frequency(VAF) values. The pathogenicity and clinical significance of the variants was interpreted according to the 2015 ACMG/AMP guidelines while the AMP/CAP/ASCO guidelines and various disease databases were used in the somatic variant calls. Results: -The mutational landscape of the 176 newly diagnosed older AML patients is detailed in Table 1. Our cohort has a higher proportion of adverse risk patients, consistent with an older AML patient population. 27 pathogenic or likely pathogenic GL variants were detected in 24 AML patients, with a germline mutation prevalence of 14% in this cohort. Deleterious GL mutations were found in the gene DDX41 (5), followed by SBDS (4), CHEK2 (4), MPL (3), BRCA2 (2), HAX1 (2), DNAH9 (2), FANCA (1), FANCL (1), SAMD9 (1), BLM (1), and ATM (1) (Table 2). The types of mutations included missense mutations (9), nonsense mutations (8), frameshift mutations (7), splice site mutations (2), and an exonic deletion (1). Family history of leukemia was available on 129 patients from this cohort. 12 patients have at least one family member with AML. Of these 12 patients, 2 had a deleterious GL alteration identified. Along with the 14% prevalence of pathogenic/likely pathogenic GL mutations , there were an additional 181 GL variants of unknown significance (VUS) in 102 patients, seen in genes implicated in inherited predisposition to hematologic malignancies, most commonly variants in DOCK8 and CREBBP(&gt;5% VUS) with both genes being implicated in leukemogenesis . As skin and/or saliva samples were collected at the time of AML diagnosis, tumor-in-normal presence was expectedly observed. The median VAF for somatic mutations was significantly lower (p &lt; 0.0001) in skin (median 6%; mean 9%; standard deviation (SD) 10%; N=562 variants) than in saliva (median 17%; mean 21%; SD 16%; N=368 variants). In 37 patients who had both saliva and skin tissue concomitantly ,skin had a significantly lower tumor-in-normal presence (median VAF 5%, mean 8%, SD 8%;) than saliva (median 15%, mean 20%, SD 16%)(p &lt; 0.0001). Conclusions: We found a prevalence of 14% pathogenic/ likely pathogenic GL mutations in cancer predisposition genes in this unique cohort of newly diagnosed older AML patients. This finding has potential clinical implications for patients and family members. We also found a large number of VUS in genes implicated in hematological malignancies. Additional studies linking candidate VUS' to familial predisposition to understand contribution to AML predisposition are needed. We are in the process of comparing the manual curation of ACMG classification of pathogenicity with a computational curation algorithm to assess the potential for automated classification of GL variants. Our study suggests the choice of source for germline DNA in patients with AML is variably impacted by leukemic contamination. Cultured skin fibroblasts are the current standard for tumor-normal paired genotyping, with the caveat of being labor intensive and not routinely performed in clinical diagnostic laboratories. This is a critical consideration for rapid GL screening of patients and family members with hematologic malignancies and suspected cancer predisposition. Disclosures Borate: Novartis: Consultancy; Takeda: Consultancy; Pfizer: Consultancy; Daiichi Sankyo: Consultancy; AbbVie: Consultancy. Mims:Abbvie: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Astellas Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Stein:Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas Pharma US, Inc: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo, Inc.: Membership on an entity's Board of Directors or advisory committees; Bioline: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees. Patel:Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Dava Oncology: Honoraria; France Foundation: Honoraria. Baer:Astellas: Research Funding; Abbvie: Research Funding; AI Therapeutics: Research Funding; Forma: Research Funding; Incyte: Research Funding; Kite: Research Funding; Takeda: Research Funding. Stock:Agios: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; UpToDate: Honoraria; Daiichi: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Membership on an entity's Board of Directors or advisory committees; Research to Practice: Honoraria. Schiller:Bristol Myer Squibb: Research Funding; Celgene: Research Funding, Speakers Bureau; Constellation Pharmaceutical: Research Funding; Daiichi Sankyo: Research Funding; Agios: Research Funding, Speakers Bureau; Amgen: Other, Research Funding; Astellas: Research Funding; Biomed Valley Discoveries: Research Funding; Eli Lilly and Company: Research Funding; FujiFilm: Research Funding; Genzyme: Research Funding; Gilead: Research Funding; Incyte: Research Funding; J&J: Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; Karyopharm: Research Funding; Novartis: Research Funding; Onconova: Research Funding; Pfizer Pharmaceuticals: Equity Ownership, Research Funding; Sangamo Therapeutics: Research Funding. Blum:AmerisourceBergen: Consultancy; Forma: Research Funding; Xencor: Research Funding; Boehringer Ingelheim: Research Funding; Celgene: Research Funding; Astellas,: Research Funding. Shami:JSK Therapeutics: Employment, Equity Ownership; Amgen: Research Funding; Pfizer: Research Funding; Cantex: Research Funding; Aptevo: Research Funding; Jazz: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Lone Star Thiotherapies: Equity Ownership. Foran:Agios: Honoraria, Research Funding. Byrd:Acerta: Research Funding; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Genentech: Research Funding; Novartis: Other: Travel Expenses, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Ohio State University: Patents & Royalties: OSU-2S; BeiGene: Research Funding; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau. Druker:OHSU (licensing fees): Patents & Royalties: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees ; Bristol-Myers Squibb: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding; Novartis: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Patents & Royalties: Patent 6958335, Treatment of Gastrointestinal Stromal Tumors, exclusively licensed to Novartis, Research Funding; Monojul: Other: former consultant; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; Beat AML LLC: Other: Service on joint steering committee; Merck & Co: Patents & Royalties: Dana-Farber Cancer Institute license #2063, Monoclonal antiphosphotyrosine antibody 4G10, exclusive commercial license to Merck & Co; Dana-Farber Cancer Institute (antibody royalty): Patents & Royalties: #2524, antibody royalty; Aileron Therapeutics: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees , Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Patents & Royalties, Research Funding; Pfizer: Research Funding; ALLCRON: Membership on an entity's Board of Directors or advisory committees; Amgen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; The RUNX1 Research Program: Membership on an entity's Board of Directors or advisory committees; Patient True Talk: Consultancy; GRAIL: Equity Ownership, Other: former member of Scientific Advisory Board; Cepheid: Consultancy, Honoraria; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Burroughs Wellcome Fund: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Pfizer: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding; ICON: Other: Scientific Founder of Molecular MD, which was acquired by ICON in Feb. 2019; CureOne: Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Other: former member of Scientific Advisory Board. Vergilio:Foundation Medicine, Inc.: Employment; F. Hoffman La Roche, Ltd.: Equity Ownership. Levine:Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; Prelude Therapeutics: Research Funding; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Amgen: Honoraria; Gilead: Consultancy; Lilly: Honoraria; Qiagen: Membership on an entity's Board of Directors or advisory committees; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Loxo: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy.
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Joseph, Catherine, Pritchard Aaron, Nasim Pourtabatabaei, et al. "Novel Mutation in CD46 in a Child with Atypical Hemolytic Uremic Syndrome (HUS) Characterized By Profound Thrombocytopenia and Anemia." Blood 128, no. 22 (2016): 4884. http://dx.doi.org/10.1182/blood.v128.22.4884.4884.
Full textAbstract:
Abstract Introduction: Atypical HUS [aHUS] is a rare disease characterized by hemolytic anemia, thrombocytopenia and renal dysfunction due to genetic mutations that lead to uncontrolled activation of the alternative complement pathway. MCP [Membrane Co-Factor protein] or CD46 is a complement factor 3b (C3b) binding molecule which is a cofactor for complement factor I dependent cleavage of C3b. Pathogenic variants in MCP gene account for 5-9% of cases with aHUS. We report the case of a pediatric patient presenting with severe thrombocytopenia, anemia and renal dysfunction who was found to have a novel mutation in MCP and complete recovery with eculizumab therapy. Case report: A previously healthy 9-year-old girl presented with a 2-day history of fever, abdominal pain, emesis and oliguria. There was no history of diarrhea, sick contacts or infectious exposures. There was no known renal disease in her family. Her exam was remarkable for mild scleral icterus, epigastric tenderness but no peritoneal signs. Her labs showed renal failure; BUN 90mg/dl, Cr 4.69 mg/dl, anemia: hemoglobin of 8.4g/dl and severe thrombocytopenia with a platelet count of 4,000/uL. Her peripheral smear had schistocytes, the LDH was elevated at 2511 units/liter and haptoglobin was low at 14 mg/dl. Coagulation parameters were normal. All findings were consistent with severe thrombotic microangiopathy (TMA). A stool assay for Shiga toxin was negative, Her C3 and C4 levels were normal Renal ultrasound showed only increased echogenicity in the right kidney. Urinalysis showed hematuria and proteinuria, culture was negative. Based on triad of hemolytic anemia, thrombocytopenia and renal dysfunction microangiopathic hemolytic anemia (MAHA) was suspected. Pending ADAMTS13 results, plasmapheresis was performed daily (x4) without significant improvement in hemolysis or renal function- as a result. Eculizumab, a human anti-C5 monoclonal antibody was initiated: after the first 2 doses, there was significant improvement in renal function, her hemolysis stopped, and platelet counts began to recover. After 2 months of treatment, renal function, hemoglobin and platelets have all normalized. Her genetic renal panel tested for mutations in 10 genes implicated in aHUS revealed a heterozygous variant in Exon 2 of the CD46 gene (c.132G>A, pMet44Ile) predicted pathogenic by 5/6 algorithms. Based on the patient presentation, normal ADAMTS13 activity (69%), negative Shiga toxin, and response to eculizumab, this case of aHUS was most likely related to the novel CD46 mutation reported for the first time in this work.. Conclusion: Our patient had an unusual presentation of Hemolytic uremic syndrome characterized by severe thrombocytopenia and hemolytic anemia. This case is unique in that we saw moderately severe renal failure, but no other organ system involvement despite severe hematologic aberrations. In atypical HUS,we often see more multiorgan involvement when platelets counts are very low, consistent with more severe thombotoc microangipathy. Her genetic test revealed a novel mutation in the CD46 gene, a regulator of complement activation. She has had an excellent response to eculizumab therapy with complete recovery of renal function and resolution of all hematologic signs of thrombotic microangiopathy, suggesting that his gene variant can be causative of atypical HUS. Disclosures Aaron: Alexion - aHUS Regional Advisory Board: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wong:UpToDate: Consultancy, Patents & Royalties.
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Derisoud, E., A. Hankele, L. Jouneau, et al. "202 Proteomic, metabolomic and fatty acid composition in lactating and non-lactating mares’ uterine fluids." Reproduction, Fertility and Development 32, no. 2 (2020): 229. http://dx.doi.org/10.1071/rdv32n2ab202.
Full textAbstract:
In the equine industry, horse breeders aim to produce one foal per mare per year. Thus, mares are bred while nursing. In high-yielding dairy cattle, concurrent lactation and conception may affect embryo quality, but effects on uterine fluid (UF) quality are unknown. No data are available in horses. The aim of our study was to analyse the effect of nursing on protein, metabolite and fatty acid (FA) compositions of UF at ~7.5 days post-ovulation. Anglo-Arab mares (multiparous (2.7±0.9 foals), 10.8±2.5 years old) that were either nursing (N; 105±12 days of lactation) or barren (B) were inseminated with the semen of the same stallion at induction of ovulation with human chorionic gonadotrophin. Ovulation was confirmed by ultrasound within 48h. At 7.4±0.7 days post-ovulation, UF was collected with a human tampon that was left for 10min in the uterus before embryo collection. Only mares for which no embryo was collected were selected (n=5 in both groups). Trypsin digestion followed by liquid chromatography-tandem mass spectrometry analysis was used for proteomic analysis, with subsequent characterisation with the PANTHER platform using an overrepresentation test (Fisher's exact type with false discovery rate correction) with the PANTHER pathway database. An untargeted approach based on liquid chromatography-tandem mass spectrometry was performed for metabolomics (normalisation to 50µg of protein), and data were analysed with the MS Peaks to Pathways tool of the MetaboAnalyst platform, using both the GSEA and mummichog algorithms (cut-off=0.05). Fatty acid composition (% of total FA) was analysed using gas chromatography. Differences between groups were analysed using a linear model with permutation using R software with the pgirmess package (P=0.05 for significance). Altogether, 2706 proteins with at least two peptides were identified in mares’ UF, with 164 being differentially expressed. Ubiquitin proteasome, involved in embryo-endometrium interactions, was the most enriched pathway in N mares (fold enrichment=15.12; P&lt;0.0001). For metabolomics, ubiquinone biosynthesis [MetaFishNet, P=0.02; NES (Enrichment Score for the variable)/(mean of all Enrichment Score in all permutations in the dataset)=1.84] was enriched in N mares. N-Glycans, mainly guanosine diphosphate mannose (Kyoto Encyclopedia of Genes and Genomes, P=0.04; NES=−1.33) and leukotriene biosynthesis (Biocyc; P=0.006; NES=−1.31), were enriched in B mares with differentially expressed leukotrienes C4/D4. The proportion of saturated FA was higher in N vs. B mares (38.4±3.6% vs. 33.2±3.6%; P=0.03), probably due to increased palmitic (P=0.08) and stearic (P=0.08) acid proportions. In conclusion, pathways involved in uterine receptivity and inflammation seem to be enriched in B mares. Fatty acids that are readily available in the diet were more present in N mares, possibly because more elaborate FA are exported to the mammary gland for milk production. Nursing could thus modify the inflammatory response in the uterine environment at ~7.5 days post-ovulation and could affect reproductive efficiency in horses.
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Luque-Tévar, M., C. Perez-Sanchez, N. Barbarroja Puerto, et al. "POS0475 INTEGRATIVE CLINICAL, MOLECULAR AND COMPUTATIONAL ANALYSES ALLOW THE IDENTIFICATION OF DISTINCTIVE PHENOTYPES OF RHEUMATOID ARTHRITIS PATIENTS RELATED TO THE CLINICAL INVOLVEMENT AND THE RESPONSE TO TNF INHIBITORS." Annals of the Rheumatic Diseases 80, Suppl 1 (2021): 469–70. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2549.
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Background:TNF inhibitors (TNFi) represent an extraordinary advance in the management of Rheumatoid Arthritis (RA). Despite their benefits, there is a percentage of patients (20–40%) that do not achieve clinical improvement. Therefore, it is necessary to search for new and easily accessible biomarkers predictive of therapeutic response that might guide precision medicine.Objectives:1. To explore changes in the molecular profile of RA patients following TNFi therapy in serum samples. 2. To search for new and reliable biomarkers predictive of TNFi response, based on clinical and molecular profiles of RA patients, by using machine learning algorithms.Methods:In a prospective multicenter study, 79 RA patients undergoing TNFi and 29 healthy donors (HD) were enrolled. Twenty-two RA patients were further included as a validation cohort. Serum samples were obtained before and after 6 months of treatment, and therapeutic efficacy was evaluated. Patients’ response was determined following EULAR response criteria. Serum inflammatory profile was analyzed by a multiplex immunoassay, along with oxidative and NETotic profiles, evaluated by commercial kits. A circulating miRNA array was also performed by next-generation sequencing. Clustering analysis was carried out to identify groups of patients with distinctive molecular signatures. Then, clinical and molecular changes induced by TNFi were delineated after 6 months of therapy. Finally, integrative clinical and molecular signatures as predictors of response were assessed at baseline by supervised machine learning methods, using regularized logistic regressions.Results:Inflammatory, oxidative stress and NETosis-derived biomolecules were found altered in RA patients versus HD, closely interconnected and associated with several deregulated miRNAs. This altered molecular profile at baseline allowed the unsupervised division of three clusters of RA patients with distinctive clinical phenotypes, further linked to TNFi effectiveness. Cluster 1 included RA patients with low levels of pro-inflammatory cytokines, associated with a medium-low disease activity score and good clinical response. Clusters 2-3 comprised patients with high levels of pro-inflammatory cytokines, associated with a high disease activity and a non-response rate of 30%.After 6 months of therapy the molecular profile found altered in RA patients was reversed in responder patients, who achieved a molecular phenotype similar to HDs. However, non-responder patients’ molecular profile remained significantly deregulated, including alterations in inflammatory mediators (IL-6, L-8, TNFα, VEGF, IL-1RA, IL-5, IL-15, GMCSF, GCSF, FGFb), oxidative stress markers (LPO) and NETosis-derived products (Elastase), along with specific miRNAs (miR-199a-5p). These molecular changes further correlated with changes in disease activity score. Machine-learning algorithms identified clinical (Creatinine, IgM, Vitamin D, Swollen Joints, C4, Disease Duration and Tryglicerides) and molecular (Nucleosomes, IL-10, miR-106a-5p, IL-13, IL-12p70, IL-15 and LPO) signatures as potential predictors of response to TNFi treatment with high accuracy. Furthermore, the integration of both features in a combined model increased the predictive value of these signatures (AUC: 0.91). These results were further confirmed in an independent validation cohort.Conclusion:1. RA patients display distinctive altered molecular profiles directly linked to their clinical status and associated with TNFi effectiveness. 2. Clinical response was associated with a specific modulation of the inflammatory profile, the reestablishment of the altered oxidative status, the reduction of NETosis and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.Disclosure of Interests:None declared
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Burd, Amy, Ross L. Levine, Amy S. Ruppert, et al. "Precision Medicine Treatment in Older AML: Results of Beat AML Master Trial." Blood 134, Supplement_1 (2019): 175. http://dx.doi.org/10.1182/blood-2019-130201.
Full textAbstract:
*equal contribution of AB, RLL, BD and JCB Background: Acute myeloid leukemia (AML) is common with increasing age, and older adults with AML rarely achieve long-term remission with chemotherapy. Gene discovery studies in older adults with AML have shown that this malignancy is characterized by a multitude of somatic genomic alterations beginning with initiating somatic events followed by acquisition of collaborative transforming mutations. Despite these important biologic insights, current therapeutic approaches to AML remain limited, particularly in adults ≥ 60 years of age. The Beat AML trial was designed to assess the feasibility of using genetic profiling to assign patients (pts) to molecularly defined, subtype-specific therapies within 7 days of the initial diagnosis in a multi-center clinical trial setting, and to delineate the role of molecularly informed first-line treatment of AML with mechanism based novel therapies. Methods: Treatment-naïve patients with AML were enrolled in this prospective trial which offered accelerated cytogenetic and comprehensive mutational testing within 7 days followed by treatment assignment using these molecular data. Pt eligibility included age ≥ 60 years with non-APML AML, no known CNS leukemia, no prior hypomethylating agent (HMA) therapy and no clinical need for emergent therapy. Eligible pts were profiled by local cytogenetics analysis and using a central next generation sequencing (NGS) assay (Foundation Medicine, Inc.) with all molecular data required for treatment assignment (TA) obtained &lt; 7 days. TA was made centrally using a pre-determined algorithm considering somatic cytogenetic and molecular alterations in the dominant clone, available targeted therapeutics for specific AML subsets, and the likelihood of cure with intensive chemotherapy. Results: From November 2016 to January 2019, 487 pts with a suspected diagnosis of AML had enrolled at 14 clinical sites; 395 were eligible for the study (77% of the patients not eligible for the study had an alternative diagnosis). The median age of eligible patients was 72 years (range: 60 to 92) with 38% being≥ 75 years and 16% with treatment-related AML. From the 395 eligible patients, 374 (94.7%) were centrally assigned to the different cytogenetic/genomic groups within 7 days. The most common groups were TP53 mutated (19%) and marker negative (18%) molecular groups. The Beat AML trial is dynamic by design, thereby allowing different arms to open over time; all trial arms are designed to evaluate for substantial clinical efficacy in small, molecularly defined patient subsets. As shown in Figure 1, 224 patients (57%) had a TA and consented to a BEAT AML sub-study. Of the remaining 171 patients, 103 received standard therapy defined as induction therapy (7+3) or hypomethylation agent (25 before TA and 78 after TA), 28 received an alternative investigational agent (5 before TA and 23 after TA), 38 received palliative care, and 2 had an unknown treatment status and are grouped with the palliative care patients in subsequent analyses; 9 patients died before TA (2 who received standard therapy and 7 in the palliative care group). Demographic, clinical, performance and molecular characteristics were not largely different between pts who elected targeted therapy as part of the BEAT AML trial versus those who elected standard therapy. As shown in Figure 2, the overall survival was significantly longer for patients enrolled in a targeted therapy arm as part of the BEAT AML trial compared to those who elected standard therapy. (p&lt;0.0001). The 30-day estimated death rate was 3.7% (95% CI: 1.9-7.2) for patients electing treatment on a BEAT AML sub-study, 20.4% (95% CI: 13.0-31.2) for those receiving standard therapy, 0% for those receiving an investigational therapy, and 72.6% (95% CI: 57.8-85.7) for the palliative care group. Conclusion: Our data support the feasibility of a rapid precision medicine approach in older patients with previously untreated AML. Patients with AML who elected treatment assigned based upon cytogenetic and molecular alterations in the dominant clone using a novel precision medicine approach had significantly improved overall survival versus those who elected standard of care treatment. Disclosures Levine: Gilead: Consultancy; Prelude Therapeutics: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Research Funding; Qiagen: Membership on an entity's Board of Directors or advisory committees; Loxo: Membership on an entity's Board of Directors or advisory committees; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Lilly: Honoraria. Mims:Abbvie: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Astellas Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Borate:Novartis: Consultancy; Takeda: Consultancy; Pfizer: Consultancy; Daiichi Sankyo: Consultancy; AbbVie: Consultancy. Stein:Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas Pharma US, Inc: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo, Inc.: Membership on an entity's Board of Directors or advisory committees; Bioline: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Patel:France Foundation: Honoraria; Dava Oncology: Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Baer:Takeda: Research Funding; Incyte: Research Funding; Kite: Research Funding; Forma: Research Funding; AI Therapeutics: Research Funding; Abbvie: Research Funding; Astellas: Research Funding. Stock:Daiichi: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Research to Practice: Honoraria; UpToDate: Honoraria; Kite, a Gilead Company: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Deininger:Pfizer: Consultancy, Honoraria, Research Funding; Ascentage Pharma: Consultancy, Honoraria; TRM: Consultancy; Sangoma: Consultancy; Fusion Pharma: Consultancy; Adelphi: Consultancy; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Humana: Honoraria; Incyte: Honoraria; Novartis: Honoraria; Sangamo: Consultancy; Blueprint: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Blum:Boehringer Ingelheim: Research Funding; Celgene: Research Funding; Astellas,: Research Funding; Xencor: Research Funding; Forma: Research Funding; AmerisourceBergen: Consultancy. Schiller:Amgen: Other, Research Funding; Agios: Research Funding, Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Constellation Pharmaceutical: Research Funding; Daiichi Sankyo: Research Funding; Eli Lilly and Company: Research Funding; FujiFilm: Research Funding; Genzyme: Research Funding; Gilead: Research Funding; Incyte: Research Funding; J&J: Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; Karyopharm: Research Funding; Novartis: Research Funding; Onconova: Research Funding; Pfizer Pharmaceuticals: Equity Ownership, Research Funding; Sangamo Therapeutics: Research Funding; Bristol Myer Squibb: Research Funding; Astellas: Research Funding; Biomed Valley Discoveries: Research Funding. Olin:Daiichi Sankyo: Research Funding; Astellas: Research Funding; Genentech: Consultancy, Research Funding; Pfizer: Research Funding; Jazz Pharmaceuticals: Consultancy, Honoraria; Revolution Medicine: Consultancy; AstraZeneca: Research Funding; Clovis: Research Funding; Ignyta: Research Funding; MedImmune: Research Funding; Mirati Therapeutics: Research Funding; Novartis: Research Funding; Spectrum: Research Funding. Foran:Agios: Honoraria, Research Funding. Lin:Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria. Traer:AbbVie: Consultancy; Agios: Consultancy; Astellas: Consultancy; Daiichi Sankyo: Consultancy; Notable Labs: Equity Ownership. Odenike:AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; Astra Zeneca: Research Funding; Astex Pharmaceuticals: Research Funding; NS Pharma: Research Funding; Gilead Sciences: Research Funding; Janssen Oncology: Research Funding; Oncotherapy: Research Funding; Agios: Research Funding; CTI/Baxalta: Research Funding. Arellano:Gilead: Consultancy. Vergilio:Roche Holding AG: Equity Ownership; Foundation Medicine: Employment. Brennan:Foundation Medicine: Employment. Vietz:Foundation Medicine: Employment. Druker:ALLCRON: Membership on an entity's Board of Directors or advisory committees; Pfizer: Research Funding; Aileron Therapeutics: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees , Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Patents & Royalties, Research Funding; Burroughs Wellcome Fund: Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Honoraria; The RUNX1 Research Program: Membership on an entity's Board of Directors or advisory committees; GRAIL: Equity Ownership, Other: former member of Scientific Advisory Board; Patient True Talk: Consultancy; Dana-Farber Cancer Institute (antibody royalty): Patents & Royalties: #2524, antibody royalty; Pfizer: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding; Merck & Co: Patents & Royalties: Dana-Farber Cancer Institute license #2063, Monoclonal antiphosphotyrosine antibody 4G10, exclusive commercial license to Merck & Co; OHSU (licensing fees): Patents & Royalties: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees ; Amgen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; Beat AML LLC: Other: Service on joint steering committee; CureOne: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Gilead Sciences: Other: former member of Scientific Advisory Board; ICON: Other: Scientific Founder of Molecular MD, which was acquired by ICON in Feb. 2019; Monojul: Other: former consultant; Novartis: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Patents & Royalties: Patent 6958335, Treatment of Gastrointestinal Stromal Tumors, exclusively licensed to Novartis, Research Funding; Bristol-Myers Squibb: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding. Byrd:Novartis: Other: Travel Expenses, Speakers Bureau; BeiGene: Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; Ohio State University: Patents & Royalties: OSU-2S; Genentech: Research Funding; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau.
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Kresch, Mindy P., Wenbin Xiao, Kamal Menghrajani, et al. "Acute Leukemia with Lineage Infidelity: Mixed Phenotype AML Exhibits a Distinct Immunophenotype with Clinical Features Overlapping Mixed Phenotype Acute Leukemia." Blood 134, Supplement_1 (2019): 2718. http://dx.doi.org/10.1182/blood-2019-131104.
Full textAbstract:
Background: Myeloid and lymphoid leukemias each have distinct diagnostic algorithms, treatment paradigms, and therapeutic options. In mixed phenotype acute leukemia (MPAL), myeloid and lymphoid immunophenotypes are expressed simultaneously. MPAL is rare, accounting for fewer than 5% of all leukemias, and carries a poor prognosis. Currently, there is no standard of care for treatment of this disease and therapeutic options tailored to this group of patients are lacking. Additionally, the biological mechanisms underlying lineage infidelity are poorly understood. Here we seek to establish more precise biological characterization of this disease and have identified a new subgroup of patients with Mixed Phenotype Acute Myeloid Leukemia (AML-MP). Methods: We developed a novel natural language processing pipeline and used it to review 830 patient records, representing all acute leukemia patients treated at Memorial Sloan Kettering Cancer Center since 2015. We identified those with ambiguous lineages based on multiparameter flow cytometry data and integrated next-generation sequencing, cytogenetics, and clinical information. Statistical methods are outlined with the associated results below. Results: Among the 830 patient records reviewed, 54 (6.5%) patients with mixed lineage characteristics were identified. Of these, 26 (48%) carried a formal diagnosis of MPAL while 28 (52%) carried a diagnosis of AML with myelodysplasia related changes (AML-MRC) or therapy related AML (t-AML). Among the cases expressing multiple lineages, 34 (25%) had B/Myeloid features (11 MPAL, 23 AML-MRC), 17 (13%) had T/Myeloid features (13 MPAL, 4 AML-MRC), 3 (0.7%) had B/T/Myeloid lineage (2 MPAL, 1 AML-MRC). Only 8 patients received prior chemotherapy at our institution > 1 year prior to diagnosis and were classified as t-AML. As a control group, we also identified 79 patients with AML-MRC exhibiting myeloid lineage alone. We pooled the mixed-lineage and myeloid-only AML-MRC cases and performed k-means clustering into 2 groups using all available molecular features (Figure 1a). Although TP53 was enriched within AML cases (p<0.01), it was insufficient either individually or in combination with other mutations to significantly distinguish AML from MPAL cases (p=1). Among patients with mixed lineage characteristics, we performed a cox proportional hazards analysis and found that RUNX1 and SRSF2 mutations were predictors of better overall survival (OS) after adjusting for age, cytogenetics, and diagnosis type (age > 60: p < 0.01, high risk cytogenetics: p<0.08, intermediate risk cytogenetics: p=0.02, RUNX1: p<0.01, SRSF2: p=0.03; overall: p < 0.01; Figure 1b, left). An MPAL diagnosis by WHO criteria failed to achieve independent statistical significance on its own (p=0.57). Among AML-MRC myeloid only cases, ASXL1 mutation was associated with improved OS and TET2 mutation showed a trend toward poorer OS after adjusting for age and cytogenetic risk (age>60: p=0.01, ASXL1: p=0.02, TET2: p=0.06; overall: p < 0.01, Figure 1b, right). Cytogenetic risk did not independently contribute to the model (high risk: p=0.3, intermediate risk: p=0.55). We also manually reviewed flow cytometry results from 64 patients diagnosed with MPAL or diagnosed with t-AML or AML-MRC despite expressing markers of multiple lineages (Mixed Phenotype AML). Using a k-nearest neighbor approach, we found that immunophenotype identified two distinct patient populations - one largely Mixed Phenotype AML, and the other with a mixture of MPAL and Mixed Phenotype AML cases (p<0.01) (Figure 1c). Conclusions: Genomic characteristics improve prognostication for patients with MPAL, with RUNX1 and SRSF2 predicting a more favorable OS and TP53 not predictive of worse OS; this is in contrast to current stratifications of patients with de novo AML. While TP53 is enriched among AML cases, it does not distinguish AML-MRC from MPAL. Additionally, AML-MRC patients with ASXL1 mutations had improved overall survival after adjusting for cytogenetic risk. Some cases that are categorized as AML-MRC and t-AML by WHO criteria should be considered Mixed Phenotype AML and may be better classified into one of two distinct immunophenotypic subsets. Our study and model provide a more refined biological classification and prognostic schema for patients with MPAL and AML-MP; further validation of these observations is required in other data sets. Figure 1 Disclosures Roshal: Auron Therapeutics: Equity Ownership, Other: Provision of services; Celgene: Other: Provision of Services; Physicians' Education Resource: Other: Provision of services. Levine:Prelude Therapeutics: Research Funding; Roche: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Qiagen: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy; Loxo: Membership on an entity's Board of Directors or advisory committees; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; Lilly: Honoraria; Amgen: Honoraria. Tallman:Indy Hematology Review: Honoraria; Hematology Oncology of Indiana: Honoraria; Salzberg Weill Cornall MSKCC Seminar in Hematologic Malignancies: Honoraria; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; International Conference in Leukemia: Honoraria; 14th Annual Miami Cancer Meeting: Honoraria; Amgen: Consultancy; Orsenix: Membership on an entity's Board of Directors or advisory committees, Research Funding; UpToDate: Patents & Royalties; Mayo Clinic: Honoraria; New Orleans Summer Cancer Conference: Honoraria; ADC Therapeutics: Research Funding; Danbury Hospital Tumor Board: Honoraria; Arog Pharmaceuticals: Research Funding; Nohla: Membership on an entity's Board of Directors or advisory committees, Research Funding; KAHR: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; BioSight: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bioline: Membership on an entity's Board of Directors or advisory committees, Research Funding; University of Oklahoma Medical Center: Honoraria; Cellerant Therapeutics: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees.
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ŞENOL, Deniz, Yusuf SEÇGİN, Şeyma TOY, Serkan ÖNER, and Zülal ÖNER. "Tipik Servikal Omurlar Makine Öğrenimi Algoritmaları Kullanılarak Birbirinden Ayırt Edilebilir mi? Radyoanatomik Yeni Belirteçler." Konuralp Tip Dergisi, April 26, 2023. http://dx.doi.org/10.18521/ktd.1177279.
Full textAbstract:
Objective: The aim of this study is to distinguish the typical cervical vertebrae that cannot be separated from one another with the naked eye by using machine algorithms (ML) with measurements made on computerized tomography (CT) images and to show the differences of these vertebrae. 
 Method: This study was conducted by examining the 536 typical cervical vertebrae CT images of 134 (between the ages of 20 and 55) individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations (Group 1: C3 – C4, Group 2: C3 – C5, Group 3: C3 – C6, Group 4: C4 – C5, Group 5: C4 – C6, Group 6: C5 – C6) were formed with parameters of each vertebrae and they were analyzed in ML algorithms. Accuracy (Acc), Matthews correlation coefficient (Mcc), Specificity (Spe), Sensitivity (Sen) values were obtained as a result of the analysis. 
 Results: As a result of this study, the highest success was obtained with Linear Discriminant Analysis (LDA) and Logistic Regression (LR) algorithms. The highest Acc rate was found as 0.94 with LDA and LR algorithm in Groups 3 and Group 4, the highest Spe value was found as 0.95 with LDA and LR algorithm in Group 5, the highest Mcc value was found as 0.90 with LDA and LR algorithm in Group 5 and the highest Sen value was found as 0.94 with LDA and LR algorithm in Groups 3 and 5. 
 Conclusion: As a conclusion, it was found that typical cervical vertebrae can be clearly distinguished from one another by using ML algorithms.
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Eguia, Martiniano, and Francisco Juan Soulignac. "Hereditary biclique-Helly graphs: recognition and maximal biclique enumeration." Discrete Mathematics & Theoretical Computer Science Vol. 15 no. 1, Graph and Algorithms (2013). http://dx.doi.org/10.46298/dmtcs.626.
Full textAbstract:
Graphs and Algorithms International audience A biclique is a set of vertices that induce a complete bipartite graph. A graph G is biclique-Helly when its family of maximal bicliques satisfies the Helly property. If every induced subgraph of G is also biclique-Helly, then G is hereditary biclique-Helly. A graph is C4-dominated when every cycle of length 4 contains a vertex that is dominated by the vertex of the cycle that is not adjacent to it. In this paper we show that the class of hereditary biclique-Helly graphs is formed precisely by those C4-dominated graphs that contain no triangles and no induced cycles of length either 5 or 6. Using this characterization, we develop an algorithm for recognizing hereditary biclique-Helly graphs in O(n2+αm) time and O(n+m) space. (Here n, m, and α= O(m1/2) are the number of vertices and edges, and the arboricity of the graph, respectively.) As a subprocedure, we show how to recognize those C4-dominated graphs that contain no triangles in O(αm) time and O(n+m) space. Finally, we show how to enumerate all the maximal bicliques of a C4-dominated graph with no triangles in O(n2 + αm) time and O(αm) space.
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Ye, Huayu, Hongrui Qin, Ying Tang, Nicha Ungvijanpunya, and Yongchao Gou. "Mapping an intelligent algorithm for predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy." Progress in Orthodontics 25, no. 1 (2024). http://dx.doi.org/10.1186/s40510-024-00523-5.
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Abstract Backgrounds and objectives The present study was designed to define a novel algorithm capable of predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy. Methods A total of 560 female cephalograms were collected, and cephalograms with unclear vertebral shapes and deformed scales were removed. 480 films from female adolescents (mean age: 11.5 years; age range: 6–19 years) were used for the model development phase, and 80 subjects were randomly and stratified allocated to the validation cohort to further assess the model’s performance. Derived significant predictive parameters from 15 anatomic points and 25 quantitative parameters of the second to fourth cervical vertebrae (C2-C4) to establish the ordinary logistic regression model. Evaluation metrics including precision, recall, and F1 score are employed to assess the efficacy of the models in each identified cervical vertebrae maturation stage (iCS). In cases of confusion and mispredictions, the model underwent modification to improve consistency. Results Four significant parameters, including chronological age, the ratio of D3 to AH3 (D3:AH3), anterosuperior angle of C4 (@4), and distance between C3lp and C4up (C3lp-C4up) were administered into the ordinary regression model. The primary predicting model that implements the novel algorithm was built and the performance evaluation with all stages of 93.96% for accuracy, 93.98% for precision, 93.98% for recall, and 93.95% for F1-score were obtained. Despite the hybrid logistic-based model achieving high accuracy, the unsatisfactory performance of stage estimation was noticed for iCS3 in the primary cohort (89.17%) and validation cohort (85.00%). Through bivariate logistic regression analysis, the posterior height of C4 (PH4) was further selected in the iCS3 to establish a corrected model, thus the evaluation metrics were upgraded to 95.83% and 90.00%, respectively. Conclusions An unbiased and objective assessment of the cervical vertebrae maturation (CVM) method can function as a decision-support tool, assisting in the evaluation of the optimal timing for treatment in growing adults. Our novel proposed logistic model yielded individual formulas for each specific CVM stage and attained exceptional performance, indicating the capability to function as a benchmark for maturity evaluation in clinical craniofacial orthopedics for Chinese female adolescents.
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Alaei, S. Hadi, Farhad Khorasheh, M. Reza Jafari Nasr, and Mahdi Arjmand. "Experimental study and kinetic modeling of methanol conversion to propylene with co-feeding of C4 and C5/C6 hydrocarbon cuts over ZSM-5 catalyst." Journal of Chemical Physics, March 2, 2023. http://dx.doi.org/10.1063/5.0139592.
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Extensive experimental data were used to develop a comprehensive kinetic model for methanol to propylene (MTP) process over ZSM-5 catalyst. Preliminary experiments were performed to determine the reaction conditions that would ensure the absence of external (film) and internal mass transfer resistances. The kinetic experiments were subsequently carried out at 420 to 500{degree sign}C under conditions where mass transfer limitations were absent. A detailed reaction network was proposed for the MTP process based on the experimental product distribution and various reported kinetic models in the literature. According to the first series of experiments (without C4 and C5/C6 recycle streams) conducted at various temperatures, the best yield for propylene production was achieved at 480{degree sign}C with water to methanol ratio of 0.7. Subsequently, kinetic experiments were performed at 480{degree sign}C and water to methanol ratio of 0.7 using feeds with different amounts of C4 and C5/C6 hydrocarbons as recycle streams. Species material balances for the integral tubular reactor along with power-law rate functions and Arrhenius equation for rate constants were employed in an optimization algorithm to obtain the kinetic parameters. The predictive ability of the model was checked against experimental data and the kinetic parameters were validated by additional experiments.
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Proost, Renee, Elisabeth Heremans, Lieven Lagae, Wim Van Paesschen, Maarten De Vos, and Katrien Jansen. "Automated sleep staging on reduced channels in children with epilepsy." Frontiers in Neurology 15 (May 10, 2024). http://dx.doi.org/10.3389/fneur.2024.1390465.
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ObjectivesThis study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE).MethodsOvernight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG.ResultsIn 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep.ConclusionWe concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.
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Yang, Po. "Modeling the Coupling of Mental Health Education and Student Management in Colleges and Universities." Applied Mathematics and Nonlinear Sciences 9, no. 1 (2024). http://dx.doi.org/10.2478/amns-2024-1970.
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Abstract Influenced by some traditional thinking, some college student managers do not recognize the importance of mental health education and fail to effectively integrate mental health education into student management, resulting in poor student management results. This paper proposes an improved optimization scheme for the ID3 algorithm, using the improved C4-5 algorithm to construct a decision tree model, selecting information gain and gain rate as the attribute measure of the model and deciding the split form of the sample. Succeeding pruning operations are used to correct model errors, and the rules are described in the form of TF-THEN. Mental health education is the application of the constructed data model. The effectiveness of student management after mental health education was differentially analyzed across the different identity perspectives of teachers and students. There are significant differences between teachers and students in the dimensions of practice effect, strengths evaluation, and direction of improvement, with p-values less than 0.05. In the study of the effectiveness of student management on students’ mental health, after the implementation of student management work, there are significant differences between the experimental group of students and the control group in the dimensions of self-confidence, emotional intelligence, and frustration tolerance, with a difference of 4.61, 1.6, and 7.9 points, respectively. There is a reciprocal interactive relationship between student management and mental health education.
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Muhammad, Kamil Abdullah, S. Subari Khazaimatol, Leo Cheang Loong Justin, and Nadia Ahmad Nurul. "Analysis of the EEG Signal for a Practical Biometric System." August 24, 2010. https://doi.org/10.5281/zenodo.1055815.
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This paper discusses the effectiveness of the EEG signal for human identification using four or less of channels of two different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because signal varies from person to person and impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes closed in 5 separate sessions conducted over a course of two weeks. Features were extracted using the wavelet packet decomposition and analyzed to obtain the feature vectors. Subsequently, the neural networks algorithm was used to classify the feature vectors. Results show that, whether or not the subjects- eyes were open are insignificant for a 4– channel biometrics system with a classification rate of 81%. However, for a 2–channel system, the P4 channel should not be included if data is acquired with the subjects- eyes open. It was observed that for 2– channel system using only the C3 and C4 channels, a classification rate of 71% was achieved.