Journal articles: 'For SNVQ Intermediate'

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Tu, Kailing, Keying Lu, Qilin Zhang, Wei Huang, and Dan Xie. "Accurate single-cell genotyping utilizing information from the local genome territory." Nucleic Acids Research 49, no. 10 (February 22, 2021): e57-e57. http://dx.doi.org/10.1093/nar/gkab106.

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Abstract Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external data, such as heterozygous profiles determined by bulk data. In most single-cell studies, information from different sources is not perfectly matched. High-accuracy SNV detection with a limited single data source remains a challenge. We developed a new variant detection method, SCOUT (Single Cell Genotyper Utilizing Information from Local Genome Territory), the greatest advantage of which is not requiring external data while base calling. By leveraging base count information from the adjacent genomic region, SCOUT classifies all candidate SNVs into homozygous, heterozygous, intermediate and low major allele SNVs according to the highest likelihood score. Compared with other genotypers, SCOUT improves the variant detection performance by 2.0–77.5% in real and simulated single-cell datasets. Furthermore, the running time of SCOUT increases linearly with sequence length; as a result, it shows 400% average acceleration in operating efficiency compared with other methods.

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Stopka, Tomas, Karin Vargova, Vojtech Kulvait, Nina Dusilkova, and Anna T. Jonasova. "Somatic Mutation-Detecting Algorithm Enables Analysis of MDS Patients during Azacitidine Therapy." Blood 124, no. 21 (December 6, 2014): 5600. http://dx.doi.org/10.1182/blood.v124.21.5600.5600.

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Abstract Introduction: Somatic mutation detection in myelodysplastic syndrome (MDS) is very important in deciphering clonal pathogenesis of every patient and if determined correctly will become useful tool in followup studies such as testing individual susceptibility to epigenetic therapy with azacitidine (AZA). While some patients respond to AZA by restoring hematologic parameters, others progress to AML. Recent identification of quite heterogeneous sets of mutated genes (Bejar R et al. 2013) suggested that: patients with specific mutation pattern/s may respond to epigenetic therapy differently. Aim: We herein set to determine mutation profiles of MDS cohort indicated to and treated by AZA and utilized TrueSight DNA amplicon NGS sequencing approach containing 54 genes all previously associated with MDS or AML. Patients: We analyzed immunomagnetically CD3-depleted bone marrows of two MDS patients - AZA responders. First patient (male, 68y), was diagnosed with RAEB2, IPSS int-2, transfusion dependent (4 TU/Mo), intermediate cytogenetics (tri21). Following 4 cycles of AZA (75 mg/m2 s.c., 5+2) the patient responded by partial remission, and AZA was discontinued after 17 cycles. Twelve months after discontinuation he progressed and AZA was readministered for additional 3 cycles and the patient achieved again partial remission. Analyzed are samples after 11 (P394) and 20 (P1380) cycles of Vidaza. Second patient (female, 64y), was diagnosed with RAEB2, IPSS high, transfusion dependent (2 TU/Mo), favorable cytogenetics (46XX). Following 4 cycles of AZA (75 mg/m2 s.c., 5+2) she responded by hematology improvement and later by partial remission. Analyzed is a sample after 4 (P1510) cycles of Vidaza. As negative controls we used two normal donor bone marrows from 41y male and 32y female. As a positive control we also used: 1 MDS/AML cell line MOLM-13 with previously identified mutations of CBL and FLT3 (DSMZ; ACC 554). Methods and approach: Samples were sequenced on Illumina MiSeq sequencer. The mapping was performed using Burrows-Wheeler Aligner algorithm. Illumina Somatic variant caller was used to identify mutations. Then we applied following filters on the data: sequencing coverage should be higher than 1000 per mutation (~80% data left), mutation should be heterozygous (~95% data left), mutation frequency should be higher than 10% (~10% data left), Illumina Somatic variant caller should flag the mutation as "PASS" (~50% data left), mutation should not be synonymous (~75% data left) and mutation should be exonic (~40% data left). These filters were also applied to find mutations in the two control samples. Those mutations which were identified also in the control samples were removed from the analysis of patient samples (~50% data left). Results: the MDS/AML cell line MOLM-13 contained mutations (SNVs or InDels) in ABL1 (SNV/frequency=46.5%), ASXL1 (SNV/49.8%), CEBPA (In/47.9%), HRAS (SNV/54.5%), TET2 (SNV/49.7%), and as expected also in the genes encoding CBL (delta-exon8/52.4%) and FLT3 (ITD/50.6%). Patient' sample P1510 contained mutations in CBL (SNV/67.9%), CUX1 (SNV/51.8%), IKZF1 (2 different SNVs/41.9 and 50.7%), KDM6A (SNV/51.6%), SF3B1 (SNV/38.9%), and SMC3 (SNV/33.1%). Patient samples P394 and P1380 contained mutations in the ASXL1 (SNV/ 35.5% and 32.6% respectively), CUX1 (2x SNVs, first SNV/46.1->64.5%, second 48.4->57.9%), and IKZF1 (SNV, 50.4->44.5%) in similar frequencies in the sample before and after 2.5 years (including 9 cycles of AZA) suggesting limited genetic heterogeneity in this AZA-responding patient. Consequently, to gain more insight into how AZA modulates mutation pattern in MDS, we now analyze a set of fourty nine additional patients before and following at least 4 cycles on AZA treatment. Conclusions: Our data support use of immunomagnetic CD3-depletion of bone marrow and addition of normal control samples in the sequencing of MDS patient samples and support this approach for testing genetic heterogeneity during MDS disease course upon AZA treatment. Disclosures Stopka: GAČR P305/12/1033 and UNCE 204021: Research Funding; Celgene: Research Funding; PersMed ltd.: Equity Ownership. Vargova:GAČR P305/12/1033 and P305/11/1745: Research Funding; UNCE 204021: Research Funding; PRVOUK P24/LF1/3: Research Funding. Kulvait:PersMed ltd.: Equity Ownership. Jonasova:PRVOUK P24/LF1/1: Research Funding; Celgene: Research Funding.

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Dubois, Frank, Ofer Shapira, Noah Greenwald, Travis Zack, Jessica W. Tsai, Ashot S. Harutyunyan, Kiran Kumar, et al. "HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii351. http://dx.doi.org/10.1093/neuonc/noaa222.322.

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Abstract BACKGROUND Driver single nucleotide variants (SNV) and somatic copy number aberrations (SCNA) of pediatric high-grade glioma (pHGGs), including Diffuse Midline Gliomas (DMGs) are characterized. However, structural variants (SVs) in pHGGs and the mechanisms through which they contribute to glioma formation have not been systematically analyzed genome-wide. METHODS Using SvABA for SVs as well as the latest pipelines for SCNAs and SNVs we analyzed whole-genome sequencing from 174 patients. This includes 60 previously unpublished samples, 43 of which are DMGs. Signature analysis allowed us to define pHGG groups with shared SV characteristics. Significantly recurring SV breakpoints and juxtapositions were identified with algorithms we recently developed and the findings were correlated with RNAseq and H3K27ac ChIPseq. RESULTS The SV characteristics in pHGG showed three groups defined by either complex, intermediate or simple signature activities. These associated with distinct combinations of known driver oncogenes. Our statistical analysis revealed recurring SVs in the topologically associating domains of MYCN, MYC, EGFR, PDGFRA & MET. These correlated with increased mRNA expression and amplification of H3K27ac peaks. Complex recurring amplifications showed characteristics of extrachromosomal amplicons and were enriched in coding SVs splitting protein regulatory from effector domains. Integrative analysis of all SCNAs, SNVs & SVs revealed patterns of characteristic combinations between potential drivers and signatures. This included two distinct groups of H3K27M DMGs with either complex or simple signatures and different combinations of associated variants. CONCLUSION Recurrent SVs associate with signatures shaped by an underlying process, which can lead to distinct mechanisms to activate the same oncogene.

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Bernasconi, Claude F., Rodney J. Ketner, Xin Chen, and Zvi Rappoport. "Detection and kinetic characterization of SNV intermediates. Reactions of thiomethoxybenzylidene Meldrum's acid with thiolate ions, alkoxide ions, OH-, and water in aqueous DMSO." Canadian Journal of Chemistry 77, no. 5-6 (June 1, 1999): 584–94. http://dx.doi.org/10.1139/v99-009.

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The reaction of thiomethoxybenzylidene Meldrum's acid (5-SMe) with thiolate and alkoxide ion nucleophiles is shown to proceed by the two-step addition-elimination SNV mechanism in which the tetrahedral intermediate accumulates to detectable levels. For the reactions with thiolate ions, rate constants for nucleophilic addition (k1RX), its reverse (k-1RX), and for conversion of the intermediate to products (k2RX) were determined. For the reactions with alkoxide ions, only k1RX and k-1RX could be obtained; the intermediate in these reactions did not yield the expected substitution products, and hence no k2RX values could be determined. The reactions with OH- and water are believed to follow the same mechanism, but the respective intermediates remain at steady-state levels, and only k1OH and k1H²O for nucleophilic attack on 5-SMe were measurable. New insights regarding structure-reactivity behavior in SNV reactions are gained from comparisons of rate and equilibrium constants in the reactions of 5-SMe with the corresponding parameters in the reactions of methoxybenzylidene Meldrum's acid (5-OMe) and benzylidene Meldrum's acid (5-H). In particular, the relative importance of steric and pi-donor effects of the MeS vs. MeO group in 5-SMe and 5-OMe, respectively, and their role in affecting the intrinsic rate constants for nucleophilic addition, has been clarified by these comparisons. Our results also add support to a previous suggestion that soft-soft type interactions tend to increase intrinsic rate constants for thiolate ion addition to vinylic substrates, especially 5-SMe with the soft MeS group.Key words: nucleophilic vinylic substitution, intrinsic rate constants, transition state imbalances, steric/pi-donor/anomeric effects.

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Tabori, Uri, Scott Ryall, Michal Zapotocky, Julie Bennett, Liana Nobre, David Ellison, Mariarita Santi, Matthias Karajannis, and Cynthia Hawkins. "LGG-50. INTEGRATED MOLECULAR AND CLINICAL ANALYSIS OF 1,000 PEDIATRIC LOW-GRADE GLIOMAS UNCOVERS NOVEL SUBGROUPS FOR CLINICAL RISK STRATIFICATION." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii375—iii376. http://dx.doi.org/10.1093/neuonc/noaa222.428.

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Abstract Pediatric low-grade gliomas (pLGG) are primarily driven by genetic alterations in the RAS/MAPK pathway, most commonly involving BRAF of NF1. Despite their molecular convergence, pLGG often show unexplained variability in their clinical outcome. To address this, we molecularly characterized a cohort of >1,000 clinically annotated pLGG. 84% of cases harbored a detectable driver mutation. The remaining 16% of patients nonetheless showed RAS/MAPK pathway up-regulation at the RNA level. The clinical presentation and outcome of pLGG appeared highly variable and linked to the alteration type: re-arrangement or SNV. Re-arrangement-driven tumors were diagnosed at a younger age (6.6 versus 10.9 years, p<0.0001), enriched for WHO grade I histology (88% versus 66%, p<0.0001), infrequently progressed (27% versus 46%, p<0.0001), and rarely resulted in death (3 versus 13%, p<0.0001) as compared to SNV-driven tumors. These included the rarest molecular drivers of pLGG, for which we now have the clinicopathologic features of including MYB, MYBL1, FGFR2 fusions, FGFR1-TACC1, FGFR1 SNVs, IDH1 p.R132H, and H3.3 p.K27M. Utilizing this information, we suggest novel risk categories of pLGG that effectively predicted patient outcome. Low-risk tumors progressed infrequently and rarely succumbed to their disease (10-year PFS of 71% and OS of 98%). Intermediate-risk pLGG had a 10-year PFS and OS of 35% and 90%, respectively. High risk pLGG almost invariably progressed (10-year PFS of 0%) and these patients often succumbed to their disease (10-year OS of 41%). These data highlight the biological and clinical differences between pLGG subtypes and offers molecular based risk stratification to these cancers.

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Karni, Miriam, Claude F. Bernasconi, and Zvi Rappoport. "Role of Negative Hyperconjugation and Anomeric Effects in the Stabilization of the Intermediate in SNV Reactions." Journal of Organic Chemistry 73, no. 8 (April 2008): 2980–94. http://dx.doi.org/10.1021/jo7017476.

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van Belzen, Ianthe A. E. M., Marc van Tuil, Shashi Badloe, Eric Strengman, Alex Janse, Eugène T. P. Verwiel, Douwe F. M. van der Leest, et al. "Molecular Characterization Reveals Subclasses of 1q Gain in Intermediate Risk Wilms Tumors." Cancers 14, no. 19 (October 5, 2022): 4872. http://dx.doi.org/10.3390/cancers14194872.

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Chromosomal alterations have recurrently been identified in Wilms tumors (WTs) and some are associated with poor prognosis. Gain of 1q (1q+) is of special interest given its high prevalence and is currently actively studied for its prognostic value. However, the underlying mutational mechanisms and functional effects remain unknown. In a national unbiased cohort of 30 primary WTs, we integrated somatic SNVs, CNs and SVs with expression data and distinguished four clusters characterized by affected biological processes: muscle differentiation, immune system, kidney development and proliferation. Combined genome-wide CN and SV profiles showed that tumors profoundly differ in both their types of 1q+ and genomic stability and can be grouped into WTs with co-occurring 1p−/1q+, multiple chromosomal gains or CN neutral tumors. We identified 1q+ in eight tumors that differ in mutational mechanisms, subsequent rearrangements and genomic contexts. Moreover, 1q+ tumors were present in all four expression clusters reflecting activation of various biological processes, and individual tumors overexpress different genes on 1q. In conclusion, by integrating CNs, SVs and gene expression, we identified subgroups of 1q+ tumors reflecting differences in the functional effect of 1q gain, indicating that expression data is likely needed for further risk stratification of 1q+ WTs.

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Bernasconi, Claude F., Aquiles E. Leyes, and Zvi Rappoport. "Kinetics of the Reaction of β-Methoxy-α-nitrostilbene with Cyanamide in 50 DMSO−50 Water. Failure to Detect the SNV Intermediate." Journal of Organic Chemistry 64, no. 8 (April 1999): 2897–902. http://dx.doi.org/10.1021/jo990044u.

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Incorvaia, Lorena, Giuseppe Badalamenti, Daniele Fanale, Bruno Vincenzi, Ida De Luca, Laura Algeri, Nadia Barraco, et al. "Not all KIT 557/558 codons mutations have the same prognostic influence on recurrence-free survival: breaking the exon 11 mutations in gastrointestinal stromal tumors (GISTs)." Therapeutic Advances in Medical Oncology 13 (January 2021): 175883592110497. http://dx.doi.org/10.1177/17588359211049779.

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Background: Although the gastrointestinal stromal tumor (GIST) genotype is not currently included in risk-stratification systems, a growing body of evidence shows that the pathogenic variant (PV) type and codon location hold a strong prognostic influence on recurrence-free survival (RFS). This information has particular relevance in the adjuvant setting, where an accurate prognostication could help to better identify high-risk tumors and guide clinical decision-making. Materials and Methods: Between January 2005 and December 2020, 96 patients with completely resected GISTs harboring a KIT proto-oncogene receptor tyrosine kinase ( KIT) exon 11 PV were included in the study. We analyzed the type and codon location of the PV according to clinicopathological characteristics and clinical outcome; the metastatic sites in relapsed patients were also investigated. Results: Tumors harboring a KIT exon 11 deletion or deletion/insertion involving the 557 and/or 558 codons, showed a more aggressive clinical behavior compared with tumors carrying deletion/deletion/insertion in other codons, or tumors with duplication/insertion/single-nucleotide variant (SNV) (7-year RFS: 50% versus 73.1% versus 88.2%, respectively; p < 0.001). Notably, among 18 relapsed patients with 557 and/or 558 deletion or deletion/insertion, 14 patients (77.8%) harbored deletions simultaneously involving 557 and 558 codons, while only 4 patients (22.2%) harbored deletions involving only 1 of the 557/558 codons. Thus, when 557 or 558 deletions occurred separately, the tumor showed a prognostic behavior similar to the GIST carrying deletions outside the 557/558 position. Remarkably, patients with GISTs stratified as intermediate risk, but carrying the 557/558 deletion, showed a similar outcome to the high-risk patients with tumors harboring deletions in codons other than 557/558, or duplication/insertion/SNV. Conclusion: Our data support the inclusion of the PV type and codon location in routine risk prediction models, and suggest that intermediate-risk patients whose GISTs harbor 557/558 deletions may also need to be treated with adjuvant imatinib like the high-risk patients.

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Bernasconi, Claude F., Shoshana D. Brown, Irina Eventova, and Zvi Rappoport. "Spectroscopic and Kinetic Evidence for an Accumulating Intermediate in an SNV Reaction with Amine Nucleophiles. Reaction of Methyl β-Methylthio-α-nitrocinnamate with Piperidine and Morpholine." Journal of Organic Chemistry 72, no. 9 (April 2007): 3302–10. http://dx.doi.org/10.1021/jo062602s.

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Bernasconi, Claude F., Rodney J. Ketner, Xin Chen, and Zvi Rappoport. "Kinetics of the Reactions of Methoxybenzylidene Meldrum's Acid with Thiolate Ions, Alkoxide Ions, OH-, and Water in Aqueous DMSO. Detection and Kinetic Characterization of the SNV Intermediate." Journal of the American Chemical Society 120, no. 30 (August 1998): 7461–68. http://dx.doi.org/10.1021/ja9743102.

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Lopez-Diaz, Fernando, Lauryn Keeler, Sally Agersborg, Lawrence Weiss, and Vincent Funari. "240 Identification of lung cancer mutational signatures and tumor drivers associated with specific bimodal PD-L1/TMB status." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A258. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0240.

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BackgroundPD-L1 expression and Tumor Mutation Burden (TMB) have independently emerged as prospective biomarkers of response to anti PD1-/PDL1 checkpoint inhibitors and even combined use of TMB, PD-L1 protein levels has been proposed. However, how the tumor genomic landscape interplays with the tumor microenvironment (TME) in defining particular predictive therapy response statuses is not clear.Methods424 FFPE clinical samples from lung cancer patients were analyzed using a CLIA-validated NGS-based assay that interrogates SNVs, indels using a 323 gene panel and by IHC for PD-L1 using the FDA approved PharmDx assay. TMB (mutations/Mb) is categorized as low (≤7), intermediate (7 15). NGS results were paired with PD-L1 status which was defined by tumor proportion scores (TPS) as: negative (TPS<1%), Low expressing (≥1–49%) and High (≥50%). In silico analyses were also performed on 5939 lung cancer samples from public databases.ResultsWe found poor correlation between PD-L1 expression and TMB in NSCLC (r2=0.266). We then classified lung cancer samples based on TMB and PD-L1 TPS and found mutational correlations specific to in each of the groups defined by PD-L1 combined with TMB scores. First, we interrogated the KRAS and EGFR mutations frequencies distribution across either TMB or PDL1 status. We find that while KRAS mutations are constant across PDL1 TPS but infrequent on TMB High tumors, EGFR mutation frequency appeared inversely correlated to both TMB and PD-L1 TPS. 67% of PD-L1 High/TMB Low samples presented mutations either on EGFR (12%), KRAS (23.5%) or in genes from known driver TRK/MAPK pathways, whereas only KRAS was part of the frequently mutated gene signature with 36.5% (13/36) samples mutated on PD-L1 High/TMB High samples. Neither EGFR nor KRAS were found frequently mutated on PD-L1 Low/TMB High group (n=46).Interestingly in patients with an intermediate TMB (7.ConclusionsGenomic alteration signatures might define subsets of lung cancer tumors with no PD-L1 expression to complement TMB and PD-L1 on the selection criteria for patients whom may benefit from checkpoint inhibitors.AcknowledgementsParis Pettersen, Hatim Husain.Ethics ApprovalThe study was approved by Neogenomics Institution’s Ethics Board and external IRB, approval number 420160280.

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Vinayak, Bhandari, Lydia Liu, Shadrielle Espirritu, Emilie Lalonde, Takafumi Yamaguchi, Lawrence Heisler, Julie Livingstone, et al. "The molecular hallmarks and clinical consequences of tumor hypoxia in prostate cancer." Journal of Clinical Oncology 37, no. 7_suppl (March 1, 2019): 81. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.81.

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81 Background: Localised prostate cancers are classified into risk-groups using clinical measurements like grade and stage to inform treatment decisions. However, these groupings are imprecise: ~30% of intermediate-risk patients suffer relapse of their disease despite precision image-guided radiotherapy or radical prostatectomy. One reason for this variability in response to treatment is the underlying cellular and molecular heterogeneity of tumours. Prostate tumour cells exist within a microenvironment characterized by gradients of oxygen levels and prostate tumours with low levels of oxygen (hypoxia) have poor clinical outcomes. Methods: Hypoxia was measured using multiple mRNA-based signatures. We examined 548 patients with localised prostate cancer and statistically assessed the association of hypoxia with copy-number alterations (CNAs), single-nucleotide variants (SNVs), genomic rearrangements, focal genomic events ( i.e. kataegis, chromothripsis), telomere length, clinical indices ( i.e. grade, stage) and subclonal architecture. Results: Elevated hypoxia was associated with allelic loss of PTEN, higher rates of chromothripsis and intraductal and cribriform carcinoma (IDC-CA). To translate these findings into a biomarker for prostate cancer precision medicine, we integrated tumour microenvironmental data with genomic and pathological information to stratify patients into distinct prognostic groups. Patients with localized prostate cancer that have polyclonal tumours with elevated hypoxia, allelic loss of PTEN and IDC-CA were at the highest risk of rapid biochemical failure (P = 3.48 x10-3, Logrank test) and metastasis (P = 4.61 x 10-3, Logrank test), even after controlling for T-category, Gleason score and pre-treatment PSA. Conclusions: These data suggest that the aggressiveness of prostate cancers is driven by the interplay of the tumour microenvironment, tumour evolutionary trajectories and its genomic mutational profile.

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Chen, Haiyan, Mark J. Cowan, Jeffrey D. Hasday, Stefanie N. Vogel, and Andrei E. Medvedev. "SMOKING INHIBITS EXPRESSION OF PROINFLAMMATORY CYTOKINES AND ACTIVATION OF IRAK-1, p38 AND NF-κB IN ALVEOLAR MACROPHAGES STIMULATED WITH TLR2 AND TLR4 AGONISTS (40.6)." Journal of Immunology 178, no. 1_Supplement (April 1, 2007): S28. http://dx.doi.org/10.4049/jimmunol.178.supp.40.6.

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Abstract Bronchiolitis is common in smokers and we hypothesized that this may be due to impaired sensing of bacterial components via TLR2 and TLR4 by smokers’ alveolar macrophages (AM). AM and PBMC obtained from smokers and non-smoking volunteers were stimulated with TLR2 and TLR4 agonists, Pam3Cys and LPS, and expression of cytokines and activation of intracellular intermediates were examined. Smokers’ AM exhibited suppressed gene expression and secretion of pro-inflammatory cytokines TNF-α, IL-1β, IL-6, IFN-γ and chemokines IL-8 and RANTES upon stimulation with LPS and Pam3Cys, whereas expression of anti-inflammatory cytokines IL-10 and IL-1RA was not affected. Comparable expression levels of these cytokines and chemokines were detected in PBMC obtained from smokers and non-smokers, indicating that the suppressive effect of smoking is restricted to the lung. TLR2/4-inducible phosphorylation of IRAK-1 and p38, and IκB-α degradation were markedly inhibited in smokers’ AM, whereas expression levels of TLR2, TLR4, CD14, MD-2 mRNA, and TLR4 protein, were not significantly changed. These results indicate that smoking induces a state of tolerance in AM manifested by suppression of TLR2/4-induced signaling that may underlie bronchiolitis associated with smoking. This work was supported by grants from Philip Morris USA Inc. and Philip Morris International, OTRD/University of Maryland, Baltimore, and NIH grants AI059524 (AEM) and AI44936 (SNV).

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Satbir, Thakur, Son Tran, Mohit Jain, Austin Lewis, Luis Murguia-Favela, Faisal M. Khan, Kevin J. Bielamowicz, et al. "A Novel Anti-Cancer Vaccine Approach for the Treatment of High-Risk Leukemia in Children." Blood 136, Supplement 1 (November 5, 2020): 25. http://dx.doi.org/10.1182/blood-2020-143381.

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Introduction: There is strong experimental and clinical data to indicate the critical involvement of immune evasion in relapsed leukemia in children. A well-defined characteristic of refractory leukemia is the accumulation of genetic aberrations and mutations that may act as drivers or passengers in the process of tumor recurrence. Many of these mutations get translated into proteins that contain tumor-specific immune-stimulatory epitopes (neoantigens) that can elicit host antitumor immune responses. Although, in general, the mutation rate is lower in pediatric tumors, recent studies have shown that almost 90% of pediatric leukemias carry potentially actionable neoepitopes. In this study, we describe the results from a comprehensive experimental approach of neoantigen prediction coupled with antigen processing and HLA-binding prediction algorithms with in vitro validation assays for the generation of neoantigen vaccines against high-risk leukemias in children. Methods: DNA and RNA from leukemia cells and matched fibroblasts were obtained. Raw reads were aligned to human reference genome and somatic variants (SNVs) were called using Strelka v1.0.1441. RNA-seq data from leukemic cells were used to predict neoantigen expression levels resulting from SNVs using STAR (2.4.1)12 and Cufflinks v2.2.1. Normalized expression data were then cross-referenced with the list of SNVs to identify leukemia-specific mutant proteins. HLA typing for each sample was carried out from RNA-seq data using seq2HLA v2.2. Using the patient's HLA phenotype, we then used NetMHCons v1.1 to predict short peptides derived from leukemia-specific mutant proteins that will bind to autologous HLA Class I molecules. These 8/9-mers were filtered to predict a high likelihood of proteasomal or immune-proteasomal processing and transporter associated with antigen processing (TAP) using NetChop v3.1 and the immune epitope database (IEDB), respectively. The peptides identified were rank-ordered based on the composite immunogenicity score derived from MHC class I binding affinities, proteasomal processing and TCR binding predictions and synthesized accordingly. Peripheral blood derived dendritic cells (DCs) and CD8+ T-cells were isolated and expanded in culture with relevant cytokines. The DCs were pulsed with peptides and then co-cultured with CD8+ T-cells. After five days, the primed CD8+ T-Cells were separated, washed and exposed to the patient's leukemic cells at varying ratios and the leukemia specific CD8+ T-cell activation was quantified by IFN gamma secretion using ELISpot assays. Results: In the leukemia specimen studied, approximately 5% of all on-target germline mutations were found only in leukemic cells. Tumor mutational burden was, on average, 0.34 mut/Mb. Analysis of the highest ranking synthetic peptides (approximately 10 per leukemia sample) showed leukemia-specific activation of patient's T-cells as measured by the mean number of spots observed in ELISpot assays. For example, in patient one (15 year old male, high-risk ALL, one year off therapy), 14 individual short sequences were identified and corresponding peptides were synthesized. Among these, three peptides were not soluble and three peptides showed significant activity above controls. Maximum leukemia specific T-cell activation was noted with peptide #7 QQSALVLL (mean 135 ELISpots compared to 72 in controls, p<0.05, triplicate) indicating a strong nonantigenic potential in this region. Furthermore, this activity was significantly diminished when an extra amino acid was added to this peptide (LQQSALVLL, mean 79 spots) showing the specificity of the approach. A number of other peptides and combinations in non-overlapping regions gave intermediate activities. Discussion: Completed data, including the vaccine peptide sequences and corresponding activities showed the feasibility of identifying pediatric leukemia neoantigen sequences in personalized mutational landscapes of these patients. In addition, we have provided an in vitro experimental approach to validate the potential of such vaccines in future clinical studies and this methodology can also be used to identify agents for effective combinations such as immune checkpoint inhibitors. A clinical trial using these strategies is in development for the treatment of high-risk leukemia in children. Disclosures No relevant conflicts of interest to declare.

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Sarkozy, Clementine, Stacy Hung, Katsuyoshi Takata, Elizabeth Chavez, Tomohiro Aoki, Gerben Duns, Graham W. Slack, et al. "Mutational Landscape of Grey Zone Lymphoma." Blood 134, Supplement_1 (November 13, 2019): 21. http://dx.doi.org/10.1182/blood-2019-127375.

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Introduction: Grey zone lymphoma (GZL), a B-cell lymphoma with features intermediate between large B-cell lymphoma (LBCL) and classical Hodgkin lymphoma (cHL), is a rare and poorly defined entity. To decipher its mutational landscape and discover new therapeutic targets, we performed exome sequencing of 31 GZL cases. Methods: GZL cases from the LYSA group (N=139) and BC Cancer (N=30) were centrally reviewed and classified as previously published (Sarkozy et al, Am J Surg Pathol 2019). Whole-exome sequencing was performed on 31 cases with available fresh frozen tissue, using laser micro-dissection (LMD, MMI technology) to enrich for tumor cells and obtain matching normal DNA from microenvironment cells. DNA was extracted (Agencourt® DNAdvance kit) and genomic libraries were constructed with the Ovation ultra-low kit (Nugen®). Exome capture was performed using Agilent SureSelectXT V6+UTR followed by paired-end sequencing (NextSeq®). Somatic nucleotide variants (SNVs) and indels were identified using VarScan, Strelka and Mutect. Parameters affecting the sensitivity and specificity of variant calling were optimized using 7 "gold standard" cases for which DNA from peripheral blood cells was additionally available. Possible oncogenic drivers were identified based on rate of recurrence, MutSigCV and literature review. Results: Among the 31 GZL cases, the median age was 41 y (14-83) with a sex ratio of 15M:16F; 21 cases had mediastinal involvement, including 15 within the thymic area; EBER in-situ hybridization (ISH) was positive in 8 cases. Seven (23%) cases were classified as group-0 (cHL morphology with 100% CD20 expression), 22 (71%) with an intermediate morphology as group-1 (N=9, cHL-like morphology) or group-2 (N=13, LBCL-like morphology) and 2 (6%) as group-3 (LBCL with 100% of CD30 expression). The mean coverage was 96X (42-203) for tumor samples. One case was excluded due to failure in the LMD process. Among the 30 cases, 6628 variants across 4826 genes were found, including 2903 coding mutations (325 indels and 2808 SNVs, mean of 104/sample, range: 15-678), 721 affecting the 5' UTR and 2774 the 3' UTR. A total of 152 genes were identified as being potential oncogenic drivers, with a mean of 11 mutated genes per case (range 2-36). The most recurrently mutated genes were SOCS1 (33%), B2M (23%), GNA13 (20%), LRRN3 (17%), and ZNF217, NCOR1, ITPKB, IRF2BP2, CSF2RB, and CSMD3 (13% each). The epigenetic SWI/SNF and transcription regulation pathway (including NCOR1/2, ARID1A, KMT2D, KMT2A) was affected in 73% of the cases, JAK/STAT in 70% and NF-kB in 19%. As assessed by CNVkit and GISTIC, the most recurrent gains/amplifications identified were in 9p24.1 (JAK2, CD274, PDCD2LG2; 69%) and 2p16.1 (REL, BCL11A; 62%), and losses in 11q14.3 (ATM; 48%) and 12q24.33 (NCOR2; 48%). Based on mutational signature analysis, individual base substitutions were linked to mutagenic processes, with the highest contributions associated with aging (29%) and defective DNA mismatch repair (27%); moreover, mutations attributable to AID/APOBEC activity (5%), were found to be significantly enriched in EBV- vs. EBV+ cases (p = 0.013). EBV+ cases had fewer total variants (mean 98 vs 258, p=0.08) and potential oncogenic variants (mean 7 vs 15, p=0.03) compared to EBV- cases. EBV+ cases also lacked mutations in the NF-kB pathway and MHC-class I components (B2M and HLA-B: 0% vs 43% in EBV-, p=0.06) but had mutations in STAT3, DHX58, ACTB and ATP13A4 (6/7 cases) not present in the 23 EBV- cases. LRRN3 and GNA13 mutations were significantly associated with thymic area involvement (40% vs 0%, p=0.01). Furthermore, fluorescence-ISH indicated that 20% (1/5) of EBV+ cases had a rearrangement in the CIITA locus (16p13.13) vs 53% (9/17) in EBV- cases. Patients with an intermediate morphology had more oncogenic variants than those in group 0 and 3 (mean of 15 vs 6 variants/case, p=0.01 affecting 12 vs 5 genes, p=0.004). Finally, NCOR1 (N=4) and NCOR2 (N=2) mutations were exclusively found in cases with intermediate morphology (23% vs 0% for those with group 0 or 3 morphology). Conclusion: These data suggest that GZL is a highly heterogenous disease harboring somatic driver events shared with PMBCL and HL. We also discovered novel gene mutations pointing to the importance of previously unrecognized pathways in the pathogenesis of GZL. The distinct mutational pattern in EBV+ GZL suggests divergent evolutionary trajectories. Disclosures Sarkozy: Takeda: Research Funding. Salles:Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; BMS: Honoraria; Amgen: Honoraria, Other: Educational events; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. Savage:BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria; Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Scott:Celgene: Consultancy; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding. Steidl:Juno Therapeutics: Consultancy; Tioma: Research Funding; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Seattle Genetics: Consultancy; Bayer: Consultancy.

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Anand, Shankara, Mark Bustoros, Romanos Sklavenitis-Pistofidis, Robert A. Redd, Eileen M. Boyle, Yu-Tzu Tai, Carl J. Neuse, et al. "Genomic Profiling of Smoldering Multiple Myeloma Classifies Molecular Groups with Distinct Pathogenic Phenotypes and Clinical Outcomes." Blood 138, Supplement 1 (November 5, 2021): 723. http://dx.doi.org/10.1182/blood-2021-150767.

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Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

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Palomo, Laura, Blanca Xicoy, Montse Arnan, Marta Cabezon, Rosa Coll, Vera Ademà, Francisco Fuster, et al. "Molecular Genetic Profiling in Chronic Myelomonocytic Leukemia with Low Risk Cytogenetic Features." Blood 126, no. 23 (December 3, 2015): 2883. http://dx.doi.org/10.1182/blood.v126.23.2883.2883.

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Abstract Background: Chronic myelomonocytic leukemia (CMML) is a heterogeneous disease that can vary from a myelodysplastic (MD) predominant profile to a myeloproliferative (MP) one. CMML has a variable course, with a median overall survival of 20 months and 15-30% of progression to acute myeloid leukemia (AML). Cytogenetic abnormalities are present in 20-40% of cases and recurrent gene mutations have been reported in several genes. Patients with low risk cytogenetic features (normal karyotype and isolated -Y) account for approximately 80% of CMML patients and often fall into the low risk categories of CMML prognostic scores. Our hypothesis is that mutational study can contribute to diagnosis and prognostic stratification in this subset of patients. Methods: A retrospective study was performed on 57 patients with CMML. Cases with normal karyotype (n=53), isolated -Y (n=2) and no metaphases (n=2) were selected. DNA was extracted from BM (n=52) and PB (n=5) samples at diagnosis. Targeted deep-sequencing was performed in a panel of 83 myeloid-related genes. Libraries were prepared with 1μg of genomic DNA using the KAPA Library Preparation Kit (Kapa Biosystems) and then enriched using the SeqCap EZ capture chemistry (Nimblegen, Roche). Libraries were sequenced with 150 bp paired-end reads on an Illumina MiSeq. Herein we present the results of 43 cases which were preliminarily analyzed using the Illumina MiSeq Reporter and Variant Studio softwares. High-probability oncogenic mutations were called by eliminating sequencing and mapping errors and known SNPs based on the available databases. An in-house bioinformatics pipeline will be designed to analyze the whole series of patients. A preliminar statistical analysis was performed with SPSS. Fisher's exact test was used to compare variables between patient subsets. Complete study, including the correlation of the sequencing findings with the clinical data, will be presented in the meeting. Results: Median age at diagnosis was 70 years (range 48-87) and there was a 2:1 male predominance. Median follow up of patients was 23 months (range 1-116) during which 23% (11/43) of cases progressed to AML. Morphological WHO subtypes were CMML-1 in 36 (84%) cases and CMML-2 in 7 (16%). According to the FAB criteria 34 (79%) cases were classified as CMML-MD and 9 (21%) as CMML-MP. According to the CMML-specific scoring system (CPSS) 28/43 (65%) patients belonged to the low-risk category, 10/43 (23%) to the intermediate-1 and 4/43 (12%) to the intermediate-2. The mean depth of the targeted resequencing per base per sample was 810-fold. After excluding sequencing and mapping errors a mean of 293 single nucleotide variants (snv) and insertions/deletions (indels) were called per sample. After filtering non-silent variants and excluding known polymorphisms a mean of 6 variants per sample were called as high-probability somatic changes. Distribution of detected variants across the patients can be seen in Figure 1. Most frequently affected genes were TET2 (70%), ASXL1 (47%) and SRSF2 (35%); followed by RUNX1 (23%), NRAS (16%), CBL (12%), EZH2 (12%), SETBP1 (12%) and ZRSR2 (12%). Variants detected in 5-10% of patients included IDH2, CRBBP, SH2B3, UMODL1, DNMT3A, JAK2, PTPN11, SF3B1 and U2AF1 genes. Statistical analysis revealed that some variants correlated with CMML subtypes: SH2B3 (P=0.010) and STEBP1 (P=0.024) associated with CMML-2; JAK2 (0.007), NRAS (P=0.026) and EZH2 (P=0.05) associated with CMML-MP. Variants in NRAS also correlated with progression to AML (P=0.04) and patients in intermediate groups of CPSS associated with JAK2 (P=0.008) and EZH2 (P=0.011) variants. Conclusions: Genetic profiling using targeted deep-sequencing is a highly promising approach for CMML diagnosis and varies according to the cytological subtypes. With the correlation of the results with the clinical data of patients, we expect to determine if targeted molecular profiling can contribute to prognostic stratification of patients with CMML and low risk cytogenetic features. For the moment, we have already found a correlation with progression to AML. Acknowledgments: Instituto de Salud Carlos III, Ministerio de Sanidad y Consumo, Spain (PI 11/02519; PI 11/02010); RTICC, FEDER (RD12/0036/0044); 2014 SGR225 (GRE) Generalitat de Catalunya; Fundació Josep Carreras, Obra Social "La Caixa" and Celgene Spain. Figure 1. Distribution of the affected genes across the 43 studied patients with CMML Figure 1. Distribution of the affected genes across the 43 studied patients with CMML Disclosures Sole: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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Lee, Hui Mei, Niloofar Zandvakili, Rhea Desai, Peter J. Browett, Purvi M. Kakadia, and Stefan K. Bohlander. "Characterization of an Acute Myeloid Leukemia Murine Model Driven By MLL/AF9: Effect of Retroviral Insertion Sites and Somatic Mutations on Gene Expression." Blood 138, Supplement 1 (November 5, 2021): 4329. http://dx.doi.org/10.1182/blood-2021-154012.

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Abstract The MLL/AF9 fusion is found in approximately 30% of MLL-rearranged leukemias and has an intermediate prognosis. Genomically well-characterized murine leukemia models enable us to understand leukemogenesis. We generated a retroviral transduction murine bone marrow transplantation leukemia model (MBMTLM) using the MLL/AF9 fusion gene. Fifteen of 20 mice transplanted with syngeneic bone marrow transduced with a MLL/AF9 carrying retrovirus developed leukemia after a median latency of 149 days. Half a million leukemic bone marrow (LBM) cells from two of these primary leukemias, MA03-P and MA86-P, were transplanted into irradiated recipient mice to establish secondary leukemias, MA03-S (n=3) and MA86-S (n=4). Half a million LBM cells from these secondary leukemias were further transplanted into irradiated recipient mice to generate tertiary leukemias, MA03-T (n=3) and MA86-T (n=4). The latency of the leukemias shortened from 141 days in MA03-P to 18 and 22 days in MA03-S and MA03-T, respectively. Similarly, MA86-P had a latency of 98 days, and the latency was reduced to about 28 days in MA986-S and MA986-T. We used retroviral insertion sites (RISs) to track leukemia clones during serial transplantation. We identified 5 RISs in MA03-P. One RIS, RIS#1-03 at chromosome 7:4602500-4609499 accounted for 52.5% of the total RIS-related reads in MA03-P, while the other four RISs were each represented by fewer than 5% of the reads. Only RIS#1-03 was detected in all of the MA03 secondary and tertiary leukemias , indicating that the cells with RIS#1-03 were the dominant clone in MA03 leukemias. Two RISs were detected in MA86-P: RIS#1-86 at chromosome 19:41338500-41341999 and RIS#2-86 at chromosome 10:127106000-127109499 at 46.7% and 2.5%, respectively . RIS#1-986 was contained in the dominant clone as only this RIS was subsequently detected in the secondary and tertiary MA86 leukemias. The relatively long latency to leukemia development in our MLL/AF9 model was most likely due to the requirement of cooperating somatic mutations. We performed whole exome sequencing on DNA from LBM (n=15) and DNA from their corresponding germline (n=2). An average of 4.5 of single nucleotide variants (SNVs) and 11.4 indels affecting protein coding sequences were found in the MA03 family of leukemias (n=7) which, among others, mutated genes involved in tyrosine kinase pathways such as Epha5 and Pik3r1. We identified an average of 14.8 (SNVs) and 0.5 indels per exome in the MA86 leukemias (n=8). Transcription regulator (Brd1) and tumor suppressor genes (Stk11 and Trp53) were affected by somatic changes in the MA86 family. RNA sequencing was performed on LBM (n=15) and healthy bone marrow (HBM) (n=8). Principal component analysis (PCA) on the expression profiles showed that LBM samples clustered together. Differential gene expression analysis identified genes such as Six1, Eya1 and Bcor which had been reported in previous studies to be essential for leukemogenesis in MLL/AF9 murine model. We also observed downregulation of genes such as Gata2, Btg1, Ifitm1, which had been implicated in other types of leukemias. We next investigated the effect of the RISs and somatic mutations on gene expression. RIS#1-903 was in intron 1 of Ppp6r1. A reduction in fragments per kilobase of transcript per Million mapped reads (FPKM) of Ppp6r1 was observed in MA03 family leukemias compared to leukemias of the MA86 family which did not have RIS#1-03 and showed no difference to HBM samples (MA03: 87.71±1.5; MA86: 132.1±5.1; HBM: 77.56±1.7, p< 0.001). We then determined the expression of Tm9sf3 as it is located 600bp away from RIS#1-986. The FPKM of Tm9sf3 was significantly higher in LBM (both of MA903 and MA986 leukemias) than in HBM (LBM: 146.0±12.7; HBM: 64.66±2.8, p<0.001). In MA86 leukemias which all have RIS#1-86, the FPKM of Tm9sf was two fold higher than in MA03 leukemias without RIS#1-86 (MA86: 189.3±4.4; MA03:97.59±1.7, p < 0.001). In contrast, none of the somatic mutations had a significant effect on the expression of any of the mutated genes. In conclusion, we have established a MBMTLM driven by the MLL/AF9 fusion gene. This well-characterized model provides insights to further understand leukemia development and drug testing. Moreover, we demonstrated that RISs can have an impact on gene expression. Future work on whether Ppp6r1 and Tm9sf3 identified by our RIS analysis are drivers in MLL/AF9 leukemias is warranted. Disclosures Browett: MSD: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria.

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Polgarova, Kamila, Vojtech Kulvait, Karina Vargova, Lubomir Minarik, Nina Dusilkova, Zuzana Zemanova, Anna Jonasova, and Tomas Stopka. "Clonal Architecture of MDS Somatic Mutations Dynamically Changes during Azacitidine Therapy and Has Very Limited Potential to Predict Patient Outcome." Blood 128, no. 22 (December 2, 2016): 4294. http://dx.doi.org/10.1182/blood.v128.22.4294.4294.

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Abstract Introduction: Myelodysplastic syndromes (MDS) are clonal disorders of myeloid hematopoietic stem cells. Recent studies has shown that nearly 90% of patients with MDS carry somatic mutations in bone marrow (BM). These findings triggered a number of studies to identify potential uses of these mutations for diagnostics and prognostics purposes. We focused on a group of 38 patients with advanced stages of the disease that were selected for Azacitidine (AZA) therapy. We then utilized a set of 98 BM samples from the patient cohort that were collected in different stages before, during, and after the period of 4-12 cycles of the therapy. Each patient provided 3 samples on average. This study excludes patients that died early on AZA. Median OS on AZA therapy was 31 Mo. Most prevalent MDS subtypes were RAEB-2 (55%), RAEB-1 (24%), and MDS/AML (13%). 20% of patients had complex karyotype or poor cytogenetics (MedOS=22Mo) and the rest had mostly normal karyotype or intermediate cytogenetics (MedOS=40Mo) prior to AZA. Progression to AML was observed in 55% of patients (PFS= 16 Mo). After 4 cycles, PR was achieved in 59% of patients, CR in 12%, while SD was maintained in 21%, and 9% of patients progressed (PD) to AML. Methods: We detected relevant mutations in MDS samples using the following approach. We collected genomic DNA from separated BM samples: either a CD3-negative population containing the myeloid compartment, or CD3-positive T cells representing an internal control. We prepared amplicon libraries from these samples using the Illumina TruSight Myeloid Panel that covers 54 key genes involved in myeloid malignancies (notably MDS and AML). We sequenced these libraries using the Illumina NGS platform. To achieve greater sensitivity in detecting SNVs and InDels we utilized two different variant calling pipelines (using samtools mpileup or FreeBayes). Since the (PCR) validation efficacy of each mutation from the single NGS run was below 60%, we improved specificity by using two independently prepared sequencing libraries. The intersection of the variant detections from both libraries was considered accurate and only these data were reported as variants. Results: When we excluded all germinal variants, 43 somatic variants in ~18 genes were identified per patient on average. The majority (31/43) of these variants had an intermediate impact (on amino acid sequence), while 12/43 had high impact on the protein structure. Importantly, the majority of them had ~1% VAF (variant allele frequency) representing putative clones with low proliferative potential. In contrast, only 8 genes (~14 variants) were mutated with VAF>2%. The following genes were mutated most frequently: TET2, STAG2, ASXL1 in approximately 60-80% of patients. Data from repeatedly analyzed patient samples on AZA therapy led to an unexpected observation that the variants with WAF>2% often exhibit dynamically changing mutation pattern while the variants of non-proliferating clones (with VAF ~1%) remain very stable. We observed prominent development of some variants (ASXL1, STAG2, CUX1, BCOR) as well as an increase in VAF of others (TP53, RUNX1, CUX1) on AZA therapy. Most of these genes when mutated were reported previously as altering prognosis of MDS (Bejar R et al, 2014). Surprisingly, in some samples we found a mutation in the RUNX1 gene before AZA therapy that was not present after the treatment however, after the treatment another not previously observed mutation of RUNX1 emerged. Furthermore, the presence of any of the mutations before AZA including SF3B1 or TP53 did not have any prognostically significant association with OS or PFS. This contention is supported by the fact that many mutations actually disappeared on AZA. Conclusions: Using an internal sample control combined with a duplicate NGS library preparation we achieved a very high accuracy of detecting somatic variants in MDS-BM sub-separated samples. We observed that variants above 2% VAF change dynamically over the course of AZA therapy while the variants with ~1% VAF remain stable. Our data suggest that development of somatic mutations in AZA-treated MDS patients is a dynamic process, which involves previously identified high risk genes including TP53, RUNX1, CUX1, ASXL1 and BCOR. Grant support: GAČR 16-05649S & P305/12/1033, AZV: 16-27790A, CZ.1.05/1.1.00/02.0109, UNCE 204021, LH15170, PRVOUK P24, LQ1604 and RVO-VFN64165. Disclosures No relevant conflicts of interest to declare.

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Oliva, Esther Natalie, Corrado Mammi', Maria Cuzzola, Yasuhito Nannya, Austin G. Kulasekararaj, Maria Grazia D'Errigo, Maria Concetta Cannatà, et al. "NGS Evaluation of the Eqol-MDS Trial: Preliminary Analysis of Eltrombopag for Thrombocytopenia of Low-Risk MDS." Blood 138, Supplement 1 (November 5, 2021): 1516. http://dx.doi.org/10.1182/blood-2021-150619.

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Abstract Background: In myelodysplastic syndromes (MDS), thrombocytopenia is an adverse risk factor. Treatments in this setting are scarce. In a randomized international phase 2 trial (EQoL-MDS, EudraCT number 2010-022890-33), we reported effecacy and safety of eltrombopag for the treatment of thrombocytopenia in the first 90 patients with lower-risk MDS with a platelet (PLT) count < 30 Gi/L (Oliva et al. Lancet Hem 2017). However, there are concerns of regulatory agencies regarding the use of thrombopoetin rececptor agonists in MDS due to previous reports signalling disease progression in clinical trials with the use of romiplostim and of eltrombopag, the latter in high risk MDS. Objective: We are further evaluating safety by conducting a comprehensive analysis of mutations in a panel of major driver or candidate driver genes in cases enrolled in the EQoL-MDS trial using targeted-capture sequencing. Methods: Serial (every 3 months) sequencing was performed using the SureSelect custom kit (Agilent Technologies), for which 350 genes were selected from known oncogenes or tumour suppressor genes in haematological malignancies. Relevant somatic mutation data with (i) VAF > 0.05; (ii) depth > 100; (iii) P value for EBCall < 0.0001, were filtered by exclusion based on (i) synonymous SNVs; (ii) variants present only in unidirectional reads; (iii) variants occurring in repetitive genomic regions; (iv) missense SNVs with VAF of 0.4-0.6 or <0.04; and (v) known variants listed in SNP databases. The present analysis has been conducted at baseline, at 12 and 24 weeks. Results: We present preliminary results of the first 21 cases (13 eltrombopag, 9 placebo) enrolled in the trial and with biological samples. Mean age was 62 (± 15) and 13 patients were male. According to the WHO 2016 classification, 11 patients had MDS with single lineage dysplasia (SLD), 7 had multi lineage dysplasia (MLD), 1 placebo case had excess blasts-1, and 1 placebo case had unclassifiable. IPSS-R risk was very low, low and intermediate in 4, 8 and 1 eltrombopag cases, respectively, and 5, 2 and 1 placebo cases, respectively. Karyotype was normal in 16 cases, del(20q) was detected in 4 cases and +14 in 1 case. At study entry, in total 49 genes were mutated (Figure), where one or more of the 49 driver genes were mutated in all but 1 placebo patient (Table). The table shows characteristics and events of patients according to treatment arm. Noteworthy, in the eltrombopag arm, two cases experienced a loss of gene mutations, one obtaining International Working Group defined complete remission of MDS, while 1 MDS EB-1 case had a gain in ZRSR2. Two placebo cases experienced a gain in mutations Conclusions: Treatment with eltrombopag in lower risk MDS is effective and safe. Preliminary analyses do not suggest that eltombopag induces disease progression neither at a clinical, nor a molecular level. Loss of mutations may occur during eltrombopag treatment with complete remission. Figure 1 Figure 1. Disclosures Oliva: Novartis: Other: Advisory Board; Celgene BMS: Consultancy, Other: Advisory Board, Patents & Royalties; Alexion: Other: Advisory Board; Argenx: Other: Advisory Board; Daiichi: Other: Advisory Board; Amgen: Other: Advisory Board. Nannya: Otsuka Pharmaceutical Co., Ltd.: Consultancy, Speakers Bureau; Astellas: Speakers Bureau. Kulasekararaj: Alexion, AstraZeneca Rare Disease Inc.: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Ra Pharma: Consultancy, Honoraria, Speakers Bureau; Alexion: Consultancy, Honoraria, Speakers Bureau; Achilleon: Consultancy, Honoraria, Speakers Bureau; Biocryst: Consultancy, Honoraria, Speakers Bureau; Akari: Consultancy, Honoraria, Speakers Bureau; Apellis: Consultancy; F. Hoffmann-La Roche Ltd.: Consultancy, Honoraria, Speakers Bureau. Latagliata: Novartis: Honoraria; Pfizer: Honoraria; BMS Cellgene: Honoraria. Santini: Astex: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Menarini: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Ogawa: Eisai Co., Ltd.: Research Funding; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Kan Research Laboratory, Inc.: Consultancy, Research Funding; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding; ChordiaTherapeutics, Inc.: Consultancy, Research Funding; Ashahi Genomics: Current holder of individual stocks in a privately-held company. OffLabel Disclosure: Eltrombopag (Revolade) is indicated for chronic immune (idiopathic) thrombocytopenic purpura (ITP) patients aged 1 year and above who are refractory to other treatments (e.g. corticosteroids, immunoglobulins).Revolade is indicated in adult patients with chronic hepatitis C virus (HCV) infection for the treatment of thrombocytopenia, where the degree of thrombocytopenia is the main factor preventing the initiation or limiting the ability to maintain optimal interferon-based therapy.Revolade is indicated in adult patients with acquired severe aplastic anaemia (SAA) who were either refractory to prior immunosuppressive therapy or heavily pretreated and are unsuitable for haematopoietic stem cell transplantation.

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Metzeler, Klaus H., Tobias Herold, Maja Rothenberg-Thurley, Susanne Amler, Cristina Sauerland, Stephanie Schneider, Nikola P. Konstandin, et al. "DNMT3A Mutations Associate with Shorter Survival and Modulate the Prognostic Impact of Mutated NPM1: an Analysis Based on Comprehensive Mutational Screening of 660 AML Patients Treated on German AML Cooperative Group (AMLCG) Trials." Blood 126, no. 23 (December 3, 2015): 3815. http://dx.doi.org/10.1182/blood.v126.23.3815.3815.

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Abstract Background: Mutations in DNA methyltransferase 3A (DNMT3A) are common in acute myeloid leukemia (AML), affecting ~20% of patients (pts) and 30-40% of those with cytogenetically normal (CN-) AML. Although several groups have investigated their prognostic relevance, most studies focused on younger adults (<60 years [y]), and their results were inconsistent. Moreover, there is conflicting data regarding possible differences between mutations affecting the 'hotspot' codon R882 and other variants. We therefore performed comprehensive mutational analyses in 660 younger and older (>=60 y) AML pts treated on German AML Cooperative Group (AMLCG) protocols, and studied the association between DNMT3A mutations and outcomes. Patients and Methods: We analyzed pretreatment blood or bone marrow specimens from 660 adult AML pts who received intensive induction chemotherapy on two consecutive phase III trials (AMLCG-1999, n=388, and AMLCG-2008, n=272; median age, 57y, range, 18-86y). Sequence variants in DNMT3A exons 7-23 and other genes known to be mutated in myeloid neoplasms were analyzed by multiplexed amplicon resequencing (Agilent Haloplex). Sequencing was performed on an Illumina MiSeq instrument using 2x250bp paired-end reads. Variants were classified as known/putative driver mutations, variants of unknown significance, or known germline polymorphisms based on published data including dbSNP, the Catalogue Of Somatic Mutations In Cancer (COSMIC) and The Cancer Genome Atlas (TCGA). Cytogenetic analyses were performed centrally. Results: We identified 223 DNMT3A mutations in 207/660 pts (31%), including 180/449 pts (40%) with intermediate-risk cytogenetics according to the MRC classification (P <.001). Missense mutations affecting codon R882 were found in 114 pts, other missense mutations in 59, and truncating mutations (nonsense SNVs or frame shift variants) in 43. Nine pts had >1 type of DNMT3A mutation. DNMT3A mutations tended to be more frequent in older compared to younger pts (35% vs. 28%, P =.08) and were associated with female sex (38% vs 26% in males; P <.001), higher leukocyte counts (P =.008) and higher marrow blast percentages (P =.005). In the entire cohort, mutated DNMT3A associated with shorter relapse-free survival (RFS, hazard ratio [HR], 1.64, P <.001) and shorter overall survival (OS; HR, 1.26; P =.02). Outcomes were similar for pts with DNMT3A codon R882 mutations, other missense mutations, or truncating mutations. Shorter RFS and OS of DNMT3A -mutated pts was also observedin the subgroup with intermediate-risk cytogenetics (RFS: HR, 1.62; P =.002 and OS: HR, 1.34; P =.02). DNMT3A mutations associated with inferior outcomes in younger pts (RFS: HR, 1.58; P =.02 and OS: HR, 1.55; P =.005), while in older pts, no significant impact of mutated DNMT3A as a single marker on RFS or OS was observed. Due to the strong association of DNMT3A mutations (which appear to be prognostically unfavorable) with mutated NPM1 (an established favorable risk marker), we studied the four subgroups defined by the combination of both mutations. DNMT3A mutations associated with shorter RFS (Fig. A) in pts with mutated NPM1 as well as in those with wild-type NPM1, and with shorter OS in NPM1-mutated pts (Fig. B). When we considered the prognostically favorable 'molecular low risk' genotype (i.e., CN-AML with mutated NPM1 without FLT3 internal tandem duplication [ITD]), DNMT3A mutations associated with shorter RFS (Fig. C) and a trend for shorter OS (Fig. D) in pts with this combination, and with significantly shorter RFS and OS in the remaining ('high molecular risk') CN-AML pts. Finally, in a multivariate model adjusting for other clinical and genetic risk factors, mutated DNMT3A remained a significant risk factor for shorter RFS (HR, 1.44; P =.01) and OS (HR, 1.26; P =.04). Conclusion: In our cohort of intensively treated AML pts covering a broad age range, we found that DNMT3A mutations associate with inferior survival and modulate the prognostic impact of mutated NPM1, confirming data recently reported by the MRC group (Gale et al., J Clin Oncol 33:2072). In contrast to this and other published reports, we observed no outcome differences between different types of DNMT3A mutations. Information on DNMT3A mutation status further refined the risk stratification of CN-AML based on the NPM1 mutated / FLT3-ITD negative genotype, supporting a role for DNMT3A mutations as a prognostic marker. Figure 1. Figure 1. Disclosures Subklewe: AMGEN Research (Munich): Research Funding. Krug:Boehringer Ingelheim: Research Funding; Novartis; BMS; Roche; Boehringer Ingelheim; Bayer: Honoraria; Sunesis: Speakers Bureau; Sunesis; Clavis Pharma; usa Pharma, Catapult Cell Therapy, Gilead, Roche: Membership on an entity's Board of Directors or advisory committees.

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Swaminathan, Mahesh, Kiyomi Morita, Yan Yuanqing, Feng Wang, Jared K. Burks, Curtis Gumbs, Latasha Little, et al. "Clinical Heterogeneity of AML Is Associated with Mutational Heterogeneity." Blood 132, Supplement 1 (November 29, 2018): 5240. http://dx.doi.org/10.1182/blood-2018-99-117287.

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Abstract BACKGROUND: AML is a group of clinically heterogeneous diseases. We hypothesized that heterogeneous presentation of AML is a reflection of equally heterogeneous genetic process during the leukemogenesis. METHODS: 536 AML patients (pts) bone marrow samples were analyzed by targeted capture exome sequencing of 295 genes. Extensive clinical-genotype correlation was performed using well annotated clinical data. RESULTS: The median age of the cohort was 62 years (IQR: 51-72) including 297 (55%) elderly (age ≥60), and 239 (45%) young (age <60) pts. Of the 536 pts, 308 (57%) pts had de novo AML (dnAML), and 103 (19%) had secondary or therapy-related AML (stAML). DNA sequencing revealed 1,586 high-confidence somatic mutations (922 SNVs and 664 indels) in 79 genes in 493 (92%) pts [median 3 (IQR 2-4) mutations/patient]. Cytogenetics were favorable in 10 (2%), intermediate in 326 (61%), and adverse in 177 (33%) (All defined by ELN 2017criteria); 23 (4%) pts had no cytogenetics data. Elderly pts and young pts had distinct mutational landscape. (1.3-9.6), p = 0.0079] were significantly more enriched in elderly AML, whereas young AML pts were enriched with mutations in FLT3 [OR 0.6 (0.4-0.9), p = 0.0089], NPM1 [OR 0.5 (0.3-0.9), p = 0.0113], PTPN11 [OR 0.2 (0.2-0.7), p = 0.0033], and WT1 [OR 0.4 (0.2-0.7), p = 0.0033]. Some of the mutations enriched in elderly pts are frequently observed in pts with clonal hematopoiesis with indeterminate potential. Based on the ontogeny of AML, PTPN11 [OR 7.6 (1-57.2), p=0.0210], NPM1 [OR 3.0 (1.5-6.1), p = 0.0007], WT1 [OR 2.9 (1.1-7.4), p=0.0279] mutations were significantly enriched in dnAML, while SF3B1 [OR 0.4 (0.18-0.89), p=0.0376], SRSF2 [OR 0.5 (0.3-0.85), p = 0.0109], TP53 [OR 0.5 (0.3-0.8), p = 0.0131], ASXL1 [OR 0.6 (0.36-0.95), p=0.0451] mutations were more enriched in stAML (Figure A). We then correlated mutation data with clinical and immunological parameters that are routinely tested in AML. Mutations in NPM1, FLT3, PTPN11 and NRAS were associated with significantly higher white blood cell (WBC) counts, bone marrow blast and LDH, which is consistent with their hyperproliferative activity as class 1 genes. In contrast, pts with mutations in TP53, STAG2 and ASXL1 presented with significantly low bone marrow blast, circulating blast, and WBC. Mutations in BCOR and ASXL1 was associated with significantly low LDH. Interestingly, pts with IDH2 mutations presented with significantly higher platelet, which is consistent with anecdotal report (DiNardo et al. Am J Hematology). Not surprisingly, TP53 mutations were associated with complex cytogenetics, whereas SRSF2, NPM1, IDH2, FLT3, and CEBPA mutations were associated with good and intermediate cytogenetics by ELN classification (Figure B). Pts with NPM1, IDH2, and IDH1 mutations were associated with less HLA-DR and CD34 expression in blast by flow cytometry. This is consistent with the frequent presentation of these AML sub-types with cuplike nuclei (Rakheja et al. BJH). DNA sequencing of a large cohort also allowed us to detect mutations that have not been as commonly reported in AML. We detected hot-spot mutations in exon 2 of MYC and MYCN genes in 9 (2%) AML pts. Additionally, internal tandem duplication (ITD) in MYC was also detected in one patient. Immunohistochemical staining showed that MYC expression was significantly elevated in patients with MYC mutations than in patients without the mutations (median H score 22 vs. 15 in MYC mutated vs. normal karyotype control, p < 0.001, 22 vs. 13.5 in MYC mutated vs. trisomy 8 control). These data suggest that a subset of AML is driven by the strong MYC signaling, consistent with a prior study (Ohanian et al. Leuk Lymphoma). CONCLUSION: Heterogeneous clinical presentation of AML has significant association with genetic heterogeneity, which suggest that distinct genetic basis of leukemogenic process has strong role in defining clinical presentation of AML. These data also help stratifying the patients for the likely target of precision medicine. Disclosures DiNardo: Medimmune: Honoraria; Celgene: Honoraria; Agios: Consultancy; Abbvie: Honoraria; Karyopharm: Honoraria; Bayer: Honoraria. Kadia:Celgene: Research Funding; Pfizer: Consultancy, Research Funding; BMS: Research Funding; Jazz: Consultancy, Research Funding; Abbvie: Consultancy; Abbvie: Consultancy; Amgen: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Consultancy, Research Funding; Jazz: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Novartis: Consultancy; Celgene: Research Funding; Novartis: Consultancy. Cortes:novartis: Research Funding. Daver:Daiichi-Sankyo: Research Funding; Pfizer: Consultancy; Alexion: Consultancy; ARIAD: Research Funding; Karyopharm: Consultancy; ImmunoGen: Consultancy; Kiromic: Research Funding; Otsuka: Consultancy; Sunesis: Consultancy; Novartis: Research Funding; BMS: Research Funding; Incyte: Consultancy; Novartis: Consultancy; Sunesis: Research Funding; Karyopharm: Research Funding; Pfizer: Research Funding; Incyte: Research Funding. Pemmaraju:SagerStrong Foundation: Research Funding; celgene: Consultancy, Honoraria; cellectis: Research Funding; samus: Research Funding; daiichi sankyo: Research Funding; Affymetrix: Research Funding; stemline: Consultancy, Honoraria, Research Funding; plexxikon: Research Funding; novartis: Research Funding; abbvie: Research Funding.

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Hirsch, Pierre, Ruoping Tang, Nassera Abermil, Pascale Flandrin, Hannah Moatti, Mohamad Mohty, Ollivier Legrand, Luc Douay, Chrystele bilhou Nabera, and Francois Delhommeau. "Clono-Specific Evaluation of Minimal Residual Disease in Acute Myeloid Leukemia." Blood 128, no. 22 (December 2, 2016): 1208. http://dx.doi.org/10.1182/blood.v128.22.1208.1208.

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Abstract Background: The genetic landscape of adult acute myeloid leukemias (AML) has been recently unravelled. This makes achievable the determination of a comprehensive profile of driver lesions for virtually all patients at diagnosis. Recent studies using multi-target minimal residual disease (MRD) strategies with around 1% sensitivity indicate that the clearance of all molecular events after chemotherapy is associated with better survival. To improve the clono-specificity and the sensitivity of this approach, after a precise determination of AML clonal composition, we combined cytogenetic, FISH, and high sensitivity deep sequencing technologies to monitor the MRD in a series of 69 patients. Methods: Forty-five consecutive patients reflecting the genetic diversity of AML were prospectively included and 24 patients were retrospectively studied. All patients received an anthracycline + cytarabine based regimen. The clonal architecture was established at diagnosis based on NGS-targeted resequencing (122 gene panel) and cytogenetic data. Lesions were next investigated in complete remission (CR). Based on the initial clonal composition, targeted resequencing panels were designed to improve the sensitivity by the use of unique molecular barcodes (Haloplex High Sensitivity, or HS-NGS assay). Cytogenetic events were evaluated by FISH. Results: In the 69 patients, a median of 4 genetic or chromosomal events were identified per patient (range 0-10). One patient had no evaluable target allowing MRD evaluation in 68/69 patients. To determine the threshold of detection of the HS NGS assay, we analyzed the frequency of mutant reads in multiple samples expected to be wild type for 31 given SNVs and 2 indels. A consensus threshold of detection was set at a variant allele frequency (VAF) of 0.2% for all lesions. In CR samples, early initiating events frequently persisted after treatment, especially mutations in DNMT3A, TET2, ASXL1, EZH2, IDH1, TP53, SRSF2, and U2AF1. Mutations in FLT3, NRAS, KIT, NPM1, CEBPA, WT1, IDH2 and BCOR were the most frequently cleared events. Seven patients did not reach CR after one course, and two had no available material after one course. In the 59 remaining patients, we tested whether the global response level of all targets was associated with prognosis. We used the median VAF of the first events of all clonal architectures to separate good responder from poor responders (i.e. VAF = 1.66%). At 2 years, there was a trend to lower leukemia free survival (LFS) probability in poor responders (31.7+/-9.9% vs 51.7+/-9.8%, p=0.08) with no translation in overall survival (OS). We next investigated if the persistence of two or more detectable markers was associated with prognosis. The 58 patients with more than one evaluable event were consequently separated in two groups: patients with 0 or 1 marker above the detection threshold after treatment (n=31), and patients with 2 or more detectable lesions (n=27). At 2 years, DFS was 64.9+/-9.3 % in patients with 0 or 1 detectable marker vs 19.8+/-8.7% in patients with 2 or more detectable markers (p=0.001). OS probability was higher in patients with 0 or 1 detectable marker 84+/-6.6% vs 57.1+/-10.5% (p=0.023). When focusing on the 40 patients with intermediate cytogenetics, persistence of 2 or more markers was associated with lower LFS (57+/-11.8% vs 19.4+/-10.5 p=0.0048) and with a trend to lower OS (85+/-8% vs 61+/-11.9% p=0.07). Similar results were observed when restricting the analyses to the 42 prospectively included patients (At 2 years: LFS 73+/-10% vs 24+/-10%, p=0.0026 and OS 90.2+/-6.6% vs 62.8+/-11.5%, p=0.036). In 50 patients with 3 or more identified events, the persistence of 3 or more markers after one course was associated with a very high risk of relapse (DFS 23.5+/-10.3 % vs 75.8 +/-7.5% at one year, p<0.0001; median DFS at 7 months in the non-responder group and not reached after two years in the responder group), and lower OS probability (84.8+/-6.2 vs 45.2+/-13.5% at 2 years, p=0.026) Conclusion Our study shows the high prognostic value of a personalized multi-target clono-specific MRD evaluation that can be used in nearly all AML patients. Detection of two or more events in more than 0.4% cells after one course is associated with lower survival, in particular in intermediate cytogenetic patients. Larger studies are needed to confirm the results and to evaluate if this strategy could be useful to guide treatment decisions. Disclosures No relevant conflicts of interest to declare.

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Liu, Zhaoyun, Bo Yu, Mu Su, Chenxi Yuan, Cuicui Liu, Zhiyong Yu, and Jinming Yu. "Construction of a model for evaluating the efficacy of neoadjuvant chemotherapy for breast cancer and dynamic monitoring of ctDNA response to neoadjuvant chemotherapy." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e12600-e12600. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e12600.

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e12600 Background: Neoadjuvant chemotherapy (NAC) is a routine treatment of choice for patients with locally advanced breast cancer. The pathological complete response (pCR) to NAC in breast cancer is closely related to a better prognosis. In addition, there have been few studies of the role of ctDNA in the dynamic monitoring of NAC, so we explored the prediction model of NAC to predict pCR and evaluated the role of ctDNA in the dynamic monitoring of NAC. Methods: A total of 269 breast cancer patients receiving NAC were enrolled, and a total of 266 tissue samples were collected. The tissue samples were sequenced using a panel covering 457 cancer-related genes to construct a pCR prediction model after NAC. A total of 267 blood samples were collected from 56 patients. Blood samples were collected at the indicated time points: before NAC (T0), after the first NAC and before cycle two (T1), during intermediate evaluation (T2), and after the end of NAC but before surgery (T3). We constructed a model to predict pCR after NAC by mutated genes and clinical factors, analyzed ctDNA of blood samples according to the mutated genes of the prediction model, and detected the dynamic monitoring role of ctDNA in NAC to predict prognosis. The median follow-up time for survival analysis was 898 days. Results: A total of 192 patients were enrolled to construct the prediction model. There were 51 patients in the additional validation set. We analysed the somatic mutations of 192 samples and constructed a predictive NAC response model including 5 SNV mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2), 4 CNV mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain), and 3 clinical factors (luminal A, Her2+ and Ki67). Analysing the ctDNA of 267 blood samples through a unique panel composed of 9 mutant genes in the prediction model, it was found that ctDNA positivity decreased with the passage of time during NAC, the ctDNA positive rates of ctDNA from T0, T1, T2 to T3 were 46%, 14%, 13% and 10%, respectively. According to survival data, pCR patients did not have disease progression after NAC. Among the non-pCR patients who had disease progression, the probability of non-pCR in patients with ctDNA cleared at T1, T2, and T3 was significantly lower than that in patients with uncleared ctDNA. Interestingly, patients who failed to achieve pCR but were ctDNA negative had a similar risk of metastasis and recurrence as those who achieved pCR. Finally, we found that the prediction model combined with ctDNA could predict pCR after NAC. It had high sensitivity and specificity and the AUC value reached 0.961. Conclusions: This study established a predictive model for predicting pCR after NAC. At the same time, ctDNA dynamic monitoring found that ctDNA status could predict NAC response and metastasis recurrence. Combining the prediction model and ctDNA status could better predict the NAC results.

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Liu, Zhaoyun, Bo Yu, Mu Su, Chenxi Yuan, Cuicui Liu, Zhiyong Yu, and Jinming Yu. "Construction of a model for evaluating the efficacy of neoadjuvant chemotherapy for breast cancer and dynamic monitoring of ctDNA response to neoadjuvant chemotherapy." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e12600-e12600. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e12600.

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e12600 Background: Neoadjuvant chemotherapy (NAC) is a routine treatment of choice for patients with locally advanced breast cancer. The pathological complete response (pCR) to NAC in breast cancer is closely related to a better prognosis. In addition, there have been few studies of the role of ctDNA in the dynamic monitoring of NAC, so we explored the prediction model of NAC to predict pCR and evaluated the role of ctDNA in the dynamic monitoring of NAC. Methods: A total of 269 breast cancer patients receiving NAC were enrolled, and a total of 266 tissue samples were collected. The tissue samples were sequenced using a panel covering 457 cancer-related genes to construct a pCR prediction model after NAC. A total of 267 blood samples were collected from 56 patients. Blood samples were collected at the indicated time points: before NAC (T0), after the first NAC and before cycle two (T1), during intermediate evaluation (T2), and after the end of NAC but before surgery (T3). We constructed a model to predict pCR after NAC by mutated genes and clinical factors, analyzed ctDNA of blood samples according to the mutated genes of the prediction model, and detected the dynamic monitoring role of ctDNA in NAC to predict prognosis. The median follow-up time for survival analysis was 898 days. Results: A total of 192 patients were enrolled to construct the prediction model. There were 51 patients in the additional validation set. We analysed the somatic mutations of 192 samples and constructed a predictive NAC response model including 5 SNV mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2), 4 CNV mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain), and 3 clinical factors (luminal A, Her2+ and Ki67). Analysing the ctDNA of 267 blood samples through a unique panel composed of 9 mutant genes in the prediction model, it was found that ctDNA positivity decreased with the passage of time during NAC, the ctDNA positive rates of ctDNA from T0, T1, T2 to T3 were 46%, 14%, 13% and 10%, respectively. According to survival data, pCR patients did not have disease progression after NAC. Among the non-pCR patients who had disease progression, the probability of non-pCR in patients with ctDNA cleared at T1, T2, and T3 was significantly lower than that in patients with uncleared ctDNA. Interestingly, patients who failed to achieve pCR but were ctDNA negative had a similar risk of metastasis and recurrence as those who achieved pCR. Finally, we found that the prediction model combined with ctDNA could predict pCR after NAC. It had high sensitivity and specificity and the AUC value reached 0.961. Conclusions: This study established a predictive model for predicting pCR after NAC. At the same time, ctDNA dynamic monitoring found that ctDNA status could predict NAC response and metastasis recurrence. Combining the prediction model and ctDNA status could better predict the NAC results.

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Nabhani, Schafiq, Sebastian Ginzel, Hagit Miskin, Shoshana Revel-Vilk, Dan Harlev, Bernhard Fleckenstein, Andrea Höhnscheid, et al. "Dysregulation of IL12 Signaling As a Novel Cause of an Autoimmune Lymphoproliferative like Syndrome." Blood 124, no. 21 (December 6, 2014): 1420. http://dx.doi.org/10.1182/blood.v124.21.1420.1420.

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Abstract Introduction Autoimmune lymphoproliferative syndrome (ALPS) is characterized by abnormal lymphocyte homeostasis caused by defective apoptosis. Mutations in genes involved in the Fas death receptor pathway (FAS, FASLG or CASP10 genes) are the cause for the pathogenesis of ALPS. However, in 20-30% of all ALPS cases, collectively classified as ALPS-U (undetermined), the genetic defect is still unknown. The objective of this study was to employ whole-exome sequencing to search for novel gene candidates underlying ALPS-U or ALPS-like disease. Resulting candidates should be validated and their impact on the Fas death receptor pathway studied. Methods Peripheral blood samples were collected from 26 patients diagnosed with ALPS (based on clinical phenotype and accumulation of DNT cells), relatives and healthy controls. PCR and Sanger sequencing were performed to check for germline and somatically acquired FAS, FASLG and CASP10 mutations. Whole-exome sequencing was carried out for all patients with unknown mutation and their parents. Candidate genes were identified by a KEGG-based protein interaction analysis interface of our in-house developed proprietary MySQL database driven workbench, termed SNuPy (Single Nucleotide Polymorphism Database). STRING 9.1 was used to identify high confidence (≥0.900) interaction partners of the Fas pathway. Single nucleotide variations (SNVs) were verified by PCR and Sanger sequencing. The impact of detected mutations on the candidate genes´ expression and functionality was analyzed by immunoblot. The candidate genes´ impact on Fas death receptor pathway and Fas/FasL expression was examined by flow cytometry, ELISA and qRT-PCR. Results Out of 26 analyzed ALPS cases 4 unrelated patients harbored heterozygous germline mutations in the death domain of the Fas receptor (p.Q282K, p.R249G, p.NVQ265-267KQT) or the extracellular domain (p.R191C), respectively. Two siblings with homozygous FASLGtruncating mutation (A69fs*138) and complete loss of FasL surface expression as a consequence were identified. Whole-exome sequencing also identified a homozygous R212* mutation in the IL12/IL23 receptor-component IL12RB1 (Interleukin 12 receptor, beta 1 subunit) in a patient who presented with classical ALPS symptoms (chronic non-malignant, noninfectious lymphadenopathy, splenomegaly, hepatomegaly, elevation of DNT cells, autoimmune cytopenias with polyclonal hypergammaglobulinemia, persistently increased vitamin B12 and IL10 levels). The p.R212* mutation in IL12RB1 leads to premature protein truncation by a stop codon gain, resulting in a complete loss of cell surface expression of IL12RB1 in the patient. IL12 is a factor known to regulate expression of both Fas and FasL. IL12 signaling was abrogated as demonstrated by deficient downstream STAT4 phosphorylation and IFNγ production. Low FasL expression on T-cells and low soluble FasL plasma levels were probably due to lack of IFNγ mediated transcriptional activation of FasL. In contrast to healthy controls, prolonged IL12 stimulation did not trigger upregulation of FasL nor apoptosis in the IL12RB1 deficient and FasL deficient (A69fs*138) patients, indicating that IL12 mediated apoptosis is FasL dependent. Heterozygous carriers of the IL12RB1 or the FASLG mutation showed an intermediate response but were asymptomatic and sub-clinically affected (showing e.g. moderate elevation of DNT cells). Conclusion Our data show that IL12 employs Fas signaling to achieve T-cell apoptosis and reveal IL12 signaling deficiency as a new cause of ALPS like disease via its impact on FasL expression. Disclosures No relevant conflicts of interest to declare.

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Foran, James M., Michael G. Heckman, Yesesri Cherukuri, Mikolaj Wieczorek, Zaid Abdel Rahman, Liuyan Jiang, Hemant S. Murthy, et al. "Epidemiologic and Clinical Analysis of Tumor Mutational Burden (TMB) in Acute Myeloid Leukemia (AML): Exome Sequencing Study of the Mayo Clinic AML Epidemiology Cohort (MCAEC)." Blood 138, Supplement 1 (November 5, 2021): 3437. http://dx.doi.org/10.1182/blood-2021-151233.

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Abstract TMB is used to guide PD-1-directed immunotherapy in solid tumor Oncology. However, it has not been systematically studied in AML, where the focus has been on cytogenetic risk and individual driver gene mutations (GM's). TMB contribution to AML epidemiology is also uncertain. We therefore studied its association with epidemiologic risk factors; driver GM's and somatic mutations (SM's) in AML risk genes which we recently demonstrated (ABCB1; CYP1A1; CYP2B6; EPHX1; ERCC1,2,& 5; MEFV; MTRR; and TERT); clinical and cytogenetic features; and outcome after therapy in the MCAEC, a highly annotated clinical epidemiology cohort of consecutive AML pts [Finn, Cancer Epidemiol 39:1084, 2015]. Methods: We obtained somatic leukemia DNA from remnant diagnostic cytogenetic pellets in 98 MCAEC patients (pts), as previously described [Foran, Blood (2017) 130:570a]. Whole exome sequencing (WES) was performed at depth of ~100 million paired end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Reads were mapped to human genome build hg19 using BWA-MEM. Single nucleotide variants (SNVs) and small INDELs were identified using Mayo Clinic (MC) analytic pipeline GenomeGPS 4.0.1 following Broad GATK variant discovery best practices of alignment, realignment and recalibration, and multi-sample joint genotyping; and filtered using both germline whole exome and genome sequencing of ~1200 MC Biobank samples and public germline variant databases of 1000 genome project, 6500 individuals in exome sequencing project, and HapMap phase 3. Remaining variants were annotated using ANNOVAR, and functional variants of non-synonymous, truncating, frame-shift, and splice-sites were used in the statistical association analyses. TMB was defined as the number of functional mutations per Mb of coding region, heterozygous or homozygous. TMB associations with epidemiologic risk, clinical characteristics, and SM's in AML risk genes (listed above) or driver GM's (occurring in 5 or more pts: ASXL1, BCOR, CEBPA, DNMT3A, FLT3, IDH2, KRAS, MLL2-5, NF1, NPM1, NRAS, PHF6, RUNX1, SF3B1, STAG2, TET2, TP53, U2AF1) were evaluated using linear regression models; a rank transformation of TMB was utilized due to its skewed distribution. Multivariable analysis (MVA) models were adjusted for variables with p-value <0.05 in unadjusted analysis. Regression coefficients ("β") were estimated, interpreted as change in mean TMB rank for specified variables or characteristic. Associations with outcomes were examined using logistic regression or Cox proportional hazards regression models. All statistical tests were two-sided (version 3.6.2; R Foundation for Statistical Computing, Vienna, Austria). Results Median age at AML diagnosis was 70 yrs (Range: 19-94 yrs), and 67 pts were male. Cytogenetic risk group was favorable (7%), intermediate-normal (46%) or abnormal (20%), and poor risk (27%). 40/61 pts (66%) achieved complete remission (CR). With a median follow-up of 8.0 months (Range: 0.1 - 186), 80 pts (82%) died and 20 (20%) underwent allogeneic transplantation (AlloBMT). Median TMB was 18.2 (Range: 15.0-70.1). In MVA, significant associations with increased TMB were observed in pts with history of prior immunosuppressive therapy or solid organ transplantation (β=19.48, P=0.015), and with FLT3 (β=21.12, P=0.015), MLL2 (β=20.91, P=0.001), and MLL3 (β=11.31, P=0.031) GM's. A borderline association was observed for U2AF1 (β=16.14, P=0.057). TMB was also associated with SM's in TERT (β=25.13, P=0.028); a borderline association with SM's in ABCB1 was not confirmed in MVA (β=-17.98, P=0.069). Additionally, cytogenetic risk group was associated with TMB in MVA (P=0.005), being highest in intermediate-normal and lowest in poor risk groups. Body Mass Index was inversely associated with TMB (unadjusted β=-16.99, P=0.005), but unconfirmed in MVA (β=-8.29, P=0.12). There was no association with CR (OR=0.93, P=0.46), use of (HR=0.96, P=0.64) or relapse risk (HR=1.00, P=0.98) following AlloBMT, or survival (HR=0.97, P=0.56) (Figure). Conclusions Measurement of TMB is feasible in this AML epidemiologic cohort, and we observed important associations with AML risk factors, risk gene SM's, cytogenetic risk group, and driver GM's. We acknowledge the relatively small sample size and possibility of type II error, and therefore these observations require validation in a large prospective cohort which is planned. Figure 1 Figure 1. Disclosures Foran: OncLive: Honoraria; certara: Honoraria; actinium: Research Funding; boehringer ingelheim: Research Funding; novartis: Honoraria; abbvie: Research Funding; servier: Honoraria; taiho: Honoraria; pfizer: Honoraria; revolution medicine: Honoraria; gamida: Honoraria; takeda: Research Funding; sanofi aventis: Honoraria; trillium: Research Funding; syros: Honoraria; aptose: Research Funding; bms: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Finn: Jazz: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; ADC Therapeutics: Consultancy, Speakers Bureau. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Genentech: Research Funding; NanoString: Research Funding; Regeneron Genetics Center: Other: Research Collaboration.

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Albitar, Maher, Andrew Ip, Andre H. Goy, Jeffrey Justin Estella, Ivan De Dios, Wanlong Ma, Andrew L. Pecora, Jamie Koprivnikar, and James K. McCloskey. "Reliability of Cell-Free DNA (cfDNA) Next Generation Sequencing in Predicting Chromosomal Structural Abnormalities and Cytogenetic-Risk Stratification of Patients with Myeloid Neoplasms." Blood 138, Supplement 1 (November 5, 2021): 3463. http://dx.doi.org/10.1182/blood-2021-148164.

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Abstract Introduction: Cytogenetic analysis is important for stratifying patients with various myeloid neoplasms. It has been reported that whole-genome sequencing can be used as an alternative to cytogenetic analysis in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). With the increasing use of liquid biopsy in the diagnosis and monitoring of patients with various types of neoplasms, we explored the potential of using liquid biopsy and next generation sequencing (NGS) in detecting chromosomal structural abnormalities or copy number variation (CNV) in patients with myeloid neoplasms. For practical approach and for capturing single nucleotide variants (SNV) and to achieve enough depth in sequencing, we used targeted sequencing for determining the chromosomal structural abnormalities in cell-free DNA (cfDNA) in patients with myeloid neoplasms. Methods: Peripheral blood plasma samples from 144 patients with myeloid neoplasms were used to extract cfDNA for NGS testing. This included 49 patients with MDS, 31 with AML, and 64 patients with myeloproliferative neoplasms (MPN). The median age was 68.5 (range: 24-96); 56 (39%) were female. cfDNA was sequenced using 275 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth was increased to more than 1000X (after removing duplicates). CNVkit software was used for analyzing and visualizing copy number variations. All samples were confirmed to be diagnostic by showing mutations in diagnostic genes with variant allele frequency >20% or by showing diagnostic chromosomal structural abnormalities (e.g., 5q deletion in MDS, 5q- syndrome). Cytogenetic data on 35 corresponding bone marrow samples (18 AML and 17 MDS) were available for comparison. Results: Of the 144 samples, 47 (33%) showed chromosomal structural abnormalities. In the AML group, 20 of 31 (65%) showed cytogenetic abnormalities by cfDNA testing. Of these positive AML patients, 18 (90%) (58% of total AML) had poor-risk cytogenetics. Therefore, the AML patients with normal cytogenetics or cytogenetic abnormalities other than high-risk constituted 42% of total AML patients. Of the MDS group, 11 of 49 MDS patients (22%) showed cytogenetic abnormalities by cfDNA testing, 6 of whom (54.5%) had high-risk cytogenetics. Overall, 12% of all MDS had poor-risk cytogenetics by cfDNA testing. In the MPN group, 16 of 64 (25%) showed cytogenetic abnormalities, 2 of which (12.5%) had 7q deletion (3% of all MPN); the rest (87.5%) of cytogenetic-positive MPN (22% of total MPN) had other abnormalities including 20q-, +8, 12q, 17p-, 11q-, trisomy 9, trisomy 21 and others. To compare chromosomal abnormalities as detected by cfDNA NGS testing with conventional cytogenetic analysis of corresponding bone marrow samples, we classified cytogenetic findings based on risk stratification into either intermediate-risk or poor-risk. Of the 36 cases, there was 100% concordance between cfDNA data and cytogenetic data when findings were grouped based on risk classification. Two of the conventional cytogenetic samples showed no metaphases while one showed intermediate-risk abnormalities by cfDNA NGS analysis and the second showed poor-risk cytogenetic abnormalities by cfDNA NGS analysis. These 36 cases included 16 cases with normal cytogenetics. Simple abnormalities such as 5q-, 7q-, +8 were called in identical fashion, but some other abnormalities such as derivative chromosome and marker chromosome were resolved or interpreted differently by the cfDNA NGS analysis. The NGS panel design used in this study does not cover fusion genes or chromosomal translocation, and chromosomal translocations were missed at this time. Conclusions: This data shows that liquid biopsy using and targeted NGS is reliable in detecting chromosomal structural abnormalities in myeloid neoplasms and potentially can replace the need for conventional cytogenetic testing. While the current study was not designed to detect chromosomal translocations, a small, targeted panel of 275 genes is adequate for standard risk classification of myeloid neoplasms into intermediate or high-risk. Considering that in the same test complete mutation profiling can also be achieved along with chromosomal structural analysis, liquid biopsy in myeloid neoplasms might be considered as an efficient replacement to bone marrow biopsy in patients with myeloid neoplasms when fusion genes are not expected. Figure 1 Figure 1. Disclosures Goy: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; OncLive Peer Exchange: Honoraria; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; LLC(Targeted Oncology): Consultancy; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Xcenda: Consultancy, Honoraria; Gilead: Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; Rosewell Park: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Honoraria, Other; Incyte: Honoraria; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Hoffman la Roche: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Michael J Hennessey Associates INC: Consultancy; Hoffman la Roche: Consultancy; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Medscape: Consultancy; Phamacyclics: Research Funding; Constellation: Research Funding; Xcenda: Consultancy; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Koprivnikar: Bristol Myers Squibb: Speakers Bureau. McCloskey: BMS: Honoraria, Speakers Bureau; COTA: Other: Equity Ownership; Takeda: Consultancy, Speakers Bureau; Pfizer: Consultancy; Novartis: Consultancy; Jazz: Consultancy, Speakers Bureau; Incyte: Speakers Bureau; Amgen: Speakers Bureau.

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Nagata, Yasunobu, Vera Grossmann, Yusuke Okuno, Ulrike Bacher, Genta Nagae, Susanne Schnittger, Yusuke Shiozawa, et al. "Landscape Of Genetic Lesions In 944 Patients With Myelodysplastic Syndromes." Blood 122, no. 21 (November 15, 2013): 521. http://dx.doi.org/10.1182/blood.v122.21.521.521.

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Abstract Background Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid neoplasms characterized by varying degrees of cytopenias and a predisposition to acute myeloid leukemia (AML). With conspicuous clinical and biological heterogeneity in MDS, an optimized choice of treatment based on accurate diagnosis and risk stratification in individual patients is central to the current therapeutic strategy. Diagnosis and prognostication in patients with myelodysplastic syndromes (MDS) may be improved by high-throughput mutation/copy number profiling. Methods A total of 944 patients with various MDS subtypes were screened for gene mutations and deletions in 104 known/putative genes relevant to MDS using targeted deep-sequencing and/or array-based genomic hybridization. Impact of genetic lesions on overall survival (OS) was investigated by univariate analysis and a conventional Cox regression, in which the Least Absolute Shrinkage and Selection Operator (lasso) was used for selecting variables. The linear predictor from the Cox regression was then used to assign the patients into discrete risk groups. Prognostic models were constructed in a training set (n=611) and confirmed using an independent validation cohort (n=175). Results After excluding sequencing/mapping errors and known or possible polymorphisms, a total of 2,764 single nucleotide variants (SNVs) and insertions/deletions (indels) were called in 96 genes as high-probability somatic changes. A total of 47 genes were considered as statistically significantly mutated (p<0.01). Only 6 genes (TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1) were mutated in >10% of the cases. Less common mutations (2−10%) involved U2AF1, ZRSR2, STAG2, TP53, EZH2, CBL, JAK2, BCOR, IDH2, NRAS, MPL, NF1, ATM, IDH1, KRAS, PHF6, BRCC3, ETV6, and LAMB4. Intratumoral heterogeneity was evident in as many as 456 cases (48.3%), even though the small number of gene mutations available for evaluation was thought substantially to underestimate the real frequency. The number of observed intratumoral subpopulations tended to correlate with the number of detected mutations and therefore, advanced WHO subtypes and risk groups with poorer prognosis. Mean variant allele frequencies (VAFs) showed significant variations among major gene targets, suggesting the presence of clonogenic hierarchy among these common mutations during clonal evolution in MDS. The impact of these genetic lesions on clinical outcomes was initially investigated in 875 patients. In univariate analysis, 25 out of 48 genes tested significantly affected overall survival negatively (P<0.05), and only SF3B1mutations were associated with a significantly better clinical outcome. Next, to evaluate the combined effect of these multiple gene mutations/deletions, together with common clinical/cytogenetic variables used for IPSS-R, OS was modeled by a conventional Cox regression. A total of 14 genes, together with age, gender, white blood cell counts, hemoglobin, platelet counts, cytogenetic score in IPSS-R, were finally selected for the Cox regression in a proportional hazard model and based on the linear predictor of the regression model, we constructed a prognostic model (novel molecular model), in which patients were classified into 4 risk groups showing significantly different OS (“low”, “intermediate”, “high”, and “very high risk”) with 3-year survival of 95.2%, 69.3%, 32.8%, and 5.3%, respectively (P<0.001). These results demonstrated that the mutation/deletion status of a set of genes could be used as variables independent of clinical parameters to build a clinically relevant prognostic score. When applied to the validation cohort, the novel molecular model was even shown to outperform the IPSS-R. Conclusions Large-scale genetic and molecular profiling by cytogenetics, NGS and array-CGH not only provided novel insights into the pathogenesis and clonal evolution of MDS, but also helped to develop a powerful prognostic model based on gene mutations and other clinical variables that could be used for risk prediction. Molecular profiling of multiple target genes in MDS is feasible and provides an invaluable tool for improved diagnosis, biologic subclassification and especially prognostication for patients with MDS. Disclosures: Grossmann: MLL Munich Leukemia Laboratory: Employment. Bacher:MLL Munich Leukemia Laboratory: Employment. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Alpermann:MLL Munich Leukemia Laboratory: Employment. Roller:MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Kohlmann:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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Guglielmelli, Paola, Laura Calabresi, Chiara Carretta, Giada Rotunno, Sandra Parenti, Simone Romagnoli, Selene Mallia, et al. "Single Cell Mutation Analysis Delineates Clonal Architecture in Leukemic Transformation of Myeloproliferative Neoplasms." Blood 138, Supplement 1 (November 5, 2021): 56. http://dx.doi.org/10.1182/blood-2021-148315.

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Abstract Introduction. Myeloproliferative neoplasms (MPN) are clonal disorders of hematopoietic stem cells that include polycythemia vera, essential thrombocythemia, and primary myelofibrosis. 10-20% of MPN pts transform to secondary acute myeloid leukemia (sAML), unresponsive to conventional therapy and associated with dismal outcome (Dunbar A, 2020). In addition to somatic driver mutations affecting JAK2, CALR or MPL, several additional variants are harbored by MPN pts inb chronic phase, and a restricted set of them were associated with risk of leukemic evolution (Vannucchi AM, Leukemia 2013; Tefferi A, Blood ASdv 2016) However, the molecular mechanisms underlying leukemic transformation remain largely unknown. Although bulk next generation sequencing (NGS) highlights the overall mutation landscape, it cannot distinguish which mutations occur in the same clone(s), nor elucidate the order of mutations or resolve clonal complexity. Conversely, single-cell sequencing (SCS) might allow to resolve clonal heterogeneity and reconstruct clonal phylogenies at each disease phase (Parenti, NPJ Prec Onc 2021)). Aim: To delineate the clonal landscape of sAML, we performed single-cell mutational profiling in 10 pts with MPNs who progressed to sAML. Methods. There were 2 set of samples/approaches: (i) 15 paired samples (chronic (CP)/blast phase (BP)) from 7 pts were analyzed using the Mission-Bio Tapestri platform and the Myeloid panel in order to target SNVs and indels across 45 myeloid genes with 312 amplicons. In one pt we also analyzed an intermediate phase corresponding to progression from PV to PPV-MF before BP development. (ii) 7 further paired CD34+ samples from 3 pts were analyzed using a 239-amplicon custom panel including 29 genes frequently mutated in myeloid neoplasms. SCS libraries were sequenced on Illumina Novaseq. Data were processed by using Mission Bio's Tapestri Pipeline and analyzed with Mission Bio's Tapestri Insights software package and R software. CNV analysis was performed by using an integrated pipeline for multiomics analysis (Mosaic, Mission Bio) Results. (i) A total of 78,354 single cells were sequenced (average 5,223) using Tapestri Myeloid panel, with an average of 28,303 reads per cell and coverage of 97X. SCS was able to identify 17 low-frequency variants not detected in bulk analysis; however, it failed to discriminate homopolymeric regions including the ASXL1 G646Wfs*12. (ii) A total of 25,417 single cells were sequenced (average 3636) using a custom panel, with a coverage of 186X and an average Allele Dropout Rate of 8.6%. This panel was able to identify ASXL1 G646Wfs*12 variant. Overall, we found a significant correlation of variant allele frequency (VAF) measured by bulk and SCs approach (R =0.84, p<.0001). Epigenetic variants (i.e. ASXL1, TET2, EZH2) account for around half of the mutations and affect a large fraction of CP cells (3 representative samples in Fig.1). In 8/10 pts, leukemic clone emerged from a driver mutation-positive cells (JAK2V617F n=4; CALR Type1 n=4). In all pts we are able to identify at least 3 mutated clones and in 7 pts the dynamics of the clones allowed to identify the one(s) responsible for evolution to sAML. In 7/10 pts, the leukemic clones were already detectable at low frequency (<2%) at CP and became dominant in BP; these low-frequency clones were missed by bulk sequencing. Furthermore, SCS revealed acquisition of 3 mutually exclusive mutations in RAS pathway in one pt: two NRAS mutations and a KRAS mutation. Copy number variation (CNV) could be assessed in 4 pts. Of note, during progression to sAML, we found single cells with amplification of ETV6 (>20 copies in 2 pts), NRAS (8 copies in 2pts) and BRAF (8 copies in 2pts). Other subclonal ploidy abnormalities were also observed in RUNX1, EZH2, U2AF1 and ZRSR2 (5-18 copies). Conclusions. Together, these data suggest that MPN present a complex clonal combination evolving over time. SCS allows to resolve this milieu, that is largely missed by conventional bulk NGS, in particularly SCS identifies rare leukemia-driving clones that were already present in chronic phase and describes their dynamics during leukemic progression. Leukemic transformation after MPN is a highly heterogeneous process with mutations and CNVs acquired in different genes and different clones. Overall, our findings provide further insights into the pathogenesis of AML transformation of MPN. Supported by AIRC, Mynerva project no.21267 Figure 1 Figure 1. Disclosures Vannucchi: BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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Roberts, Kathryn G., Samuel W. Brady, Zhaohui Gu, Lei Shi, Stanley Pounds, Deqing Pei, Cheng Cheng, et al. "The Genomic Landscape of Childhood Acute Lymphoblastic Leukemia." Blood 134, Supplement_1 (November 13, 2019): 649. http://dx.doi.org/10.1182/blood-2019-124881.

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Introduction: Although recent studies have refined the classification of B-progenitor and T-lineage acute lymphoblastic leukemia into gene-expression based subgroups, a comprehensive integration of significantly mutated genes and pathways for each subgroup is needed to understand disease etiology. Methods: We studied 2789 children, adolescents and young adults (AYA) with newly diagnosed B-ALL (n=2,322 cases) or T-ALL (n=467) treated on Children's Oncology Group (n=1,872) and St. Jude Children's Research Hospital trials (n=917). The cohort comprised childhood NCI standard-risk (41.8%; age range 1-9.99 yrs, WBC ≤ 50,000/ml), childhood NCI high-risk (44.5%; age range ≥10 to 15.99 yrs) and AYA (9.9%; age range 16-30.7 yrs). Genomic analysis was performed on tumor and matched-remission samples using whole transcriptome sequencing (RNA-seq; tumor only; n=1,922), whole exome sequencing (n=1,659), whole genome sequencing (n=757), and single nucleotide polymorphism array (n=1,909). Results: For B-ALL, 2104 cases (90.6%) were classified into 26 subgroups based on RNA-seq gene expression data and aneuploidy or other gross chromosomal abnormalities (iAMP21, Down syndrome, dicentric), deregulation of known transcription factors by rearrangement or mutation (PAX5 P80R, IKZF1 N159Y), or activation of kinase alterations (Ph+, Ph-like). For T-ALL, cases were classified into 9 previously described subtypes based on dysregulation of transcription factor genes and gene expression. In 1,659 cases subject to exome sequencing (1259 B-ALL, 405 T-ALL) we identified 18,954 nonsynonymous single nucleotide variants (SNV) and 2,329 insertion-deletion mutations (indels) in 8,985 genes. Overall, 161 potential driver genes were identified by the mutation-significance detection tool MutSigCV or by presence of pathogenic variants in known cancer genes. Integration of sequence mutations and DNA copy number alteration data in B-ALL identified 7 recurrently mutated pathways: transcriptional regulation (40.6%), cell cycle and tumor suppression (38.0%), B-cell development (34.5%), epigenetic regulation (24.7%), Ras signaling (33.0%), JAK-STAT signaling (12.0%) and protein modification (ubiquitination or SUMOylation, 5.0%). The top 10 genes altered by deletion or mutation in B-ALL were CDKN2A/B (30.1%), ETV6 (27.0%), PAX5 (24.6%), CDKN1B (20.3%), IKZF1 (17.6%), KRAS (16.5%), NRAS (14.6%), BTG1 (7.5%) histone genes on chromosome 6 (6.9%) and FLT3 (6.1%), and for T-ALL, CDKN2A/B (74.7%), NOTCH1 (68.2%), FBXW7 (21.3%), PTEN (20.5%) and PHF6 (18.2%) (Figure 1A). We identified 17 putative novel driver genes involved in ubiquitination (UBE2D3, UBE2A, UHRF1, and USP1), SUMOylation (SAE1, UBE2I), transcriptional regulation (ZMYM2, HMGB1), immune function (B2M), migration (CXCR4), epigenetic regulation (DOT1L) and mitochondrial function (LETM1). We also observed variation in the frequency of genes and pathways altered across B-ALL subtypes (Figure 1B). Interestingly, alteration of SAE1 and UBA2, novel genes that form a heterodimeric complex important for SUMOylation, and UHRF1 were enriched in ETV6-RUNX1 cases. Deletions of LETM1, ZMYM2 and CHD4 were associated with near haploid and low hypodiploid cases. Deletion of histone genes on chromosome 6 and alterations of HDAC7 were enriched in Ph+ and Ph-like ALL. Mutations in the RNA-binding protein ZFP36L2 were observed in PAX5alt, DUX4 and MEF2D subgroups. Genomic subtypes were prognostic. ETV6-RUNX1, hyperdiploid, DUX4 and ZNF384 ALL were associated with good outcome (5-yr EFS 91.1%, 87.2%, 91.9% and 85.7%, respectively), ETV6-RUNX1-like, iAMP21, low hyperdiploid, PAX5 P80R and PAX5alt were associated with intermediate outcome (5-yr EFS 68.6%, 72.2%, 70.8%, 77.0% and 70.9%, respectively), whilst KMT2A, MEF2D, Ph-like CRLF2 and Ph-like other conferred a poor prognosis (55.5%, 67.1%, 51.5% and 62.1%, respectively). TCF3-HLF and near haploid had the worst outcome with 5-yr EFS rates of 27.3% and 47.2%, respectively. Conclusions: These findings provide a comprehensive landscape of genomic alterations in childhood ALL. The associations of mutations with ALL subtypes highlights the need for specific patterns of cooperating mutations in the development of leukemia, which may help identify vulnerabilities for therapy intervention. Disclosures Gastier-Foster: Bristol Myers Squibb (BMS): Other: Commercial Research; Incyte Corporation: Other: Commercial Research. Willman:to come: Patents & Royalties; to come: Membership on an entity's Board of Directors or advisory committees; to come: Research Funding. Raetz:Pfizer: Research Funding. Borowitz:Beckman Coulter: Honoraria. Zweidler-McKay:ImmunoGen: Employment. Angiolillo:Servier Pharmaceuticals: Consultancy. Relling:Servier Pharmaceuticals: Research Funding. Hunger:Jazz: Honoraria; Amgen: Consultancy, Equity Ownership; Bristol Myers Squibb: Consultancy; Novartis: Consultancy. Loh:Medisix Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees. Mullighan:Amgen: Honoraria, Other: speaker, sponsored travel; Loxo Oncology: Research Funding; AbbVie: Research Funding; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel.

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Stasik, Sebastian, Jan Moritz Middeke, Michael Kramer, Christoph Rollig, Alwin Krämer, Sebastian Scholl, Andreas Hochhaus, et al. "EZH2 Mutations and Impact on Clinical Outcome Analyzed in 1604 Patients with Acute Myeloid Leukemia." Blood 132, Supplement 1 (November 29, 2018): 1528. http://dx.doi.org/10.1182/blood-2018-99-114421.

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Abstract Purpose: The enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase and key epigenetic regulator involved in transcriptional repression and embryonic development. Loss of EZH2 activity by inactivating mutations is associated with poor prognosis in myeloid malignancies such as MDS. More recently, EZH2 inactivation was shown to induce chemoresistance in acute myeloid leukemia (AML) (Göllner et al., 2017). Data on the frequency and prognostic role of EZH2-mutations in AML are rare and mostly confined to smaller cohorts. To investigate the prevalence and prognostic impact of this alteration in more detail, we analyzed a large cohort of AML patients (n = 1604) for EZH2 mutations. Patients and Methods: All patients analyzed had newly diagnosed AML, were registered in clinical protocols of the Study Alliance Leukemia (SAL) (AML96, AML2003 or AML60+, SORAML) and had available material at diagnosis. Screening for EZH2 mutations and associated alterations was done using Next-Generation Sequencing (NGS) (TruSight Myeloid Sequencing Panel, Illumina) on an Illumina MiSeq-system using bone marrow or peripheral blood. Detection was conducted with a defined cut-off of 5% variant allele frequency (VAF). All samples below the predefined threshold were classified as EZH2 wild type (wt). Patient clinical characteristics and co-mutations were analyzed according to the mutational status. Furthermore, multivariate analysis was used to identify the impact of EZH2 mutations on outcome. Results: EZH2-mutations were found in 63 of 1604 (4%) patients, with a median VAF of 44% (range 6-97%; median coverage 3077x). Mutations were detected within several exons (2-6; 8-12; 14-20) with highest frequencies in exons 17 and 18 (29%). The majority of detected mutations (71% missense and 29% nonsense/frameshift) were single nucleotide variants (SNVs) (87%), followed by small indel mutations. Descriptive statistics of clinical parameters and associated co-mutations revealed significant differences between EZH2-mut and -wt patients. At diagnosis, patients with EZH2 mutations were significantly older (median age 59 yrs) than EZH2-wt patients (median 56 yrs; p=0.044). In addition, significantly fewer EZH2-mut patients (71%) were diagnosed with de novo AML compared to EZH2-wt patients (84%; p=0.036). Accordingly, EZH2-mut patients had a higher rate of secondary acute myeloid leukemia (sAML) (21%), evolving from prior MDS or after prior chemotherapy (tAML) (8%; p=0.036). Also, bone marrow (and blood) blast counts differed between the two groups (EZH2-mut patients had significantly lower BM and PB blast counts; p=0.013). In contrast, no differences were observed for WBC counts, karyotype, ECOG performance status and ELN-2017 risk category compared to EZH2-wt patients. Based on cytogenetics according to the 2017 ELN criteria, 35% of EZH2-mut patients were categorized with favorable risk, 28% had intermediate and 37% adverse risk. No association was seen with -7/7q-. In the group of EZH2-mut AML patients, significantly higher rates of co-mutations were detected in RUNX1 (25%), ASXL1 (22%) and NRAS (25%) compared to EZH2-wt patients (with 10%; 8% and 15%, respectively). Vice versa, concomitant mutations in NPM1 were (non-significantly) more common in EZH2-wt patients (33%) vs EZH2-mut patients (21%). For other frequently mutated genes in AML there was no major difference between EZH2-mut and -wt patients, e.g. FLT3ITD (13%), FLT3TKD (10%) and CEBPA (24%), as well as genes encoding epigenetic modifiers, namely, DNMT3A (21%), IDH1/2 (11/14%), and TET2 (21%). The correlation of EZH2 mutational status with clinical outcomes showed no effect of EZH2 mutations on the rate of complete remission (CR), relapse free survival (RFS) and overall survival (OS) (with a median OS of 18.4 and 17.1 months for EZH2-mut and -wt patients, respectively) in the univariate analyses. Likewise, the multivariate analysis with clinical variable such as age, cytogenetics and WBC using Cox proportional hazard regression, revealed that EZH2 mutations were not an independent risk factor for OS or RFS. Conclusion EZH mutations are recurrent alterations in patients with AML. The association with certain clinical factors and typical mutations such as RUNX1 and ASXL1 points to the fact that these mutations are associated with secondary AML. Our data do not indicate that EZH2 mutations represent an independent prognostic factor. Disclosures Middeke: Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Rollig:Bayer: Research Funding; Janssen: Research Funding. Scholl:Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Abbivie: Other: Travel support; Alexion: Other: Travel support; MDS: Other: Travel support; Novartis: Other: Travel support; Deutsche Krebshilfe: Research Funding; Carreras Foundation: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Hochhaus:Pfizer: Research Funding; Incyte: Research Funding; Novartis: Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Research Funding. Brümmendorf:Janssen: Consultancy; Takeda: Consultancy; Novartis: Consultancy, Research Funding; Merck: Consultancy; Pfizer: Consultancy, Research Funding. Burchert:AOP Orphan: Honoraria, Research Funding; Bayer: Research Funding; Pfizer: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Research Funding. Krause:Novartis: Research Funding. Hänel:Amgen: Honoraria; Roche: Honoraria; Takeda: Honoraria; Novartis: Honoraria. Platzbecker:Celgene: Research Funding. Mayer:Eisai: Research Funding; Novartis: Research Funding; Roche: Research Funding; Johnson & Johnson: Research Funding; Affimed: Research Funding. Serve:Bayer: Research Funding. Ehninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; Bayer: Research Funding; GEMoaB Monoclonals GmbH: Employment, Equity Ownership. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding.

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N’Guessan, Arnaud, Ilana Lauren Brito, Adrian W. R. Serohijos, and B. Jesse Shapiro. "Mobile Gene Sequence Evolution within Individual Human Gut Microbiomes Is Better Explained by Gene-Specific Than Host-Specific Selective Pressures." Genome Biology and Evolution 13, no. 8 (June 16, 2021). http://dx.doi.org/10.1093/gbe/evab142.

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Abstract Pangenomes—the cumulative set of genes encoded by a population or species—arise from the interplay of horizontal gene transfer, drift, and selection. The balance of these forces in shaping pangenomes has been debated, and studies to date focused on ancient evolutionary time scales have suggested that pangenomes generally confer niche adaptation to their bacterial hosts. To shed light on pangenome evolution on shorter evolutionary time scales, we inferred the selective pressures acting on mobile genes within individual human microbiomes from 176 Fiji islanders. We mapped metagenomic sequence reads to a set of known mobile genes to identify single nucleotide variants (SNVs) and calculated population genetic metrics to infer deviations from a neutral evolutionary model. We found that mobile gene sequence evolution varied more by gene family than by human social attributes, such as household or village. Patterns of mobile gene sequence evolution could be qualitatively recapitulated with a simple evolutionary simulation without the need to invoke the adaptive value of mobile genes to either bacterial or human hosts. These results stand in contrast with the apparent adaptive value of pangenomes over longer evolutionary time scales. In general, the most highly mobile genes (i.e., those present in more distinct bacterial host genomes) tend to have higher metagenomic read coverage and an excess of low-frequency SNVs, consistent with their rapid spread across multiple bacterial species in the gut. However, a subset of mobile genes—including those involved in defense mechanisms and secondary metabolism—showed a contrasting signature of intermediate-frequency SNVs, indicating species-specific selective pressures or negative frequency-dependent selection on these genes. Together, our evolutionary models and population genetic data show that gene-specific selective pressures predominate over human or bacterial host-specific pressures during the relatively short time scales of a human lifetime.

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Marston, Nicholas A., James P. Pirruccello, Giorgio E. M. Melloni, Satoshi Koyama, Frederick K. Kamanu, Lu-Chen Weng, Carolina Roselli, et al. "Predictive Utility of a Coronary Artery Disease Polygenic Risk Score in Primary Prevention." JAMA Cardiology, December 28, 2022. http://dx.doi.org/10.1001/jamacardio.2022.4466.

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ImportanceThe clinical utility of polygenic risk scores (PRS) for coronary artery disease (CAD) has not yet been established.ObjectiveTo investigate the ability of a CAD PRS to potentially guide statin initiation in primary prevention after accounting for age and clinical risk.Design, Setting, and ParticipantsThis was a longitudinal cohort study with enrollment starting on January 1, 2006, and ending on December 31, 2010, with data updated to mid-2021, using data from the UK Biobank, a long-term population study of UK citizens. A replication analysis was performed in Biobank Japan. The analysis included all patients without a history of CAD and who were not taking lipid-lowering therapy. Data were analyzed from January 1 to June 30, 2022.ExposuresPolygenic risk for CAD was defined as low (bottom 20%), intermediate, and high (top 20%) using a CAD PRS including 241 genome-wide significant single-nucleotide variations (SNVs). The pooled cohort equations were used to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk and classify individuals as low (&lt;5%), borderline (5-&lt;7.5%), intermediate (7.5-&lt;20%), or high risk (≥20%).Main Outcomes and MeasuresMyocardial infarction (MI) and ASCVD events (defined as incident clinical CAD [including MI], stroke, or CV death).ResultsA total of 330 201 patients (median [IQR] age, 57 [40-74] years; 189 107 female individuals [57%]) were included from the UK Biobank. Over the 10-year follow-up, 4454 individuals had an MI. The CAD PRS was significantly associated with the risk of MI in all age groups but had significantly stronger risk prediction at younger ages (age &lt;50 years: hazard ratio [HR] per 1 SD of PRS, 1.72; 95% CI, 1.56-1.89; age 50-60 years: HR, 1.46; 95% CI, 1.38-1.53; age &gt;60 years: HR, 1.42; 95% CI, 1.37-1.48; P for interaction &lt;.001). In patients younger than 50 years, those with high PRS had a 3- to 4-fold increased associated risk of MI compared with those in the low PRS category. A significant interaction between CAD PRS and age was replicated in Biobank Japan. When CAD PRS testing was added to the clinical ASCVD risk score in individuals younger than 50 years, 591 of 4373 patients (20%) with borderline risk were risk stratified into intermediate risk, warranting initiation of statin therapy and 3198 of 7477 patients (20%) with both borderline or intermediate risk were stratified as low risk, thus not warranting therapy.Conclusions and RelevanceResults of this cohort study suggest that the predictive ability of a CAD PRS was greater in younger individuals and can be used to better identify patients with borderline and intermediate clinical risk who should initiate statin therapy.

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Gupta, Sonakshi, Immaculata Xess, Gagandeep Singh, Saumya CS, Azka Iram, and Manish Soneja. "P400 A study of the ecology, evolution and resistance mechanism of Candida auris at a tertiary care center in North India." Medical Mycology 60, Supplement_1 (September 2022). http://dx.doi.org/10.1093/mmy/myac072.p400.

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Abstract Poster session 3, September 23, 2022, 12:30 PM - 1:30 PM Aim: To study the ecology, evolution, and resistance mechanism of Candida auris, using samples from patients, healthcare workers, hospital and environmental niches, using amplified fragment length polymorphism (AFLP) and antifungal susceptibility testing (AFST). Methods A total of 720 samples were screened for C. auris, including clinical samples from patients (tissue, body fluids), surveillance samples from patients (axillar/groin swabs), swabs and water samples from environmental locations and objects, surface swabs from hospital locations, and screening samples from healthcare personnel for hand carriage of C. auris. Samples were cultured on Sabouraud Dextrose agar (SDA) and CHROMagar. Colonies morphologically suggestive of C. auris were identified by Matrix Assisted Laser Desorption-Time of Flight (MALDI-TOF) and isolates were subjected to antifungal susceptibility testing (AFST) by broth micro-dilution method. DNA was extracted for analysis by amplified fragment length polymorphism (AFLP) and cluster analysis. The amplicons were subjected to capillary electrophoresis and fluorescent amplified length polymorphism (FALP) for the generation of a heat map and dendrogram to evaluate single nucleotide polymorphisms and single nucleotide variations (SNPs and SNVs). Results Out of 720 samples, C. auris was isolated and identified by MALDI-TOF from 50, including 37 from routine patient samples, 12/674 axillar/groin surveillance swabs, and 1/66 samples from hands of healthcare workers. C. auris was not isolated from any environmental samples or hospital surfaces. AFST revealed high overall rates of resistance to three important antifungal drugs−93.22%, 38.98%, and 52.54% of isolates were resistant to fluconazole, voriconazole, and amphotericin B respectively. Resistance to echinocandins was lower−1.81% of isolates were resistant to caspofungin, and micafungin. Additionally, 18 isolates showed only intermediate sensitivity to both voriconazole and caspofungin. The highest rates of resistance to amphotericin B, and azoles were observed in isolates from blood (62.5% of isolates) and axillar/groin swabs (44.5% of isolates) respectively. Resistance to caspofungin was seen in 14.28% of isolates from both groups. AFLP and capillary electrophoresis of extracted DNA revealed 188 variations in the range of 300-662 nucleotides. A total of 10 samples had no change in the nucleotides and were labeled as ‘constant’. The dendrograms generated by bioinformatic analysis of FALP results yielded two different clusters provisionally designated as cluster I and cluster II. Cluster I could be further distinguished into sub-cluster Ia and sub-cluster Ib, indicating further variations. Conclusions: Candida auris is a pathogen of emerging importance in our center, with significant levels of resistance to several important antifungal drugs. Incidence of both the pathogen and antifungal drug resistance was observed in samples collected from patients, but not from the hospital or environment, and minimally from healthcare personnel. This suggests that the source of most C. auris infections is colonizers from the patient rather than environmental sources or healthcare workers, and infection control measures should be tailored accordingly.

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