Co-Occurring Mutation Clusters Predict Drug Sensitivity in Acute Myeloid Leukemia

• Published in: Blood 2020-11, Vol.136 (Supplement 1), p.12-13
• Main Authors: Qin, Guangrong, Ilya, Shmulevich, Kim, Taek-Kyun, Tercan, Bahar, Martins, Timothy J, Dai, Jin, Chien, Sylvia, Carson, Andrew, Patay, Bradley, Estey, Elihu H., Loeb, Lawrence A., Monnat, Raymond J., Becker, Pamela S.
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2020-142727

Background The molecular origin of cancer drug resistance is not apparent for most cases of acute myeloid leukemia (AML). Clonal evolution appears to be associated with increasing drug resistance. We sought to determine whether the mutation patterns are associated with drug susceptibility in AML. Methods Seventy-two patient blood or marrow samples were enriched for CD34+ blasts by immunomagnetic bead selection. High throughput drug sensitivity screens were performed for 223 drugs after a 72-hour exposure to 8-12 customized drug concentrations (within the range of 5pM to 100µM) of each drug spanning 4-5 logs. Post exposure viability was determined using CellTiter Glo luminescent reagent. XLFit was used to analyze the data and generate dose response curves based on a standard 4-parameter logistic fit. Mutation analysis was performed by MyAML™ utilizing next generation sequencing (NGS) to analyze the 3’ and 5’ UTRs and exonic regions of 194 AML-associated genes and genomic breakpoints. Co-mutation and mutual exclusivity scores of gene aberrations were computed using three different statistical methods on three large publicly available datasets: 1) TCGA-AML, 2) BeatAML, and 3) data from 1540 patients (Moritz Gerstung, et al., Nat Genet. 2017). The scores for each co-mutation or mutually exclusive gene pair were then logarithmically transformed from the aggregated p-value using a robust rank aggregation method and used to construct a graph from which communities of co-mutated genes could be detected, resulting in higher mutual exclusivity between modules and higher co-occurrence within modules. Results We identified five main groups of co-mutations, including the following: 1) RUNX1 group, 2) CEBPA group, 3) NPM1 group, 4) TP53 group, and 5) RAS group (see Fig 1A). The co-mutation community in the RUNX1 group is featured with transcriptional dysregulation (RUNX1, ASXL1 and EZH2), and dysregulation in splicing (U2AF1, SRSF2 or SF3B1). The TP53 and CEBPA groups exhibit transcriptional dysregulation, and transcription factor alterations. The NPM1 and RAS groups exhibit signaling alteration, particularly the Ras/MAPK signaling pathway. Among each co-mutation community, the variant allele frequencies (VAFs) exhibited differences for specific mutations. For example, in the NPM1 group, FLT3 exhibits the lowest VAF, followed by NPM1 while DNMT3A shows the highest VAF. The five co-mutation groups are associated with different overall survival, including better survival