Somatic Mutational Screen For Improved Prediction Of The Outcomes Of Epigenetic Therapy In MDS

Hypomethylating agents decitabine and azacitidine are standard treatments for MDS. In their use, one hopes to rectify cytopenias and prolong survival by retarding further disease progression. However, individual treatment responses vary from complete remissions (CR) to complete refractoriness. In general, at least months of therapy is needed prior to assessing response. Thus, patients may need to be subjected to prolonged exposure to ineffective therapy, suffering toxicities without clinical benefit, while alternative and potentially more effective treatments are delayed. Currently, there are no reliable phenotypic or mutational markers for predicting response to hypomethylating agents.

Once whole exome sequencing (WES) became available for more routine analysis, we theorized that somatic mutational patterns may help identify patients who would most benefit from these drugs, thereby maximizing response rate by rational patient selection. To pursue this hypothesis, we screened a cohort of 168 patients with MDS who received either azacitidine or decitabine for the presence of somatic mutations. Only those who received sufficient therapy, i.e., completed at least 4 cycles, were selected for outcome analysis. WES and targeted deep NGS for a subset of 60 genes most frequently affected by somatic mutations in MDS (as determined in a set of 200 exome MDS project, see abstract from our group) was applied to 94 evaluable patients.

Median age was 68 years (range, 26-85), 34% were female, and MDS subtypes were as follows: RA/RCUD/RARS 7%, RCMD 20%, RAEB-1/2 32%, MDS/MPN & CMML-1/2 21%, and sAML 16%. Response was assessed using IWG 2006 criteria at 4 and 7 months after therapy initiation. Overall response was 34%; rate of CR (including marrow/cytogenetic CR) was 22%, any HI 7%, SD 19%, and no response 35%. The cohort was then dichotomized into “responders” (N=64) and “non-responders” (N=69) with responders classified as those achieving CR/PR or any HI. Baseline patient characteristics were similar between both groups, including average age at treatment initiation, sex, disease subtypes, proportion of abnormal/complex karyotypes, and presence of common cytogenetic aberrations. Overall, the most frequently mutated genes include: TET2 (12%), IDH1/IDH2 (5%), SRSF2 (11%), ASXL1 (28%), SF3B1 (13%), RUNX1 (11%), EZH2/EED/SUZ12 (11%), SETBP1 (6%), CBL (7%), and PPFIA2 (10%). For some analyses we also divided mutations into functional gene families; e.g., DNMT family (DNMT1, DNMT3A, DNMT3B), PRC2 family (EZH2, EED, SUZ12, JARID2, RBBP4, PHF1), IDH family (IDH1, IDH2), CBL family (CBL, CBLB), RAS family (NRAS, KRAS, HRAS, NF1, NF2, RIT1, PTPN11), and among others.

The most common molecular abnormalities in responders included the presence of complex karyotype (19% vs. 26% in refractory), del7q/-7 (18% vs. 22%), del5q (19% vs. 15%), and mutations in DNMT3 (25% vs. 22%), ASXL1 (25% vs. 32%), and others. Similarly, the most common defects found in refractory included also the U2AF1/2 family of genes (16% vs. 7% in responders). When compared and selected by the lowest p value, the top mutations in terms of predicting response were SRSF2 (OR 2.4), cohesin (5.1), ATM (OR 5.6) and PHF6 (OR 4.22). Mutations predicting non-response include RAS (OR .3), U2AF1/2 (OR 0.4) and LUC7L defects (OR .53). To generate better predictors, we have combined mutations in “either/or” fashion. For instance, the presence of either SRSF2 and cohesin (p=.0318) or cohesin and PHF6 mutations (p=.02) will be considered predictors of response and the presence of either or RAS/U2AF1(p=.019) and cohesin/ATM (p=.008) and SRSF2 (p=.006) predictors of refractoriness.

In sum, mutational patterns may be helpful in identifying patients who may benefit from hypomethylating therapies. Identification of the most predictive genes could guide development of molecular marker-based selection of patients for hypomethylating agent therapy, but will require ongoing analysis and additional prospective testing for validation