The Complexity of Interpreting Genomic Data in Patients with Primary and Secondary Acute Myeloid Leukemia (AML)

Background Acute myeloid leukemia (AML) is a complex, heterogeneous neoplasm characterized by the accumulation of complex genetic alterations that are responsible for the initiation and progression of the disease. Secondary AML (sAML) represents a progression from antecedent hematologic disorders such as myelodysplastic syndromes (MDS) or myeloprolifrative neoplasms (MPN). Certain acquired mutations have been reported to be specific for sAML when compared to primary AML (pAML), but many limitations exist when cytogenetic grouping or other parameters are taken into account. In addition, some mutations have been shown to impact survival in some studies, but not others. Methods We performed targeted deep sequencing on samples from bone marrow and peripheral blood of pts diagnosed with sAML and pAML and treated at our institution between 1/2003-1/2013. Additional data on pAML was added from The Cancer Genome Atlas (TCGA). A panel of 62 gene mutations described as frequently recurrent mutations in myeloid malignancies were assessed. Cytogenetic grouping was defined by CALGB/Alliance criteria. Differences were compared using Fisher9s exact test and the Mann-Whitney U test for categorical and continuous variables, respectively. Overall survival (OS) was calculated from the time of diagnosis to last follow up or death. Results: A total of 496 pts included: 273 with pAML and 223 with sAML. Comparing pAML to sAML, pts were younger (median age 59 vs. 68 years, p 9 /L, p 3 abnormalities) compared to 11% and 16% for pAML ( p ASXL1 ( p JAK2 ( p =.014), CBL ( p =.05), BCOR ( p =.02), STAG2 ( p =.003), SF3B1 ( p =.04), SRSF2 ( p =.001 ), and U2AF1 ( p =.03) were highly specific for the sAML phenotype, whereas mutations in NPM1 (p FLT3 ( p DNMT3A ( p IDH2 ( p =.02) were more specific for pAML. When the analysis was restricted to pts with NK cytogenetics, only ASXL1 (p DNMT3A (p ASXL1 (p=.01) remained specific for sAML. No other mutations were specific for pAML. We then evaluated whether the mutations that were specific to each AML phenotype had an impact on OS. We observed different mutations that impacted OS in each phenotype: DNMT3A (HR 1.81, 95% CI 1.28-2.57, p TP53 (HR 3.1, 95% 1.74-5.53, p SUZ12 (HR 3.18, 95% CI 1.01-10, p=.05) led to worse OS in pAML, whereas mutations in EZH2 (HR 2.12, 95% CI 1.07-4.21, p =.03), PRPF8 (HR 2.32, 95% CI 1.20-4.46, p=.01), and TP53 ( HR 2.92, 95% CI 1.69-5.04, p FLT3 (HR 2.15, 95% CI 1.37- 3.35, p DNMT3A (HR 2.41, 95% CI 1.57-3.70, p BCOR (HR 2.41, 95% CI 1.57-3.70, p TP53 (HR 2.41, 95% CI 1.57-3.70, p BOCR (HR 2.95, 95% CI 1.03-8.50, p SF3B1 (HR .19, 95% CI .05-.82, p SUZ12 (HR .12, 95% CI .01-.99, p TP53 (HR 1.9, 95% CI 1.09-3.46, p Conclusion Clear genomic variations exist between sAML and pAML. Although some of these genomic changes are more specific to each phenotype in general, this specificity and the impact on OS differed for each cytogenetic subgroup, highlighting the complexity of interpreting genomic information in pts with AML and the need to incorporate both cytogenetic and molecular data in prognosis-driven treatment decisions. Disclosures Sekeres: TetraLogic: Membership on an entity9s Board of Directors or advisory committees; Celgene Corporation: Membership on an entity9s Board of Directors or advisory committees; Amgen: Membership on an entity9s Board of Directors or advisory committees.