Clinical Heterogeneity of AML Is Associated with Mutational Heterogeneity

Abstract

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.