Program: Oral and Poster Abstracts
Session: 617. Acute Myeloid Leukemia: Biology, Cytogenetics, and Molecular Markers in Diagnosis and Prognosis II
Cases (n=212) included subjects with centrally adjudicated AML diagnosis. Peripheral blood samples were available from baseline and follow up (year 1 and 3) all prior to diagnosis of overt AML (average time to AML was 9.06 years).
Controls (n=212) (healthy non-AML), were matched by age, timing of serial sample collection and history of cancer at WHI baseline and follow-up. Matching was done in a time forward manner to ensure similar follow up times.
Methods: Ultra-deep targeted exome sequencing was performed using a 200 gene capture panel of recurrently mutated genes in hematologic malignancies. Variants present at 2.5% VAF minimum were selected and categorized as pathogenic significance vs. variant of uncertain significance (VUS) following rigorous quality control and filtration of SNPs. Analysis was focused on 67 genes recurrently mutated in MDS/AML in addition to categorical analysis of mutations (epigenetic regulators, activated signaling, spliceosome). Chromosomal rearrangements were assessed using information from split and paired end reads mapped to intronic regions of known breakpoints captured by the gene panel.
Baseline characteristics of cases and controls were compared by using t-test or Wilcoxon rank-sum test for continuous variables, and the chi-square test or Fisher’s exact test for categorical variables. Fisher’s exact test was used to calculate the mutation status among cases and controls and to calculate the association of baseline mutation with AML diagnosis. Time to development of AML in cases was assessed by using regression analyses with the mutation status as a covariate. All p-values are two-sided with statistical significance evaluated at the 0.05 alpha levels.
Results: The median age of cases and controls were 65.5 and 65 years respectively with average time to AML being 9.06 years. Myeloid specific mutations were detected in 35.4 % of controls and 59.04% of cases. The most commonly found mutations and their frequencies among cases and controls respectively were DNMT3A (22.8% and 10%), TET2 (19.5% and 4.2%) and ASXL1/ASXL2 (8% and 6.8%). TP53 (6.6%) and RUNX1 (2%) mutations were only seen in participants who eventually developed AML and were absent in controls. Subclonal mutations in DNMT3A, TET2, TP53, and SF3B1 revealed a general pattern of mutual exclusivity. Among cases, a >30-fold enrichment in pathogenic mutations was observed relative to controls (14.76 % vs.0.47%). One case demonstrated detectable MLL-ELL rearrangement 6 years prior to diagnosis of AML. There was no association of WBC, platelets or hemoglobin count with the presence of any mutation at the same time.
There was an increased prevalence of myeloid specific mutation among cases compared to controls (OR 2.87, C.I. 1.90-4.37, p value <0.001). The presence of a pathogenic myeloid mutation was also significantly different among cases and controls (p value <.0001). In this matched case control analysis, the presence of a myeloid specific mutation at baseline was associated with the development of AML (OR 2.9, p value <0.001). Among cases, time to development of AML in participants with and without a prior myeloid specific mutation was 8.2 years and 9.6 years respectively (p value >0.05). Strikingly, all participants with a RUNX1 and TP53 and majority with the targetable IDH2 R140Q mutation (10/11) eventually developed AML many years later.
Conclusions: The likelihood of having a myeloid -specific mutation at baseline, prior to the development of AML, was significantly higher in cases compared to controls from this WHI cohort. IDH2, TP53 and RUNX1 mutations were almost always predictive of development of AML. These findings suggest that subclonal stepwise acquisition of mutations may occur years prior to disease onset and that deep molecular monitoring using NGS may enable early intervention in selected patients.
Disclosures: Desai: Agios: Other: Ad Board; Argenx: Other: Ad Board. Mencia-Trinchant: Cellectis: Research Funding. Ritchie: Bristol-Myers Squibb: Other: Research funding to my institution; NS Pharma: Other: Research funding to my institution; Novartis: Consultancy, Other: Research funding to my institution, and travel, Speakers Bureau; Pfizer: Consultancy, Other: Research funding to my institution; Celgene: Consultancy, Other: Travel, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Astellas Pharma: Other: Research funding to my institution. Guzman: Cellectis: Research Funding. Roboz: AbbVie, Agios, Amgen, Amphivena, Array Biopharma Inc., Astex, AstraZeneca, Celator, Celgene, Clovis Oncology, CTI BioPharma, Genoptix, Immune Pharmaceuticals, Janssen Pharmaceuticals, Juno, MedImmune, MEI Pharma, Novartis, Onconova, Pfizer, Roche Pharmace: Consultancy; Cellectis: Research Funding. Hassane: Cellectis: Research Funding.
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