Molecular Characterization of Acute Myeloid Leukemia Patients with Normal Karyotype

Background

Conventional cytogenetic classification remains one of the most important prognostic factors in acute myeloid leukemia (AML). Approximately 50-60 % of patients (pts) with AML have normal karyotype (NK). NK status has traditionally been associated with intermediate risk AML, but actually represents a heterogeneous group with variable outcomes. Adding mutational data such as NPM1, FLT3-ITD, and CEBPa can improve risk stratification for a subset of pts but does not reflect the genomic complexity and mutation interactions that may impact the overall outcome.

In this study, we evaluated the association among several mutations and overall survival (OS) in pts with NK AML using unbiased advanced analytics approaches.

Method

Genomic and clinical data of 2793 primary AML (pAML) pts were analyzed. A panel of 35 genes that are commonly mutated in AML and myeloid malignancies was included. OS was calculated from the time of diagnosis to time of death or last follow up. To study the association of mutations and OS using an unbiased approach, we applied several machine learning algorithms that included random survival forest and recommender system algorithms (machine learning algorithm analogues to Amazon or Netflix recommender systems, in which a customer who buys A and B is likely to buy C; mutations A and B are determined to be likely associated with mutation C).

Results

Of 2793 pts with pAML, 1352 (48%) had NK and included in the final analysis. The median age of NK pts was 55 years (range, 18-93). Median WBC, hemoglobin, and platelet count at diagnosis were; 21.3 X 10⁹(range, .2-600), 9.1 g/L (range, 2.7-17.6), and 61 X 10⁹ (range, 5-950), respectively. The median number of mutations/sample was 3 (range, 0-7). The most commonly mutated genes were: NPM1 (49%), DNMT3A (37%), FLT3-ITD (24%), CEBPa (19%), TET2 (17%), IDH2 (17%), and RUNX1 (15%). In univariate Cox regression analysis, mutations in NPM1 (HR .81, median OS 58.3 months[m], p= .008), and CEBPa (single mutant, HR, .8, median OS 91.4 m, and double mutant, HR .69, median OS not reached, p < .001, respectively) were all associated with longer OS, while mutations in DNMT3a (HR 1.26, median OS 24 m, p= .003), FLT3-ITD (HR 1.49, median OS 15.2 m, p< .001), TET2 (HR 1.26, median OS 22.3 m, p= .02), RUNX1 (HR 1.36, median OS 22.3 m, p= .003), SRSF2 (HR 1.58, median OS 20.5 m, p< .001), IDH1 (HR 1.29, median OS 20.5 m, p< .001), and ASXL1 (HR 1.89, median OS 15.6 m, p < .001) were associated with shorter OS. The median OS for pts with 0-2 mutations was 59.3 m (95%CI 38.2- 99.1) compared to 34.1m (95%CI 25.3-49.6) for pts with 3-4 mutations and 16.1m (95%CI 12.4- 24.1) for pts with ≥ 5 mutations, p < .001. Association rules identified several combinations of mutations that impacted OS and are summarized in Figure 1. Based on the median OS of each combination, we divided our pt cohort into favorable, intermediate-1, intermediate-2, and unfavorable categories with median OS of 174.9 m (95%CI 79.4-Not reached), 54.8 m (95%CI 28.7-Not reached), 29.2 m (95%CI 22.8-49.6), and 13.8 m (95%CI 12.2-16.1), respectively, p < .001), Figure 1.

These findings suggest that the prognostic impact of molecular data on OS in AML pts with NK is limited when using only one or two mutations (in the exception of TP53 mutations which are present in ~ 1% of NK AML pts), Figure 1. For example, pts with NPM1 mutations can have variable OS depending on presence or absence of mutations in other genes. The median OS for pts with NMP1^Mut/FLT3-ITD^Wt/DNMT3A^Wt(Mut = mutated, Wt = wild type) was 99.1 m compared to 13.4 m for pts with NPM1^Mut/FLT3-ITD^Mut/DNMT3A^Mut (triple positive), p < .001, Figure 1.

Conclusions

We developed genomic combinations that can improve the risk stratification of AML pts with NK using unbiased advanced analytic approaches. These combinations can divide patients into 4 risk categories that may aid physicians in treatment decisions. Such approaches account for the impact of individual mutations and the complexity of genomic interactions on outcomes.

Figure 1

View largeDownload slide

Figure 1

View largeDownload slide

Close modal