CMML2AML: machine-learning discovery of co-mutations and specific single mutations predictive of blast transformation in chronic myelomonocytic leukemia

Abstract

Contemporary risk models in chronic myelomonocytic leukemia (CMML) focus on the prognostic relevance of individual rather than concurrent mutations. In the current study of 605 Mayo Clinic patients with CMML, we applied machine-learning algorithms in order to examine the influence of cooperative mutational interactions on blast transformation (BT). A hierarchical clustering algorithm was developed and tailored for patient stratification using survival outcomes and co-occurrence of genomic alterations. Five molecular clusters were identified with 3-year blast BT rates ranging from 0% to 100% (AUC at 3 years 0.78). A subsequent Cox regression analysis confirmed independent detrimental impact of specific mutations or their combinations including NPM1 (HR 26.7; p < 0.01), “NRAS + SETBP1” (HR 12.6; p < 0.01), “ASXL1 + BCOR” (HR 8.4; p < 0.01), “ASXL1 + RUNX1” (HR 2.2, p < 0.01), JAK2 (HR 2.1; p < 0.01), and “ASXL1 + TET2” (HR 1.7; p = 0.02) while “PHF6+wild-type ASXL1” (HR 5.61e−10; p < 0.01) had a favorable impact. Furthermore, compared to NPM1 wild-type cases, NPM1-mutated patients were less likely to have co-occurring mutations involving ASXL1 (0% vs. 43%, p < 0.01), RUNX1 (0% vs. 17%, p = 0.02), and SRSF2 (7% vs. 39%, p < 0.01) and were more likely DNMT3A (71% vs. 7%, p < 0.01). The prognostic relevance of “NRAS + SETBP1”, “ASXL1 + RUNX1”, NPM1 and BCOR was validated in an external cohort from Italy (N = 501). Taken together, these observations highlight i) the possibility of prognostic interaction of mutations in CMML that should be considered in the development of future risk models and ii) the distinct genotypic and prognostic characteristics of NPM1-mutated CMML.