Whole-exome sequencing enhances prognostic classification of myeloid malignancies

Display Omitted A robust prognostic classifier built from exome sequencing data.Prognostic categories are strongly reproducible in an independent validation set.Classifier that operates in a pan-diagnostic manner (MDS, MDS/MPN, and AML).Classifier outperforms standard prognostic classifiers within diagnoses.Classifier is distributed publicly as a web-based tool. PurposeTo date the standard nosology and prognostic schemes for myeloid neoplasms have been based on morphologic and cytogenetic criteria. We sought to test the hypothesis that a comprehensive, unbiased analysis of somatic mutations may allow for an improved classification of these diseases to predict outcome (overall survival). Experimental designWe performed whole-exome sequencing (WES) of 274 myeloid neoplasms, including myelodysplastic syndrome (MDS, N=75), myelodysplastic/myeloproliferative neoplasia (MDS/MPN, N=33), and acute myeloid leukemia (AML, N=22), augmenting the resulting mutational data with public WES results from AML (N=144). We fit random survival forests (RSFs) to the patient survival and clinical/cytogenetic data, with and without gene mutation information, to build prognostic classifiers. A targeted sequencing assay was used to sequence predictor genes in an independent cohort of 507 patients, whose accompanying data were used to evaluate performance of the risk classifiers. ResultsWe show that gene mutations modify the impact of standard clinical variables on patient outcome, and therefore their incorporation hones the accuracy of prediction. The mutation-based classification scheme robustly predicted patient outcome in the validation set (log rank P=6.7710-21; poor prognosis vs. good prognosis categories HR 10.4, 95% CI 3.2133.6). The RSF-based approach also compares favorably with recently-published efforts to incorporate mutational information for MDS prognosis. ConclusionThe results presented here support the inclusion of mutational information in prognostic classification of myeloid malignancies. Our classification scheme is implemented in a publicly available web-based tool (http://myeloid-risk.case.edu/).