Somatic Mutations of ASXL1, RUNX1 and SETBP1 Improve Prognostic Stratification of Patients with Chronic Myelomonocytic Leukemia

Chronic myelomonocytic leukemia (CMML) is a myelodysplastic/myeloproliferative neoplasm characterized by a highly variable clinical course. Based on clinical, hematologic and cytogenetic parameters, we previously developed a CMML-specific Prognostic Scoring System (CPSS) that stratifies patients into 4 different risk groups [Blood 2013;121:3005-15]. Recently, recurrent somatic mutations have been identified in CMML, and preliminary evidence suggests that selected mutated genes may provide useful prognostic information.

In this study, we performed a comprehensive mutation analysis of genes implicated in myeloid malignancies in a large and well characterized cohort of CMML patients with the aim to dissect relationships between genotype and disease phenotype and to integrate somatic mutations into a clinical/molecular prognostic model.

Thirty-eight genes were analyzed by high throughput sequencing (Illumina MiSeq, San Diego, CA) in a cohort of 199 CMML patients (pts) diagnosed according to WHO classification, and in 12 pts with monocytosis not fulfilling WHO diagnostic criteria. Myelodysplastic and myeloproliferative subtypes (CMML-MD and CMML-MP, respectively) were defined according to FAB criteria. Least absolute shrinkage and selection operator (Lasso) and Cox proportional hazards methods were adopted to select and weight variables for prognostic scoring.

Ninety-three percent of pts showed at least one somatic mutation (median number per patient: 2, range 0-6). The most frequently mutated genes were TET2 (44%), SRSF2 (43%), ASXL1 (34%), KRAS (11%), NRAS (10%), CUX1 (10%), CBL (9%), RUNX1 (7%), SETBP1 (7%), JAK2 (6%), SF3B1 (6%), and U2AF1 (5%). A significant association was found between mutations in TET2 and RNA splicing factors (P=.037), 42 of 199 CMML pts (21%) showing co-occurrence of TET2 and SRSF2 mutations. Mutations in genes involved in signaling were significantly associated with CMML-MP (P=.002), whereas SF3B1 mutations were associated with CMML-MD (P=.024).

The number of mutations per patient inversely correlated with overall survival (OS) (HR=1.32, P=.021). In univariate analysis, mutations in ASXL1 (HR=2.31, P=.026) , RUNX1 (HR=3.53, P=.02) and SETBP1 (HR=3.85, P=.005) significantly affected OS. Focusing the analysis on disease subtype, ASXL1 mutations significantly affected survival in CMML-MD (HR=3.45, P=.025), whereas CUX1 and SETBP1 had a significant prognostic value in CMML-MP (HR=4.33, P=.013 and HR=4.4, P=.025, respectively).

In order to investigate the additive value of somatic mutations to current prognostic assessment, we first fitted a Lasso Cox regression model for genetic variable selection. The selected variables were then included in an unpenalized Cox regression in order to obtain unbiased coefficients. The statistically significant variables were CPSS-specific cytogenetic risk groups (HR=2.49, P=.001), mutations in ASXL1 (HR=2.77, P=.018), RUNX1 (HR=5.39, P=.009) and SETBP1 (HR=3.96, P=.013). Based on regression coefficients, we defined a CMML-specific genetic risk score that was able to identify three different groups (Low risk: normal karyotype and –Y; Intermediate: other abnormalities, mutations in ASXL1; High: trisomy 8, complex karyotype, mutations in RUNX1 or SETBP1), with significantly different OS (HR=2.24, P<.001). The Akaike information criterion showed that this genetic risk score performed better than the original CPSS cytogenetic risk classification (AIC 274 vs. 282, respectively). According to the CMML-specific genetic risk score, 36% of patients had a shift toward a higher risk category compared with cytogenetic risk classification. Then, the new genetic risk categories were integrated in the CPSS (CPSS-Mol), which was able to identify 4 risk groups with different OS (HR=2.29, P<.001) and risk of disease progression (HR=2.62, P<.001). As a robustness analysis, we also fitted a Lasso Cox regression model including clinical and genetic variables, and all variables included in the clinical/molecular score were confirmed. The Akaike information criterion showed that the CPSS-Mol performed better than the original CPSS (AIC 251 vs. 253, respectively).

In conclusion, this study showed that mutation pattern in CMML significantly correlates with disease phenotype. The integration of somatic mutations in current scoring systems significantly improves prognostic stratification of patients.