Integrating clinical features and genetic lesions in the risk assessment of patients with chronic myelomonocytic leukemia

Abstract

Chronic myelomonocytic leukemia (CMML) is a myelodysplastic/myeloproliferative neoplasm with variable clinical course. To predict the clinical outcome, we previously developed a CMML-specific prognostic scoring system (CPSS) based on clinical parameters and cytogenetics. In this work, we tested the hypothesis that accounting for gene mutations would further improve risk stratification of CMML patients. We therefore sequenced 38 genes to explore the role of somatic mutations in disease phenotype and clinical outcome. Overall, 199 of 214 (93%) CMML patients carried at least 1 somatic mutation. Stepwise linear regression models showed that these mutations accounted for 15% to 24% of variability of clinical phenotype. Based on multivariable Cox regression analyses, cytogenetic abnormalities and mutations in RUNX1, NRAS, SETBP1, and ASXL1 were independently associated with overall survival (OS). Using these parameters, we defined a genetic score that identified 4 categories with significantly different OS and cumulative incidence of leukemic evolution. In multivariable analyses, genetic score, red blood cell transfusion dependency, white blood cell count, and marrow blasts retained independent prognostic value. These parameters were included into a clinical/molecular CPSS (CPSS-Mol) model that identified 4 risk groups with markedly different median OS (from >144 to 18 months, hazard ratio [HR] = 2.69) and cumulative incidence of leukemic evolution (from 0% to 48% at 4 years, HR = 3.84) (P < .001). The CPSS-Mol fully retained its ability to risk stratify in an independent validation cohort of 260 CMML patients. In conclusion, integrating conventional parameters and gene mutations significantly improves risk stratification of CMML patients, providing a robust basis for clinical decision-making and a reliable tool for clinical trials.

Figure 1

Mutation patterns observed in patients with CMML in the learning cohort. The plot represents the distribution of somatic lesions in genes mutated in ≥1% of patients. Each column represents an individual patient sample.

Figure 2

Multivariable models to predict hematologic variables from driver mutations. (A) Multivariable model to predict hemoglobin value from driver mutations. The step curve shows the cumulative proportion of variance (y-axis, left) in hemoglobin levels explained by each of the genetic variables. The gray shaded area represents the 95% confidence interval (CI) for this curve. Coefficient estimates for each gene in the model including all variables (y-axis, right) are shown as circles (coefficients >0 indicate positive correlation with hemoglobin levels, ie, the covariate is associated with higher hemoglobin levels; coefficients <0 indicate negative correlation with hemoglobin levels, ie, the covariate is associated with lower hemoglobin levels). (B) Multivariable model to predict WBC from driver mutations, as for (A). (C) Multivariable model to predict BM blast count from driver mutations, as for panels A-B.

Figure 3

OS and cumulative incidence of leukemic evolution according to the CPSS-Mol in the learning cohort. (A) OS and (B) cumulative incidence of evolution into AML of patients classified into CPSS-Mol risk groups. The number of patients (N) in each category is reported: low risk group accounted for 22% of patients, intermediate-1 for 29%, and intermediate-2 and high-risk groups for 33% and 16% of patients, respectively.

Figure 4

OS and cumulative incidence of leukemic evolution according to the CPSS-Mol in the validation cohort. (A) OS and (B) cumulative incidence of evolution into AML of patients classified into CPSS-Mol risk groups. The number of patients (N) in each category is reported: low risk group accounted for 14% of patients, intermediate-1 for 20%, and intermediate-2 and high-risk groups for 38% and 27% of patients, respectively.