An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies

Abstract

Since its reclassification as a distinct disease entity, clinical research efforts have attempted to establish baseline characteristics and prognostic scoring systems for chronic myelomonocytic leukemia (CMML). Although existing data for baseline characteristics and CMML prognostication have been robustly developed and externally validated, these results have been limited by the small size of single-institution cohorts. We developed an international CMML data set that included 1832 cases across eight centers to establish the frequency of key clinical characteristics. Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7.5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%. We additionally interrogated existing CMML prognostic models and found that they are all valid and have comparable performance but are vulnerable to upstaging. Using random forest survival analysis for variable discovery, we demonstrated that the prognostic power of clinical variables alone is limited. Last, we confirmed the independent prognostic relevance of ASXL1 gene mutations and identified the novel adverse prognostic impact imparted by CBL mutations. Our data suggest that combinations of clinical and molecular information may be required to improve the accuracy of current CMML prognostication.

Figure 1

KM survival analysis of seven existing CMML prognostic models. KM survival analysis of (a) IPSS, (b) R-IPSS, (c) MD Anderson Scoring System, (d) MD Anderson Prognostic Score, (e) DUSS, (f) MAYO and (g) CPSS. Number of evaluable cases for each model and P-value from log-rank test is shown.

Figure 2

Relative prognostic power of existing CMML models using the entire cohort. ROC curves of all clinical models tested in 1011 evaluable cases in shown for OS (a) and LFS (b) at 36 months. A comparison between the AUC of the ROC curves and the Harrell's C-index is shown in (c). *P<0.05 when comparing AUC of R-IPSS to IPSS.

Figure 3

Relative prognostic power of existing CMML models parsed by MDS-CMML and MPN-CMML. The OS of our international database parsed by MDS-CMML and MPN-CMML (a). The ROC curves of all clinical models tested for MDS-CMML (b) and MPN-CMML (c) is shown for OS at 36 months.

Figure 4

Random forest survival analysis generates a novel CMML model that is comparable to existing models. The results of the random forest survival analysis for OS (a) and LFS (b) are shown. The ROC curves for all clinical models, including the new model generated using the variables discovered with random forest analysis is shown for OS (c) and LFS (d).

Figure 5

Prognostic significance of genetic data in the international CMML database. The frequency and distribution of mutations is shown using the cbioportal oncoprinter for two clinically relevant subgroups and the number of cases contributed from each center (a and b). The KM survival analysis for (c) ASXL1, (d) CBL, (e) RUNX1, (f) SRSF2, (g) TET2, (h) SETBP1, (i) NRAS, (j) JAK2 and (k) EZH2. The number of evaluable cases for each gene and P-value from log-rank test is shown.