An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies
Year: 2015
Type: article
Study design: Observational Study
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.
Source: Blood Cancer Journal
DOI: 10.1038/bcj.2015.53
Institutions: University of South Florida, Moffitt Cancer Center, The University of Texas MD Anderson Cancer Center, Mayo Clinic, Hôpital Avicenne, Cleveland Clinic, Memorial Sloan Kettering Cancer Center, Hôpital Saint-Louis, Université Paris Diderot, Royal Bournemouth Hospital, University of Milan, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Université Paris-Saclay, Inserm, Institut Gustave Roussy
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,290
OpenAlex ID: W1656026419
