As reported in the Journal of Clinical Oncology, Lanino et al have developed a new model (international chronic myelomonocytic leukemia [CMML] prognostic scoring system [iCPSS]) for characterizing CMML by combining molecular information with clinical information.
Study Details
The study involved use of a retrospective cohort of 3,013 patients (training cohort) to develop models of molecular-based disease taxonomy and prognostication that could affect clinical decision-making in CMML. The model was assessed in a prospective cohort of 516 patients (validation cohort).
Key Findings
In the training cohort, nine sets of characteristics were identified as having distinct genomic features and clinical outcomes (all P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. It was observed that approximately 15% of patients exhibited molecular/clinical overlap with other myeloid neoplasms.
Molecular characteristics and clinical information were integrated to construct the iCPSS, which incorporated mutations in nine genes with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups having distinct probabilities of overall survival and leukemia-free survival in both the training and validation cohorts (all P < .001), with the system outperforming existing prognostic models.
Overall, 55% of patients in the retrospective cohort had restratified risk, including downstaging in 35.3% and upstaging in 20.1%. In the prospective cohort, 61% were restratified.
Decision analysis indicated that iCPSS could refine optimal timing of allogeneic transplantation at the individual patient level. Compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases; the changes resulted in a significant gain-in-life expectancy in the eligible patient population (P < .001).
The investigators concluded: “Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.”
Matteo Giovanni Della Porta, MD, of IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy, is the corresponding author for the Journal of Clinical Oncology article.
DISCLOSURE: The study was supported by the European Union–Horizon 2020/2023 program and Innovative Health Initiative and others. For full disclosures of the study authors, visit ascopubs.org.
