Garcia-Manero, Guillermo

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Abstract Recent updates to monocyte count thresholds recognize oligomonocytic chronic myelomonocytic leukemia (OM-CMML) as an early form of CMML. However, the clinical validity of these changes remains uncertain without incorporating biological and genomic factors. In this study, we analyzed a cohort of 911 patients (249 with OM-CMML, 359 with overt CMML, and 303 with myelodysplastic syndromes) using unsupervised clustering to evaluate the role of genomic determinants in refining CMML diagnosis. Our findings show that CMML molecular signatures (biallelic TET2 mutations or SRSF2-TET2 comutations) are linked to a distinct transcriptome, monocytic bias, classical monocytosis, and a higher risk of progression to overt CMML in OM-CMML cases. We developed a weighted genomic model and diagnostic workflow showing that combining genomic signatures with bone marrow monocyte frequencies in OM-CMML more accurately predicts progression to overt CMML. These findings support integrating genomic determinants and our clinic-ready diagnostic workflow into the CMML diagnostic framework to improve accuracy. Significance: Through comprehensive clinical and genomic profiling of a large patient cohort, alongside immunophenotypic and transcriptional cellular analyses, this study provides evidence that incorporating genomic determinants into the diagnostic criteria for OM-CMML improves diagnostic accuracy and refines the identification of early-stage CMML, thereby preventing misclassification.

AbstractThe progress with intensive chemotherapy and supportive care measures has improved survival in patients with newly diagnosed acute myeloid leukemia (AML). Given the recent development of effective low intensity therapies, an optimal decision on the therapy intensity may improve survival through the avoidance of early mortality. We reviewed the outcome of 3728 patients with newly diagnosed AML who received intensive chemotherapy between August 1980 and May 2020. Intensive chemotherapy was defined as a cumulative cytarabine dose ≥ 700 mg/m2 during induction therapy. We divided the whole cohort into a training and validation group at a 3:1 ratio. The population was divided into a training (2790 patients) and a validation cohort (938 patients). The median age was 55 years (range, 15‐99). Among them, 442 patients (12%) had core‐binding factor AML. Binary logistic regression identified older age, worse performance status, hyperbilirubinemia, elevated creatinine, hyperuricemia, cytogenetic abnormalities other than CBF and ‐Y, and pneumonia as adverse prognostic factors for an early 4‐week mortality. This risk classification for early mortality was verified in the validation cohort of patients. In the validation cohort of more recently treated patients from 2000 to 2017, the 4‐week mortality rates with intensive chemotherapy were 2%, 14%, and 50% in the low‐, high‐, and very high‐risk group, respectively. The mortality rates with low intensity therapies were 3%, 9%, and 20%, respectively. The risk classification guides treatment intensity by the assessment of age, frailty, organ dysfunction, cytogenetic abnormality, and infection to avoid early mortality.