Guillermo Montalban-Bravo, Chong Wu, Juan José Rodríguez‐Sevilla, Yue Wei, K. Chien, Ian Bouligny, Rashmi Kanagal-Shamanna, Ziyi Li, Anuya Natu, Mark E. Gurney, Alexandre Bazinet, Danielle E. Hammond, Alex Bataller, Gautam Borthakur, Nicholas J. Short, Courtney D. DiNardo, Tapan M. Kadia, Farhad Ravandi, Naval Daver, Naveen Pemmaraju, Elias Jabbour, Ghayas C. Issa, Sa A. Wang, Keyur P. Patel, Guilin Tang, L. Jeffrey Medeiros, Terra L. Lasho, Christy M. Finke, Aref Al‐Kali, Clifford M. Csizmar, Hassan Alkhateeb, Naseema Gangat, Abhishek A. Mangaonkar, David Román, Leonor Arenillas, Ayalew Tefferi, Hagop M. Kantarjian, Guillermo Garcia-Manero, Xavier Calvo, Mrinal M. Patnaik, Sanam Loghavi
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.