‘Integrative transcriptomic and preclinical modelling to identify targetable signaling axes in chronic myelomonocytic leukemia’
Integrative transcriptomic and preclinical modelling to identify targetable signaling axes in chronic myelomonocytic leukemia
An SWG Grant-supported project initiated by the SWG on Myelodysplastic Syndromes (MDS).
Full project title
Project lead
Dr Yu-Hung Wang, MD, MSc, PhD
National Taiwan University Cancer Center (NTUCC), Taiwan
Project background and aims
Chronic myelomonocytic leukaemia (CMML) is an aggressive myeloid malignancy with overlapping dysplastic and proliferative features and a highly variable clinical course. Although recurrent mutations in genes such as TET2, ASXL1, SRSF2 and K/NRAS contribute to disease development, these alone do not account for the striking heterogeneity in outcomes, nor are they sufficient to guide treatment choices. Existing therapies, including hydroxycarbamide, hypomethylating agents and supportive care, provide modest and inconsistent benefit, while prospective CMML trials remain limited. There is therefore a clear need for biologically informed biomarkers that more accurately define disease subsets and identify patients who may respond to targeted interventions.
Recent studies in AML have demonstrated that patients without FLT3 mutations may still display FLT3-like transcriptional programmes and gain meaningful benefit from FLT3 inhibition. Drawing on this concept, our project seeks to determine whether analogous “mutation-like” transcriptomic signatures exist in CMML. Through RNA sequencing of primary CMML samples, we will examine the activity of key signalling pathways, including RAS/MAPK, PI3K/AKT, JAK/STAT and FLT3-associated programmes, to establish whether these patterns can refine risk stratification beyond mutational profiling and better correlate with clinical behaviour.
The EHA SWG grant is pivotal in enabling this work, supporting sequencing, bioinformatic analyses and the integration of molecular and clinical datasets. This funding will allow us to develop a biologically grounded framework for CMML classification, identify signalling-defined patient subgroups, and generate the preliminary evidence required to explore new therapeutic avenues, including pathway-directed agents with potential relevance to CMML. It will also strengthen collaborative research across CMML communities and provide a foundation for future translational studies aimed at improving outcomes for patients who currently have few effective treatment options.