New Blood Cancer Test Identifies Aggressive Tumors and Predicts Patient Survival
Researchers have identified a specific subtype of lymphoma cells that drives aggressive disease and developed a machine-learning test to predict which patients will fare poorly. The advance could help oncologists decide who needs more intensive treatment, potentially improving outcomes while sparing others from unnecessary toxicity.
Originaltitel: Identifying Distinct Molecular Subtypes and Establishing a Prognostic Framework for DLBCL Patients via Multiomics Analysis and Machine Learning Approaches.
Diffuse large B-cell lymphoma (DLBCL) is characterized by profound heterogeneity that underpins varied clinical outcomes. To decipher this complexity, we performed an integrated single-cell and genomic analysis. Using scRNA-seq data (GSE182434), we identified six distinct malignant B-cell subclusters (MB1-MB6) within the DLBCL ecosystem. Cell-cell communication analysis revealed intricate interaction networks, particularly involving the MIF and Complement pathways. Prognostic analysis of bulk transcriptomic data (GSE32918) identified the MB5-related gene signature as the most critical factor associated with poor overall survival. This MB5 subgroup was associated with enhanced proliferative processes, a higher tumor mutational burden, and specific comutations. Leveraging MB5 marker genes, we developed and validated a robust CoxBoost-RSF machine-learning model that effectively stratified patient risk in independent cohorts. Our study defines the MB5 malignant B-cell subgroup as a key driver of DLBCL aggressiveness and provides both a novel prognostic biomarker and a framework for personalized therapeutic targeting.