Machine Learning-Derived Immune Gene Signature Predicts Prognosis and Therapeutic Vulnerabilities in Multiple Myeloma

The immune microenvironment contributes substantially to the biological and clinical heterogeneity of multiple myeloma (MM), yet immune-related molecular biomarkers with reproducible prognostic value remain limited. Here, we developed a 12-gene immune-related gene signature (IRGS) using an integrative machine-learning framework and evaluated its prognostic performance across multiple MM cohorts. The IRGS consistently stratified overall survival and remained independently associated with outcome after adjustment for established clinical covariates. Its prognostic discrimination was comparable to that of IFM15 and generally exceeded that of MRCIX6 and a mitophagy-related signature across the evaluated validation datasets. Single-cell RNA sequencing further revealed marked cell type-dependent variation in the activity of the 12-gene module, with comparatively low activity in plasma cells and higher activity in several non-plasma compartments, indicating that the bulk-derived IRGS reflects a multicellular bone marrow transcriptional context rather than an exclusively malignant plasma cell intrinsic program. Somatic mutation analysis identified distinct mutational patterns between IRGS-defined groups, including relative enrichment of DIS3 mutations in the low-IRGS group and MUC16 mutations in the high-IRGS group, together with a modestly higher tumor mutational burden in low-IRGS patients. Transcriptome-based drug-response prediction further suggested differential therapeutic vulnerabilities, with high- and low-IRGS groups showing distinct predicted sensitivity patterns across apoptosis-, DNA damage-, BET-, checkpoint-, and replication-stress-related agents. Collectively, these findings define the IRGS as a complementary immune-associated molecular biomarker for prognostic stratification in MM and provide a framework linking prognosis with multicellular transcriptional context, somatic mutational characteristics, and candidate therapeutic vulnerabilities.