Multi-omics analysis of multiple myeloma patients with differential response to first-line treatment.

Abstract

The genome backgrounds of multiple myeloma (MM) would affect the efficacy of specific treatment. However, the mutational and transcriptional landscapes in MM patients with differential response to first-line treatment remains unclear. We collected paired whole-exome sequencing (WES) and transcriptomic data of over 200 MM cases from MMRF-COMPASS project. R package, maftools was applied to analyze the somatic mutations and mutational signatures across MM samples. Differential expressed genes (DEG) was calculated using R package, DESeq2. The feature selection of the predictive model was determined by LASSO regression. In silico analysis revealed newly discovered recurrent mutated genes such as TTN, MUC16. TP53 mutation was observed more frequent in nonCR (complete remission) group with poor prognosis. DNA repair-associated mutational signatures were enriched in CR patients. Transcriptomic profiling showed that the activity of NF-kappa B and TGF-β pathways was suppressed in CR patients. A transcriptome-based response predictive model was constructed and showed promising predictive accuracy in MM patients receiving first-line treatment. Our study delineated distinctive mutational and transcriptional landscapes in MM patients with differential response to first-line treatment. Furthermore, we constructed a 20-gene predictive model which showed promising accuracy in predicting treatment response in newly diagnosed MM patients.