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

Area of Science:

• Oncology
• Genomics
• Transcriptomics

Background:

• Multiple myeloma (MM) treatment efficacy varies based on genomic background.
• The specific mutational and transcriptional landscapes influencing differential treatment responses in MM remain largely uncharacterized.

Purpose of the Study:

• To delineate the distinct mutational and transcriptional landscapes in MM patients with differential responses to first-line treatment.
• To develop a predictive model for treatment response in MM patients.

Main Methods:

• Whole-exome sequencing (WES) and transcriptomic data from over 200 MM patients (MMRF-COMPASS project) were analyzed.
• Somatic mutations and mutational signatures were assessed using R package maftools.
• Differential gene expression was calculated using R package DESeq2, with feature selection via LASSO regression.

Main Results:

• Newly discovered recurrent mutated genes (e.g., TTN, MUC16) were identified. TP53 mutations were more frequent in the non-complete remission (nonCR) group.
• DNA repair-associated mutational signatures were enriched in CR patients, while NF-kappa B and TGF-β pathway activity was suppressed.
• A transcriptome-based predictive model with 20 genes demonstrated promising accuracy for predicting treatment response.

Conclusions:

• Distinct mutational and transcriptional profiles correlate with treatment response in multiple myeloma.
• A novel 20-gene predictive model shows potential for guiding first-line treatment decisions in newly diagnosed MM patients.