The Functional Transcriptomic Landscape Informs Therapeutic Strategies in Multiple Myeloma

Area of Science:

• Oncology
• Genomics
• Pharmacology

Background:

• Multiple myeloma (MM) is a cancer of plasma cells with several approved therapies.
• Identifying predictive biomarkers for drug response is crucial for optimizing MM treatment strategies.

Purpose of the Study:

• To perform an integrated functional genomic analysis of MM patient samples to discover predictive biomarkers for drug sensitivity.
• To investigate the mechanistic basis of drug response and resistance and to identify novel therapeutic strategies.

Main Methods:

• Integrated analysis of ex vivo drug sensitivity, clinical variables, cytogenetics, mutational profiles, and transcriptomes from MM patient samples.
• Development of a multiple myeloma transcriptomic topology to identify predictive "footprints" for drug sensitivity.
• Validation of transcriptomic footprints for daratumumab (DARA) and selinexor (SELI) using clinical response data.

Main Results:

• A multiple myeloma transcriptomic topology was established, revealing "footprints" associated with ex vivo drug sensitivity.
• Transcriptomic footprints accurately classified clinical responses to DARA and SELI.
• Anticorrelated resistance mechanisms for DARA and SELI were identified, suggesting a rationale for sequential therapy.

Conclusions:

• Functional genomic analysis provides predictive biomarkers and mechanistic insights for multiple myeloma drug response.
• Sequential therapy with SELI following DARA-containing regimens is associated with improved survival, supporting an evolutionary-based treatment strategy.
• The developed computational framework can guide the identification of improved therapeutic strategies for multiple myeloma.