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The Functional Transcriptomic Landscape Informs Therapeutic Strategies in Multiple Myeloma.

Praneeth Reddy Sudalagunta1, Rafael R Canevarolo1, Mark B Meads2

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|October 30, 2024
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Summary

Researchers identified gene expression patterns that predict patient response to multiple myeloma drugs. These "footprints" revealed a strategy for sequential therapy with daratumumab and selinexor, improving patient survival.

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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.