Overcoming Drug Resistance in Multiple Myeloma

  • Bergsagel, Leif (PI)
  • Kumar, Shaji Kunnathu (CoPI)
  • Meurice, Nathalie (CoPI)
  • Stewart, A. Keith (PI)
  • Ness, Brian Van (PI)

Project: Research project

Project Details


OVERALL ABSTRACT MM is a plasma cell neoplasm within the bone marrow with significant complexity and heterogeneity at the molecular level. Despite improvements in the clinical outcomes achieved with newer therapies, wide inter-individual variation in response to treatment is a major limitation in achieving consistent therapeutic effects, or cures. Not all patients respond to initial therapies (innate resistance), and those that do frequently relapse with refractory disease (acquired resistance). The central hypothesis we are addressing is that rational therapeutic development in MM should be based on an understanding of the underlying genetics and biology of the tumor that identify sensitivity and resistance to current and future drugs. In this Myeloma-DRSC we are proposing 3 Projects that will: 1) develop a high throughput drug screening platform for myeloma cell lines representing the wide diversity of MM biology; and use this platform to screen primary patient tumor cells for most effective responses, including combination therapies; 2) develop a comprehensive mutational and germline variation panel that will be correlated to drug responses in vitro and in vivo, as well as toxicities; 3) identify the mechanism of response and resistance for two of the most common therapeutics used in MM treatment: IMiDS and proteasome inhibitors; 4) develop genetic and immunophenotypic signatures that effectively predict tumor response, resistance, and patient toxicities; 5) identify tumor sub-populations that may contribute to emerging resistance; and 6) identify strategies to predict drug resistance and approaches to overcome resistance.
Effective start/end date9/30/178/31/22


  • National Cancer Institute: $1,054,898.00


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.