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secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy
Harish Kumar1, Suman Mazumder1,2, Sayak Chakravarti1
1Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL, USA.
Abstract:
Multiple myeloma, the second-most common hematopoietic malignancy in the United States, still remains an incurable disease with dose-limiting toxicities and resistance to primary drugs like proteasome inhibitors (PIs) and Immunomodulatory drugs (IMiDs).We have created a computational pipeline that uses pharmacogenomics data-driven optimization-regularization/greedy algorithm to predict novel drugs ("secDrugs") against drug-resistant myeloma. Next, we used single-cell RNA sequencing (scRNAseq) as a screening tool to predict top combination candidates based on the enrichment of target genes. For in vitro validation of secDrugs, we used a panel of human myeloma cell lines representing drug-sensitive, innate/refractory, and acquired/relapsed PI- and IMiD resistance. Next, we performed single-cell proteomics (CyTOF or Cytometry time of flight) in patient-derived bone marrow cells (ex vivo), genome-wide transcriptome analysis (bulk RNA sequencing), and functional assays like CRISPR-based gene editing to explore molecular pathways underlying secDrug efficacy and drug synergy. Finally, we developed a universally applicable R-software package for predicting novel secondary therapies in chemotherapy-resistant cancers that outputs a list of the top drug combination candidates with rank and confidence scores.Thus, using 17AAG (HSP90 inhibitor) + FK866 (NAMPT inhibitor) as proof of principle secDrugs, we established a novel pipeline to introduce several new therapeutic options for the management of PI and IMiD-resistant myeloma.
Insights
Researchers developed a computational pipeline to identify novel drug combinations for treating multiple myeloma (MM) that is resistant to standard therapies. This approach offers new therapeutic options for patients with relapsed or refractory MM.
Area of Science:
- Computational biology and bioinformatics
- Hematologic oncology
- Pharmacogenomics
Background:
- Multiple myeloma (MM) is a significant hematopoietic malignancy with limited treatment options and frequent drug resistance.
- Existing therapies, including proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs), face challenges due to dose-limiting toxicities and resistance.
- There is a critical need for novel therapeutic strategies to overcome drug resistance in multiple myeloma.
Purpose of the Study:
- To develop and validate a computational pipeline for predicting novel secondary drugs ('secDrugs') against drug-resistant multiple myeloma.
- To identify effective drug combinations for overcoming resistance to proteasome inhibitors and immunomodulatory drugs.
- To establish a universally applicable R-software package for predicting secondary therapies in chemotherapy-resistant cancers.
Main Methods:
- Utilized a pharmacogenomics data-driven optimization-regularization/greedy algorithm to predict novel drugs.
- Employed single-cell RNA sequencing (scRNAseq) for screening and predicting combination candidates based on target gene enrichment.
- Conducted in vitro validation using human myeloma cell lines with varying resistance profiles, alongside ex vivo single-cell proteomics (CyTOF), bulk RNA sequencing, and CRISPR-based gene editing.
Main Results:
- Successfully established a novel computational pipeline for predicting secondary drugs against drug-resistant myeloma.
- Identified promising drug combinations, exemplified by 17AAG (HSP90 inhibitor) + FK866 (NAMPT inhibitor), for overcoming PI and IMiD resistance.
- Developed a versatile R-software package to rank and score potential drug combination therapies for resistant cancers.
Conclusions:
- The developed computational pipeline and R-software package provide a novel framework for discovering therapeutic strategies in drug-resistant multiple myeloma.
- This approach offers potential new treatment options for patients with refractory or relapsed multiple myeloma.
- The methodology is adaptable for predicting secondary therapies across various chemotherapy-resistant cancers.
