bioRxivSystems BiologyPreprint
Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy
Jeremy WorleyiD, Heeju NohiD, Daoqi YouiD, Mikko M. TuruneniD, Hongxu DingiD, Evan PaulliD, Aaron Timothy GriffiniD, Adina GrunniD, Mingxuan ZhangiD, Kristina C. GuillaniD, Erin C. BushiD, Samantha BrosiusiD, Hanina H. HibshooshiD, Prabhjot Singh MundiiD, Peter A. SimsiD, Piero D. DalerbaiD, Filemon S. Dela CruziD, Andrew L. KungiD, Andrea CalifanoiD
Abstract
Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential--often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel--a commonly used chemotherapeutic --displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations. Statement of significanceNetwork-based approaches, as shown in a study on metastatic breast cancer, can develop effective combinatorial therapies targeting complementary subpopulations. By analyzing scRNA-seq data and using clinically relevant drugs, researchers identified and depleted chemoresistant Cancer Stem-Like Cells, enhancing the efficacy of standard chemotherapies.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 1
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