Analysis of Gene Expression Heterogeneity Reveals Therapeutic Targets and Novel Regulators of Metastasis
Abstract
Tumor cell heterogeneity has been implicated in metastatic progression of solid tumors such as triple-negative breast cancer (TNBC), leading to resistance and recurrence. We hypothesized that genes with low cell-to-cell transcriptional variability may be effective therapeutic targets, and that analysis of variability may facilitate identification of new metastatic regulators. Here we demonstrate, using single cell RNA sequencing, that the metastasis suppressor Raf Kinase Inhibitory Protein (RKIP) reduced overall transcriptional variability in TNBC xenograft tumors. Focusing on genes with reduced variability in response to RKIP, we identified targetable gene sets such as oxidative phosphorylation and showed that metformin could inhibit RKIP-expressing but not control tumor growth. We also found many regulators of cancer progression including a novel epigenetic metastasis suppressor, KMT5C. These studies demonstrate that a metastatic regulator can alter transcriptional variability in tumors and reveal the importance of genes involved in heterogeneity as potential therapeutic targets and regulators of metastatic progression in cancer.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank Sebastian Pott for valuable advice on experimental and computational methods, and Robert Rosner for helpful discussion of variability. We also thank Jonathan Willner for interpreting pathological features from IHC, Damian Berardi for advice on mouse models, and former and current members of the Rosner Lab (Ali Yesilkanal, Marcelo Fernandez De La Mora, Leticia Stock, Long Nguyen, and Margarite Matossian) for helpful feedback on the manuscript. We thank the University of Chicago Research Computing Center, Integrated Light Microscopy Core (Shirley Bond), Human Tissue Resource Center (Terri Li and Can Gong), Animal Resources Center (Ani Solanki), Genomics Facility (Pieter Faber), Cellular Screening Center, Center for Research Informatics, DNA Sequencing Facility, and Integrated Small Animal Imaging Research Resource for providing technical assistance. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was supported by NIH R01 GM121735-01 (M.R.R), the Janet D. Rowley Discovery Fund (M.R.R), the Goldblatt Endowment Fund (D.Y.), and the Fitch Scholarship Fund (D.Y.). G.B. was supported by NIH R35 GM122561, the Laufer Center for Physical and Quantitative Biology, a Stony Brook Cancer Center Engineering, Physical Sciences and Oncology Pilot Fund and the Laufer Center for Physical and Quantitative Biology. The University of Chicago Genomics Facility is supported by the Cancer Center Support Grant (P30 CA014599). Author Contributions. Conceptualization: MRR, DY Methodology: MRR, DY, GB, MC Software: DY Formal Analysis: DY, GB Investigation: DY, CD, AV, LR-M, MH Resources: MRR Data Curation: DY Writing: MRR, DY, GB Visualization: DY, GB, MC Supervision: MRR Project Administration: MRR Funding Acquisition: MRR Data Availability statement. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Raw and processed RNA-seq data were deposited into the GEO 20 database (GSE220671). The authors have declared no competing interest.Attached Files
Submitted - 2022.12.16.520816v1.full.pdf
Supplemental Material - media-1.xlsx
Supplemental Material - media-2.xlsx
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Additional details
- Eprint ID
- 120164
- Resolver ID
- CaltechAUTHORS:20230316-182695000.52
- R01 GM121735-01
- NIH
- University of Chicago
- R35 GM122561
- NIH
- Stony Brook University
- P30 CA014599
- NIH
- Created
-
2023-03-18Created from EPrint's datestamp field
- Updated
-
2023-03-18Created from EPrint's last_modified field