American Association for Cancer Research
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Table S1 from Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma

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posted on 2023-04-14, 08:23 authored by Lijun Yao, Julia T. Wang, Reyka G. Jayasinghe, Julie O'Neal, Chia-Feng Tsai, Michael P. Rettig, Yizhe Song, Ruiyang Liu, Yanyan Zhao, Omar M. Ibrahim, Mark A. Fiala, Julie M. Fortier, Siqi Chen, Leah Gehrs, Fernanda Martins Rodrigues, Michael C. Wendl, Daniel Kohnen, Andrew Shinkle, Song Cao, Steven M. Foltz, Daniel Cui Zhou, Erik Storrs, Matthew A. Wyczalkowski, Smrithi Mani, Scott R. Goldsmith, Ying Zhu, Mark Hamilton, Tao Liu, Feng Chen, Ravi Vij, Li Ding, John F. DiPersio

Clinical information of patients in scRNA-seq discovery cohort, flow cytometry validation cohort and proteomics validation cohort, including age, gender, race, ISS stage, time to disease progression (TTPD). Disease stage for WashU cohort 1.

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Paula and Rodger Riney Foundation

National Cancer Institute (NCI)

United States Department of Health and Human Services

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ARTICLE ABSTRACT

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy. Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

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