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Published March 20, 2024 | Version Published
Journal Article Open

Clonal differences underlie variable responses to sequential and prolonged treatment

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Abstract

Cancer cells exhibit dramatic differences in gene expression at the single-cell level, which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor. A record of this paper’s transparent peer review process is included in the supplemental information.

Copyright and License

© 2024 Elsevier.

Acknowledgement

We thank the members of the Shaffer Lab for input on the experiments and figures of the manuscript. We thank the Arjun Raj Lab at the University of Pennsylvania for the barcoding plasmid library and Nimbus Image software. A.J.F., P.E.W., and S.M.S. recognize support from Grants for Faculty Mentoring Undergraduate Research (A.J.F. and S.M.S., 2021; P.E.W. and S.M.S., 2022). S.M.S. recognizes support from the Wistar/Penn SPORE (P50 CA261608) and NIH Director’s Early Independence Award (DP5OD028144).

Contributions

Conceptualization, D.L.S., A.J.F., and S.M.S.; methodology, D.L.S., A.J.F., and S.M.S.; software, D.L.S., A.J.F., and R.J.V.V.; formal analysis, D.L.S., A.J.F., and R.J.V.V.; investigation, D.L.S., A.J.F., and P.E.W.; data curation, D.L.S., A.J.F., P.E.W., and R.J.V.V.; writing – original draft, D.L.S., A.J.F., P.E.W., R.J.V.V., and S.M.S.; writing – review & editing, D.L.S., A.J.F., P.E.W., R.J.V.V., and S.M.S.; visualization, D.L.S., A.J.F., P.E.W., R.J.V.V., and S.M.S.; funding acquisition, S.M.S., A.J.F., and P.E.W.; supervision, S.M.S.

Conflict of Interest

The authors declare no competing interests.

Additional Information

During the preparation of this work the author(s) used ChatGPT in order to help in paraphrasing and avoiding redundancy in our STAR Methods section. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

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Identifiers

PMCID
PMC11003565

Funding

National Institutes of Health