Sahaana
Sankaran
Characterizing Progressive DTP States Across Cancer Cell Models
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Authors:
Sahaana Sankaran
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About Paper:
Drug tolerant persisters (DTP) are a small population of cancer cells that resist anti-cancer treatment. The DTP cell population is not defined by prior genetic differences, and little is known about molecular changes that occur during drug exposure. However, at the point of cancer relapse, clinical treatments are less effective, highlighting the need for an improved understanding of DTP progression. This project characterizes progressive DTP states across different cancer cell models and anti-cancer drugs. We hypothesize that the DTP population of cells is heterogeneous, with subpopulations progressing through diverging trajectories. We first performed dose-response experiments on ten cell line models, representing ovarian, pancreatic, gastric, lung, and breast cancer, to establish appropriate inhibitor concentrations to generate DTPs. Next, we generated cell survival curves for the cell lines across 6 days of treatment. We then performed live-cell imaging experiments using FUCCI reporters. All cell lines had a surviving DTP population by the final day of treatment and a high proportion of quiescent cells. However, cytotoxic and cytostatic survival dynamics differed across cell lines. Finally, we performed single-cell RNA sequencing on the cell lines treated across several days. Our preliminary analysis of single-cell sequencing data showed that the DTP population were highly heterogeneous, displaying molecular phenotypes that dynamically changed across days of treatment. These results show that while DTPs exit the cell cycle in response to treatment, they are a dynamic, heterogeneous population that undergoes different transcriptional changes and exhibit varying survival dynamics. This study fills a critical knowledge gap regarding the earliest cellular states that enable cell tolerance to anti-cancer therapies. Recognizing DTPs as a heterogeneous group, rather than a unified subpopulation, will allow for improved therapeutic targeting. SOOSOHO STH OOOH SHC OSS SOHC SSS OHO OHCOHOOCH ESL OHCHCOECOHO SPPBDFIDIDDFIPPDDDIPSDIDPIIFFDDISISOHIDIHDIFIHIIIIIOOO
Source:
University of Illinois Chicago
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Co-authors:
Sahaana Sankaran