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Pre-existing cell states predict resistance to multiple treatments

GSE279162 Homo sapiens Expression profiling by high throughput sequencing 9 samples Submitted 2024/10/14 Platform GPL18573
Summary
Pre-existing differences between individual cancer cells can predict which cells will become resistant upon the application of treatment. This understanding has been furthered by novel methods in DNA barcoding that allow tracking of clones and their cell states during treatment. However, previous studies using these techniques have been limited in their scope, focusing on how single cell-states lead to resistance to a single treatment. In this study, we performed multi-treatment, high-throughput clonal tracking and single-cell RNA-sequencing to trace rare clones through the development of resistance to many different treatments in parallel with the goal of identifying cell states associated with multi-treatment resistance. We found that clones that will go on to develop resistance to one treatment had an increased chance of separately developing resistance to other treatments with diverse mechanisms of action. Additionally, we identified high CD44 expression in treatment-naive cells as a predictor of future resistance to multiple different treatments. Furthermore, for cells within the same treatment condition, we found that differences in gene expression states prior to treatment can lead cells to follow divergent paths towards their ultimate resistance fate. This work provides a framework for extracting targetable gene expression states from complex resistance dynamics across multiple treatments to eliminate multi-treatment resistance.
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Direct links to NCBI, no account and no request form: the whole study as GSE279162_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 9 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1171004 and SRA study SRP537501. Searching any of these in the dataset finder brings you back here.

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