Tracing back primed resistance in cancer via sister cells

Jun Dai, Shuyu Zheng, Matías M. Falco, Jie Bao, Johanna Eriksson, Sanna Pikkusaari, Sofia Forstén, Jing Jiang, Wenyu Wang, Luping Gao, Fernando Perez-Villatoro, Olli Dufva, Khalid Saeed, Yinyin Wang, Ali Amiryousefi, Anniina Färkkilä, Satu Mustjoki, Liisa Kauppi, Jing Tang () and Anna Vähärautio ()
Additional contact information
Jun Dai: University of Helsinki
Shuyu Zheng: University of Helsinki
Matías M. Falco: University of Helsinki
Jie Bao: University of Helsinki
Johanna Eriksson: University of Helsinki
Sanna Pikkusaari: University of Helsinki
Sofia Forstén: University of Helsinki
Jing Jiang: University of Helsinki
Wenyu Wang: University of Helsinki
Luping Gao: University of Helsinki
Fernando Perez-Villatoro: University of Helsinki
Olli Dufva: University of Helsinki
Khalid Saeed: University of Helsinki
Yinyin Wang: University of Helsinki
Ali Amiryousefi: University of Helsinki
Anniina Färkkilä: University of Helsinki
Satu Mustjoki: University of Helsinki
Liisa Kauppi: University of Helsinki
Jing Tang: University of Helsinki
Anna Vähärautio: University of Helsinki

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

Date: 2024
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DOI: 10.1038/s41467-024-45478-7

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