Abstract
Nongenetic adaptive resistance to chemotherapy, driven by transcriptional rewiring, is emerging as a significant mechanism in tumor survival. In this study we combined longitudinal transcriptomics with temporal pattern analysis to investigate patient-specific mechanisms underlying acquired resistance in breast cancer. Matched tumor biopsies (pretreatment, posttreatment, and adjacent normal) were collected from breast cancer patients who received neoadjuvant chemotherapy. Transcriptomes were analyzed by longitudinal gene-pattern classification to track patient-specific gene expression alterations that occur during treatment. Our findings reveal that resistance-associated genes were already dysregulated in primary tumors, suggesting the presence of a preexisting drug-tolerant state. While each patient displayed unique resistance-associated gene rewiring, these alterations converged into a limited number of dysregulated functional modules. Notably, patients receiving the same treatment exhibited distinct rewiring of genes and pathways, revealing parallel, individualized routes to resistance. In conclusion, we propose that tumor cells survive chemotherapy by sustaining or amplifying a preexisting drug-tolerant state that circumvents drug action. We suggest that individualized “chemoresistome maps” could identify cancer vulnerabilities and inform personalized therapeutic strategies to overcome or prevent resistance.
Original language | English |
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Journal | Molecular Oncology |
DOIs | |
Publication status | Published Online - 28 Apr 2025 |
Funding
This research was supported by a grant from the Israel Ministry of Science and Technology (Funding agency: 10.13039/501100006245) (grant # 3\u201011175). This study makes use of data generated by the Molecular Taxonomy of Breast Cancer International Consortium [ 8 ]. The Consortium Data Access Committee approved application for data access. GF is the Incumbent of the David and Stacey Cynamon Research fellow Chair in Genetics and Personalized Medicine. We thank Jonathan Barlev and Barak Markus for helpful discussions regarding data analysis.
All Science Journal Classification (ASJC) codes
- Molecular Medicine
- Oncology
- Genetics
- Cancer Research