Abstract
Acquired resistance and clonal heterogeneity are critical challenges in cancer treatment, and the lack of effective computational tools hampers the discovery of new treatments to overcome resistance. Using high-throughput transcriptomic databases of compound perturbation profiles, we have developed a bioinformatic strategy for identifying candidate drugs to overcome resistance with combinatorial therapy. We devised this strategy during an investigation into the acquired resistance against PARP inhibitors (PARPi) in a triple-negative inflammatory breast cancer cell line. In this study, we derived multiple PARPi-resistant clones and characterized their transcriptomic adaptations compared to the parental clone. The transcriptomes of the resistant clones showed substantial heterogeneity, highlighting the importance of characterizing multiple clones from the same tumour. Surprisingly, we found that these transcriptomic changes may not actually confer PARPi resistance, but they may nevertheless induce a shared secondary vulnerability. By modeling our data in relation to transcriptomic perturbation profiles of compounds, we uncovered deficiencies in Ras signaling that resulted from transcriptional adaptation to long-term PARPi treatment across multiple resistant clones. Due to these induced deficiencies, we predicted that the resistant clones would be sensitive to pharmacological reinforcement of PARPi-induced transcriptional adaptation. We then experimentally validated this predicted vulnerability that is shared by multiple resistant clones. Our results thus provide a promising paradigm for integrating transcriptomic data with compound perturbation profiles in order to identify drugs that can exploit an induced vulnerability and overcome therapeutic resistance, thus providing another strategy towards precision oncology.