Vasaikar, Suhas

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

Identifying bioenergetics that facilitate the epithelial to mesenchymal transition (EMT) in breast cancer cells may uncover targets to treat incurable metastatic disease. Metastasis is the number one cause of cancer-related deaths; therefore, it is urgent to identify new treatment strategies to prevent the initiation of metastasis. To characterize the bioenergetics of EMT, we compared metabolic activities and gene expression in cells induced to differentiate into the mesenchymal state with their epithelial counterparts. We found that levels of GLS2, which encodes a glutaminase, are inversely associated with EMT. GLS2 down-regulation was correlated with reduced mitochondrial activity and glutamine independence even in low-glucose conditions. Restoration of GLS2 expression in GLS2-negative breast cancer cells rescued mitochondrial activity, enhanced glutamine utilization, and inhibited stem-cell properties. Additionally, inhibition of expression of the transcription factor FOXC2, a critical regulator of EMT in GLS2-negative cells, restored GLS2 expression and glutamine utilization. Furthermore, in breast cancer patients, high GLS2 expression is associated with improved survival. These findings suggest that epithelial cancer cells rely on glutamine and that cells induced to undergo EMT become glutamine independent. Moreover, the inhibition of EMT leads to a GLS2-directed metabolic shift in mesenchymal cancer cells, which may make these cells susceptible to chemotherapies.