Abstract 739: Patient-derived xenografts allow deconvolution and prediction of chemotherapy responses
Authors: Jonathan T. Lei, Lacey E. Dobrolecki, Chen Huang, Ramakrishnan R. Srinivasan, Suhas V. Vasaikar, Alaina N. Lewis, Christina Sallas, Na Zhao, Jin Cao, John D. Landua, Chang I. Moon, Yuxing Liao, Susan G. Hilsenbeck, C K. Osborne, Mothaffar F. Rimawi, Matthew J. Ellis, Varduhi Petrosyan, Bo Wen, Kai Li, Alexander B. Saltzman, Antrix Jain, Anna Malovannaya, Gerburg M. Wulf, Elisabetta Marangoni, Shunqiang Li, Daniel C. Kraushaar, Tao Wang, Senthil Damodaran, Xiaofeng Zheng, Funda Meric-Bernstam, Gloria V. Echeverria, Meenakshi Anurag, Xi Chen, Bryan E. Welm, Alana L. Welm, Bing Zhang, Michael T. Lewis
Journal: Cancer Research (2025)

Abstract Introduction: Combination chemotherapy remains essential for clinical management of triple-negative breast cancer (TNBC), making it impossible to assess responses to multiple agents in a single patient. Herein, we conduct multi-omic analyses of TNBC patient-derived xenografts (PDXs) treated with single agent carboplatin and docetaxel, or the combination, to identify candidate mechanisms of resistance, as well as predictive biomarkers to individual treatments, and to develop therapeutic strategies to overcome resistance. Methods: Genomic, transcriptomic, and proteomic profiles of baseline tumors from 50 TNBC PDXs were associated with responses to human equivalent doses of either single agent carboplatin, docetaxel, and the combination. Integration of external TNBC PDX and clinical datasets was performed using ComBat. Protein Marker Selection (ProMS) tool was used to integrate both RNA and protein data to select 5-10 RNA feature combinations for optimized prediction of chemotherapy response in a logistic regression model. Results: Combination responses were generally no better than the best single agent, with enhanced response in only ∼13% of PDX, and apparent antagonism in a comparable percentage. Single ome comparisons showed largely non-overlapping results between genes associated with single agent and combination treatment that validated in independent patient cohorts. Multi-omic analyses of PDXs identified agent-specific treatment-associated biomarkers and predictive biomarker combinations. Notably, integrating proteomic with mRNA data improved machine learning models that achieved AUROC performance of 0.85 to predict pathologic complete response to combination chemotherapy. These models await further evaluation in datasets that may become available, including data from the BEAUTY (NCT02022202), TBCRC 030 (NCT01982448), and RESPONSE (NCT05020860) clinical trials, or in future experimental settings using PDX/PDX organoids. In PDXs responsive to any treatment, several basal cytokeratins were among the top upregulated proteins compared to PDXs resistant to all treatments. KRT5 was validated by IHC in PDX tumors, achieving an AUROC of 0.83 for predicting responsiveness, indicating its potential as a future chemoresponse marker in TNBC. PDXs refractory to all treatments showed dysregulated mitochondrial function. Treatment with romidepsin, an HDAC inhibitor that targets this process indirectly, increased DNA damage, and enhanced carboplatin response, in a chemoresistant PDX model with high abundance of HDAC proteins. Conclusion: Multi-omic characterization identifies molecular mechanisms and predictive biomarkers for stratifying TNBC tumors for single or combination chemotherapy treatments, suggests targeted therapies to augment chemotherapy response, and provides a valuable resource for researchers and clinicians. Citation Format: Jonathan T. Lei, Lacey E. Dobrolecki, Chen Huang, Ramakrishnan R. Srinivasan, Suhas V. Vasaikar, Alaina N. Lewis, Christina Sallas, Na Zhao, Jin Cao, John D. Landua, Chang I. Moon, Yuxing Liao, Susan G. Hilsenbeck, C K. Osborne, Mothaffar F. Rimawi, Matthew J. Ellis, Varduhi Petrosyan, Bo Wen, Kai Li, Alexander B. Saltzman, Antrix Jain, Anna Malovannaya, Gerburg M. Wulf, Elisabetta Marangoni, Shunqiang Li, Daniel C. Kraushaar, Tao Wang, Senthil Damodaran, Xiaofeng Zheng, Funda Meric-Bernstam, Gloria V. Echeverria, Meenakshi Anurag, Xi Chen, Bryan E. Welm, Alana L. Welm, Bing Zhang, Michael T. Lewis. Patient-derived xenografts allow deconvolution and prediction of chemotherapy responses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 739.