Triple negative breast cancer (TNBC) is a very challenging disease because it is biologically aggressive, there are no targeted therapies, and, consequently, patients have poor prognosis. Although immunotherapy is promising for treating many cancers, TNBC lacks specific molecular targets, no predictive biomarkers to chemotherapy response have yet been identified, and treatment response is difficult to evaluate using current biomarker assessments. Patient-derived xenograft (PDX) models of TNBC offer the exciting opportunity of evaluating this disease in terms of molecular features (e.g., genomic copy number, whole exome sequence, and mRNA expression) to identify candidate ?omic? biomarkers that best predict the ultimate response to treatment and could provide surrogate endpoints to validate novel imaging biomarkers in co-clinical trial human trails. Moreover, emerging quantitative MRI methods, such as dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI), contain rich physiological signals in the images for predicting treatment response, but it is challenging to integrate both animal and human data to reliably predict the treatment response. A paradigm of ?co-clinical trials? is emerging in which new treatments are evaluated in animals, and the results guide treatments in clinical trials, but there is a paucity of informatics tools and resources to enable analyses in such animal-to-human work. We believe that an informatics-based methodology that integrates molecular `omics' and imaging data will propel advances in TNBC by enabling development of machine learning models to predict the response to therapies. In order to develop research resources that will encourage consensus on how quantitative imaging methods are optimized to improve the quality of imaging results for co-clinical trials, we will leverage an ongoing co-clinical trial we are undertaking to pursue the following specific aims: (1) Identify molecular biomarkers that predict response in TNBC patient-derived xenografts (PDX); (2) Identify quantitative MRI biomarkers that predict response in TNBC patient-derived xenografts; and (3) Evaluate our informatics tools in a prospective co-clinical trial. Our proposed research is significant and innovative because it leverages advances in basic cancer biology, state-of-the-art imaging technologies, and informatics methods to develop a resource to catalyze discovery in this important disease. Our PDX-based approach will provide the cancer community with a rational, iterative, combined pre-clinical and clinical methodology and supporting data resource for making progressively more refined and personalized therapeutic regimens for TNBC patients. Our methods and tools will likely also generalize to other cancers and could, therefore, substantially benefit the care of all cancer patients.
Determining the optimal therapies for a specific triple negative breast cancer tumor is currently not possible, as the number of possible treatment combinations is too large to evaluate experimentally in clinical trials. Our proposed methods that leverage patient derived xenografts and machine learning analysis of integrated `omics and quantitative imaging data will provide the cancer community with a rational, iterative, combined pre-clinical and clinical trial approach and supporting data resource for making progressively more refined and personalized therapeutic regimens in these patients. Our methods and tools will likely also generalize to other cancers and could, therefore, substantially benefit the care of all cancer patients.
