Washington University Co-Clinical Imaging Research Resource

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ABSTRACT Breast cancer (BC) is the most common cancer diagnosed in women. Approximately 70% of BCs are estrogen receptor (ER) positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-). Endocrine therapy (ET) reduces recurrence risk and improves survival for many in this group. However, despite standard of care and adjuvant ET, over 20% patients with ER+/HER2- BC experience metastatic recurrence in the years to come, and virtually all patients with metastatic disease eventually experience disease progression on ET due to intrinsic or acquired resistance mechanisms. There are currently no biomarkers that reliably identify which of these advanced breast cancer patients will benefit from ET-based approaches so that chemotherapy could be avoided or delayed. To address this unmet need, the objective of this proposal is to develop co-clinical quantitative PET/CT imaging strategies integrated genoproteomic discovery to predict response to ET in patients with ER+/HER2- metastatic breast cancer (MBC). To that end, we will interface with a recently awarded phase II multicenter Translational Breast Cancer Research Consortium (TBCRC) trial to assess the functional status of estrogen receptor in patients with ER+/HER2- MBC. The U24 will have three specific aims: in Aim 1 we will optimize animal modeling and the quantitative accuracy of PET imaging agents of response to ET in ER+/HER2- BC patient-derived tumor xenografts (PDX). In Aim 2 we will implement optimal quantitative methods to predict response to ET in ER+/HER2- and integrate with multi-scale genoproteomic data across the co-clinical trial. And in Aim 3 we will populate content from the co-clinical investigation on a web-accessible research resource and expand capabilities of co-clinical database (CCDB). In addition, high value multi-scale analytic data will be generated, including whole exome sequencing (WES), RNASeq, pathology, and CODetection by indEXing (CODEX) to characterize tumor heterogeneity. All data will be uploaded to an informatics resource available to the co-clinical community to test new algorithms and mine for novel leads integrating imaging and multi-scale analytic data to predict therapeutic response. Overall, this proposal aims to have a far-reaching and high impact on the implementation of precision medicine in identifying, stratifying, and predicting response to ET+CDK4/6i in patients with ER+/HER2- MBC, integrating quantitative imaging with genoproteomic discovery.