PROJECT SUMMARY/ABSTRACT Across all cancers a significant problem in oncology is the current lack of reliable means to predict response to anti-cancer treatments for individual patients. Specifically in locally advanced rectal cancer (LARC), it is known that patients can benefit from chemotherapy, radiation, and operative management. However, not all of these therapies may be required for each individual patient. With standard practices, patients are exposed to the toxicities of all of these modalities without knowing the necessity of each. In addition, for some patients they require escalation of therapies beyond these due to resistant disease. A method to predict treatment response is urgently needed to better tailor the neoadjuvant treatment paradigm for individual patients and this is the goal of this proposal. The long-term objective of this proposal is to utilize optical metabolic imaging (OMI) of patient- derived colorectal cancer (CRC) organoid cultures to predict sensitivity to therapeutic regimens. This proposal develops novel cellular-level imaging technologies to predict treatment response in individual cancer patients using optical metabolic imaging (OMI) of organoids cultures from their own tumors. Since tumor genetics can have profound impacts on cellular metabolism, a better understanding of the underlying mechanisms by which tumor genetics alter OMI in the pre- and post-treatment settings is needed. In addition, assessment of cell-level heterogeneity within patient samples is required to fully predict the treatment response. To interrogate these mechanistic inquiries, we have generated multiple tools and research techniques. Our data to date indicate that OMI in primary CRCs and other tumor types predicts in vivo drug response in mice and patients. In this proposal, we test the hypothesis that OMI of patient-derived organoid cultures will predict treatment response for patients with CRC undergoing chemotherapy and/or radiation. In this extension proposal, we build on the prior aims to assess the use of OMI to evaluate clonal evolution and correlate these studies with molecular evolution of these cells. Additionally, we further validate the OMI response thresholds to predict treatment response identified in our initial aims with an additional cohort of CRC patients. Finally, we will perform a multivariate analysis to evaluate multiple OMI parameters in combination with change in organoid diameter, response on clinical imaging, pre-treatment clinical stage, age, gender, race/ethnicity, clinical treatment dose intensity, tumor infiltrating lymphocyte counts, and the molecular profile as predictors of clinical outcomes for these CRC patients. Overall, these additional aims represent logical extensions of the initial aims and will be key to further supporting the incorporation of OMI of patient-derived organoids into future prospective clinical trials.