Purpose: Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed. Experimental Design: Patient-derived cancer organoids were derived across multiple histologies. The histologic characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single-cell levels, respectively. Results: We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer. Conclusions: These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation.

PIK3CA mutations are common in clinical molecular profiling, yet an effective means to target these cancers has yet to be developed. MTORC1 inhibitors are often used offlabel for patients with PIK3CA mutant cancers with only limited data to support this approach. Here we describe a cohort of patients treated with cancers possessing mutations activating the PI3K signaling cascade with minimal benefit to treatment with the MTORC1 inhibitor everolimus. Previously, we demonstrated that dual PI3K/mTOR inhibition could decrease proliferation, induce differentiation, and result in a treatment response in APC and PIK3CA mutant colorectal cancer. However, reactivation of AKT was identified, indicating that the majority of the benefit may be secondary to MTORC1/2 inhibition. TAK-228, an MTORC1/2 inhibitor, was compared with dual PI3K/mTOR inhibition using BEZ235 in murine colorectal cancer spheroids. A reduction in spheroid size was observed with TAK-228 and BEZ235 (À13% and À14%, respectively) compared with an increase of >200% in control (P < 0.001). These spheroids were resistant to MTORC1 inhibition. In transgenic mice possessing Pik3ca and Apc mutations, BEZ235 and TAK-228 resulted in a median reduction in colon tumor size of 19% and 20%, respectively, with control tumors having a median increase of 18% (P ¼ 0.02 and 0.004, respectively). This response correlated with a decrease in the phosphorylation of 4EBP1 and RPS6. MTORC1/2 inhibition is sufficient to overcome resistance to everolimus and induce a treatment response in PIK3CA mutant colorectal cancers and deserves investigation in clinical trials and in future combination regimens.

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Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.