Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity.

Representative models are needed to screen new therapies for patients with cancer. Cancer organoids are a leap forward as a culture model that faithfully represents the disease. Mouse-derived cancer organoids (MDCOs) are becoming increasingly popular, however there has yet to be a standardized method to assess therapeutic response and identify subpopulation heterogeneity. There are multiple factors unique to organoid culture that could affect how therapeutic response and MDCO heterogeneity are assessed. Here we describe an analysis of nearly 3500 individual MDCOs where individual organoid morphologic tracking was performed. Change in MDCO diameter was assessed in the presence of control media or targeted therapies. Individual organoid tracking was identified to be more sensitive to treatment response than well-level assessment. The impact of different generations of mice of the same genotype, different regions of the colon, and organoid specific characteristics including baseline size, passage number, plating density, and location within the matrix were examined. Only the starting size of the MDCO altered the subsequent growth. These results were corroborated using ~ 1700 patient-derived cancer organoids (PDCOs) isolated from 19 patients. Here we establish organoid culture parameters for individual organoid morphologic tracking to determine therapeutic response and growth/response heterogeneity for translational studies.

Patient-Derived Cancer Organoid Cultures to Predict Sensitivity to Chemotherapy and Radiation.

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.