Abstract
New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer.
Highlights
Tumor organoids have emerged as an appealing method to tailor anti-cancer treatments by performing high-throughput drug screening directly on a patient’s tumor cells [1,2,3]
Organoids provide a platform to discover new drugs and drug combinations to treat pancreatic cancer (PC) patients, who currently suffer from a severe lack of effective treatment options
Existing methods to evaluate drug response in organoids ignore cellular heterogeneity, which can lead to patient relapse
Summary
Tumor organoids have emerged as an appealing method to tailor anti-cancer treatments by performing high-throughput drug screening directly on a patient’s tumor cells [1,2,3]. Methods for measuring drug effects in organoids have involved either cell viability assays, pooling of proteins, DNA, and RNA from many organoids, or tracking of organoid diameter changes. These methods homogenize the response of an entire organoid or many organoids and ignore cellular heterogeneity, which drives tumor treatment resistance [14,15,16,17]. It is possible for minority subpopulations of lethal drug-resistant cells to go completely undetected without more advanced assessment tools. A study of inter-tumor metabolic heterogeneity detected unique metabolomic profiles in each of over 180 melanoma patient tumors [21], highlighting the importance of metabolism in personalized medicine
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