Evaluating efficacy on tumor biopsy tissue ex vivo before treatment for equitable cancer care.

e13502 Background: Nearly half the world will be diagnosed with cancer. Each year, > 1.7 million new cancers are diagnosed in the US. Worse, systemic therapy turns out to be ineffective in 70% of patients because those drugs do not match the patient’s tumor, imposing a substantial physical, emotional, and financial burden. Each tumor’s distinct composition and heterogeneity can result in diverse responses to the same treatment. There is a critical and urgent need to develop ex vivo personalized cancer models that match patients with the right treatment – before treatment. Here we present a low-cost ex vivo personalized cancer model to rapidly evaluate effectiveness of various therapeutics on intact core biopsy tissue obtained from a patient’s tumor. Objective: The goal of this study is to deliver multiple isolated fluid streams to spatially distinct regions (SDRs) of intact core biopsy specimens for evaluating efficacy of multiple anticancer agents simultaneously. Methods: Core biopsies were generated from xenograft and resected human tumor tissue using 18-gauge and 20-gauge spring-loaded biopsy systems. Xenografts were established using cell lines with known sensitivity and resistance profiles. Fluid streams with anticancer agents, nucleic acid stains, and mock drugs were delivered to spatially distinct regions (SDRs) on intact core biopsy specimens in our patented Personalized Oncology Efficacy Test (POET) lab-chip bioreactors. High content imaging and custom image processing algorithms were used to measure differential activity. Results: No lateral fluid surface conduction, and only minimal lateral diffusion, was observed up to 200 μm deep in tissue. Extent of observed lateral diffusion was within operational parameters of the bioreactor. Integrity of spatially distinct regions on intact core tissue was maintained. Differential activity from anticancer agents compared to mock drugs was observed and quantitatively measured using custom image processing algorithms. Conclusions: The NCI warns that “current methods to assess potential cancer treatments are cumbersome, expensive, and often inaccurate.” A personalized medicine approach, based on efficacy observed on tissue directly from the patient’s own tumor, is fundamentally better than the current approach. Low-cost next generation ex vivo cancer models that can rapidly match treatments to tumors, such as POET, could transform cancer treatment and make a significant impact on the lives of patients.