Abstract 185: Monitoring metabolite dynamics in patient-derived tumoroids using automated microfluidic Pu·MA System
Authors: Nikolov Ekaterina , Sirenko Oksana , Hammer Matthew , Brock Courtney , Thai Anthony , Burow Matthew , Collins-Burow Bridgette , Cromwell Evan 
Published: June 2022

Introduction: Cancer cells are characterized by metabolic modifications to meet high bioenergetic and anabolic demand during tumorigenesis. For most tumors aerobic glycolysis is a major driver of cancer progression, resistance to therapy, poor patient outcome. Lactate, a product of glycolysis, plays a role in tumor progression and is linked to drug resistance in breast cancer. Patient-derived 3D cell models are valuable tools for research, drug development and personalized medicine, because they recapitulate features of tumor microenvironment. Therefore, monitoring metabolite dynamics and lactate production in physiologically relevant patient-derived models provides valuable data for understanding metabolic perturbations during disease progression, drug response and resistance. In this study we assayed the glucometabolic changes in response to anti-cancer drugs in triple negative breast cancer (TNBC) tumoroids. Our approach utilizes an automated 3D assay system. The Pu·MA system with microfluidic flowchips allows for in situ supernatant sampling, sensitive luminescence Lactate-Glo assay and high content imaging.

Methods: Tumoroids were formed in ULA plate from primary cells from a TNBC biopsy, TU-BcX-4IC (4IC). Single tumoroids were assayed in flowchips in the Pu·MA system. The workflow comprised of a) measuring pre-treatment tumoroid viability with RT-Glo assay b) time-course automated supernatant sampling for measuring lactate using Lactate-Glo assay and c) post-treatment imaging of viability response for IC50 calculation.

Results: RT-Glo assay of 4IC tumoroids showed sufficient signal-to-background ratio of 44 and signal-to-noise ratio of 690. Before treatment, the coefficient of variation of signal from samples was 26% indicating high starting uniformity of tumoroid size and viability, which is important for the validity of the conclusions. Treatment of 4IC tumoroids for 48 hr with paclitaxel, romidepsin (HDAC1/2 inhibitor) and trametinib (MEK1/2 inhibitor) showed varying sensitivities to these drugs. IC50 values for romidepsin and trametinib were ~10 nM, as compared to paclitaxel &gt;1 μM, which is much greater than typically reported for breast cancer models. This is consistent with previously published taxane resistance of 4IC primary tumor. Tumoroid treatment of over 9 - 12 hr also resulted in statistically significant increase in lactate secretion for paclitaxel (p &lt; 0.05) and romidepsin (p &lt; 0.01) indicating drug-induced shift toward glycolysis. These results corroborate previous publications linking treatment with paclitaxel to increased lactate production and chemoresistance in breast cancer.

Conclusions: Profiling of patient-derived tumoroids using the automated Pu·MA System for dynamic metabolic changes shows great potential for studying disease progression, drug resistance, identifying new therapies and advancing personalized medicine.

Citation Format: Ekaterina Nikolov, Oksana Sirenko, Matthew Hammer, Courtney Brock, Anthony Thai, Matthew Burow, Bridgette Collins-Burow, Evan Cromwell. Monitoring metabolite dynamics in patient-derived tumoroids using automated microfluidic Pu·MA System [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 185.