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Garassino, M. C.

Publications and source records attributed to Garassino, M. C..

2 recordsLinked to original sources

MCT1 activity defines an aggressive metabolic phenotype and therapeutic target in non-small cell lung cancer

Lactate uptake through monocarboxylate transporter 1 (MCT1) is associated with aggressive disease in non-small cell lung cancer (NSCLC), but how lactate transport supports tumor metabolism remains incompletely understood. Integrating stable isotope tracing, metabolomics, and transcriptomics across patient tumors, animal models, and cultured cells, we show that elevated lactate utilization and SLC16A1 expression correlate with worse clinical outcomes in NSCLC. Although MCT1 inhibition with AZD3965 does not significantly impair tumor growth as a monotherapy, it depletes purine-associated metabolite pools, alters redox balance, and induces a conserved transcriptional response involving MYC target dysregulation and suppression of nucleotide metabolism genes. Addition of exogenous hypoxanthine, or expression of the E. coli-derived NADH-producing enzyme soluble transhydrogenase, partially rescues the effects of MCT1 inhibition. Importantly, this metabolically compromised state sensitizes NSCLC to nucleotide-targeting chemotherapies, including pemetrexed. These findings reveal that MCT1 mediated lactate uptake sustains nucleotide homeostasis in NSCLC, and that its inhibition exposes a context-dependent vulnerability that can be leveraged to enhance chemotherapy efficacy.

cancer biology↗

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

Deployment and access to state-of-the-art diagnostic technologies remains a fundamental challenge in providing equitable global cancer care to low-resource settings. The expansion of digital pathology in recent years and its interface with computational biomarkers provides an opportunity to democratize access to personalized medicine. Here we describe a low-cost platform for digital side capture and computational analysis composed of open-source components. The platform provides low-cost ($200) digital image capture from glass slides and is capable of real-time computational image analysis using an open-source deep learning (DL) algorithm and Raspberry Pi ($35) computer. We validate the performance of deep learning models performance using images captured from the open-source workstation and show similar model performance when compared against significantly more expensive standard institutional hardware.

pathology↗