Metabolic Signatures of Immune Checkpoint Inhibitor Response in Gynecologic Cancers: Insights from Flux Balance Analysis

Abstract

Modifiers of immune checkpoint inhibitor (ICI) responses in cancer patients are complex and remain poorly characterized, especially in gynecologic cancers. In this study, we explored fluxomic biomarkers that differentiate responders from non-responders to ICIs in a series of 49 patients with gynecologic cancers, including ovarian, cervical, and endometrial cancers. By applying metabolic enzyme expression as constraints, we utilized an objective-customizable flux balance analysis within a genome-scale metabolic model to predict the metabolic flux differences between responders versus non-responders of ICI treatment. We identified three reactions with consistent differential activity across all ten different optimization objectives: Succinate Dehydrogenase (SUCD1m) in the citric acid cycle, NADH: Guanosine-5-Phosphate Oxidoreductase (r0276) involved in purine catabolism, and Ornithine Transaminase Reversible, Mitochondrial (ORNTArm) in the urea cycle. Additionally, reactions within the folate cycle subsystem, particularly involving MTHFD2, demonstrated significance in distinguishing treatment responses, aligning with previous findings linking MTHFD2 to immune evasion and tumor progression. To further analyze the association between metabolic features and survival outcomes, we implemented machine learning models that integrate multi-omics data. Our model included clinical-pathologic, molecular-genomic features (gene expression, TGF-{beta} score, immune cell abundance from transcriptomic deconvolution), and significant reaction fluxes. Our findings suggest that SUCD1m, MTHFDm and ORNTArm are important metabolic biomarkers that could serve as predictive indicators for ICI response and, if validated in a larger cohort, may guide the development of targeted therapies to enhance treatment efficacy for gynecologic cancer patients. This study highlights the use of genome-scale metabolic modeling to identify clinically relevant biomarkers and improve therapeutic strategies.