Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic acid (TCA) cycle, characterized by mitochondrial-to-cytosolic citrate export, has emerged as critical for embryonic stem cell differentiation. However, its role in cancer remains poorly understood.

Here, we present a two-step computational framework to systematically analyze the activity of this non-canonical TCA cycle across over 500 cancer cell lines and investigate its role in shaping hallmarks of malignancy. First, we applied constraint-based modeling to infer cycle activity, defining two complementary metrics: Cycle Propensity , measuring the likelihood of its engagement in each cell line, and Cycle Flux Intensity , quantifying average flux through the reaction identified as rate-limiting. We identified distinct tumor-specific patterns of pathway utilization. Notably, cells with high Cycle Propensity preferentially rerouted cytosolic citrate via aconitase 1 (ACO1) and isocitrate dehydrogenase 1 (IDH1), promoting α -ketoglutarate ( α KG) and NADPH production. Elevated engagement of this cycle strongly correlated with Warburg-like metabolic shifts, including decreased oxygen consumption and increased lactate secretion.

In the second step, to uncover non-metabolic transcriptional signatures associated with non-canonical TCA cycle activity, we performed machine learning–based feature selection using ElasticNet and Random Forest, identifying robust gene signatures predictive of cycle activity. Over-representation analysis revealed enrichment in genes involved in invasiveness, angiogenesis, stemness, and key oncogenic pathways. Analysis of DepMap gene dependency data revealed that TCA cycle activity correlates with differential vulnerability to perturbation of these oncogenic pathways, reinforcing the functional relevance of identified transcriptional signatures. To further interpret the predictive models, SHapley Additive exPlanations (SHAP) was applied to prioritize genes contributing most to non-canonical cycle activity, suggesting novel candidates for experimental investigation.

Overall, our framework enables comprehensive analysis of non-canonical TCA cycle dynamics and uncovers potential links between metabolic plasticity and malignant phenotypes.