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March 4th, 2025
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Department of Biosciences and Biotecnologies, University of Milano-Bicocca, Milan, Italy
systems biology
biorxiv

Spatial Flux Balance Analysis reveals region-specific cancer metabolic rewiring and metastatic mimicking

Maspero, D.Open in Google Scholar•Marteletto, G.Open in Google Scholar•Lapi, F.Open in Google Scholar•Galuzzi, B. G.Open in Google Scholar•Ruano, I.Open in Google Scholar•Vandenbosch, B.Open in Google Scholar•Yin, K.Open in Google Scholar•Tejpar, S.Open in Google Scholar•Graudenzi, A.Open in Google Scholar•Heyn, H.Open in Google Scholaret al.

To fully understand how cancer metabolism differs between primary tumors and metastases, resolving cell metabolism with spatial precision is essential. Yet, spatial fluxomics lags behind advancements in spatial transcriptomics. To address this gap, we generated high-resolution spatial transcriptomics datasets from paired primary colorectal tumors and liver metastases, designed to capture metabolic adaptations across distinct tumor sites. Concurrently, we developed the Spatial Flux Balance Analysis (spFBA) computational framework to leverage them. Since broad metabolic differences between tumors and healthy tissues are established, we first validated spFBA on a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA detected cancer metabolic hallmarks, like enhanced glucose uptake and metabolic growth, but with unprecedented resolution, revealing lactate production with sustained oxygen consumption at the tumor interface and with reduced respiration in the core. Next, applying spFBA to our colorectal cancer dataset, we provided biological insights, confirming that metastases mimic the metabolic traits of their tissue of origin. Additionally, our approach uncovered the first in vivo evidence of lactate-consuming cancer cells, marking a significant advance in understanding cancer metabolism. spFBA stands out as a powerful approach to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.

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