Cancer cells reprogram their metabolism to support rapid growth, survive in nutrient-poor environments, and resist treatment. In prostate cancer, metabolic changes are particularly striking: normal prostate cells are known to accumulate high levels of citrate and zinc, while cancer cells shift toward active energy production and altered lipid metabolism to fuel tumor growth.
Understanding precisely which metabolic pathways are active in prostate cancer cells, and how that activity varies across different regions of the same tumor, could reveal new therapeutic targets. Drugs that block a metabolic enzyme expressed only in cancer cells and not in normal tissue could selectively kill the tumor while sparing healthy cells.
Until recently, studying spatial metabolic variation within a tumor was technically very difficult. Spatial transcriptomics now makes it possible to measure gene expression at defined spots across a tissue section while preserving spatial context, opening the door to building location-aware metabolic maps of cancer tissue.
The researchers applied spatial transcriptomics to three prostate cancer tissue sections, measuring RNA levels across a grid of spots that each represent a small cluster of cells. This produced gene expression data that retained the physical location of each measurement within the tissue.
They then used these spatially resolved gene expression profiles to construct genome-scale metabolic models (GEMs) for each tissue spot. A GEM is a mathematical representation of all the known biochemical reactions in a cell, calibrated by the gene expression level of each enzyme. By constraining the model with measured expression data, the researchers could predict which metabolic reactions were active or inactive at each spatial location.
Comparing the metabolic models built from cancer-containing spots versus normal adjacent tissue spots allowed the team to identify reactions that were specifically upregulated or downregulated in cancer cells. Reactions with high activity in cancer but low activity in normal tissue represent candidate vulnerabilities: if those reactions can be blocked pharmacologically, cancer cells may be selectively harmed while normal cells remain unaffected.
The analysis was conducted on three independent tissue sections to test whether identified patterns were consistent across patients and slides, providing an initial assessment of reproducibility.
One of the most striking findings was that metabolic gene expression varied substantially across different regions of the same tumor, a phenomenon the authors call intratumoral metabolic heterogeneity. Neighboring tissue spots could show markedly different metabolic profiles despite being within the same histological cancer region.
This heterogeneity has important implications for drug development: a metabolic target that is highly expressed in one region of the tumor may be absent or low in another. A therapy targeting only that pathway could kill some cancer cells but leave others unaffected, potentially allowing resistant clones to repopulate the tumor.
The metabolic models also revealed spatial organization of metabolic activity that correlated with histological features. Areas with higher tumor grade showed distinct metabolic signatures compared to lower-grade regions, suggesting that metabolic reprogramming tracks with cancer aggressiveness and tissue architecture in a spatially organized way.
The spatial metabolic modeling analysis identified SCD1 (stearoyl-CoA desaturase 1) as a gene with significantly elevated expression and predicted metabolic activity in cancer tissue compared to adjacent normal prostate tissue. SCD1 is a key enzyme in fatty acid desaturation, converting saturated fatty acids into unsaturated forms that are important for membrane synthesis and cell signaling.
The researchers also identified SLCO2A1, a prostaglandin transporter, as specifically active in cancer regions. This transporter plays a role in inflammatory signaling pathways that can support tumor growth and immune evasion, making it an interesting candidate for further investigation as a cancer-specific vulnerability.
A particularly intriguing finding was the spatial separation of two related metabolic processes: fatty acid synthesis and fatty acid desaturation appeared to be concentrated in different spatial zones within the tumor. This suggests a metabolic division of labor between tumor regions that may reflect underlying cellular heterogeneity or microenvironmental gradients in oxygen and nutrients.
A metabolic target is most therapeutically useful when it is essential for cancer cell survival but dispensable in normal cells. The cancer-selective upregulation of SCD1 activity identified in this study suggests that inhibiting this enzyme could preferentially harm cancer cells. SCD1 inhibitors have already been investigated in other cancer types, making this a potentially actionable finding for prostate cancer research.
The SLCO2A1 transporter, involved in prostaglandin signaling, represents a less conventionally targeted pathway in prostate cancer. Its identification through unbiased spatial metabolic modeling illustrates how this computational approach can surface non-obvious targets that might not be prioritized in hypothesis-driven research focused only on known cancer pathways.
Importantly, drug development targeting these candidates would need to account for the intratumoral heterogeneity revealed in this study. Combination strategies targeting multiple metabolic nodes, or approaches that identify the dominant metabolic dependency in a given patient's tumor, may be needed to translate these findings into effective therapies.
This study establishes that genome-scale metabolic modeling applied to spatial transcriptomics data is a feasible and informative approach for identifying cancer-selective metabolic vulnerabilities in prostate cancer tissue. The combination of spatial resolution and comprehensive metabolic coverage is a key advantage over prior methods.
The identified targets, particularly SCD1 and SLCO2A1, provide concrete starting points for in vitro and in vivo validation studies. If follow-up experiments confirm that inhibiting these targets selectively kills prostate cancer cells, the path toward clinical translation would involve medicinal chemistry and preclinical toxicology work.
More broadly, this study contributes to the emerging field of spatial systems biology, demonstrating that location matters in tumor metabolism and that metabolic maps of cancers can reveal structural and functional organization that bulk RNA sequencing would miss. Applying similar approaches to larger patient cohorts and more advanced disease stages will be important next steps.