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Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Yuliang Wang, Shuyi Ma, Walter L. Ruzzo
doi: https://doi.org/10.1101/719294
Yuliang Wang
1Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
2Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
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  • For correspondence: yuliangw@cs.washington.edu
Shuyi Ma
3Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle WA, USA
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Walter L. Ruzzo
2Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
4Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
5Fred Hutchinson Cancer Research Center, Seattle, WA, 98102, USA
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Abstract

Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted October 19, 2019.
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Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities
Yuliang Wang, Shuyi Ma, Walter L. Ruzzo
bioRxiv 719294; doi: https://doi.org/10.1101/719294
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Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities
Yuliang Wang, Shuyi Ma, Walter L. Ruzzo
bioRxiv 719294; doi: https://doi.org/10.1101/719294

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