In silico reconstruction of primary and metastatic tumor architecture using GIS-augmented spatial transcriptomics

Yoo, J. Y.; Akter, S.; Zuo, Q.; Kazemi, A.; Wu, W.; Goel, M.; Lam, A.; Dutta, D.; Barnick, B.; Pekmezci, M.; Grosse Perdekamp, M.; Soliman, A.; Madak-Erdogan, Z.

doi:10.1101/2025.10.16.682879

bioRxiv · 10.1101/2025.10.16.682879

In silico reconstruction of primary and metastatic tumor architecture using GIS-augmented spatial transcriptomics

Yoo, J. Y.·Akter, S.·Zuo, Q.·Kazemi, A.·Wu, W.·Goel, M.·Lam, A.·Dutta, D.·Barnick, B.·Pekmezci, M.·Grosse Perdekamp, M.·Soliman, A.·Madak-Erdogan, Z.

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Abstract

The tumor microenvironment (TME) comprises different cell populations that interact, contributing to tumor heterogeneity and therapy response. Spatial transcriptomics offers valuable insights into transcriptional complexity and heterogeneity of the TME. We established Geographic Information System (GIS)-augmented In-Silico Reconstruction of Tumor Architecture (GIS-ROTA), a biologically informed analytic framework that integrates pathway or cell type-based enrichment analysis with local Morans I to uncover functional spatial domains. In our Visium dataset of primary and metastatic estrogen receptor-positive breast tumor samples, GIS-ROTA revealed extensive co-localization of estrogen response with metabolic pathway gene sets and mutual exclusivity with metastasis-related and specific immune-related pathway gene sets. The novelty of our approach lies in considering biological functions prior to identifying any spatial domains, providing direct interpretability and minimizing the subjectivity of interpreting clusters observed from conventional analytic methods. Overall, our GIS-ROTA framework integrates biological knowledge first, yielding spatial patterns with functional relevance and enabling identification of novel targets for development of therapeutic strategies.

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Yoo, J. Y., Akter, S., Zuo, Q., Kazemi, A., Wu, W., Goel, M., Lam, A., Dutta, D., Barnick, B., Pekmezci, M., Grosse Perdekamp, M., Soliman, A., Madak-Erdogan, Z.. 2025-10-17. In silico reconstruction of primary and metastatic tumor architecture using GIS-augmented spatial transcriptomics. https://doi.org/10.1101/2025.10.16.682879

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