A deep learning-based multiscale integration of spatial omics with tumor morphology.

Schmauch, B.; Herpin, L.; Olivier, A.; Duboudin, T.; Dubois, R.; Gillet, L.; Schiratti, J.-B.; Di Proietto, V.; Le Corre, D.; Bourgoin, A.; Taïeb, J.; Emile, J.-F.; Fridman, W. H.; Pronier, E.; Laurent-Puig, P.; Durand, E. Y.

doi:10.1101/2024.07.22.604083

bioRxiv · 10.1101/2024.07.22.604083

A deep learning-based multiscale integration of spatial omics with tumor morphology.

Schmauch, B.·Herpin, L.·Olivier, A.·Duboudin, T.·Dubois, R.·Gillet, L.·Schiratti, J.-B.·Di Proietto, V.·Le Corre, D.·Bourgoin, A.·Taïeb, J.·Emile, J.-F.·Fridman, W. H.·Pronier, E.·Laurent-Puig, P.·Durand, E. Y.

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Abstract

Spatial Transcriptomics (spTx) offers unprecedented insights into the spatial arrangement of the tumor microenvironment, tumor initiation/progression and identification of new therapeutic target candidates. However, spTx remains complex and unlikely to be routinely used in the near future. Hematoxylin and eosin (H&E) stained histological slides, on the other hand, are routinely generated for a large fraction of cancer patients. Here, we present a novel deep learning-based approach for multiscale integration of spTx with tumor morphology (MISO). We trained MISO to predict spTx from H&E on a new unpublished dataset of 72 10X Genomics Visium samples, and derived a novel estimate of the upper bound on the achievable performance. We demonstrate that MISO enables near single-cell-resolution, spatially-resolved gene expression prediction from H&E. In addition, MISO provides an effective patient representation framework that enables downstream predictive tasks such as molecular phenotyping or MSI prediction.

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Schmauch, B., Herpin, L., Olivier, A., Duboudin, T., Dubois, R., Gillet, L., Schiratti, J.-B., Di Proietto, V., Le Corre, D., Bourgoin, A., Taïeb, J., Emile, J.-F., Fridman, W. H., Pronier, E., Laurent-Puig, P., Durand, E. Y.. 2024-07-23. A deep learning-based multiscale integration of spatial omics with tumor morphology.. https://doi.org/10.1101/2024.07.22.604083

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