Back

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis

Shiau, C.; Cao, J.; Gregory, M. T.; Gong, D.; Yin, X.; Cho, J.-W.; Wang, P. L.; Su, J.; Wang, S.; Reeves, J. W.; Kim, T. K.; Kim, Y.; Guo, J. A.; Lester, N. A.; Schurman, N.; Barth, J. L.; Weissleder, R.; Jacks, T.; Qadan, M.; Hong, T. S.; Wo, J. Y.; Roberts, H.; Beechem, J. M.; Fernandez-del Castillo, C.; Mino-Kenudson, M.; Ting, D. T.; Hemberg, M.; Hwang, W. L.

2023-06-29 cancer biology
10.1101/2023.06.28.546848 bioRxiv
Show abstract

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.