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Friday, December 3 • 6:00pm - 6:10pm
OP 34 - DEGAS: Mapping clinical metrics to spatial transcriptomics with deep learning

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DEGAS: Mapping clinical metrics to spatial transcriptomics with deep learning

Presenting Author: Justin Couetil, Indiana University School of Medicine

Justin Couetil, Indiana University School of Medicine
Jie Zhang, Indiana University School of Medicine
Kun Huang, Indiana University School of Medicine
Travis Johnson, Indiana University School of Medicine

Abstract: To search for links between cancer genotype and phenotype, we developed the DEGAS framework to map disease information to spatially resolved tumors.<br><br>In the era of precision medicine, spatial transcriptomics (ST) offers a unique opportunity to characterize tumor morphology and transcriptional heterogeneity simultaneously. We train deep transfer learning networks on ST and bulk-RNA seq with disease information (i.e., survival, treatment response, disease status, risk factors) to infer these characteristics spatially on the ST slide. Using the breast cancer data from TCGA, normal tissue from GTEX, and three 10x Genomics ST data sets of breast ductal adenocarcinoma, we identify high-risk regions of tumor tissue that align with 76-84% of the clusters derived from ST data alone. This shows that we can infer clinical attributes while maintaining the transcriptional differences in the ST slide.

Our methodology includes gold-standard preprocessing, feature selection, model training, post-processing, and data visualization tools. This represents a robust framework to use clinical data to identify regions of tumor which may reflect resistance to certain therapies, have certain mutations, or RNA signatures corresponding to lifestyle risk factors like smoking. As spatial transcriptomics become higher resolution and less costly, we hope our framework can be used as a “spotlight” to show researchers which subpopulations and spatial organizations of tumor cells may contribute to a patient’s clinical trajectory.

We plan to develop multimodal DEGAS models, allowing researchers to use this framework to link clinical phenotype to genomic (e.g. circulating tumor DNA), histologic, transcriptional, and proteomic data.

Friday December 3, 2021 6:00pm - 6:10pm MST
Ballroom Salon 1