Using deep learning models to infer spatial transcriptomics from H&E slides
Principal Investigator
Name
Eytan Ruppin
Degrees
MD, Ph.D
Institution
Cancer Data Science Laboratory, NCI NIH
Position Title
Chief Cancer Data Science Laboratory
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-1832
Initial CDAS Request Approval
Feb 19, 2025
Title
Using deep learning models to infer spatial transcriptomics from H&E slides
Summary
Spatial transcriptomics have the potential to revolutionize precision medical oncology. However, one of its biggest caveats is the high cost of performing it on large cohorts.
In this project we aim to enhance and validate our current model path2space which infers spatial transcriptomics from breast cancer H&E slides. The goal is to expand and continue to validate our approach to be able to asses different types of cancer from H&E slides. This tool could help find spatial markers from large datasets that couldn’t be before.
We believe that the PLCO dataset is a good fit for our tool as it has comprehensive information regarding each of the patients . This information could further help us find biomarkers for different subgroups of the disease.
In this project we aim to enhance and validate our current model path2space which infers spatial transcriptomics from breast cancer H&E slides. The goal is to expand and continue to validate our approach to be able to asses different types of cancer from H&E slides. This tool could help find spatial markers from large datasets that couldn’t be before.
We believe that the PLCO dataset is a good fit for our tool as it has comprehensive information regarding each of the patients . This information could further help us find biomarkers for different subgroups of the disease.
Aims
We have a few aims
1. Expand and validate our current path2space model in order to make it work on various types of cancer.
2. Assess different techniques that could help make a better model.
3. Find pan-cancer and cancer specific biomarkers that could help in turn find new disease pathways.
4. Find pan-cancer and cancer specific biomarkers for response to treatment or overall survival
5. Find new drug targets from the inferred spatial transcriptomics.
Collaborators
none at the moment