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Using deep learning models to infer spatial transcriptomics from H&E slides

Principal Investigator
Name
Eytan Ruppin
Degrees
M.D, Ph.D
Institution
Cancer Data Science Laboratory, NCI
Position Title
Chief Cancer Data Science Laboratory
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-1353
Initial CDAS Request Approval
Nov 5, 2024
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 the model path2space which infers spatial transcriptomics from H&E slides. The goal is to expand and continue to validate our approach to be able to asses lung cancer H&E slides. This tool could help find spatial markers from large datasets that couldn’t be before.

We believe that the NLST dataset is a good fit for our tool as it has comprehensive information regarding each of the patients who did progress and got cancer after the screening. 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 lung cancer.

2. Assess different techniques that could help make a better model.

3. Find lung cancer specific biomarkers that could help in turn find new disease pathways.

4. Find lung cancer biomarkers for response to treatment or overall survival

5. Find new drug targets from the inferred spatial transcriptomics.

Collaborators

No current collaborators known.