AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology
To Know
Where:
Online Webinar
Organizer:
CBIIT
Presented By:
Eytan Ruppin, MD, Ph.D (CCR Cancer Data Science Lab)
About this Class
Join Dr. Eytan Ruppin, NCI investigator in the Center for Cancer Research, as he discusses Path2Space, a new and unpublished deep learning approach that predicts spatial gene expression directly from histopathology slides.
Spatial transcriptomics (ST) is transforming our understanding of tumor heterogeneity by providing high-resolution, location-specific mapping of gene expression within tumors and their microenvironment. However, high costs have restricted the size of cohorts, limiting large-scale biomarker discovery.
With Path2Space, you can:
- predict the spatial expression of over 4,300 breast cancer genes in independent validations, thereby outperforming existing ST predictors.
- accurately infer cell-type abundances in the tumor microenvironment (TME).
- apply to over 1,000 breast tumor histopathology slides from the TCGA, characterizing their TME on an unprecedented scale, and identify new spatially grounded breast cancer subgroups with distinct survival rates.
- infer TME landscapes, enabling more accurate predictions of patients’ response to chemotherapy and trastuzumab.
- operate a transformative, fast, and cost-effective approach to robustly delineate the TME.