# Deep-learning Integration of Histopathology and Proteogenomics at a Pan-cancer Level - Resubmission

> **NIH NIH F30** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $41,300

## Abstract

PROJECT SUMMARY
Discovering and understanding novel pathological features that correlate with proteogenomics is crucial in
improving and streamlining cancer prognosis and treatment. Currently, biomedical research efforts to predict
cancer outcomes rely on sequencing approaches not readily accessible in a clinical setting. Instead, clinicians
frequently rely on histopathology images to visually assess for aberrant changes to tissue morphology.
Subsequently, a tool to infer clinical and molecular signatures directly from histopathology images would harness
the power of omics research with the feasibility of image-based diagnosis.
Histopathology imaging data is still vastly underutilized in the quest to better understand tumor biology, largely
because of inadequate tools for analysis and data integration. To identify pan-cancer hallmarks and conserved
pathways of tumorigenesis, I therefore propose to develop multi-resolution deep convolutional neural network
(CNN) models across 10 different cancer types and predict clinical annotations, histology outcomes, and critical
mutations based on tumor histopathology images (Aim 1). Through our lab’s collaboration with the National
Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have access to multi-omics,
clinical, and histopathologic data obtained from 1,602 patients. In addition, to understand the biological
mechanisms driving morphology changes, I propose to develop computational methods that integrate
transcriptomic and proteomic expression datasets with imaging to facilitate pathway-level knowledge discovery
(Aim 2). Our proposal is the first to correlate expression perturbations with morphology patterns and identify
enriched canonical pathways directly from histopathology images. Importantly, this proposal aims to connect
scientific efforts of biomedical research with the diagnostic tools of clinicians to expand diagnostic power and
improve clinical practice.

## Key facts

- **NIH application ID:** 10802110
- **Project number:** 5F30CA271622-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Joshua Wang
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $41,300
- **Award type:** 5
- **Project period:** 2023-03-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10802110

## Citation

> US National Institutes of Health, RePORTER application 10802110, Deep-learning Integration of Histopathology and Proteogenomics at a Pan-cancer Level - Resubmission (5F30CA271622-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10802110. Licensed CC0.

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