Wednesday, July 30, 2025 7/30/2025

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

Award Number: F30CA271622
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 03/01/2023
PERIOD OF PERFORMANCE END DATE: 06/30/2025

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $20,297 )
2025	2025	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	001	3	5/22/2025	NON-COMPETING CONTINUATION	$188
2025	2025	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	000	3	2/18/2025	NON-COMPETING CONTINUATION	$20,109
														Subtotal = $20,297

Issue Date FY: 2024 ( Subtotal = $41,300 )
2024	2024	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	000	2	1/31/2024	NON-COMPETING CONTINUATION	$40,020
2024	2024	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	001	2	5/7/2024	NON-COMPETING CONTINUATION	$1,280
														Subtotal = $41,300

Issue Date FY: 2023 ( Subtotal = $40,020 )
2023	2023	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	000	1	2/22/2023	NEW	$40,020
2023	2023	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Cancer Research Manpower	001	1	2/22/2023	NEW	$0
														Subtotal = $40,020

Grand Total All Awards = $101,617

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

Award Number: F30CA271622

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 03/01/2023

PERIOD OF PERFORMANCE END DATE: 06/30/2025

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