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Developing and Evaluating Individual and Integrative Proteomics Risk Prediction Models to Improve Risk Stratification for Smoking-Related Cancers

Award Number: K00CA294242
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2024
PERIOD OF PERFORMANCE END DATE: 11/30/2028

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Developing and Evaluating Individual and Integrative Proteomics Risk Prediction Models to Improve Risk Stratification for Smoking-Related Cancers - Cancer is the second leading cause of death globally, with lung and bronchus cancers accounting for the highest number of cancer-related deaths. Tobacco smoking has been linked to increased risks of various cancers. Smoking-related cancers may share common etiologic pathways and overlapping molecular signatures. Current cancer screening tests are limited and typically recommended for specific higher-risk groups (e.g., long-term smokers for lung cancer), yet they often yield false-positives and false-negatives. Hence, improving risk stratification to identify individuals most or least likely to benefit from screening is essential for optimizing the benefit-risk balance of early detection. Additionally, integrating molecular insights across smoking-related cancers can improve personalized risk communication for ever-smokers, support smoking cessation efforts, and guide more effective preventive strategies across multiple cancer types. Growing interest has been directed toward proteomics-based analyses of multiple cancer types, as proteomic signatures can capture dynamic molecular changes in response to environmental exposures as well as during cancer development and progression. Several studies have developed proteomic risk scores (ProRS) for cancers, demonstrating superior predictive performance. Despite promising evidence, the development and application of predictive models using circulating proteins, either alone or in combination with genetic and nongenetic risk factors, remain in their infancy. In addition to relative risk, further evaluation of absolute risk is particularly important for assessing the utility of prediction models, which are essential for guiding clinical decision-making for prevention and screening. In K00 phase, I will develop and evaluate individual and integrative proteomics risk prediction models for smoking-related cancers to strengthen risk stratification, guiding early detection and informing risk communication to promote smoking cessation. In Aim 1.1, I will develop and evaluate ProRS for the 10-year incidence of lung cancer. Lung cancer-associated proteins will be selected using machine learning approach. The ProRS and corresponding absolute risk models based on selected proteins will be constructed and assessed for predictive performance. Models will be stratified by smoking history and time to diagnosis. In Aim 1.2, I will build integrative prediction models that combine ProRS, genetic, and non-genetic risk factors for lung cancer. I will leverage publicly available polygenic risk score (PRS) weights derived from large-scale genome-wide association studies to calculate PRS for participants. Important non-genetic risk factors will be identified through machine learning approach. Multiple Individual and integrative prediction models will be constructed and evaluated. The model with the best predictive performance will be used for constructing the absolute risk model. In Aim 2, I will extend the similar workflow as Aim 1.1 to four other smoking-related cancers, including bladder, kidney, pancreas, and head and neck cancers.


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: 2026 ( Subtotal = $89,872 )
2026	2026	THE JOHNS HOPKINS UNIVERSITY	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Cancer Treatment Research	000	2	3/24/2026	EXTENSION WITH OR WITHOUT FUNDS	$89,872
														Subtotal = $89,872

Grand Total All Awards = $89,872

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Developing and Evaluating Individual and Integrative Proteomics Risk Prediction Models to Improve Risk Stratification for Smoking-Related Cancers

Award Number: K00CA294242

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2024

PERIOD OF PERFORMANCE END DATE: 11/30/2028

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