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Statistical methods for cancer genomics and cell-free DNA analysis

Award Number: R01CA240299
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2020
PERIOD OF PERFORMANCE END DATE: 05/31/2025

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Statistical methods for cancer genomics and cell-free DNA analysis - PROJECT SUMMARY/ABSTRACT If detected early, many cancers can be successfully treated, leading to a high rate of survival. Unfortunately, cancer is often detected only at late stages since current screening technologies have insufﬁcient sensitiv- ity and speciﬁcity at low tumor fractions. Further, screening itself is often invasive or even harmful, leading health policy experts to recommend delaying or avoiding screening since the disadvantages may outweigh the beneﬁt. Cell-free DNA (cfDNA) sequencing presents an exciting recent possibility for highly accurate, non- invasive cancer screening. When cells die, they often release small fragments of their DNA into the body, and these cell-free DNA fragments temporarily circulate in the bloodstream. Thus, when cancer is present, plasma obtained from routine blood draws contains DNA fragments from cancer cells. By performing genome sequencing on this plasma cfDNA, it is possible to non-invasively detect and analyze cancers. However, ad- vanced statistical methods are needed to extract the signal from the noise. The fraction of tumor-derived cfDNA fragments is very small, on the order of 1/1000 or less for early stage cancers. The main objective of the proposed project is to develop and test a ﬂexible suite of statistical methods for cancer detection and analysis using cfDNA sequencing data at low tumor fractions. Our central hypothesis is that structured prob- abilistic models of genomic signals of cancer in cfDNA data, along with careful handling of errors and biases, will enable cancer detection and classiﬁcation with high sensitivity and speciﬁcity. (Aim 1) Develop robust non- parametric Poisson regression framework, applied to mutational signatures. The mutational processes that lead to cancer exhibit characteristic genome-wide signatures that are naturally modeled using nonnegative matrix factorization (NMF). We generalize the Poisson NMF model to a nonparametric hierarchical Bayesian regression model with priors informed by latent cancer type/subtype, covariates, known biological structure, and large databases of cancer genomes. (Aim 2) Develop grammar-based methods for complex models of sequential data, applied to SCNAs. Accurate genome-wide SCNA modeling requires continuous and dis- crete latent states, asynchronous emissions, inhomogeneous transition kernels, and informed priors based on previously observed cancer/normal genomes. We develop a grammar and algorithms for complex sequence models with these features. (Aim3) Develop integrated Bayesian framework for robust cancer detection from cfDNA sequencing. We will combine the methods from Aims 1 and 2 in a hierarchical model with cancer type/subtype as a latent variable. (Aim 4) Develop software, provide documentation, and disseminate results to facilitate reproducibility. We will provide user-friendly open-source software, preprocessed public data, and thorough documentation to enable reproducibility and maximize ease-of-use.


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 = $0 )
2026	2024	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	1033 MASSACHUSETTS AVE STE 3	CAMBRIDGE	MA	02138	MIDDLESEX	USA	Cancer Cause and Prevention Research	000	5	12/4/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2024	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	1033 MASSACHUSETTS AVE STE 3	CAMBRIDGE	MA	02138	MIDDLESEX	USA	Cancer Cause and Prevention Research	000	5	5/13/2025	NON-COMPETING CONTINUATION	$0
2025	2024	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	1033 MASSACHUSETTS AVE STE 3	CAMBRIDGE	MA	02138	MIDDLESEX	USA	Cancer Cause and Prevention Research	001	5	9/24/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $329,084 )
2024	2024	HARVARD COLLEGE PRESIDENT & FELLOWS OF	677 HUNTINGTON AVE	BOSTON	MA	02115	SUFFOLK	USA	Cancer Cause and Prevention Research	000	5	5/20/2024	NON-COMPETING CONTINUATION	$329,084
														Subtotal = $329,084

Issue Date FY: 2023 ( Subtotal = $339,476 )
2023	2023	HARVARD COLLEGE PRESIDENT & FELLOWS OF	677 HUNTINGTON AVE	BOSTON	MA	02115	SUFFOLK	USA	Cancer Cause and Prevention Research	000	4	5/9/2023	NON-COMPETING CONTINUATION	$339,476
														Subtotal = $339,476

Issue Date FY: 2022 ( Subtotal = $346,404 )
2022	2022	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	677 HUNTINGTON AVE	BOSTON	MA	02115	SUFFOLK	USA	Cancer Cause and Prevention Research	000	3	4/22/2022	NON-COMPETING CONTINUATION	$346,404
														Subtotal = $346,404

Issue Date FY: 2021 ( Subtotal = $346,404 )
2021	2021	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	677 HUNTINGTON AVE	BOSTON	MA	02115	SUFFOLK	USA	Cancer Cause and Prevention Research	000	2	5/27/2021	NON-COMPETING CONTINUATION	$346,404
														Subtotal = $346,404

Issue Date FY: 2020 ( Subtotal = $366,573 )
2020	2020	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	677 HUNTINGTON AVE	BOSTON	MA	02115	SUFFOLK	USA	Cancer Cause and Prevention Research	000	1	8/25/2020	NEW	$366,573
														Subtotal = $366,573

Grand Total All Awards = $1,727,941

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Statistical methods for cancer genomics and cell-free DNA analysis

Award Number: R01CA240299

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/01/2020

PERIOD OF PERFORMANCE END DATE: 05/31/2025

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