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Early detection and interception of cancer using the mutational signature methodology

Award Number: K22CA288779
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 08/13/2026
PERIOD OF PERFORMANCE END DATE: 07/31/2029

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Early detection and interception of cancer using the mutational signature methodology - PROJECT SUMMARY Mutations in genes responsible for accurate DNA repair increase the risk of cancer. Computational analysis of mutational signatures is a powerful way to capture these DNA repair deﬁciencies in tumors and premalignant tissues. The analysis of mutational signatures is based on the observation that the genomic sequence context of point mutations and characteristics of structural variants distinguish different mutational processes. In this project, we will expand the mutational signature methodology for applications in cancer detection and interception for high-risk patients with genetic predisposition. In Aim 1, to characterize a broader spectrum of DNA repair deﬁciencies that lead to cancer, we will expand the predictive framework of mutational signatures to predict more types of DNA repair deﬁciencies observable in tumors, other than homologous recombination deﬁciency. The novel features of structural variants, combined with the algorithm that draws signals from multiple types of mutations, will become a precise biomarker for new cancer therapies targetting aberrant DNA repair. The algorithms will be able to predict types of mutagenesis given individual patient risk. In Aim 2, we will estimate the chronology of DNA repair deﬁciencies before cancer diagnosis. To do so, we will reﬁne the methodology for timing the accumulation of mutational signatures over time. Preliminary data show that in cancers with germline mutations in BRCA1 and BRCA2 genes, the onset of homologous recombination deﬁciency precedes cancer diagnosis by years. We will expand this methodology to consider new mutation types and seek to validate the ﬁndings through computational analysis of data from normal tissue and precursor lesions. Understanding the chronology of cancer development will inform future screening strategies that will consider the expected mutational patterns. In Aim 3, we will estimate mutational signatures in normal tissue before clonal expansion. We will use new sequencing technologies that can accurately proﬁle mutations in single DNA molecules, develop new algorithms for mutational signature analysis, and deconvolute mutational signatures operative in different cell types. We will apply these algorithms to data from circulating tumor DNA and normal tissue from patients with genetic cancer predisposition. The assays and algorithms developed will capture mutagenesis with higher sensitivity needed for both early detection and studies of premalignant tissue. Overall, this proposal with enhance our understanding of mutagenesis linked to DNA repair deﬁciencies across time, informing new cancer therapies and early detection strategies.


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 = $191,484 )
2026	2026	BETH ISRAEL DEACONESS MEDICAL CENTER, INC.	330 BROOKLINE AVE	BOSTON	MA	02215	SUFFOLK	USA	Cancer Research Manpower	000	1	8/13/2026	NEW	$191,484
														Subtotal = $191,484

Grand Total All Awards = $191,484

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Early detection and interception of cancer using the mutational signature methodology

Award Number: K22CA288779

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 08/13/2026

PERIOD OF PERFORMANCE END DATE: 07/31/2029

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