Evidence map›Paper›PMID 42001218›Full record

ArticleJournal of the National Cancer Institute2026

Prioritizing context-specific genetic risk mechanisms in 11 solid cancers.

Xueyao Wu, Artem Kim, Charles E Breeze, Tracy A O'Mara, Dhanya Ramachandran, Thilo Dörk, Stella Koutros, Nathaniel Rothman, Ludmila Prokunina-Olsson, Nicholas Mancuso and 2 more

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Article in Journal of the National Cancer Institute, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Xueyao WuTrans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Artem KimDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Charles E BreezeOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Tracy A O'MaraCancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia.
Dhanya RamachandranGynaecology Research Unit, Hannover Medical School, Hannover, Germany.
Thilo DörkGynaecology Research Unit, Hannover Medical School, Hannover, Germany.
Stella KoutrosOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Nathaniel RothmanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Ludmila Prokunina-OlssonLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Nicholas MancusoDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Sara LindströmDepartment of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
Peter KraftTrans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile genome-wide association studies (GWAS) have identified hundreds of cancer-associated genetic variants, the specific biological contexts where these variants exert their effects remain largely unknown. We aimed to prioritize context-specific genetic risk mechanisms for 11 solid cancers at both genome-wide and single-variant resolutions.

methodsWe integrated cancer GWAS summary statistics from European ancestry samples (avg. n cases = 47,856) with 1,473 context-specific annotations representing candidate cis-regulatory elements. For genome-wide analysis, we applied CT-FM, a method that jointly models heritability enrichments across annotations to select likely disease-relevant biological contexts. Following functionally informed fine-mapping to identify high-confidence (PIP ≥ 0.5) causal SNPs, we used CT-FM-SNP to identify relevant contexts for individual variants. A combined SNP-to-gene framework was applied to construct putative {regulatory SNP-context-gene-cancer} quadruplets.

resultsStratified LD score regression analysis identified 141 annotations showing significant heritability enrichment (FDR q ≤ 0.05). CT-FM prioritized four high-confidence (PIP ≥ 0.5) biological contexts mammary luminal epithelial cells for overall and estrogen receptor (ER)-positive breast cancer, a prostate cancer epithelial cell line (VCaP) for prostate cancer, and bulk tumor tissue contexts for colorectal and renal cancers. Variant-level analysis of hundreds of putatively causal SNPs aligned with these findings and identified additional high-confidence contexts for ER-negative breast, endometrial, lung, and bladder cancers. A total of 489 putative regulatory quadruplets were constructed, proposing specific molecular hypotheses underlying the observed GWAS signals.

conclusionThese findings advance our understanding of genetic susceptibility to different cancers. Future work in larger, more diverse GWAS, coupled with more comprehensive annotation atlases, is essential to expand upon and validate our results.

Identifiers

PMID42001218

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.