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Record W2122541748 · doi:10.1093/jnci/djv246

Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer

2015· review· en· W2122541748 on OpenAlexaff
Cornelia M. Ulrich, Ellen L. Goode, Yonathan Brhane, Kenneth Muir, Andrew T. Chan, Loı̈c Le Marchand, Joellen M. Schildkraut, John S. Witte, Rosalind A. Eeles, Paolo Boffetta, Margaret R. Spitz, Julia G. Poirier, David N. Rider, Brooke L. Fridley, Zhihua Chen, Christopher Haiman, Fredrick R. Schumacher, Douglas F. Easton, Maria Teresa Landi, Paul Brennan, Richard S. Houlston, David C. Christiani, John K. Field, Heike Bickeböller, Angela Risch, Zsofia Kote‐Jarai, Fredrik Wiklund, Henrik Grönberg, Stephen J. Chanock, Sonja I. Berndt, Peter Kraft, Sara Lindström, Ali Amin Al Olama, Honglin Song, Catherine Phelan, Nicholas Wentzensen, Ulrike Peters, Martha L. Slattery, Thomas A. Sellers, Graham Casey, Stephen B. Gruber, David J. Hunter, Christopher I. Amos, Brian E. Henderson

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Cancer InstituteNational Institutes of HealthCancer Research UKNational Human Genome Research InstituteWorld Health Organization
KeywordsColorectal cancerMedicineProstateCancerBreast cancerOncologyProstate cancerOvaryInflammationCancer researchLung cancerLungInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Inflammation has been hypothesized to increase the risk of cancer development as an initiator or promoter, yet no large-scale study of inherited variation across cancer sites has been conducted. METHODS: We conducted a cross-cancer genomic analysis for the inflammation pathway based on 48 genome-wide association studies within the National Cancer Institute GAME-ON Network across five common cancer sites, with a total of 64 591 cancer patients and 74 467 control patients. Subset-based meta-analysis was used to account for possible disease heterogeneity, and hierarchical modeling was employed to estimate the effect of the subcomponents within the inflammation pathway. The network was visualized by enrichment map. All statistical tests were two-sided. RESULTS: We identified three pleiotropic loci within the inflammation pathway, including one novel locus in Ch12q24 encoding SH2B3 (rs3184504), which reached GWAS significance with a P value of 1.78 x 10(-8), and it showed an association with lung cancer (P = 2.01 x 10(-6)), colorectal cancer (GECCO P = 6.72x10(-6); CORECT P = 3.32x10(-5)), and breast cancer (P = .009). We also identified five key subpathway components with genetic variants that are relevant for the risk of these five cancer sites: inflammatory response for colorectal cancer (P = .006), inflammation related cell cycle gene for lung cancer (P = 1.35x10(-6)), and activation of immune response for ovarian cancer (P = .009). In addition, sequence variations in immune system development played a role in breast cancer etiology (P = .001) and innate immune response was involved in the risk of both colorectal (P = .022) and ovarian cancer (P = .003). CONCLUSIONS: Genetic variations in inflammation and its related subpathway components are keys to the development of lung, colorectal, ovary, and breast cancer, including SH2B3, which is associated with lung, colorectal, and breast cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.376
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations76
Published2015
Admission routes1
Has abstractyes

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