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Record W2105956673 · doi:10.1001/jamaoncol.2014.168

Hereditary Diffuse Gastric Cancer Syndrome

2015· article· en· W2105956673 on OpenAlexaff
Samantha Hansford, Pardeep Kaurah, Hector Li-Chang, Michelle Woo, Janine Senz, Hugo Pinheiro, Kasmintan A. Schrader, David F. Schaeffer, Karey Shumansky, George Zogopoulos, Teresa Almeida‐Santos, Isabel Claro, Joana Carvalho, Cydney Nielsen, Sarah Padilla, Amy Lum, Aline Talhouk, Katie Baker-Lange, Sue Richardson, I.J. Lewis, Noralane M. Lindor, Erin Pennell, Andrée MacMillan, Bridget A. Fernandez, G. Keller, Henry T. Lynch, Sohrab P. Shah, Parry Guilford, Steven Gallinger, Giovanni Corso, Franco Roviello, Carlos Caldas, Carla Oliveíra, Paul D.P. Pharoah, David G. Huntsman

Bibliographic record

VenueJAMA Oncology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsUniversity of TorontoDr. H. Bliss Murphy Cancer CentreLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalVancouver General HospitalOccupational Cancer Research CentreMcGill University Health CentreUniversity of British ColumbiaBC Cancer Agency
FundersNational Institute for Health and Care ResearchCancer Research UK
KeywordsGermline mutationMedicineBreast cancerCancerPenetranceCHEK2ProbandGeneticsOncologyMutationCDH1Genetic testingInternal medicineBiologyGene

Abstract

fetched live from OpenAlex

IMPORTANCE: E-cadherin (CDH1) is a cancer predisposition gene mutated in families meeting clinically defined hereditary diffuse gastric cancer (HDGC). Reliable estimates of cancer risk and spectrum in germline mutation carriers are essential for management. For families without CDH1 mutations, genetic-based risk stratification has not been possible, resulting in limited clinical options. OBJECTIVES: To derive accurate estimates of gastric and breast cancer risks in CDH1 mutation carriers and determine if germline mutations in other genes are associated with HDGC. DESIGN, SETTING, AND PARTICIPANTS: Testing for CDH1 germline mutations was performed on 183 index cases meeting clinical criteria for HDGC. Penetrance was derived from 75 mutation-positive families from within this and other cohorts, comprising 3858 probands (353 with gastric cancer and 89 with breast cancer). Germline DNA from 144 HDGC probands lacking CDH1 mutations was screened using multiplexed targeted sequencing for 55 cancer-associated genes. MAIN OUTCOMES AND MEASURES: Accurate estimates of gastric and breast cancer risks in CDH1 mutation carriers and the relative contribution of other cancer predisposition genes in familial gastric cancers. RESULTS: Thirty-one distinct pathogenic CDH1 mutations (14 novel) were identified in 34 of 183 index cases (19%). By the age of 80 years, the cumulative incidence of gastric cancer was 70% (95% CI, 59%-80%) for males and 56% (95% CI, 44%-69%) for females, and the risk of breast cancer for females was 42% (95% CI, 23%-68%). In CDH1 mutation-negative index cases, candidate mutations were identified in 16 of 144 probands (11%), including mutations within genes of high and moderate penetrance: CTNNA1, BRCA2, STK11, SDHB, PRSS1, ATM, MSR1, and PALB2. CONCLUSIONS AND RELEVANCE: This is the largest reported series of CDH1 mutation carriers, providing more precise estimates of age-associated risks of gastric and breast cancer that will improve counseling of unaffected carriers. In HDGC families lacking CDH1 mutations, testing of CTNNA1 and other tumor suppressor genes should be considered. Clinically defined HDGC families can harbor mutations in genes (ie, BRCA2) with different clinical ramifications from CDH1. Therefore, we propose that HDGC syndrome may be best defined by mutations in CDH1 and closely related genes, rather than through clinical criteria that capture families with heterogeneous susceptibility profiles.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations687
Published2015
Admission routes1
Has abstractyes

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