Evidence map›Paper›PMID 24359602›Full record

ReviewBreast cancer research : BCR2013

Common breast cancer risk variants in the post-COGS era: a comprehensive review.

Kara N Maxwell, Katherine L Nathanson

Open access · goldAbstract readReview
In one paragraph

Review in Breast cancer research : BCR, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Discriminatory power of common genetic variants in personalized breast cancer diagnosis.Proceedings of SPIE--the International Society for Optical Engineering · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Kara N Maxwell
Katherine L Nathanson
University of Pennsylvania · US

Funding

Post GWA Studies in Testicular Germ Cell TumorsU01CA164947 · NCI · UNIVERSITY OF PENNSYLVANIA · PI KANETSKY, PETER A, NATHANSON, KATHERINE L. · 2012 to 2024
$11.7M
MEDICAL GENETICS RESEARCH TRAINING GRANTT32GM008638 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Rebecca Ganetzky · 1997 to 2026
$8.7M
Inherited genetic variation and predisposition to testicular germ cell tumorR01CA114478 · NCI · UNIVERSITY OF PENNSYLVANIA · PI NATHANSON, KATHERINE L. · 2007 to 2011
$3.1M
NCI NIH HHS R01 CA114478NCI NIH HHS R01 CA164947NIGMS NIH HHS 5T32GM008638-15
6 · The paper itself

Abstract

Breast cancer has a strong heritable component, with approximately 15% of cases exhibiting a family history of the disease. Mutations in genes such as BRCA1, BRCA2 and TP53 lead to autosomal dominant inherited cancer susceptibility and confer a high lifetime risk of breast cancers. Identification of mutations in these genes through clinical genetic testing enables patients to undergo screening and prevention strategies, some of which provide overall survival benefit. In addition, a number of mutant alleles have been identified in genes such as CHEK2, PALB2, ATM and BRIP1, which often display incomplete penetrance and confer moderate lifetime risks of breast cancer. Studies are underway to determine how to use the identification of mutations in these genes to guide clinical practice. Altogether, however, mutations in high and moderate penetrance genes probably account for approximately 25% of familial breast cancer risk; the remainder may be due to mutations in as yet unidentified genes or lower penetrance variants. Common low penetrance alleles, which have been mainly identified through genome-wide association studies (GWAS), are generally present at 10 to 50% population frequencies and confer less than 1.5-fold increases in breast cancer risk. A number of single nucleotide polymorphisms (SNPs) have been identified and risk associations extensively replicated in populations of European ancestry, the number of which has substantially increased as a result of GWAS performed by the Collaborative Oncological Gene-environment Study consortium. It is now estimated that 28% of familial breast cancer risk is explained by common breast cancer susceptibility loci. In some cases, SNP associations may be specific to different subsets of women with breast cancer, as defined by ethnicity or estrogen receptor status. Although not yet clinically established, it is hoped that identification of common risk variants may eventually allow identification of women at higher risk of breast cancer and enable implementation of breast cancer screening, prevention or treatment strategies that provide clinical benefit.

Indexed as

Genetic Predisposition to DiseaseAllelesBRCA1 ProteinBRCA2 ProteinBreast NeoplasmsFemaleGenetic TestingGenome-Wide Association StudyHumansPolymorphism, Single NucleotideWhite PeopleBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinBRCA2 protein, human

Identifiers

PMID24359602
PMCPMC3978855
OpenAlexW2103016976

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.