Evidence map›Paper›PMID 25342391›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2014

The role of genome sequencing in personalized breast cancer prevention.

Weiva Sieh, Joseph H Rothstein, Valerie McGuire, Alice S Whittemore

Abstract readComparative Study
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Cancer prevention: state of the art and future prospects.Journal of preventive medicine and hygiene · 2015
    Review
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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

4 authors.

Weiva SiehStanford University School of Medicine, Department of Health Research and Policy, Stanford, California.
Joseph H RothsteinStanford University School of Medicine, Department of Health Research and Policy, Stanford, California.
Valerie McGuireStanford University School of Medicine, Department of Health Research and Policy, Stanford, California.
Alice S WhittemoreStanford University School of Medicine, Department of Health Research and Policy, Stanford, California. alicesw@stanford.edu.

Funding

Statistical Methods for Genetic EpidemiologyR01CA094069 · NCI · STANFORD UNIVERSITY · PI WHITTEMORE, ALICE · 2002 to 2016
$7.7M
Genetic Epidemiology of Ovarian and Prostate CancerK07CA143047 · NCI · STANFORD UNIVERSITY · PI SIEH, WEIVA · 2010 to 2014
$867k
NCI NIH HHS K07 CA143047NCI NIH HHS K07CA143047NCI NIH HHS R01 CA094069NCI NIH HHS R01CA094069
6 · The paper itself

Abstract

backgroundThere is uncertainty about the benefits of using genome-wide sequencing to implement personalized preventive strategies at the population level, with some projections suggesting little benefit. We used data for all currently known breast cancer susceptibility variants to assess the benefits and harms of targeting preventive efforts to a population subgroup at highest genomic risk of breast cancer.

methodsWe used the allele frequencies and effect sizes of 86 known breast cancer variants to estimate the population distribution of breast cancer risks and evaluate the strategy of targeting preventive efforts to those at highest risk. We compared the efficacy of this strategy with that of a "best-case" strategy based on a risk distribution estimated from breast cancer concordance in monozygous twins, and with strategies based on previously estimated risk distributions.

resultsTargeting those in the top 25% of the risk distribution would include approximately half of all future breast cancer cases, compared with 70% captured by the best-case strategy and 35% based on previously known variants. In addition, current evidence suggests that reducing exposure to modifiable nongenetic risk factors will have greatest benefit for those at highest genetic risk.

conclusionsThese estimates suggest that personalized breast cancer preventive strategies based on genome sequencing will bring greater gains in disease prevention than previously projected. Moreover, these gains will increase with increased understanding of the genetic etiology of breast cancer. IMPACT: These results support the feasibility of using genome-wide sequencing to target the women who would benefit from mammography screening.

Indexed as

Genetic TestingModels, StatisticalSequence Analysis, DNABreast NeoplasmsFemaleGene FrequencyGenetic VariationGenomeHumansPrecision MedicineRisk AssessmentRisk Factors

Identifiers

PMID25342391
PMCPMC4221442

What OpenQuestion holds

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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.