Evidence map›Paper›PMID 34381935›Full record

ArticleJCO precision oncology2021

Polygenic Breast Cancer Risk for Women Veterans in the Million Veteran Program.

Jessica Minnier, Nallakkandi Rajeevan, Lina Gao, Byung Park, Saiju Pyarajan, Paul Spellman, Sally G Haskell, Cynthia A Brandt, Shiuh-Wen Luoh

Abstract readValidation Study
In one paragraph

Article in JCO precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 4 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

12 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

9 authors.

Jessica MinnierOHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR.ORCID 0000-0002-3527-2757
Nallakkandi RajeevanClinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT.ORCID 0000-0002-2045-0872
Lina GaoOHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR.
Byung ParkOHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR.ORCID 0000-0003-3516-7516
Saiju PyarajanVA Boston Healthcare System, Boston, MA.ORCID 0000-0002-9047-3762
Paul SpellmanKnight Cancer Institute, Oregon Health & Science University, Portland, OR.
Sally G HaskellVA Connecticut Healthcare System, West Haven, CT.
Cynthia A BrandtYale Center for Medical Informatics (YCMI), Yale School of Medicine, New Haven, CT.
Shiuh-Wen LuohKnight Cancer Institute, Oregon Health & Science University, Portland, OR.ORCID 0000-0001-8355-5332

Funding

BLRD VA I01 BX004188
6 · The paper itself

Abstract

Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND

methodsClinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk.

resultsThree hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%.

conclusionClinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.

Indexed as

VeteransAdultAgedBlack PeopleBreast NeoplasmsCohort StudiesFemaleGenetic Predisposition to DiseaseHumansMiddle AgedPolymorphism, Single NucleotideRisk AssessmentRisk FactorsWhite People

Identifiers

PMID34381935
PMCPMC8345920

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.