Evidence map›Paper›PMID 26673457›Full record

ArticleBBA clinical2014

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population.

Eldon R Jupe, Kathie M Dalessandri, John J Mulvihill, Rei Miike, Nicholas S Knowlton, Thomas W Pugh, Lue Ping Zhao, Daniele C DeFreese, Sharmila Manjeshwar, Bobby A Gramling and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BBA clinical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

12 authors at 5 institutions in 1 country.

Eldon R JupeResearch and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
Kathie M DalessandriSurgeon-Scientist, Point Reyes Station, CA, USA.
John J MulvihillDepartment of Pediatrics, Section of Genetics, University of Oklahoma, Oklahoma City, OK, USA.
Rei MiikeDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Nicholas S KnowltonNSK Statistical Solutions LLC, Choctaw, OK, USA.
Thomas W PughResearch and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
Lue Ping ZhaoDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Daniele C DeFreeseResearch and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
Sharmila ManjeshwarResearch and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
Bobby A GramlingResearch and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
John K WienckeDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Christopher C BenzDivision of Hematology-Oncology, University of California, San Francisco, CA, USA ; Buck Institute for Research on Aging, Novato, CA, USA.
University of California, San Francisco · USBear Valley · USBuck Institute for Research on Aging · USFred Hutch Cancer Center · USUniversity of Oklahoma · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population.

methodsA polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA.

resultsThe optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant. CONCLUSIONS AND GENERAL SIGNIFICANCE: Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

Indexed as

Aldosterone synthase variant (CYP11B2, -344T/C)Breast cancerPolyfactorial risk model (PFRM)Single nucleotide polymorphisms (SNPs)

Identifiers

PMID26673457
PMCPMC4633888
OpenAlexW2070887742

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.