Evidence map›Paper›PMID 25118203›Full record

ReviewJournal of the National Cancer Institute2014

How many etiological subtypes of breast cancer: two, three, four, or more?

William F Anderson, Philip S Rosenberg, Aleix Prat, Charles M Perou, Mark E Sherman

Abstract readReview
In one paragraph

Review in Journal of the National Cancer Institute, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
144citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Postpartum breast cancer: evidence for a distinct phenotype.Journal of the National Cancer Institute · 2026
    Article
  7. Review
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  11. Gut Microbiome and Estrogen.Journal of menopausal medicine · 2025
    Review
  12. Article
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  14. Article
  15. Review
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  19. Review
  20. Article

84 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

William F AndersonDivision of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP). wanderso@mail.nih.gov.
Philip S RosenbergDivision of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP).
Aleix PratDivision of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP).
Charles M PerouDivision of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP).
Mark E ShermanDivision of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP).

Funding

Tissue Procurement & PathologyP50CA058223 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KATHERINE A. HOADLEY · 1992 to 2026
$59.3M
Descriptive Studies and Record LinkageZIACP010183 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI SHIELS, MEREDITH · 2009 to 2025
$10.7M
Intramural NIH HHSNCI NIH HHS P50 CA058223NCI NIH HHS P50-CA58223-09A1
6 · The paper itself

Abstract

Breast cancer is a heterogeneous disease, divisible into a variable number of clinical subtypes. A fundamental question is how many etiological classes underlie the clinical spectrum of breast cancer? An etiological subtype reflects a grouping with a common set of causes, whereas a clinical subtype represents a grouping with similar prognosis and/or prediction. Herein, we review the evidence for breast cancer etiological heterogeneity. We then evaluate the etiological evidence with mRNA profiling data. A bimodal age distribution at diagnosis with peak frequencies near ages 50 and 70 years is a fundamental characteristic of breast cancer for important tumor features, clinical characteristics, risk factor profiles, and molecular subtypes. The bimodal peak frequencies at diagnosis divide breast cancer overall into a "mixture" of two main components in varying proportions in different cancer populations. The first breast cancer tends to arise early in life with modal age-at-diagnosis near 50 years and generally behaves aggressively. The second breast cancer occurs later in life with modal age near 70 years and usually portends a more indolent clinical course. These epidemiological and molecular data are consistent with a two-component mixture model and compatible with a hierarchal view of breast cancers arising from two main cell types of origin. Notwithstanding the potential added value of more detailed categorizations for personalized breast cancer treatment, we suggest that the development of better criteria to identify the two proposed etiologic classes would advance breast cancer research and prevention.

Indexed as

Age DistributionAge FactorsBiomarkers, TumorBreast NeoplasmsEvidence-Based MedicineFemaleGene Expression ProfilingGenetic Predisposition to DiseaseHumansImmunohistochemistryIncidenceNeoplasm ProteinsPrognosisRisk FactorsRNA, NeoplasmBiomarkers, TumorNeoplasm ProteinsRNA, Neoplasm

Identifiers

PMID25118203
PMCPMC4148600

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.