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Record W2190954851 · doi:10.1056/nejmoa1510764

Prospective Validation of a 21-Gene Expression Assay in Breast Cancer

2015· article· en· W2190954851 on OpenAlexafffund
Joseph A. Sparano, Robert J. Gray, Della Makower, Kathleen I. Pritchard, Kathy S. Albain, Daniel F. Hayes, Charles E. Geyer, Elizabeth Claire Dees, Edith A. Perez, John A. Olson, JoAnne Zujewski, Tracy Lively, Sunil Badve, Thomas J. Saphner, Lynne I. Wagner, Timothy J. Whelan, Matthew J. Ellis, Soonmyung Paik, William C. Wood, Peter M. Ravdin, Maccon Keane, Henry Gómez, Pavan S. Reddy, Timothy F. Goggins, Ingrid A. Mayer, Adam Brufsky, Deborah Toppmeyer, Virginia Kaklamani, James N. Atkins, Jeffrey L. Berenberg, George W. Sledge

Bibliographic record

VenueNew England Journal of Medicine · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsSunnybrook HospitalJuravinski Cancer Centre
FundersYonsei University College of MedicineUniversity of North Carolina at Chapel HillSunnybrook Research InstituteYonsei UniversityNational Institutes of HealthECOG-ACRIN Cancer Research GroupSchool of Medicine, Virginia Commonwealth UniversityVanderbilt UniversityVirginia Commonwealth UniversityNorthwestern UniversityNational Cancer InstituteEmory UniversityUniversity of PittsburghSchool of Medicine, Indiana UniversityJohns Hopkins UniversityWake Forest UniversityAmerican College of Radiology Imaging Network
KeywordsBiomarkerBreast cancerOncologyProspective cohort studyMedicineInternal medicineCancerGene expressionGeneBioinformaticsBiologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Prior studies with the use of a prospective-retrospective design including archival tumor samples have shown that gene-expression assays provide clinically useful prognostic information. However, a prospectively conducted study in a uniformly treated population provides the highest level of evidence supporting the clinical validity and usefulness of a biomarker. METHODS: We performed a prospective trial involving women with hormone-receptor-positive, human epidermal growth factor receptor type 2 (HER2)-negative, axillary node-negative breast cancer with tumors of 1.1 to 5.0 cm in the greatest dimension (or 0.6 to 1.0 cm in the greatest dimension and intermediate or high tumor grade) who met established guidelines for the consideration of adjuvant chemotherapy on the basis of clinicopathologic features. A reverse-transcriptase-polymerase-chain-reaction assay of 21 genes was performed on the paraffin-embedded tumor tissue, and the results were used to calculate a score indicating the risk of breast-cancer recurrence; patients were assigned to receive endocrine therapy without chemotherapy if they had a recurrence score of 0 to 10, indicating a very low risk of recurrence (on a scale of 0 to 100, with higher scores indicating a greater risk of recurrence). RESULTS: Of the 10,253 eligible women enrolled, 1626 women (15.9%) who had a recurrence score of 0 to 10 were assigned to receive endocrine therapy alone without chemotherapy. At 5 years, in this patient population, the rate of invasive disease-free survival was 93.8% (95% confidence interval [CI], 92.4 to 94.9), the rate of freedom from recurrence of breast cancer at a distant site was 99.3% (95% CI, 98.7 to 99.6), the rate of freedom from recurrence of breast cancer at a distant or local-regional site was 98.7% (95% CI, 97.9 to 99.2), and the rate of overall survival was 98.0% (95% CI, 97.1 to 98.6). CONCLUSIONS: Among patients with hormone-receptor-positive, HER2-negative, axillary node-negative breast cancer who met established guidelines for the recommendation of adjuvant chemotherapy on the basis of clinicopathologic features, those with tumors that had a favorable gene-expression profile had very low rates of recurrence at 5 years with endocrine therapy alone. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT00310180.).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.288
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1,439
Published2015
Admission routes2
Has abstractyes

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