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Record W2207394670 · doi:10.18632/oncotarget.5562

A four gene signature predicts benefit from anthracyclines: evidence from the BR9601 and MA.5 clinical trials

2015· article· en· W2207394670 on OpenAlexafffundabout
Melanie Spears, Fouad Yousif, Nicola Lyttle, Paul C. Boutros, Alison F. Munro, Chris Twelves, Kathleen I. Pritchard, Mark N. Levine, Lois E. Shepherd, John M.S. Bartlett

Bibliographic record

VenueOncotarget · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of TorontoHamilton Health SciencesSunnybrook Health Science CentreQueen's UniversityMcMaster UniversityOntario Institute for Cancer Research
FundersOntario Ministry of Research and InnovationTerry Fox Research InstituteCanadian Institutes of Health ResearchGovernment of Ontario
KeywordsAnthracyclineInternal medicineMedicineOncologyMultivariate analysisBreast cancerHazard ratioUnivariate analysisProportional hazards modelPathologicalCancerConfidence interval

Abstract

fetched live from OpenAlex

// Melanie Spears 1 , Fouad Yousif 2 , Nicola Lyttle 1 , Paul C. Boutros 2,3,4 , Alison F. Munro 5 , Chris Twelves 6 , Kathleen I. Pritchard 7,8 , Mark N. Levine 9 , Lois Shepherd 10 and John MS. Bartlett 1,5 1 Transformative Pathology, Ontario Institute for Cancer Research, MaRS Centre, Toronto, ON, Canada 2 Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, Toronto, ON, Canada 3 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 4 Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada 5 Edinburgh Cancer Research UK Centre, MRC IGMM, University of Edinburgh, Crewe Road South, Edinburgh, UK 6 Leeds Institute of Cancer and Pathology and Cancer Research UK Centre, St James’ University Hospital, Leeds, UK 7 Sunnybrook Odette Cancer Centre, Toronto, ON, Canada 8 University of Toronto, Toronto, ON, Canada 9 McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada 10 NCIC Clinical Trials Group (NCIC CTG] and Queen’s University, Kingston, ON, Canada Correspondence to: Melanie Spears, email: // Keywords : breast cancer, anthracycline, chromosome instability, predictive biomarker Received : July 20, 2015 Accepted : August 10, 2015 Published : September 10, 2015 Abstract Chromosome instability (CIN) in solid tumours results in multiple numerical and structural chromosomal aberrations and is associated with poor prognosis in multiple tumour types. Recent evidence demonstrated CEP17 duplication, a CIN marker, is a predictive marker of anthracycline benefit. An analysis of the BR9601 and MA.5 clinical trials was performed to test the role of existing CIN gene expression signatures as predictive markers of anthracycline sensitivity in breast cancer. Univariate analysis demonstrated, high CIN25 expression score was associated with improved distant relapse free survival (DRFS) (HR: 0.74, 95% CI 0.54-0.99, p = 0.046). High tumour CIN70 and CIN25 scores were associated with aggressive clinicopathological phenotype and increased sensitivity to anthracycline therapy compared to low CIN scores. However, in a prospectively planned multivariate analysis only pathological grade, nodal status and tumour size were significant predictors of outcome for CIN25/CIN70. A limited gene signature was generated, patients with low tumour CIN4 scores benefited from anthracycline treatment significantly more than those with high CIN4 scores (HR 0.37, 95% CI 0.20-0.56, p = 0.001). In multivariate analyses the treatment by marker interaction for CIN4/anthracyclines demonstrated hazard ratio of 0.35 (95% CI 0.15-0.80, p = 0.012) for DRFS. This data shows CIN4 is independent predictor of anthracycline benefit for DRFS in breast cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.128
GPT teacher head0.380
Teacher spread0.252 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2015
Admission routes3
Has abstractyes

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