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Abstract P3-11-03: Validation of CIN4 in the DBCG 89D clinical cohort

2019· article· en· W2944359344 on OpenAlexaff
Melanie Spears, Jensen Mb, Nicola Lyttle, Lori Liao, B Ejitlertsen, JM Bartlett

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsAnthracyclineInternal medicineOncologyMedicineProportional hazards modelCohortBreast cancerHazard ratioCancerConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Chromosome instability (CIN) in solid tumours is associated with poor prognosis and results in numerical and structural chromosomal aberrations. Our group previously have developed the CIN signatures and have demonstrated the CIN signatures as prognostic biomarkers in breast cancer cohorts. Furthermore, our work in the BR9601 and MA.5 clinical cohorts CIN4 provided level IIB evidence that CIN4 was predictive of anthracycline sensitivity. An analysis of the DBCG 89D clinical trial was now performed to validate the role of CIN gene expression signatures as a marker of anthracycline sensitivity. Methods: RNA was extracted from patients in DBCG 89D clinical trial analysed through NanoString technology. The prognostic and predictive values of the signatures on distant relapse-free survival (DRFS) were explored using Cox proportional hazard models. Multivariate models included menopausal status, tumour size, nodal status, ER and Her2 status, histological type and grade, and treatment regimen. Results: All of the 594 samples available from the DBCG 89D we successfully analysed. CIN25 and CIN70 gene expression signatures did not associate with any of the clinicopathological characteristics tested. In addition, CIN25 and CIN70 were not prognostic or predictive of distant relapse free or breast cancer specific survival in this clinical cohort. Low CIN4 score was associated with ER negativity (p=0.02), HER2 normal expression (p<0.05). Conclusion: In this study we demonstrated that CIN4 was associated with aggressive disease. We were however in DBCG 89D unable to validate the predictive value of CIN4 concerning anthracycline sensitivity. Citation Format: Spears M, Jensen M-B, Lyttle N, Liao L, Laenkholm A-V, Ejitlertsen B, Bartlett JM. Validation of CIN4 in the DBCG 89D clinical cohort [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-11-03.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.079
GPT teacher head0.448
Teacher spread0.369 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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