Abstract P2-05-05: A four gene signature predicts anthracycline benefit: Evidence from the BR9601 and MA5 breast cancer trials
Bibliographic record
Abstract
Abstract Background: Chromosome instability (CIN) in solid tumours is associated with poor prognosis and results in numerical and structural chromosomal aberrations. Recent evidence from both the BR9601 and MA.5 trials has demonstrated CEP17 duplication as a predictive marker of anthracycline benefit. CIN25 and CIN70 gene expression profiles have previously been published and predict survival response. An analysis of the BR9601 and MA5 clinical trials was performed to test the role of CIN gene expression signatures as a marker of anthracycline sensitivity. Methods: RNA was extracted from patients in both the BR9601 and MA5 studies and analysed through Nanostring technology. Log-rank analyses explored the prognostic values of the signatures on distant relapse-free survival (DRFS). Cox-regression models tested independent prognostic value on DRFS in the presence of treatment, age, tumour size, nodal status, ER status and grade, and treatment by marker interactions. Results: Of the 761 samples available from the BR9601 and MA5 cohorts we successfully analysed 703 (92.4%). High CIN25 and CIN70 scores were associated with age (p<0.0001), grade (p0.0001), PgR negativity (p<0.0001) and ER negativity (p<0.0001). In univariate analysis, high CIN25 score was associated with decreased DRFS (HR: 0.74, 95% CI 0.54-0.99, p=0.046). In a multivariate analysis with adjustment for size, nodal status, ER, pathological grade, HER2, CIN25, treatment and treatment by marker only pathological grade, nodal status and tumour size were significant predictors of outcome. A more limited set of genes that reflected CIN was established by examining the expression profile of the genes and clustering them. The combined cohort was split into a 60% training and 40% validation set. The area under the curve (AUC) was calculated and the gene signature with the greatest AUC was selected and termed CIN4. Patients with low CIN4 score benefited from anthracycline treatment compared to those that had high CIN4 score (HR 2.72, 95% CI 1.48-5.02, p=0.001). No significant benefit with CMF treatment was observed in (HR: 1.02, 95% CI 0.58-1.82, p=0.92). After multivariate analysis the treatment by marker interaction for CIN4 had a hazard ratio of 2.10 (95% CI 2.18-30.38, p= 0.001). Conclusion: High CIN70 and CIN25 scores were associated with an aggressive phenotype and showed a potential increased sensitivity to anthracycline therapy compared to those with low CIN scores. CIN4 was an independent predictor of anthracycline benefit for DRFS. However, further work in larger patient cohorts such as NEAT is warranted. Citation Format: Melanie Spears, Nicola S Lyttle, Fouad Yousif, Alison F Munro, Christopher Twelves, Kathleen I Pritchard, Mark N Levine, Lois Shepherd, John MS Bartlett. A four gene signature predicts anthracycline benefit: Evidence from the BR9601 and MA5 breast cancer trials [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-05-05.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".