Copy-number and targeted sequencing analyses to identify distinct prognostic groups: Implications for patient selection to targeted therapies amongst anti-endocrine therapy resistant early breast cancers.
Bibliographic record
Abstract
524 Background: Hormone receptor positive breast cancer is a therapeutic challenge. Despite optimal anti-endocrine therapies, most breast cancer deaths follow a diagnosis of early luminal cancer. To understand the impact of multiple aberrations in the context of current therapy, we assessed the prognostic ability of genomic signatures as a putative stratification tool to targeted therapies. Methods: This a priori study is based on molecular pathways which might predict response to targeted therapies. DNA from 420 patients from the phase III TEAM pathology cohort were used. Patients with a distant recurrence within 5 years were matched by clinical variables to those disease-free at follow up. Copy number analysis was performed using the Affymetrix Oncoscan Assay. Targeted sequencing was performed in a subset of samples for genes based on signaling cassettes mined from the ICGC. Pathways were identified as aberrant if there were copy number variations (CNVs) and/or mutations in any of the pre-determined pathway genes: 1) CCND1/CCND2/CCND3/CDK4/CDK6; 2) FGFR1/FGFR2/FGFR2/FGFR4; and 3) AKT1/AKT2/PIK3CA/PTEN. Kaplan-Meier and log-rank analyses were used for DFS between groups. Hazard ratios were calculated using the Cox proportional hazard models adjusted for age, tumour size, grade, lymph node and HER2 status. Results: 390/420 samples passed informatics QC filters. For the CCND/CDK pathway, patients with no CNV changes experienced a better DFS (HR = 1.7, 95% CI 1.3-2.3, p < 0.001). For the FGFR/FGF pathway, a similar outcome is seen among patients without CNVs (HR = 1.5, 95% CI 1.1-2.0; p = 0.005). For AKT/PIK3CA, a decrease in DFS was seen in those with aberrations (HR = 1.4, 95% CI 1.0-1.8, p = 0.03). Conclusions: We demonstrated that CNVs of genes within CDK4/CCND, PIK3CA/AKT and FGFR pathways are independently linked to high risk of relapse following endocrine treatment, with implications for identifying those patients who are at high-risk for recurrence despite optimal anti-endocrine therapy and linking molecular features driving these cancers to targeted therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".