Identification of Distinct Prognostic Groups: Implications for Patient Selection to Targeted Therapies Among Anti-Endocrine Therapy–Resistant Early Breast Cancers
Bibliographic record
Abstract
PURPOSE Hormone receptor–positive breast cancer remains an ongoing therapeutic challenge, despite optimal anti-endocrine therapies. In this study, we assessed the prognostic ability of genomic signatures to identify patients at risk for recurrence after endocrine therapy. Analysis was performed on the basis of an a priori hypothesis related to molecular pathways, which might predict response to existing targeted therapies. PATIENTS AND METHODS A subset of patients from the Tamoxifen Versus Exemestane Adjuvant Multinational trial ( ClinicalTrials.gov identifiers: NCT00279448 and NCT00032136, and NCT00036270) pathology cohort were analyzed to determine the prognostic ability of mutational and copy number aberration biomarkers that represent the cyclin D/cyclin-dependent kinase (CCND/CDK), fibroblast growth factor receptor/fibroblast growth factor (FGFR/FGF), and phosphatidylinositol 3-kinase/protein kinase B (PI3K/ATK) pathways to inform the potential choice of additional therapies to standard endocrine treatment. Copy number analysis and targeted sequencing was performed. Pathways were identified as aberrant if there were copy number aberrations and/or mutations in any of the predetermined pathway genes: CCND1/CCND2/CCND3/CDK4/CDK6, FGFR1/FGFR2/FGFR2/FGFR4, and AKT1/AKT2/PIK3CA/PTEN. RESULTS The 390 of 420 samples that passed quality control were analyzed for distant metastasis–free survival between groups. Patients with no changes in the CCND/CDK pathway experienced a better distant metastasis–free survival (hazard ratio, 1.94; 95% CI, 1.45 to 2.61; P < .001) than those who possessed aberrations. In the FGFR/FGF and PI3K/AKT pathways, a similar outcome was observed (hazard ratio, 1.43 [95% CI, 1.07 to 1.92; P = .017] and 1.34 [95% CI, 1.00 to 1.81; P = .053], respectively). CONCLUSION We show that aberrations of genes in these pathways are independently linked to a higher risk of relapse after endocrine treatment. Improvement of the clinical management of early breast cancers could be made by identifying those for whom current endocrine therapies are sufficient, thus reducing unnecessary treatment, and secondly, by identifying those who are at high risk for recurrence and linking molecular features that drive these cancers to treatment with 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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".