Identifying clinically relevant prognostic subgroups in node-positive postmenopausal HR+ early breast cancer patients treated with endocrine therapy: A combined analysis of 2,485 patients from ABCSG-8 and ATAC using the PAM50 risk of recurrence (ROR) score and intrinsic subtype.
Bibliographic record
Abstract
506 Background: Most postmenopausal women with node positive HR+ EBC receive adjuvant chemotherapy. We hypothesized that a molecular-based characterization of residual risk after endocrine therapy using the ROR score and IS may identify node-positive patient subgroups with limited long-term recurrence risk after endocrine therapy better than clinical-pathological risk assessment by clinical treatment score (CTS) alone. Methods: Long-term follow-up and tissue samples were obtained from 2,485 postmenopausal HR+ patients from the ABCSG-8 (N=1,478) and transATAC (N=1,007) trials. The PAM50 test was conducted on RNA extracted from paraffin blocks using the NanoString nCounter Analysis system. The ability of ROR, IS and ROR-defined risk groups (ROR-RG) to add prognostic information to CTS was assessed by the likelihood ratio test in a prospectively defined analysis plan. Results: Patients in the combined data set were grouped by the number of positive nodes into 1 (N1), 2 (N2), or 2 or 3 (N2-3),Baseline hazards for these subgroups were similar in the two trials. ROR score, IS and ROR-RG added statistically significant prognostic information (10-year distant recurrence risk) beyond CTS in all groups. In patients with one positive node, the absolute 10-year risk of distant recurrence was 6.6% [95% CI: 3.3%-12.8%] in the PAM-50-low risk group (40% of patients) and 8.4 % [5.3%-13.3%] in the Luminal A subgroup (69% of patients). Conclusions: The results of this combined analysis demonstrate that a significant proportion of N1 EBC patients have very limited long term recurrence risk and suggest the same for some N2 patients. The PAM50 ROR score, IS and ROR-RG reliably provide additional prognostic information beyond CTS and may be useful in deciding which women with node-positive HR+ EBC can be spared adjuvant chemotherapy. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".