Comprehensive Transcriptomic Profiling Identifies Breast Cancer Patients Who May Be Spared Adjuvant Systemic Therapy
Bibliographic record
Abstract
PURPOSE: There is currently no molecular signature in clinical use for adjuvant endocrine therapy omission in breast cancer. Given the unique trial design of SweBCG91-RT, where adjuvant endocrine and chemotherapy were largely unadministered, we sought to evaluate the potential of transcriptomic profiling for identifying patients who may be spared adjuvant endocrine therapy. EXPERIMENTAL DESIGN: We performed a whole-transcriptome analysis of SweBCG91-RT, a randomized phase III trial of ± radiotherapy after breast-conserving surgery for node-negative stage I-IIA breast cancer. Ninety-two percent of patients were untreated by both adjuvant endocrine therapy and chemotherapy. We calculated 15 transcriptomic signatures from the literature and combined them into an average genomic risk, which was further used to derive a novel 141-gene signature (MET141). All signatures were then independently examined in SweBCG91-RT and in the publicly available METABRIC cohort. RESULTS: In SweBCG91-RT, 454 patients were node-negative, postmenopausal, and systemically untreated with ER-positive, HER2-negative cancers, which constitutes a low-risk subgroup and potential candidates for therapy omission. Most transcriptomic signatures were highly prognostic for distant metastasis, but considerable discordance was observed on the individual patient level. Within the MET141 low-risk subgroup (lowest 25th percentile of scores), 95% of patients were free of metastasis at 15 years, even in the absence of adjuvant endocrine therapy. In a clinically low-risk subgroup of the METABRIC cohort not treated with systemic therapy, no breast cancer death occurred among the MET141 low-risk patients. CONCLUSIONS: Transcriptomic profiling identifies patients with an excellent outcome without any systemic adjuvant therapy in clinically low-risk patients of the SweBCG91-RT and METABRIC cohorts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".