Discovery and validation of a genomic signature to identify women with early-stage invasive breast cancer who may safely omit adjuvant radiotherapy after breast-conserving surgery.
Bibliographic record
Abstract
512 Background: Adjuvant radiotherapy (RT) is currently the standard of care for women with early-stage invasive breast cancer (BC) treated with breast conserving surgery (BCS). However, some women may have very low risk of recurrence and could safely be spared RT. This study aimed to identify these women using a molecularly-based approach. Methods: We performed an analysis of the SweBCG91-RT cohort, a trial randomizing women with node-negative stage I-II invasive BC +/- RT following breast conserving surgery, with sparse use of adjuvant systemic therapy. Only patients with ER+, HER2- tumors, and not treated with adjuvant systemic therapy, were included in this analysis. Transcriptome-wide profiling of tumors was performed using the Affymetrix Human Exon 1.0 ST microarray. The SweBCG91-RT cohort was divided into a training cohort of 243 patients and a validation cohort of 354 patients. Biological gene sets and individual genes related to locoregional recurrence in patients not receiving RT of the training set were identified, and a 16-gene signature was trained using elastic net regression. The signature, named Profile for the Omission of Local Adjuvant Radiation (POLAR), was locked prior to validation. Results: In the validation cohort, POLAR was prognostic for locoregional recurrence (LRR) in patients not treated with RT (multivariable Cox model adjusting for age, grade, tumor size, and luminal A vs luminal B: HR = 1.7 [1.2,2.3], p < 0.001). Patients categorized as POLAR low-risk had a 10-year locoregional recurrence rate of 7% in the absence of RT. Notably, there was no significant benefit from RT for these POLAR low-risk patients (HR = 1.1 [0.38,3.3], p = 0.83), whereas patients categorized as POLAR high-risk had a significant decreased risk of locoregional recurrence when treated with RT (recurrence rate without RT at 10-years 19%, HR = 0.43 [0.24,0.78], p = 0.0053). Conclusions: These data suggest that the novel POLAR genomic signature based on LRR biology can not only identify patients who have a low risk of LRR without adjuvant RT after BCS but who also would not benefit from RT, thus being prime candidates for RT omission.
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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 | 0.000 | 0.000 |
| 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".