SETER/PR: a robust 18-gene predictor for sensitivity to endocrine therapy for metastatic breast cancer
Why this work is in the frame
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Bibliographic record
Abstract
Abstract There is a clinical need to predict sensitivity of metastatic hormone receptor-positive and HER2-negative (HR+/HER2−) breast cancer to endocrine therapy, and targeted RNA sequencing (RNAseq) offers diagnostic potential to measure both transcriptional activity and functional mutation. We developed the SET ER/PR index to measure gene expression microarray probe sets that were correlated with hormone receptors ( ESR1 and PGR ) and robust to preanalytical and analytical influences. We tested SET ER/PR index in biopsies of metastastic HR+/HER2− breast cancer against the treatment outcomes in 140 patients. Then we customized the SET ER/PR assay to measure 18 informative, 10 reference transcripts, and sequence the ligand-binding domain (LBD) of ESR1 using droplet-based targeted RNAseq, and tested that in residual RNA from 53 patients. Higher SET ER/PR index in metastatic samples predicted longer PFS and OS when patients received endocrine therapy as next treatment, even after adjustment for clinical-pathologic risk factors (PFS: HR 0.534, 95% CI 0.299 to 0.955, p = 0.035; OS: HR 0.315, 95% CI 0.157 to 0.631, p = 0.001). Mutated ESR1 LBD was detected in 8/53 (15%) of metastases, involving 1−98% of ESR1 transcripts (all had high SET ER/PR index). A signature based on probe sets with good preanalytical and analytical performance facilitated our customization of an accurate targeted RNAseq assay to measure both phenotype and genotype of ER-related transcription. Elevated SET ER/PR was associated with prolonged sensitivity to endocrine therapy in patients with metastatic HR+/HER2− breast cancer, especially in the absence of mutated ESR1 transcript.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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 it