Is Oncotype DX testing informative for breast cancers with low ER expression? A retrospective review from a biomarker testing referral center
Bibliographic record
Abstract
PURPOSE: It remains unclear whether patients with HER2-negative, low-estrogen receptor (ER-low)-positive early breast cancer (BC) benefit from Oncotype DX® (ODX) testing. METHODS: We conducted a retrospective review of cases referred for ODX testing over a seven-year period from a breast biomarker testing referral center (n = 854). For each case, we recorded the ODX Recurrence Score (RS) along with percentage of ER nuclear positivity and staining intensity on immunohistochemistry. Our criteria for ER-low was defined as ≤10% cells with nuclear positivity and/or weak intensity of staining. Slides from all ER-low cases were reviewed and the reported ODX ER gene scores were recorded. We randomly selected a comparator group of 56 patients with ER > 10% positivity and non-weak staining intensity (ER-high). RESULTS: We identified 27 cases (3.2%) that met our criteria for ER-low. Of these, 92.6% had a high RS (>25), and 7.4% had a RS of 25. All cases with ≤10% ER nuclear positivity had a high RS. Most ER-low cases (85.2%) had ODX quantitative ER gene scores in the negative range, whereas all (100%) ER-high cases had positive ER gene scores. CONCLUSION: ODX does not appear to add significant additional information to inform treatment decisions for most patients with ER-low BC. Incorporating weak ER staining intensity in addition to low percentage of nuclear positivity identifies about twice as many ER-low patients, although with reduced specificity for high RS. Our study supports the contention that most ER-low early BC should be regarded similarly to ER-negative BC.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".