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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, 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".