Quantitative hormone receptors, triple-negative breast cancer (TNBC), and molecular subtypes: A collaborative effort of the BIG-NCI NABCG.
Bibliographic record
Abstract
1008 Background: Most TNBC trials focusing on biology of the basal-like subtype (BLBC) allow borderline (1-10% staining) estrogen receptor (ER) and progesterone receptor (PgR) expression by immunohistochemistry (IHC); however the optimal ER and PgR cut points to enrich for non-luminal subtypes has not been studied. In this study,we compared quantitative ER/PgR status with gene expression-based intrinsic subtype in order to determine if borderline cases should be included in TNBC trials. Methods: ER, PgR, and HER2 status was determined by central review of tumors collected from three phase III randomized trials: GEICAM 9906 (n=820), NCIC CTG MA.5 (n=476) and MA.12 (n=398). PAM50 intrinsic subtyping (BLBC, HER2-enriched, Luminal A, Luminal B and Normal-like) was performed using the qRT-PCR-based assay. Quantitative ER/PgR expression by IHC and subtype was tested using ANOVA and Fisher’s exact test. Results: Of 1,694 tumors, 15% were BLBC, 21% HER2-Enriched, 33% Luminal A, 25% Luminal B and 4% Normal-like. BLBC subtypes were significantly associated with low expression of ER and PgR (median = 0.05%) compared to other subtypes (p < 0.001). The vast majority of BLBC (96%) did not express any ER or PgR protein by IHC. BLBC represented 73% of TNBC (borderline cases not included) and significantly more than the additional TNBC with borderline ER/PgR (p < 0.001). Within borderline ER/PgR and HER2-negative cases only, 17% were BLBC and 46% were luminal subtypes (Table). Conclusions: BLBC rarely express ER or PgR by IHC. The majority of borderline TNBC (1-10% ER/PgR) are not BLBC; half of them are categorized as luminal categories that may be endocrine sensitive. TNBC trials seeking to target BLBC tumor biology should use the ASCO/CAP guidelines of 0% as the cutoffs for ER and PgR negativity. [Table: see text]
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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 | 0.051 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".