A Majority of Low (1-10%) ER Positive Breast Cancers Behave Like Hormone Receptor Negative Tumors
Bibliographic record
Abstract
BACKGROUND: The 2010 guidelines by ASCO-CAP have mandated that breast cancer specimens with ≥1% positively staining cells by immunohistochemistry should be considered Estrogen Receptor (ER) positive. This has led to a subclass of low-ER positive (1-10%) breast cancers. We have examined the biology and clinical behavior of these low ER staining tumors. METHODS: We have developed a probabilistic score of the "ER-positivity" by quantitative estimation of ER related gene transcripts from FFPE specimens. Immunohistochemistry for ER was done on 240 surgically excised tumors of primary breast cancer. Relative transcript abundance of 3 house-keeping genes and 6 ER related genes were determined by q-RT PCR. A logistic regression model using 3 ER associated genes provided the best probability function, and a cut-off value was derived by ROC analysis. 144 high ER (>10%), 75 ER negative and 21 low-ER (1-10%) tumors were evaluated using the probability score and the disease specific survival was compared. RESULTS: Half of the low-ER positive tumors were assigned to the ER negative group based on the probability score; in contrast 95% of ER negative and 92% of the high ER positive tumors were assigned to the appropriate ER group (p<0.0001). The survival of the low-ER group was intermediate between that of the high ER positive and ER negative groups (p<0.05). CONCLUSION: Our results suggest that the newly lowered ASCO-CAP criteria for ER positivity, leads to the false categorization of biologically ER negative tumors as ER positive ones. This may have particular relevance to India, where we have a much higher proportion of ER negative tumors in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".