A Majority of Low (1-10%) ER Positive Breast Cancers Behave Like Hormone Receptor Negative Tumors
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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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it