ER+ PR- breast cancer defines a unique subtype of breast cancer that is driven by growth factor signaling and may be more likely to respond to EGFR targeted therapies
Bibliographic record
Abstract
514 Background: Hormonal based therapy has long been the mainstay for treatment of ER+ breast cancer. ER+ PR- disease is now known to exhibit different clinical behavior compared to ER+PR+ disease. Recent data indicate that ER+PR- disease is characterized by a lower response rate to estrogen deprivation, has a worse prognosis compared to ER+ PR+ disease, and may be dependent on other signaling pathways. To evaluate the role of the EGFR tyrosine kinase inhibitor gefitnib in the treatment of breast cancer, we conducted a pre-surgical study in women with operable disease. Methods: Frozen core biopsies were obtained at baseline. Patients then received a short-term exposure to gefitinib (at least 2 weeks) prior to definitive surgery when a frozen tumor specimen was obtained. Tissue integrity and composition was verified by H and E and RNA was isolated for microarray analysis. 59 women were enrolled in the study of which 43 were evaluable for molecular analysis. Baseline microarrays were performed on the initial biopsies to classify the ‘subtype‘ of breast cancer (e.g. basal, luminal, HER2 amplified). To analyze the genetic changes that occur in breast cancer tissue with exposure to gefitinib, a direct comparison of the baseline sample and post-treatment tumor was performed. In addition, ER and PR status were determined by immunohistochemistry and compared to the microarray findings. Changes in Ki67 and a set of cell cycle genes were used to define ‘molecular response” to gefitinib. Of the 43 samples evaluated by microarray, 11 patients were categorized as exhibiting molecular growth inhibition, 10 patients as molecular growth proliferation, and 22 did not have a significant change in Ki67 and the cell cycle gene set to assign a response. When grouped by subtype, ER+PR- and HER2 amplified tumors define a subgroup more likely to show molecular growth inhibition with gefitinib. Conversely, ER+PR+ tumors were more likely to show molecular growth proliferation. Conclusions: These results support the hypothesis that ER+PR- breast cancer is growth factor dependent and constitutes a unique subgroup of ER+ patients which may be more likely to benefit from EGFR inhibition. [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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".