Data from β<sub>1</sub>-Integrin Circumvents the Antiproliferative Effects of Trastuzumab in Human Epidermal Growth Factor Receptor-2–Positive Breast Cancer
Bibliographic record
Abstract
Abstract Resistance to trastuzumab, the monoclonal antibody targeting human epidermal growth factor receptor 2 (HER-2), is a major concern for HER-2–positive metastatic breast cancer (MBC) patients. To date, HER-2 status is the only available biomarker for selecting patients for trastuzumab-based therapy. β1-Integrin, an adhesion molecule involved in cell survival and drug resistance, shares common downstream signaling elements with HER-2, such as the phosphatidylinositol 3-kinase/Akt and extracellular signal-regulated kinase-1/2 (ERK1/2) pathways. The significance of β1-integrin expression in HER-2–positive breast cancer and its involvement in a patient's response to trastuzumab-based therapy are unknown. We show here that overexpression of β1-integrin is an independent negative prognostic factor for tumor progression of HER-2–positive MBC patients treated with trastuzumab-based chemotherapy. Enforced overexpression of β1-integrin, its small interfering RNA–induced knockdown or treatment with a β1-integrin–blocking antibody in HER-2–positive breast cancer cells, identified a strong inverse relationship between expression level of β1-integrin and in vitro sensitivity to trastuzumab. Notably, β1-integrin overexpression increased the phosphorylation of Akt-Ser473 and ERK1/2, thereby promoting survival and mitogenic signals to bypass the antiproliferative effects of trastuzumab. Our findings show that β1-integrin provides a novel independent prognostic biomarker of trastuzumab response in HER-2–positive MBC patients and suggest a new target to augment the antiproliferative effects of trastuzumab. [Cancer Res 2009;69(22):8620–8]
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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) | 0.000 | 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 | 0.001 |
| 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".