Strategies for Delaying or Treating<i>In vivo</i>Acquired Resistance to Trastuzumab in Human Breast Cancer Xenografts
Bibliographic record
Abstract
PURPOSE: Acquired resistance to trastuzumab (Herceptin) is common in patients whose breast cancers show an initial response to the drug. The basis of this acquired resistance is unknown, hampering strategies to delay or treat such acquired resistance, due in part to the relative lack of appropriate in vivo tumorigenic models. EXPERIMENTAL DESIGN: We derived an erbB-2-positive variant called 231-H2N, obtained by gene transfection from the highly tumorigenic erbB-2/HER2-negative human breast cancer cell line, MDA-MB-231. Unlike MDA-MB-231, the 231-H2N variants was sensitive to trastuzumab both in vitro and especially in vivo, thus allowing selection of variant resistant to drug treatment in the latter situation after showing an initial response. RESULTS: The growth of established orthotopic tumors in severe combined immunodeficient mice was blocked for 1 month by trastuzumab, after which rapid growth resumed. These relapsing tumors were found to maintain resistance to trastuzumab, both in vitro and in vivo. We evaluated various therapeutic strategies for two purposes: (a) to delay such tumor relapses or (b) to treat acquired trastuzumab resistance once it has occurred. With respect to the former, a daily oral low-dose metronomic cyclophosphamide regimen was found to be particularly effective. With respect to the latter, an anti-epidermal growth factor receptor antibody (cetuximab) was effective as was the anti-vascular endothelial growth factor (anti-VEGF) antibody bevacizumab, which was likely related to elevated levels of VEGF detected in trastuzumab-resistant tumors. CONCLUSIONS: Our results provide a possible additional rationale for combined biological therapy using drugs that target both erbB-2/HER2 and VEGF and also suggest the potential value of combining less toxic metronomic chemotherapy regimens not only with targeted antiangiogenic agents but also with other types of drug such as trastuzumab.
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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) | 0.001 | 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 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".