A Model of Postsurgical Advanced Metastatic Breast Cancer More Accurately Replicates the Clinical Efficacy of Antiangiogenic Drugs
Bibliographic record
Abstract
The failure rate of randomized phase III oncology clinical trials is extremely high, even when preceded by encouraging preclinical studies and phase II trial results of the same therapy. Thus, there is considerable effort being made to improve the predictive clinical potential of preclinical models, in addition to improving phase II trial design. With respect to the former, preclinical models have historically relied heavily on treatment of primary spontaneous or transplanted tumors rather than the more common and therapeutically challenging clinical trial circumstance of advanced metastatic disease. Here, we show that the oral antiangiogenic tyrosine kinase inhibitor (TKI), sunitinib, which failed to meet primary or secondary survival endpoints in 4 separate phase III metastatic breast cancer (MBC) trials, either alone or with chemotherapy, similarly failed to show monotherapy or combination chemotherapy efficacy in a model of postsurgical advanced MBC using a metastatic variant of the MDA-MB-231 triple-negative human breast cancer. In contrast, the drug was effective when used to treat established orthotopic primary tumors. Similar results were obtained with pazopanib monotherapy, another antiangiogenic oral TKI. However, when an antibody targeting the VEGF pathway (DC101) was tested, it showed a trend in modestly improving the efficacy of paclitaxel therapy, thus resembling to a degree prior phase III clinical results of bevacizumab plus paclitaxel in MBC. Our results suggest the potential value of treating postsurgical advanced metastatic disease as a possible strategy to improve preclinical models for predicting outcomes in patients with metastatic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".