Pre- and post-operative anti-PD-L1 plus anti-angiogenic therapies in mouse breast or renal cancer models of micro- or macro-metastatic disease
Bibliographic record
Abstract
There are phase 3 clinical trials underway evaluating anti-PD-L1 antibodies as adjuvant (postoperative) monotherapies for resectable renal cell carcinoma (RCC) and triple-negative breast cancer (TNBC); in combination with antiangiogenic VEGF/VEGFR2 inhibitors (e.g., bevacizumab and sunitinib) for metastatic RCC; and in combination with chemotherapeutics as neoadjuvant (preoperative) therapies for resectable TNBC. This study investigated these and similar clinically relevant drug combinations in highly translational preclinical models of micro- and macro-metastatic disease that spontaneously develop after surgical resection of primary kidney or breast tumours derived from orthotopic implantation of murine cancer cell lines (RENCA luc or EMT-6/CDDP, respectively). In the RENCA luc model, adjuvant sunitinib plus anti-PD-L1 improved overall survival compared to either drug alone, while the same combination was ineffective as early therapy for unresected primary tumours or late-stage therapy for advanced metastatic disease. In the EMT-6/CDDP model, anti-PD-L1 was highly effective as an adjuvant monotherapy, while its combination with paclitaxel chemotherapy (with or without anti-VEGF) was most effective as a neoadjuvant therapy. Our preclinical data suggest that anti-PD-L1 plus sunitinib may warrant further investigation as an adjuvant therapy for RCC, while anti-PD-L1 may be improved by combining with chemotherapy in the neoadjuvant but not the adjuvant setting of treating breast cancer.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.001 | 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 teacher head, 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".