Therapeutic impact of Nintedanib with paclitaxel and/or a PD-L1 antibody in preclinical models of orthotopic primary or metastatic triple negative breast cancer
Bibliographic record
Abstract
BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive malignancy with poor prognosis, in part because of the current lack of any approved molecularly targeted therapy. We evaluated various combinations of three different drugs: nintedanib, an antiangiogenic TKI targeting VEGF receptors, paclitaxel (PTX), or a PD-L1 antibody, using models of orthotopic primary or advanced metastatic TNBC involving a metastatic variant of the MDA-MB-231 human cell line (called LM2-4) in SCID mice and two mouse lines (EMT-6 and a drug-resistant variant, EMT-6/CDDP) in immunocompetent mice. These drugs were selected based on the following: PTX is approved for TNBC; nintedanib combined with docetaxel has shown phase III clinical trial success, albeit in NSCLC; VEGF can act as local immunosuppressive factor; and PD-L1 antibody plus taxane therapy was recently reported to have encouraging phase III trial benefit in TNBC. METHODS: Statistical analyses were performed with ANOVA followed by Tukey's Multiple Comparison Test or with Kruskal-Wallis test followed by Dunn's Multiple Comparison Test. Survival curves were analyzed using a Log-rank (Mantel Cox) test. Differences were considered statistically significant when p values were < 0.05. RESULTS: Toxicity analyses showed that nintedanib is well tolerated when administered 5-days ON 2-days OFF; PTX toxicity differed in mice, varied with cell lines used and may have influenced median survival in the metastatic EMT6/CDDP model; while toxicity of PD-L1 therapy depended on the cell lines and treatment settings tested. In the LM2-4 system, combining nintedanib with PTX enhanced overall antitumor efficacy in both primary and metastatic treatment settings. In immunocompetent mice, combining nintedanib or PTX with the PD-L1 antibody improved overall antitumor efficacy. Using the advanced metastatic EMT-6/CDDP model, optimal efficacy results were obtained using the triple combination. CONCLUSIONS: These results suggest circumstances where nintedanib plus PTX may be potentially effective in treating TNBC, and nintedanib with PTX may improve PD-L1 therapy of metastatic TNBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".