Sorafenib in combination with ionizing radiation has a greater anti-tumour activity in a breast cancer model
Bibliographic record
Abstract
High expression of vascular endothelial growth factor (VEGF) in patients with breast cancer has been associated with a poor prognosis, indicating that VEGF could be linked to the efficacy of chemotherapy and radiotherapy. It has also been suggested that radiation resistance is partly due to tumour cell production of angiogenic cytokines, particularly VEGF receptor (VEGFR). This evidence indicates that inhibition of VEGFR might enhance the radiation response. Sorafenib tosylate (Bay 54-9085) is an oral, small-molecule multikinase inhibitor of several targets including RAF/MEK/ERK MAP kinase signalling, VEGFR-2, VEGFR-3 and platelet-derived growth factor receptor-beta. Sorafenib has shown clinical efficacy in treating solid tumours such as renal cell and hepatocellular carcinomas. However, strategies are yet to be identified to prolong and maximize the anticancer effect of this multikinase inhibitor. The objective of this study was to determine whether a combination of Sorafenib and radiation will enhance the treatment response in vitro and in vivo. Radio-modulating effect of Sorafenib was assessed by performing clonogenic assays. In addition, cell cycle analyses as well as annexin-V apoptosis assays were performed 24 and 48 h after treatment, respectively. To confirm our in-vitro results, tumour growth delay assays were performed. Our results showed a strong and supra-additive antitumour effect of radiation combined with Sorafenib in vitro (dose enhancement factor of 1.76). The combined therapy demonstrated a strong and significant G2/M cell cycle arrest (combined treatment vs. irradiated alone: P<0.0008). Moreover, annexin-V staining showed a significant increase in the level of apoptosis (combined treatment vs. irradiated alone: P<0.0004). Study of the syngeneic model demonstrated the superior potency of the Sorafenib combined with radiotherapy. Our results demonstrate that higher antitumour activity can be achieved when radiation and Sorafenib are combined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".