Targeting the CXCL12/CXCR4 pathway to reduce radiation treatment side effects
Bibliographic record
Abstract
High precision, image-guided radiotherapy (RT) has increased the therapeutic ratio, enabling higher tumor and lower normal tissue doses, leading to improved patient outcomes. Nevertheless, some patients remain at risk of developing serious side effects.In many clinical situations, the radiation tolerance of normal tissues close to the target volume limits the dose that can safely be delivered and thus the potential for tumor control and cure. This is particularly so in patients being re-treated for tumor progression or a second primary tumor within a previous irradiated volume, scenarios that are becoming more frequent in clinical practice.Various normal tissue 'radioprotective' drugs with the potential to reduce side effects have been studied previously. Unfortunately, most have failed to impact clinical practice because of lack of therapeutic efficacy, concern about concurrent tumor protection or excessive drug-related toxicity. This review highlights the evidence indicating that targeting the CXCL12/CXCR4 pathway can mitigate acute and late RT-induced injury and reduce treatment side effects in a manner that overcomes these previous translational challenges. Pre-clinical studies involving a broad range of normal tissues commonly affected in clinical practice, including skin, lung, the gastrointestinal tract and brain, have shown that CXCL12 signalling is upregulated by RT and attracts CXCR4-expressing inflammatory cells that exacerbate acute tissue injury and late fibrosis. These studies also provide convincing evidence that inhibition of CXCL12/CXCR4 signalling during or after RT can reduce or prevent RT side effects, warranting further evaluation in clinical studies. Greater dialogue with the pharmaceutical industry is needed to prioritize the development and availability of CXCL12/CXCR4 inhibitors for future RT studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".