Combined-Modality Treatment of Solid Tumors Using Radiotherapy and Molecular Targeted Agents
Bibliographic record
Abstract
PURPOSE: Molecular targeted agents have been combined with radiotherapy (RT) in recent clinical trials in an effort to optimize the therapeutic index of RT. The appeal of this strategy lies in their potential target specificity and clinically acceptable toxicity. DESIGN: This article integrates the salient, published research findings into the underlying molecular mechanisms, preclinical efficacy, and clinical applicability of combining RT with molecular targeted agents. These agents include inhibitors of intracellular signal transduction molecules, modulators of apoptosis, inhibitors of cell cycle checkpoints control, antiangiogenic agents, and cyclo-oxygenase-2 inhibitors. RESULTS: Molecular targeted agents can have direct effects on the cytoprotective and cytotoxic pathways implicated in the cellular response to ionizing radiation (IR). These pathways involve cellular proliferation, DNA repair, cell cycle progression, nuclear transcription, tumor angiogenesis, and prostanoid-associated inflammation. These pathways can also converge to alter RT-induced apoptosis, terminal growth arrest, and reproductive cell death. Pharmacologic modulation of these pathways may potentially enhance tumor response to RT though inhibition of tumor repopulation, improvement of tumor oxygenation, redistribution during the cell cycle, and alteration of intrinsic tumor radiosensitivity. CONCLUSION: Combining RT and molecular targeted agents is a rational approach in the treatment of solid tumors. Translation of this approach from promising preclinical data to clinical trials is actively underway.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".