Nanotechnology‐enhanced radiotherapy and the abscopal effect: Current status and challenges of nanomaterial‐based radio‐immunotherapy
Bibliographic record
Abstract
Rare but consistent reports of abscopal remission in patients challenge the notion that radiotherapy (RT) is a local treatment; radiation-induced cancer cell death can trigger activation and recruitment of dendritic cells to the primary tumor site, which subsequently initiates systemic immune responses against metastatic lesions. Although this abscopal effect was initially considered an anomaly, combining RT with immune checkpoint inhibitor therapies has been shown to greatly improve the incidence of abscopal responses via modulation of the immunosuppressive tumor microenvironment. Preclinical studies have demonstrated that nanomaterials can further improve the reliability and potency of the abscopal effect for various different types of cancer by (1) altering the cell death process to be more immunogenic, (2) facilitating the capture and transfer of tumor antigens from the site of cancer cell death to antigen-presenting cells, and (3) co-delivering immune checkpoint inhibitors along with radio-enhancing agents. Several unanswered questions remain concerning the exact mechanisms of action for nanomaterial-enhanced RT and for its combination with immune checkpoint inhibition and other immunostimulatory treatments in clinically relevant settings. The purpose of this article is to summarize key recent developments in this field and also highlight knowledge gaps that exist in this field. An improved mechanistic understanding will be critical for clinical translation of nanomaterials for advanced radio-immunotherapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".