Oncolytic Viruses and Immunotherapy for the Treatment of Uveal Melanoma and Retinoblastoma: The Current Landscape and Novel Advances
Bibliographic record
Abstract
Intraocular malignant tumors are rare; however, they can cause serious life-threatening complications. Uveal melanoma (UM) and retinoblastoma (RB) are the most common intraocular tumors in adults and children, respectively, and come with a great disease burden. For many years, several different treatment modalities for UM and RB have been proposed, with chemotherapy for RB cases and plaque radiation therapy for localized UM as first-line treatment options. Extraocular extension, recurrence, and metastasis constitute the major challenges of conventional treatments. To overcome these obstacles, immunotherapy, which encompasses different treatment options such as oncolytic viruses, antibody-mediated immune modulations, and targeted immunotherapy, has shown great potential as a novel therapeutic tool for cancer therapy. These anti-cancer treatment options provide numerous advantages such as selective cancer cell death and the promotion of an anti-tumor immune response, and they prove useful in preventing vision impairment due to macular and/or optic disc involvement. Numerous factors such as the vector choice, route of administration, dosing, and patient characteristics must be considered when engineering an oncolytic virus or other forms of immunotherapy vectors. This manuscript provides an in-depth review of the molecular design of oncolytic viruses (e.g., virus capsid proteins and encapsulation technologies, vectors for delivery, cell targeting) and immunotherapy. The most recent advances in preclinical- and clinical-phase studies are further summarized. The recent developments in virus-like drug conjugates (i.e., AU011), oncolytic viruses for metastatic UM, and targeted immunotherapies have shown great results in clinical trials for the future clinical application of these novel technologies in the treatment algorithm of certain intraocular tumors.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".