Smart Oncolytic Adenovirotherapy to Induce Killing of Cancer Cells and Elicit Antitumor Immunity
Bibliographic record
Abstract
Cancer is one of the leading causes of death in the world, accounting for over 30% of all deaths in Canada. Various chemotherapy and therapeutic agents are currently in practice to help combat and treat cancerous growths and to lead to cancer remission. Virotherapy is an emerging treatment that uses biotechnology to convert viruses into therapeutic agents for the treatment of specific types of cancer. This process reprograms viruses to become oncolytic and target tumor cells in the body for lysis. It also uses these viruses to recruit inflammatory and vaccination responses by the immune system to help kill surrounding tumor cells while also establishing a long immune memory to help in the case of later infections. Adenoviruses are a group of viruses that infect the membranes of the respiratory tract, eyes, intestines, urinary tract, and nervous system of humans and causing fever as well as many cold symptoms. It is also a commonly used oncolytic virus and has been demonstrated in recent studies to be a great potential tool for eliciting appropriate inflammatory responses from the immune system to kill cancer cells and inducing cell-mediated immunity to prevent against later re-infection by the specific cancer type. Advances to this virotherapy has progressed towards overcoming tumor-mediated immunosuppression, which usually allows cancerous cells to evade the immune system and escape cell destruction, especially when combined with other therapy treatments. (Goradel et al., 2019). This review will focus on the mechanism as to how engineered modified viruses stimulate the immune system for cell killing and cell-mediated immunity. There will also be an examination of several research papers with some evidence to understand the synergy being oncolytic adenovirotherapy and the immune system function to kill cancer cells. Some disadvantages and issues with using this form of therapeutic treatment will also be presented, as well as some present and future research operating to fix these issues as well as increase the overall efficacy of this cancer treatment oncolytic adenovirotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
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".