Targeted Oncolytic Herpes Simplex Viruses for Aggressive Cancers
Bibliographic record
Abstract
Herpes simplex virus (HSV) is a well-known vector that is often used for gene therapy to treat cancers. The most attractive feature of HSV is its ability to destroy tumors through a distinctive oncolytic mechanism where the virus can destroy cancer cells via cell lysis, a killing function that no anti-cancer drugs can mimic. Importantly, HSV is a safe and effective virus that can be easily manipulated to preferentially replicate in tumor cells. In the last 20 years of reengineering efforts, a number of HSV designs, including the classical G207, have been focused on deleting viral genes in order to render the virus tumor specific. Although such designs can successfully destroy tumor xenografts in animal models, with minimal impact on normal tissues, a common trade-off is the marked attenuation of the virus. This problem is most profound in many clinical tumors, where virus dissemination is often hindered by the difficult cellular and molecular terrain of the human tumor mass. In order to harness all of HSV's replication potential to destroy tumor cells, efforts in our lab, as well as others, last several years have been focused on engineering an oncolytic HSV to target tumor cells without deleting any viral genes, and have since generated highly tumor specific viruses including our transcriptional translational dually regulated HSV (TTDR-HSV). In this review, we will discuss the improvements associated with the newer TTDR-HSV design compared to the classical defective HSV designs such as G207 and tk- HSV. Lastly, we will review additional cellular features of aggressive tumors, such as their immense cellular heterogeneity and volatility, which may serve to hinder the dissemintation of TTDR-HSV. The challenge for future studies would be to explore how TTDRHSV could be redesigned and/or employed with combinatorial approaches to better target and destroy the heterogeneous and dynamic cell populations in the aggressive tumor mass.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".