Background:

Tumour cells have long been an area of interest when it comes to finding effective cancer treatments, and oncolytic viruses are deemed to be the solution to uncontrolled tumour growth. As a form of immunotherapy, oncolytic viruses attack cancer cells while leaving healthy cells unharmed. It is a preferred method of cancer treatment, for surgery, chemotherapy, radiotherapy, and multimodal therapies have had many drawbacks, such as severe side effects and the inability of these treatments to attack different types of tumours.

However, tumour evasion mechanisms limit the effectiveness of oncolytic viruses. Data from Frontiers in Oncology’s clinical trials involving these viruses only showed an increase in about four months (from 18.9 to 23.3 months) between melanoma patients who were injected with the Herpes Simplex Type 1 oncolytic virus and those who were not when it came to their median survival time. Furthermore, not every subject who was treated with an oncolytic virus had a long-term cellular response targeting the melanoma tumour. While small gains in advanced melanoma patient survival rates demonstrate the effectiveness of the viruses in targeting cancer cells, it is evident that there needs to be more research to overcome cellular defence mechanisms protecting tumours against these viruses.

Research Questions:
How can computational modeling help to predict what genetic composition of oncolytic
viruses be modified to produce a stronger, longer-term, or even permanent solution to melanoma
than what is currently seen in clinical trials? That is, can we use machine learning algorithms to
identify changes in genetic material that need to be made to decrease melanoma tumour growth?

Existing Literature:

Published in the U.S National Library of Medicine, “Oncolytic Viruses – Naturally and Genetically Engineered Cancer Immunotherapies” discusses the effects engineered oncolytic viruses have on the creation of antitumor immunity. Before researchers at Frontiers in Oncology conducted clinical trials involving 642 patients with melanoma, integrating this immunotherapy, the following questions were raised that could assist with the production of more effective tumour suppression using the virus:

❖ What is the success rate in the way the immune system responds to the different types of oncolytic viruses (which can be differentiated by examining their genetic composition) to create antitumor immunity in humans?

❖ Do oncolytic viruses in later trial design phases cause more CD8+ and CD4+ T-cells to become activated and initiate immune responses to the viruses, such as to continue minimising – or ending – the growth of tumours?

❖ Do the oncolytic viruses in later trial design phases have a positive, indirect effect on PD-L1 expression in immune cells, in which the viruses help reduce, or stop, the binding of PD-L1 to PD-1 (this binding enables t-cell suppression, and hence, growth of tumours)? The clinical study involved patients with an advanced form of melanoma. The subjects

were given a Phase I dose of a type of oncolytic virus, Coxsackievirus, and 26.7% of patients who received the treatment displayed some form of response, allowing for tumour growth to stabilise. PD-L1 was expressed, which enabled immune cells to target the tumour. Additionally, there were more CD8+ t-cells to assist with this response. When Phase II of the trial occurred, where a genetically modified Coxsackievirus intended to improve on the shortcomings of Phase Ib (patients who did not receive the cellular response targeting tumours) was implemented in subjects, only 38.6% of patients received the same durable response. While there have been some improvements in targeting tumours as the Coxsackievirus stimulated more genes to activate t-cells, a significant number of patients can benefit from more advanced genetic modification in oncolytic viruses capable of stimulating stronger responses to tumours.

One solution to tumour suppression that involves genetically engineering strong immune responses to cancers was seen in “TG6050, an oncolytic vaccinia virus encoding interleukin-12 and anti-CTLA-4 antibody induces tumour regression through profound tumour microenvironment remodelling and strong antitumor adaptive immune response,” published in the U.S National Library of Medicine. Researchers used a colon carcinoma model in mice to evaluate the level of interleukin-12 in these organisms, which was essential in analysing how well this antibody stimulated inhibitors to target cancer cells. The oncolytic virus would release interleukin-12 into the tumour cells. Because that activates the antibodies, the introduction of these viruses increased the production of CD8+ t-cells suppressing tumours in the mice colon model. Hence, there is evidence that oncolytic viruses can be genetically modified to produce specific types of antibodies, causing powerful stimulation in cellular response mechanisms targeting tumours. Can this be done in human melanoma patients?

Methodology:
There should be existing data of around two hundred patients with melanoma tumours treated with oncolytic viruses, and from these patients, one needs to collect RNA sequences, immunological and proteomic data from melanoma patients treated with oncolytic viruses. In RNA sequencing analysis to identify genetic sequences associated with outcomes after these patients were treated with specific viruses and controls, one can use bioinformatics such as the
spliced transcripts aligning to a reference point in a genome, as well as statistical tools to compare gene expression levels, specifically that of CD8+ t-cell markers and PD-L1 expression, between treated and control conditions to determine which genes are responding to each treatment. The usage of agent-based modeling, a computational modeling technique, should be used throughout to simulate interactions between each type of oncolytic virus tested on patients and tumour cells, as well as that between oncolytic viruses and immune cells, especially CD8+ t-cells. This will be followed by predicting how encoding interleukin-12 and anti-CTLA-4 antibodies in oncolytic viruses affects tumour cell death and immune cell response, which can be accomplished by
utilising machine learning algorithms to predict patient responses to various types of oncolytic viruses based on viral and host genomic sequences. Model interpretation tools in these machine learning algorithms will then be used to interpret the results produced by models and identify predictive features in relation to cellular responses to the viruses. These predictions will be
cross-referenced with oncolytic patient data across databases and different phases of the trials conducted by Frontiers in Oncology.

Research Significance:

Oncolytic viruses are not as effective as they could be despite efforts to help them increase stimulation of immune mechanisms targeting tumours. Tumour cells develop an immune response to the viruses targeting them over time because there are genes in cancer cells coding for this viral immunity that are favoured and replicate. Thus, some patients with advanced cancer do not experience long-term immune system responses that target tumours. Hence, there is a need for further research to modify the viruses in some way that improves their ability to be successful in destroying tumour cells through strong adaptive mechanisms. If every patient wants to overcome their struggles with cancer, it is through improving current treatments to target a variety of tumours. This research aims to assist with the design of novel oncolytic viruses with genetic changes intended to suppress tumours without using clinical trials and offers a proposal for future clinical research by suggesting promising oncolytic virus candidates to be developed.

References:

Alwithenani, A., Hengswat, P., & Chiocca, E. A. (2025, March 26). Oncolytic viruses as cancer therapeutics: From mechanistic insights to clinical translation. Molecular Therapy. http://www.cell.com/molecular-therapy-family/molecular-therapy/abstract/S1525-0016(1 6)30851-6

Deforges, J. (2024, February 27). TG6050, an oncolytic vaccinia virus encoding interleukin-12 and anti-CTLA-4 antibody induces tumour regression through profound tumour microenvironment remodeling and strong antitumor adaptive immune response. National Centre for Biotechnology Information. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE259379&utm_.

Jhawar, S. R., Thandoni, A., Bommareddy, P. K., Hassan, S., Kohlhapp, F. J., Goyal, S., Schenkel, J. M., Silk, A. W., & Zloza, A. (2017a, September 11). Oncolytic viruses-natural and genetically engineered cancer immunotherapies. National Centre for Biotechnology Information. https://pmc.ncbi.nlm.nih.gov/articles/PMC5600978/

Sultan, M. H., Zhan, Q., Jin, H., Jia, X., & Wang, Y. (2025, January 22). Epigenetic modulation by oncolytic viruses: Implications for cancer therapeutic efficacy. National Centre for Biotechnology Information. https://pubmed.ncbi.nlm.nih.gov/39855579/