Oncolytic Viruses for Therapeutic Cancer Vaccination

By Lina Sorg

Cytotoxic chemotherapy utilizes a combination of cytotoxic drugs, also called cytostatics, to destroy cancer cells. These drugs travel throughout the body via the bloodstream and inhibit cell division, thus attacking cancerous tumors, diminishing metastases, improving outcomes of surgeries or radiotherapy, and generally treating cancer symptoms. The effectiveness of chemotherapy depends on the type and severity of the targeted tumors, their composition and rate of development, and the number of cancerous cells in the surrounding area.

Although it is the primary treatment method for most cancers, cytotoxic chemotherapy is often hard for patients to tolerate. Cytostatics target all dividing cells, including those that comprise healthy tissues. They kill neutrophils, which are white blood cells that heal damaged tissue and resolve bacterial or viral infections. Depending on its timing, chemotherapy therefore increases the severity of neutropenia and leaves patients vulnerable to life-threatening infections.

As a result, researchers continue to explore alternate approaches to cancer treatment. Immunotherapeutic strategies are one such alternative, as these methods leverage the immune system to target specific cancers and reduce toxicities in the body. One specific technique involves the use of oncolytic viruses (OVs) — genetically modified viruses that boost immune response and target tumor cells without harming healthy cells. During a minisymposium presentation at the 2020 SIAM Conference on the Life Sciences, which took place virtually last month, Morgan Craig of the Université de Montréal explored the potential of OVs as therapeutic cancer vaccines.

OVs selectively infect tumor cells and elicit an immune system response by recruiting immune cells—such as neutrophils—to the site of contagion, which bolsters the killing effect. Craig modeled this phenomenon with a normal viral kinetic model that accounts for target cells, the viral particles (virion), and infected cells. The OV infects target cells and transforms them into infected cells. They remain infected for a period of time and undergo the cell cycle, after which the virion is ejected via cellular lysis.

The first OV to reach Western markets was T-VEC, which was approved in 2015 to treat late-stage melanoma. T-VEC codes for the immune stimulatory protein GM-CSF, short for granulocyte-macrophage colony-stimulating factor. Unfortunately, developing a new drug is quite expensive; for example, a candidate drug that enters phase 1 testing in today’s market requires nearly four billion dollars of investment. Despite initial promise, many immunotherapies—including OVs—fail during clinical trials. Failure along this pipeline is very costly. “We want to improve the process so we can test effective and nontoxic therapies in humans earlier, and with schedules that are likely to lead to their approval,” Craig said.

Craig and her collaborators are using in silico trials to do so. They generate virtual patients that reflect clinical cohorts, then examine various drug schedules and doses at different times and intervals to identify the best way to use multiple drugs. These actions result in personalized, enhanced clinical trials that ultimately produce optimized treatments that are more effective than standard combination therapy. This leads to the next part of Craig’s project, which involves therapeutic cancer vaccines to strengthen patients’ immune responses. An intramuscular injection primes the immune system to recognize and destroy cancerous tumor cells. Meanwhile, the OV is simultaneously and intravenously delivered to replicate and selectively target, infect, and destroy harmful cells. “Oncolytic viruses can be delivered directly into the tumor and express tumor-specific antigens,” Craig said. This collective treatment results in long-term, immune-mediated destruction of any residual cancer cells.

Craig then launched into work based on a previous model to understand the process of effectively combining diverse viral strains, given that different viruses elicit different immune responses. She focused on vesicular stomatitis virus (VSV) and vaccinia virus (VV); the former is a genetically simple RNA virus that rapidly replicates in tumors, while the latter allows the virus to establish local pockets of infection. Craig’s team augmented their original model to include different viral strains while maintaining the same in silico clinical trial strategy. The group established parameters based on the results, sampled from the parameter distribution, simulated the model, calculated the residuals, and accepted virtual patients based on physiological parameters. Because they did not want to include individuals who were not realistic based on cancer progression, they simulated each individual’s distinct evolution. They also inferred clinically-actionable schedules and identified strategies to stratify patients based on characteristics that determine their success.

Next, Craig worked to optimize the enhancer multiplicity. “How many injections of a primer or enhancer are necessary if we fix the injection of the virus as seven days?” she asked. She chose this seven-day lag based on her experimental work with VV and VSV. Because the cohorts are heterogeneous, there is a heterogeneous response. Craig found that one enhancer was sufficient for the vast majority of cohorts. She also attempted to optimize the “booster” lag — the delay in VSV administration. Her slow growth protocol involved seven enhancers with a 15-day lag and the fast growth protocol involved one enhancer with a one-day lag.

To confirm her results, Craig generated two new cohorts of individuals: those with the top 10 percent of tumor growth and those with the bottom 10 percent of tumor growth. She retested the fast growth protocol for the top 10 percent and slow growth protocol for the bottom 10 percent, then reversed the procedures (i.e., tested for aggressively growing tumors in the slow growth protocol and vice versa). Slow-growing tumors do fine on a regiment of seven primers but suffer under an aggressive growth protocol. Thus, tumor aggressivity is a significant determinant for optimal protocols and serves as the primary driver of enhancer multiplicity and booster lag. 

Craig proves that researchers can use intrinsic tumor growth rate to stratify patients into appropriate protocols. The in silico trial platform recapitulates population heterogeneity. Ultimately, Craig’s comprehensive modeling approach offers hope for the future design and clinical use of therapeutic cancer vaccines that utilize OVs.

Lina Sorg is the managing editor of SIAM News.