Scientists use new 'computational cell biology' to kill cancer cells by making them sick

One doesn't often think of cancer cells themselves being vulnerable to infections, but a team of scientists in Ottawa, Ontario is using advanced mathematical modeling to engineer viruses that will infect and destroy cancer cells. According to a paper published Friday in Nature Communications, the team uses predictive modeling to investigate how treatment techniques and genetic modification might allow cancer-killing (oncolytic) viruses to overcome cancer cells' anti-infection defenses and kill them.

Dr. Mads Kaern, a medical faculty member and the Canada Research Chair at the University’s Ottawa Institute of Systems Biology, and Dr. John Bell, a senior biologist with the Ottowa Hospital Research Institute and professor at the University of Ottawa’s Faculty of Medicine, were lead authors on the report, which details how the team used mathematical modeling to create techniques to render cancer cells highly vulnerable to infection by oncolytic viruses while leaving healthy tissue untouched. The modified viruses zero in on the very thing that makes cancer cells so destructive -- their potential to proliferate and grow explosively and unchecked, and blocks it.

"These viruses tend to replicate better in cancer cells," said Kaern to Raw Story, "because cancer cells tend to grow and divide more with an increased metabolism. The viruses are sort of exploiting that by replicating more aggressively, specifically in cancer cells."

The trick, Kaern said, is to engineer viruses that do that, but with minimal harm to surrounding healthy cells. The engineered viruses are built to not propagate in healthy tissues. In terms of cancer cells, however, it only takes one oncolytic virus making contact with one cancer cell to begin the propagation process.

"It's a self-amplifying process," he said, in which the medicine against the cancer replicates itself inside the human body.

"You don't really have to overload the system with tons of chemotherapy, which also targets specific cancers, right? But you have to ingest these large amounts intravenously and people get really sick from that because all the cells in the body are affected," he explained. "So the advantage of the viruses is that they will find where they have to go and you only need one to start to process."

Cancer cells and normal cells are equipped with defensive mechanisms that protect them from invading cells. By using mathematical models, the Ottawa team has managed to equip oncolytic viruses with a gene that helps them override many kinds of cancer cancer cells' natural defenses, slowing the cancer's reproduction and also making it more vulnerable to other infections.

Raw Story asked Kaern if it would be oversimplification to say that the team is learning how to beat cancer cells by making them sick. He laughed and replied, "I don't think so. I never thought of it that way, but yeah. You're compromising their immunity."

Kaern said in the team's press release that mathematical models allowed them to predict outcomes more quickly and circumvent the normal, time-consuming trial-and-error process of testing treatments.

"By using these mathematical models to predict how viral modifications would actually impact cancer cells and normal cells, we are able to accelerate the pace of research," he said.

Kaern and Bell constructed a mathematical model of the process of infection of a cancer cell with an oncolytic virus, including how the virus would replicate, spread itself and override the cancer's biological defenses. The study used predictive models to understand how the viruses might better overcome the cancer's defenses, models that turned out to be surprisingly accurate.

Bell said in the release, however, that there is still a great deal to learn about the processes of other cancers.

"What is remarkable is how well we could actually predict the experimental outcome based on computational analysis," said Bell. "This work creates a useful framework for developing similar types of mathematical models in the fight against cancer."

He said that there is still a great deal to learn, though. "Unfortunately, cancer is a very complicated and diverse disease, and some viruses work well in some circumstances and not well in others. As a result, there has been a lot of effort in trying to modify the viruses to make them safe, so they don't target healthy tissue and yet are more efficient in eliminating cancer cells."

While a "magic bullet" anti-cancer panacea is probably not going to arise in the near future, the use of mathematical modeling is speeding up the research process and opening up exciting possibilities.

"From my perspective, that's the most interesting part," said Kaern. "The most fascinating thing is to challenge existing knowledge represented in a mathematical model and try to understand why these models sometimes fail. It's a very exciting opportunity to be a part of this, and I am glad that our efforts in training students in computational cell biology have resulted in such a significant advancement."

[image of cancer researcher in lab via Shutterstock.com]