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Medical News Today: Cancer evolution: how the laws of nature could predict tumor growth

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Cancer is one of the most challenging diseases of our time; the number of new cases is expected to rise by around 70% worldwide over the next 20 years. But what if doctors could predict how a cancer evolves? This would be sure to pave the way for more effective treatment strategies. According to the results of a new study, such a possibility could be in the cards.
[An illustration of a human cancer cell]
Researchers say cancer evolution could be predicted through a mathematical model based on patterns of natural laws.


In the journal Nature Genetics, researchers reveal how the growth of cancer could be predicted by using a mathematical model based on patterns that influence the flow of rivers and brightness of the stars, known as the “power law distribution model.”


Study co-leader Dr. Andrea Sottoriva, Chris Rokos Fellow in Evolution and Cancer at the Institute of Cancer Research, UK, and colleagues note that previous studies have attempted to determine how tumors evolve, but interpreting the overwhelming array of genomic data to uncover such information has proven challenging.


“The lack of a rigorous theoretical framework able to make predictions on existing data means that results from cancer genomic profiling studies are often difficult to interpret,” they explain.


As such, the team stresses the need for a mathematical model that can be applied to data on different tumor profiles in order to predict how tumors will grow.


“This predictability means that the vast amount of genetic data we can generate from tumor biopsies could tell us how a given cancer will develop over time – which mutations will come to drive it into more aggressive disease, when they will emerge, and which drugs are best to treat them,” says Dr. Sottoriva.


“Like in a game of chess, the aim is anticipating the next move of the adversary, to ultimately win the game.”

Model predicted evolution of more than 300 tumors


For their study, the team analyzed 904 tumors from 14 different cancer types, including bowel, stomach, brain, lung and pancreatic cancers.


Fast facts about cancer

  • Up to January 2014, around 14.5 million people with a history of cancer were living in the US
  • More than 1.6 million new cancer cases were diagnosed in the US last year
  • In 2015, around 589,430 people in the US died from cancer.


Learn more about cancer


The researchers found that the evolution of 323 of these tumors – including those from stomach, bowel and some lung cancers – could be predicted by applying a power law distribution model.


The team explains that important cancer genes in these tumors were already present when the tumors started growing. Additionally, they found that any new mutations that arose inside these tumors had no effect on growth, effectively making them “passenger” mutations.


These passenger mutations accumulated following a power law distribution pattern that arises in an array of natural laws, according to the researchers, such as those that drive the flow of the River Nile and the brightness of the stars.


“Our study shows that the spread of mutations through a cancer follows natural laws – and is therefore theoretically predictable, just as we can predict the movement of celestial bodies or the weather,” says Dr. Sottoriva.


While their model was less effective for predicting the evolution of some tumors – such as those from brain and pancreatic cancers – the researchers note that creating more elaborate mathematical models could enable such evolution to be predicted in the future.

Predictability of cancer evolution could advance cancer treatment


Overall, the team says their findings have important clinical implications, as they could help doctors distinguish mutations that drive tumor growth and require treatment from passenger mutations that may be harmless.


Dr. Kat Arney, science information manager at Cancer Research UK – which helped fund the study – believes that if such a tool comes into play, it could open up more effective treatment strategies for cancer patients.


“If doctors were able to reliably predict how cancers change with time, it could help them choose the most effective treatments for each patient,” she explains. “Advances in DNA sequencing technology mean that we’re now able to track how individual tumors change over time at the deepest genetic level.”


Commenting on the team’s results, study co-leader Dr. Trevor Graham, head of the Evolution and Cancer Laboratory at the Barts Cancer Institute at Queen Mary University of London, UK, says:

“We often think of cancers as being the chaotic and uncontrolled growth of cells within the body. But counter to this intuition, our study shows how cancer evolution is in fact often highly ordered and can even be explained by a straightforward mathematical rule.


This rule is important because it hugely simplifies our view of how cancers evolve. Now that we know the rule, we can attempt to bend it in our favor to improve patient outcomes.”


The study was funded by a donation from Chris Rokos to the International Cancer Institute, as well as the Wellcome Trust, Cancer Research UK and the Medical Research Council, among other organizations.


Dr. Sottoriva talks more about the team’s research in this short video.


Earlier this month, Medical News Today reported on a study detailing the development of an injectable fluorescent agent that could help surgeons remove all cancerous tissue on the first attempt.

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About Basil Ita

I’m Basil Ita Ekuson, a freelancer, a blogger with a passion for research. The owner of basilita.com. Basil Ita mainly addresses the needs of small business owners that want to maximize their online visibility via internet marketing specifically solo ads, blogging, content marketing, SEO, social media marketing, visual marketing and affiliate marketing. Via this particular blog, I share my thoughts on HOW TO related issues, and some TIPS. My goal is to help as many people as I can to live laptop fantasy lifestyle… Feel free to drop me a line or ask me a question!

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