Harnessing information theory to find cancer genes

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A team of scientists has used an area of ​​math to help them discover cancer genes.

The researchers at Johns Hopkins Medicine and Johns Hopkins Kimmel Cancer Center applied information theory – a well-known area of ​​mathematics designed to study how information is measured, stored, and shared – to look at genes that influence cancer. They say they have discovered a likely major genetic culprit in the development of acute lymphocytic leukemia (ALL) – the most common form of childhood leukemia.

The findings are published in Nature Biomedical Engineering.

Apply information theory

The team used information theory and applied an analysis that relies on sequences of zeros and ones – the binary system of symbols common in computer languages ​​and codes – to identify variables or outcomes of a particular process. The scientists focused on a chemical process in cells called DNA methylation, discovered by a founder of the field of cancer epigenetics, Feinberg, in the 1980s, in which certain chemical groups attach to regions of genes that control the on / off switches of genes . This is now recognized as a way to change the DNA without changing the code.

Andrew Feinberg, MD, MPH, Bloomberg Distinguished Professor at Johns Hopkins University School of Medicine, Whiting School of Engineering and Bloomberg School of Public Health, said, “ This study shows how a mathematical language of cancer can help us understand how cells are supposed. to behave and how changes in that behavior affect our health. “

Feinberg and his team say that using information theory to find genes driving cancer can be applicable to a wide variety of cancers and other diseases.

“Most people are familiar with genetic changes in DNA, namely mutations that change the DNA sequence. Those mutations are like the words that make up a sentence, and methylation is like punctuation in a sentence, with pauses and stops while we read, ”said Feinberg.

“We wanted to use this information to identify genes that stimulate cancer development, even though their genetic code is not mutated,” said Michael Koldobskiy, MD, Ph.D., pediatric oncologist and assistant professor of oncology at Johns Hopkins Kimmel. Cancer Center, who explained that methylation at a particular gene location is binary – methylation or no methylation – and a system of zeros and ones can represent these differences, just as they are used to represent computer codes and instructions.

Analyze DNA

For the study, the team analyzed DNA extracted from bone marrow samples of 31 children newly diagnosed with ALL at Johns Hopkins Hospital and Texas Children’s Hospital, sequencing the DNA to determine which genes were methylated throughout the genome and which were not .

By assigning zeros and ones to pieces of genetic code that were methylated or unmethylated and by using concepts of information theory and computer programs to recognize methylation patterns, the scientists were able to find regions of the genome that were consistently methylated in patients with leukemia and patients with leukemia . without cancer.

They also saw genome regions in the leukemia cells that were more randomly methylated, compared to the normal genome, a signal to scientists that those spots may be specifically related to leukemia cells. In particular, one gene, called UHRF1, stood out among other gene regions in leukemia cells that showed differences in DNA methylation compared to the normal genome.

“It was a great surprise to find this gene, as it has been suggested to be linked to prostate and other cancers but has never been identified as a cause of leukemia,” says Feinberg.

Experiments by the team show that lab-grown leukemia cells without activity of the UHRF1 gene cannot self-renew and perpetuate additional leukemia cells.

“Leukemia cells are meant to survive, and the best way to ensure survival is to vary the epigenetics in many genome regions so that no matter what the cancer is trying to kill, at least some will survive,” says Koldobskiy.

“This new approach could lead to more rational ways to address the changes that cause these and likely many other cancers.”

The Johns Hopkins team plans to use information theory to analyze methylation patterns in other cancers, and they will determine whether epigenetic changes in URFH1 are related to treatment resistance and disease progression in patients with childhood leukemia.

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