Cancer markers mapped to the entire genome using the Netflix algorithm

Cancer markers mapped to the entire genome using the Netflix algorithm

Mutations are a major driver of cancer development, but much of our understanding focuses on changes in individual genes in cancer.

An international team of scientists has used artificial intelligence to map the entire genome that indicates the development of cancer. The researchers said their algorithm was similar to that used by Netflix, as it identified 21 common errors that occur in human DNA when cancer begins to grow.

Dr. Ludmil Alexandrov, assistant professor at the University of California, San Diego, and co-author of the study said in the journal Nature. “Just as Netflix can predict which series to choose next, we think we’ll be able to predict how a cancer will behave based on changes it has previously undergone in its genome,” he added.

The researchers used their algorithm to examine the genomic data of 9,873 patients with 33 different types of cancer. Studies have identified 21 copy number changes (changes in the structure and length of chromosomes) in the genome. At least one of these 21 signatures was present in 97 percent of the samples they analyzed. These signatures can then be linked to different characteristics of the tumors, including how aggressive the tumor is or how well it survives.

“To be one step ahead of cancer, we need to anticipate how it will adapt and change,” said Dr Nishalan Pillai, associate professor at University College London and co-author of the research.

According to the researcher, mutations are a major driver of cancer development, but much of our understanding of cancer focuses on changes in individual genes. There is a lack of a comprehensive picture of how huge blocks of genes are copied, transferred or deleted without serious tumor consequences.

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To Cosmos, Pillay added, “Understanding how these events occur will help us reclaim our advantage over cancer. Thanks to the results of genome sequencing, we can now see how these changes occur in different types of cancer and learn how to respond to them effectively.”

The team is now working on a blueprint that researchers can use to predict how aggressive a cancer is and where there are weaknesses or weaknesses in possible treatments. It is hoped that doctors will also be able to use the scheme.

The algorithm and its other software tools were made freely available to other researchers so that other scientists could build their own libraries. “We believe that making these high-performance computing tools free to other scientists will accelerate progress toward creating a personalized cancer blueprint with the best chance of survival for patients,” said Christopher Steele, the study’s first author. Researcher at University College London.

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