{"id":191,"date":"2020-09-08T20:28:42","date_gmt":"2020-09-08T20:28:42","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/09\/08\/telltale-signs-ai-researchers-trace-cancer-risk-factors-using-tumor-dna\/"},"modified":"2020-09-08T20:28:42","modified_gmt":"2020-09-08T20:28:42","slug":"telltale-signs-ai-researchers-trace-cancer-risk-factors-using-tumor-dna","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/09\/08\/telltale-signs-ai-researchers-trace-cancer-risk-factors-using-tumor-dna\/","title":{"rendered":"Telltale Signs: AI Researchers Trace Cancer Risk Factors Using Tumor DNA"},"content":{"rendered":"
\n

Life choices can change a person\u2019s DNA \u2014 literally.<\/p>\n

Gene changes that occur in human cells over a person\u2019s lifetime, known as somatic mutations, cause the vast majority of cancers. They can be triggered by environmental or behavioral factors such as exposure to ultraviolet light or radiation, drinking or smoking.<\/p>\n

By using NVIDIA GPUs to analyze the signature, or molecular fingerprint, of these mutations, researchers can better understand known causes of cancer, discover new risk factors and investigate why certain cancers are more common in certain areas of the world than others.<\/p>\n

The Cancer Grand Challenges\u2019 Mutographs team, an international research group funded by Cancer Research U.K., is using NVIDIA GPU-accelerated machine learning models to study DNA from the tumors of 5,000 patients with five cancer types: pancreas, kidney and colorectal cancer, as well as two kinds of esophageal cancer.<\/p>\n

Using powerful NVIDIA DGX<\/a> systems, researchers from the Wellcome Sanger Institute \u2014 a world leader in genomics \u2014 and the University of California, San Diego, collaborated with NVIDIA developers to achieve more than 30x acceleration when running their machine learning software SigProfiler<\/a>.<\/p>\n

\u201cResearch projects such as the Mutographs Grand Challenge are just that \u2014 grand challenges that push the boundary of what\u2019s possible,\u201d said Pete Clapham, leader of the Informatics Support Group at the Wellcome Sanger Institute. \u201cNVIDIA DGX systems provide considerable acceleration that enables the Mutographs team to not only meet the project\u2019s computational demands, but to drive it even further, efficiently delivering previously impossible results.\u201d<\/p>\n

Molecular Detective Work<\/b><\/h2>\n

Just as every person has a unique fingerprint, cancer-causing somatic mutations have unique patterns that show up in a cell\u2019s DNA.<\/p>\n

\u201cAt a crime scene, investigators will lift fingerprints and run those through a database to find a match,\u201d said Ludmil Alexandrov, computational lead on the project and an assistant professor of cellular and molecular medicine at UCSD. \u201cSimilarly, we can take a molecular fingerprint from cells collected in a patient\u2019s biopsy and see if it matches a risk factor like smoking or ultraviolet light exposure.\u201d<\/p>\n

Some somatic mutations have known sources, like those Alexandrov mentions. But the machine learning model can pull out other mutation patterns that occur repeatedly in patients with a specific cancer, but have no known source.<\/p>\n

When that happens, Alexandrov teams up with other scientists to test hypotheses and perform large-scale experiments to discover the cancer-causing culprit.<\/p>\n

Discovering a new risk factor can help improve cancer prevention. Researchers in 2018 traced back a skin cancer mutational signature to an immunosuppressant drug<\/a>, which now lists the condition as one of its possible side effects, and helps doctors better monitor patients being treated with the drug.<\/p>\n

Enabling Whirlwind Tours of Global Data<\/b><\/h2>\n

In cases where the source of a mutational signature is known, researchers can analyze trends in the occurrence of specific kinds of somatic mutations (and their corresponding cancers) in different regions of the world as well as over time.<\/p>\n

\u201cCertain cancers are very common in one part of the world, and very rare in others. And when people migrate from one country to another, they tend to acquire the cancer risk of the country they move to,\u201d said Alexandrov. \u201cWhat that tells you is that it\u2019s mostly environmental.\u201d<\/p>\n

Researchers on the Mutographs project are studying a somatic mutation linked to esophageal cancer<\/a>, a condition some studies have correlated with the drinking of scalding beverages like tea or mat\u00e9.<\/p>\n

Esophageal cancer is much more common in Eastern South America, East Africa and Central Asia than in North America or West Africa. Finding the environmental or lifestyle factor that puts people at higher risk can help with prevention and early detection of future cases.<\/p>\n

\"map
Cases of esophageal squamous cell carcinoma vary greatly around the world. (Image courtesy of Mutographs project. Data source: GLOBOCAN 2012.)<\/figcaption><\/figure>\n

The Mutographs researchers teamed up with NVIDIA to accelerate the most time-consuming parts of the SigProfiler AI framework on NVIDIA GPUs. When running pipeline jobs with double precision on NVIDIA DGX systems, the team observed more than 30x acceleration compared to using CPU hardware. With single precision, Alexandrov says, SigProfiler runs significantly faster, achieving around a 50x speedup.<\/p>\n

The DGX system\u2019s optimized software and NVLink interconnect technology<\/a> also enable the scaling of AI models across all eight NVIDIA V100 Tensor Core GPUs<\/a> within the system for maximum performance in both model development and deployment.<\/p>\n

For research published in Nature<\/i><\/a> this year, Alexandrov\u2019s team analyzed data from more than 20,000 cancer patients, which used to take almost a month.<\/p>\n

\u201cWith NVIDIA DGX, we can now do that same analysis in less than a day,\u201d he said. \u201cThat means we can do much more testing, validation and exploration.\u201d<\/p>\n

Subscribe to NVIDIA healthcare news here<\/a>.<\/em><\/p>\n

Main image credit: Wellcome Sanger Institute<\/i><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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