{"id":34493,"date":"2018-09-06T16:41:21","date_gmt":"2018-09-06T20:41:21","guid":{"rendered":"https:\/\/sciencebusiness.technewslit.com\/?p=34493"},"modified":"2018-09-07T11:53:53","modified_gmt":"2018-09-07T15:53:53","slug":"machine-learning-detects-more-cancer-mutations","status":"publish","type":"post","link":"https:\/\/technewslit.com\/sciencebusiness\/?p=34493","title":{"rendered":"Machine Learning Detects More Cancer Mutations"},"content":{"rendered":"
\"Population<\/a>
(National Human Genome Research Institute, NIH)<\/figcaption><\/figure>\n

6 September 2018. Medical researchers and data scientists developed a system for analyzing genomic data from cancer patients that accurately detects more cancer-causing mutations than other current techniques. An academic-industry team reports its findings in yesterday’s issue of the journal Science Translational Medicine<\/em><\/a> (paid subscription required).<\/p>\n

Researchers from the lab of cancer researcher Victor Velculescu<\/a> at Johns Hopkins University and the company Personal Genome Diagnostics<\/a>, both in Baltimore, are seeking better methods for detecting mutations in the genomes of cancer patients that develop and change as tumors progress. Accurately detecting and characterizing these non-inherited, or somatic, mutations better define the targets for more precise therapies, but according to the authors, today’s analytical techniques — even those using next-generation or high-throughput genomic sequencing — can vary in accuracy. In addition, some of these methods require high-quality specimen samples that may not always be available.<\/p>\n

The study evaluated a technology being developed by Personal Genome Diagnostics called Cerebro that applies machine learning, a form of artificial intelligence, to genomic analysis of suspected tumor specimens and blood samples. Cerebro uses a random-forest algorithm<\/a> that builds data into decision-trees, then merges the data from the trees together, thus the “forest” reference, for more accurate and stable predictions. The team led by Velculescu and Samuel Angiuoli<\/a>, chief information officer at Personal Genome Diagnostics, trained the algorithm with next-generation sequencing data from blood samples. The training data had some 30,000 known genomic variations associated with tumors, as well as 2 million more errors and artifacts from genomic sequencing that could erroneously be considered as mutations. Once trained, Cerebro uses about 1,000 decision trees to classify each mutation.<\/p>\n

The team assessed Cerebro’s ability to accurately detect and classify each genomic variation as a cancer-causing somatic mutation. When compared to data from 1,368 cases in the Cancer Genome Atlas<\/a>, a collection and catalogue of cancer-related mutations, Cerebro matched 74 percent of the variations, but also highlighted false negatives and positives in the data, including those in actionable genes. And Cerebro’s analysis of simulated low-purity tumor data, similar to those taken from many patients, was more accurate and had higher predictive value than 6 other current mutation-detection techniques.<\/p>\n

In addition, the researchers coupled Cerebo’s analysis with next-generation sequencing of tumors from 22 lung cancer patients, to determine their likelihood to respond to immunotherapies. In this analysis, the team looked particularly for tumor mutation burden<\/a>, a quantitative measure of acquired somatic mutations in tumors and an indicator of response to treatment. The results show that adding Cerebro to 3 other next-generation sequencing methods more accurately classifies tumors by their likelihood to respond to immunotherapy.<\/p>\n

“Our data showed improved classification of patients when using Cerebro and highlights the importance of accurate mutation detection on treatment decisions,” says Angiuoli in a company statement. Personal Genome Diagnostics is a spin-off enterprise from Johns Hopkins University, founded in 2010<\/a> by Velculescu and Luis Diaz<\/a>, now head of solid tumor oncology at Memorial Sloan Kettering Cancer Center in New York, but previously a postdoctoral researcher at Johns Hopkins.<\/p>\n

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