{"id":887,"date":"2020-08-26T09:32:18","date_gmt":"2020-08-26T09:32:18","guid":{"rendered":"https:\/\/clinlabint.3wstaging.nl\/machine-learning-finds-tumour-gene-variants-and-sensitivity-to-drugs\/"},"modified":"2021-01-08T11:08:46","modified_gmt":"2021-01-08T11:08:46","slug":"machine-learning-finds-tumour-gene-variants-and-sensitivity-to-drugs","status":"publish","type":"post","link":"https:\/\/clinlabint.com\/machine-learning-finds-tumour-gene-variants-and-sensitivity-to-drugs\/","title":{"rendered":"Machine learning finds tumour gene variants and sensitivity to drugs"},"content":{"rendered":"<p>Matching unique genetic information from cancer patients\u2019 tumours with treatment options \u2013 an emerging area of precision medicine efforts \u2013 often fails to identify all patients who may respond to certain therapies. Other molecular information from patients may reveal these so-called \u201chidden responders,\u201d according to a Penn Medicine.<br \/>\n\u201cTargeted sequencing can find individuals with certain mutations that are thought to confer susceptibility to anti-cancer drugs,\u201d said senior author Casey Greene, PhD, an assistant professor of Pharmacology in the Perelman School of Medicine at the University of Pennsylvania. \u201cBut many people may lack these mutations, and as machine learning approaches improve they may help guide these patients to appropriate therapies.<br \/>\n\u201dGreene and first author and doctoral student Gregory P. Way used machine learning to classify abnormal protein activity in tumours. This branch of artificial intelligence develops computer programs that can use new data to learn and make predictions. The algorithm they devised to search TCGA integrates genetic data from 33 different cancer types. Greene and Way used information from the transcriptome, the grand total of all messenger RNAs expressed within an individual.<br \/>\nThey specifically applied their model to the Ras pathway, a family of genes that make proteins that govern cell replication and death. Changes in the normal function of Ras proteins \u2013 mutations which are responsible for 30 percent of all cancers \u2013 can power cancer cells to grow and spread. These mutations are often referred to as the \u201cundruggable Ras,\u201d having beaten back a variety of investigational inhibitor drugs and vaccine-based therapies.<br \/>\n\u201cThis model was trained on genetic data from human tumours in The Cancer Genome Atlas and was able to predict response to certain inhibitors that affect cancers with overactive Ras signalling in an encyclopaedia of cancer cell lines,\u201d Greene said. The upshot is that the transcriptome is underused in bringing precision to oncology, but when combined with machine learning it can aid in identifying potential hidden responders.<br \/>\nThe Penn team collaborated with co-author Yolanda Sanchez, PhD, a cancer biologist from the Geisel School of Medicine at Dartmouth College. They are working together to mesh her identification of compounds that target tumors with runaway Ras activity and tumour data (analysed by machine learning) to find patients who could benefit from these potential cancer drugs.<br \/>\n\u201cFor precision medicine to benefit individuals in real time, we must develop robust models to efficiently test efficacy of potential therapies,\u201d Sanchez said. \u201cWe can use this very powerful combined approach of machine learning-guided drug discovery using Avatars, which are mice carrying identical copies of a patient\u2019s tumors. The Avatars allow our interdisciplinary team to identify the tumours with runaway Ras activity and evaluate and compare multiple therapies in real time.\u201d<br \/>\n<span lang=\"EN-GB\">Penn Medicine<link https:\/\/www.pennmedicine.org\/news\/news-releases\/2018\/april\/seeking-hidden-responders-machine-learning _blank external-link-new-window \"Opens external link in new window\">www.pennmedicine.org\/news\/news-releases\/2018\/april\/seeking-hidden-responders-machine-learning<\/link><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Matching unique genetic information from cancer patients\u2019 tumours with treatment options \u2013 an emerging area of precision medicine efforts \u2013 often fails to identify all patients who may respond to certain therapies. Other molecular information from patients may reveal these so-called \u201chidden responders,\u201d according to a Penn Medicine. \u201cTargeted sequencing can find individuals with certain [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[35],"tags":[],"_links":{"self":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/887"}],"collection":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/comments?post=887"}],"version-history":[{"count":0,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/887\/revisions"}],"wp:attachment":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/media?parent=887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/categories?post=887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/tags?post=887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}