Genetic Engineering & Biotechnology News

DEC 2018

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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8 | DECEMBER 2018 | Genetic Engineering & Biotechnology News | GENengnews.com Using Big Data to Treat the Whole Patient Continued from page 1 individually, perhaps check all possible triplets, quadruplets, etc., but the number of possible combinations grows expo- nentially," he explained. "We call this 'combinatorial explo- sion.' It's totally impractical to compute the statistics for all possible combinations, even if your computer was the size of the sun, so you need clever computational techniques to take shortcuts—evaluating the most likely solutions first." Dr. Baumbach specializes in using artificial intelligence (AI) approaches, such as machine learning and random for- est, to find the most likely combinations of genes. His trick is to pick ensembles of genes that, although not individually linked to a disease, are part of the same molecular pathways. In patients, these groups of genes can work together to drive a disease state. He also uses unsupervised learning to stratify patients with, for instance, asthma into subgroups from scratch (called de novo endophenotyping) using disease mecha- nisms, rather than individual genes or gene panels. With this technique, patients are deliberately not grouped by pheno- type, but are—instead—clustered in subgroups by an AI. According to Dr. Baumbach, "If you try a new medi- cine on 10,000 asthma patients, it might be very effective in 20%, but not effective in the other 80%, so there's no chance of getting this drug registered, unless you have an effective means of telling the 20% from the 80%. If we can stratify these individuals into subgroups that share common mechanisms, we can check the mechanism against the drug target and look at other treatment options." Dr. Baumbach hopes that in the future this technology could be used to tailor treatments to individuals—perhaps by giving multiple drugs at lower doses to reduce side effects or to repurpose drugs that target the molecular networks involved in their disease. Developing Clinical Tools Classification of diseases by their molecular mechanisms was also the topic of a talk by Timothy Radstake, Ph.D., a professor of translational immunology at the University Medical Center Utrecht. He discussed using molecular data to develop clinical models of Sjögren's syndrome and in- terocular lymphoma. "We started five years ago to really delve into this niche in a data-driven manner. Most researchers focus on certain molecules, but we decided to use big data to look at multiple subsets of immune cells," he explained. Dr. Radstake uses a variety of techniques, including RNA sequencing, proteomics, transcriptomics, metabolomics, and sequencing of the epigenome to classify diseases by their mo- lecular biomarkers. From there, his team has developed a computer model to predict the risk that patients with uveitis will develop intraocular lymphoma. They developed a tool where they punctured the eye and extracted a little fluid, before doing a simple proteomics analysis. "We think it predicts intraocular lymphoma two years earlier so, if successful, this would potentially lead to earlier treatment for these patients," he said. The model is now being trialed as a diagnostic aid. Dr. Radstake is considering moving to clinical trials, but warned that "the real application in clinical decision mak- ing is far from reality. We are making the first steps right now, but we have to overcome a lot of challenges." Among these challenges are carrying out large-scale trials on patient groups from different laboratories. "When we analyze 15 different patient subgroups in the same laboratory, this al- lows us to have direct comparison, but when we work to- gether with another group, this data isn't transferrable," he said. Clinicians specializing in autoimmune diseases are working to overcome these hurdles, but "we're just at the beginning of how that might work." Tackling Big Data Aridaman Pandit, Ph.D., an assistant professor from the University Medical Center Utrecht, works closely with Dr. Radstake and gave a workshop on big data for clinicians. "We are realizing that medicine can't turn into personalized medicine without looking at big data," he said. "But typical Drug Discovery At Hidden Health Solutions, data about people's daily experiences are collected and studied using a six-step method called Participatory Narrative Inquiry (PNI). At present, Hidden Health Solutions is using PNI to discern patterns in the experiences of people with chronic illness, and to gain insights about what improves their quality of life. The nal PNI step, "return insights," may occur online or via mailings or meetings. Grand Forest, an ensemble learning platform developed at the University of Southern Denmark and the Technical University of Munich, has been optimized for discovering disease-associated modules from genomic pro ling data. The platform may be accessed online, and it provides for supervised and unsupervised analyses of decision trees.

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