Clinical OMICS

JUL-AUG 2019

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

Issue link:

Contents of this Issue


Page 28 of 51 July/August 2019 Clinical OMICs 27 differences," Rehm said. Kingsmore is strongly for more sequencing of these underrepresented populations. "Thus far, the majority of sequencing has been disproportionately performed on the Caucasian population," he said. "Ide- ally, we need to make sure that all pop- ulations have access to this testing and we need multiple reference genomes that include diverse populations to make sure the analysis is robust for individuals of all backgrounds." More advanced analytical tools are also coming into use. Artificial Intelligence (AI) and machine learn- ing (ML) are two very popular top- ics now. SOPHiA DDM integrates SOPHiA AI, which detects, annotates and pre-classifies all types of genomic variants such as SNVs, Indels, CNVs, amplifications, and fusions, in one single experiment. Its clinical grade solutions are used to detect and char- acterize variants associated with multiple conditions, including can- cers and hereditary diseases. The company reports that more than 970 healthcare organizations from around the world have adopted SOPHiA's platform, which "learns" from all the data contributed by user-members while securing data privacy. But the ultimate goal is push-button diagnosis. "In the future all this will be automated," said Kingsmore. His team recently published a new rap- id-fire approach to genetic diagnosis. (Clark et al. Science Translational Med- icine, 2019.) The Rady team applied automated machine-learning and clinical natural language processing (CNLP) to reduce the need for the labor-intensive manual analysis of genomic data. This work was done in collaboration with technology and data-science developers Alexion, Clin- ithink, Diploid, Fabric Genomics, and Illumina. Kingsmore and others believe that a driving force for the continued mat- uration of clinical genome analysis will be Illumina's acquisition of Edico Genome in 2018. Edico has been a lead- ing provider of data analysis solutions for NGS. Its algorithms are expected to help improve and accelerate the analysis process for Illumina cus- tomers. "Today people have to knit together all these public and commercial tools and it still takes weeks for most of them to generate a diagnosis," King- smore says. He envisions a day when something that sits on the sequencer automatically delivers the diagnosis. "That is where Illumina is evolving," he said. "They produce results, not sequences." Rady is at the head of the curve, having set up their lab specifically to do speedy genetic analysis. The group has already delivered genetic diagnoses on approximately 1000 children since 2016. "It wasn't long ago when it took us weeks to do just the interpretation step," said Chowdhury, Kingsmore's colleague. "Now we can do a tertiary analysis in a single day." That's what patients all over the world are waiting for—a time when anyone with a condition that is related to a genetic cause can walk into a doctor 's office and get a dependable diagnosis within days rather than weeks, months, or even years. "A lot of expertise is needed to go from the sequence data to interpreting and validating your results." —Heidi Rehm, chief genomics officer, department of medicine, Massachusetts General Hospital Building a New Data Tool As the applications and use of NGS has expanded, new analysis and interpreta- tion tools have been needed. For exam- ple, FitzPatrick and his collaborators built VEP-G2P, which is an extension to Ensem- bl Variant Effect Predictor (VEP), another popular publicly available program that is used to predict the possible effects of a particular variant. VEP-G2P was built specifically to help diagnose patients with genetically heterogeneous clinical pre- sentation, like those FitzPatrick sees in the clinic. Those are particularly hard cases. "The main problem is that each of us has several thousand variants," he explains. "The challenge is filtering out the irrele- vant ones." This project was powered by data from the Deciphering Developmental Disor- ders Study (DDD). That project recruited more than 13,000 patients with previous- ly undiagnosed severe developmental disorders (DD) from the U.K. and the Re- public of Ireland. Those patients, and their parents, were all se- quenced. Next, a da- tabase of all known loci causing DDs was created, contin- ually updated, and used in studies of DD. FitzPatrick and colleagues used the basic architec- ture and processes employed in build- ing that database to create VEP-2GP and associated tools. Basically, they filter out variants that are found in healthy patients and then predict which of the remaining variants are likely to be pathogenic, that means going from a few thousand to 2-4 target variants. The program shows high sensitivity and precision compared to oth- er public tools in a recent report in Nature Communications. —Malorye Branca David FitzPatrick, Ph.D., University of Edinburgh

Articles in this issue

Links on this page

Archives of this issue

view archives of Clinical OMICS - JUL-AUG 2019