Genetic Engineering & Biotechnology News

MAY15 2017

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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14 | MAY 15, 2017 | | Genetic Engineering & Biotechnology News for a platform that combined publicly available cancer ge- nome and transcriptome datasets after several large-scale collaborations left the company with a large amount of se- quencing data to analyze. OASIS, an open-access cancer ge- nomics web portal, was Pfizer's solution. "Raw omics data is difficult to analyze," said Julio Fernández Banet, Ph.D., principal scientist on Pfizer's oncol- ogy research team. "Our aim in developing OASIS was to minimize barriers for scientists to access trusted omics da- tasets and, as easily as possible, perform ad hoc analyses to answer their questions." The OASIS platform integrates numerous datasets from Pfizer collaborations and established sources such as The Cancer Genome Atlas (TCGA), the Cancer Cell Line En- cyclopedia (CCLE), and the Genotype-Tissue Expression (GTEx) project. The portal includes sample-level annota- tions and gene-level mutation, copy number variation, and expression data on tens of thousands of samples across doz- ens of cancers and tissues. Not only can the data be easily and freely mined, but OA- SIS also has built-in analytics. Researchers can plot expres- sion data and compare with sample mutation status, find cell lines with a particular mutation of interest, and gener- ate reports summarizing alterations for a list of genes across multiple cancer types. In version 3.0, available in the second half of this year, OASIS will also include proteomic data, an exciting enhancement to connect genetic aberrations with protein translation. As more relevant connections are made among biologi- cal data, more meaningful conclusions can be made to drive drug discovery and development. Large datasets are a start- ing point for integrating and connecting data; however, there is also critical biological information that exists discreetly buried within millions of scientific publications. Accessing and linking this information requires an intelligence beyond human processing. Data4Cure's all-inclusive bioinformatics platform, the Biomedical Intelligence Cloud, is powered by a dynamic graphical knowledge base named CURIE, which uses ad- vanced machine learning to automatically mine molecular datasets as well as published texts. "CURIE combines a variety of technologies including bioinformatics, machine learning, and natural language processing, as well as a proprietary semantic AI integration engine to continuously extract and integrate knowledge from multiple sources," explained Data4Cure's CEO, Ja- nusz Dutkowski, Ph.D. "The knowledge graph can also be automatically expanded with user's proprietary data in a private cloud mode, an important feature for the pharma- ceutical industry." The CURIE graph is continually expanded and integrates omics data, methylation profiles, genotype/phenotype as- sociations, biomarker databases, and even patient sample metadata from clinical trials. The graph currently spans over 150 thousand nodes, each representing a different biologi- cal entity or property, and 100 million relationships among them. Relationships are strengthened when data is supported by multiple sources or agrees with known pathways, giving the model higher predictive properties. The robust network of biological information con- tained within the Biomedical Intelligence Cloud allows users to visualize their favorite biological molecule at a systems level. Even more impressive is the way in which the data can be analyzed to stratify patients into disease subtypes, a capability that can lead to predictions of thera- peutic efficacy. For example, a pathway activity analysis using patient ex- pression data can identify immune cell-infiltrated and non- infiltrated tumor subtypes and associates them with specific genomic alterations. This information can then be used in immuno-oncology clinical trial planning by linking subtypes to treatment response. Use of Data4Cure's bioinformatic platform ensures an informed drug development plan which is beneficial for everyone. Standardizing NGS in the Clinic In an effort to treat patients using a targeted approach, more and more hospitals are incorporating sequencing pro- grams for diseased patients where genetic variation plays a role. Unfortunately, the programs are suffering from a lack of standardization, a deficit that can lead to discrepancies in care among healthcare facilities. Sophia Genetics hopes OMICS Feature See Big Biodata on page 16 Machines Learn to Sift Big Biodata Continued from page 1 The volcano plot feature of Pfizer's Oasis system provides a way to identify genes that are up-regulated (red dots) or down- regulated (blue dots) in tumors when compared with normal samples. Current ranges classify genes as up/down- regulated when the fold-change (FC; log2) is higher than 1.6 or lower than −1.6 and the false discovery rate (FDR; −log10) value is higher than 1. Petabytes of data stored as 1's and 0's are generated in clinical NGS programs. The immense amount of processing required to analyze the data requires a high- performance computing (HPC) environment and optimized computing instructions.

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