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38 | OCTOBER 1, 2016 | GENengnews.com | Genetic Engineering & Biotechnology News See Precision Medicine on page 40 has been proven, even if it was just proven yesterday, clinicians have the job of rolling it out very quickly into clinical care," she in- sists. Alessandra Cesano, M.D., Ph.D., chief medical officer, NanoString Technologies, concurs: "The revolution of precision medi- cine is not just in the new science; it is also in the application of these scientific discoveries to the clinic so the patient benefits." Speeding up the translational research con- tinuum and implementing precision medicine initiatives were key themes at the Global En- gage and GTC Precision Medicine Conferenc- es held in London and Boston, respectively. Highlights included strategies and technolo- gies to exploit the vast and ever-expanding amount of scientific data and information. Some of the outstanding conference presenta- tions are discussed herein. Interrogative Systems Biology Traversing the translational research con- tinuum has historically been a lengthy pro- cess. "BERG combines high-throughput mo- lecular profiling and an artificial intelligence- based analytics system to expedite the process of understanding how molecules interact in human cells or the body," says Leonardo Ro- drigues, Ph.D., associate director, advanced analytics, BERG. "The BERG Interrogative Biology™ plat- form operates with any sample-related data including clinical information, treatment out- comes, and high-throughput molecular data such as proteomics, lipidomics, and metabo- lomics," details Dr. Rodrigues. Trillions of data points are generated from single samples that may include thousands of compounds such as proteins, lipids, and metabolites. Also, data points may reflect various kinds of biological activity. Essentially, the BERG platform integrates systems biology with artificial intelligence and machine-learning algorithms to analyze data and generate unbiased multi-omics out- put. "Our platform can be used for the dis- covery of novel therapeutics and biomark- ers," explains Dr. Rodrigues. "We have used it extensively to build our own pipeline." Discovered by an Algorithm BPM 31510 is the first drug developed through artificial intelligence, asserts Dr. Ro- drigues. BPM 31510, a coenzyme Q10-con- taining proprietary formulation, may reverse the Warburg effect associated with altered lipid metabolism in cancer cells and elicit anti- cancer responses such as apoptosis. BERG has clinical trials involving its BPM 31510 drug, including a Phase I trial open for all solid tumors and a Phase II trial for advanced pancreatic cancer. The clinical trials incorporate precision medicine approaches through the BERG In- terrogative Biology platform. For example, patients' biological samples such as blood, urine, and tissue, along with clinical infor- mation, are analyzed, and integrative cause- and-effect maps are developed. The maps can help determine which patients might respond to a given drug or predict adverse events. Causal Machine Learning GNS Healthcare's causal machine learn- ing platform REFS™ (reverse engineering and forward simulation) turns healthcare data into actionable insights to guide preci- sion medicine and scientific discovery. "We reverse engineer causal models from health- care data," explains Diane Wuest, Ph.D., associate director, precision medicine initia- tives, GNS Healthcare. "Once these mod- els are built, they can be forward simulated and queried to answer 'what if' scenarios on outcomes." The platform aggregates all data types found in healthcare environments, such as electronic medical records and clinical, mo- bile, and omics data (genomic, proteomic, metabolomic, etc.), and builds unbiased causal disease models. "We input billions of data points that represent millions of variables and their in- teractions," continues Dr. Wuest. "Then we conduct analyses to discern complex causal mechanisms and predictors across popula- tions and on individual levels." REFS models can be built in days to weeks depending on the data types and size available. Actionable Insights Models can predict patient outcomes. For diabetes, certain predictors of progres- sion have been identified based on patient registries, making it possible to benchmark a new patient against the models to anticipate the progression of diabetes in that patient. Web-based dashboard tools help clinicians predict if the patient is at high or low risk for diabetes progression and inform treatment interventions. REFS data-driven modeling technology is increasingly being used for translational scientific research and identifying clinically relevant information. "With any of the models that we build, typically about one-third of the insights are previously known," informs Dr. Wuest. "Another third, also known, may seem re- mote from the exact disease area and hence of questionable relevance, at least until they are given a bit of thought. The final third are novel pieces of information that need additional investigation." All three insights are represented in the results of a collaboration between GNS Healthcare and the Multiple Myeloma Re- search Foundation, which analyzed the CoMMpass Study™ (NCT01454297). Results identified were the known RN7SK (7SK RNA), the partially known PDXP (pyridoxal phosphatase), and the new MIR3648-1 (MicroRNA 3648-1) disease- associated markers. Implementing Actionable Knowledge "It is our job as clinicians to make sure that every cancer patient who walks in the door is tested appropriately for actionable mutations," states Dr. Berman. Given her position as a radiation oncologist, Dr. Ber- man has a particular perspective on the draft Dynamic Shifts in Precision Medicine Diagnostic biomarkers are powerful decision-making tools that can enable more accurate diagnoses and better disease stratification for improved clinical management of patients. One class of biomarkers are autoantibodies (AAB), which are expressed early in a number of autoimmune diseases and can be indicative of disease stage and severity. The human autoimmune profile covers a huge number of AABs, which provide an enormous resource to identify novel AABs, which provide an enormous resource to identify novel marker candidates. This resource can be utilized for the better identification of diagnostic biomarkers allowing for the im- proved stratification of patients based upon their disease profile. Autoimmune diseases are influenced by a range of factors, meaning it is unlikely that a single given biomarker will be use- ful for patient stratification. "Researchers, therefore, are utilizing multiple biomarker identification tools such as Protagen's SeroTag® biomarker de- velopment engine for the high-throughput screening of AABs in autoimmune diseases such as systemic lupus erythematosus (SLE), systemic sclerosis (SSc), and rheumatoid arthritis (RA)," said Peter Schulz-Knappe, M.D., CSO of Protagen Diagnostics. "In one study, investigators used thousands of human autoantigens to develop a comprehensive landscape by which disease sub- groups could be identified, based upon their highly differenti- ated autoantibody signature." Screening over 4,000 serum samples, a bead-based suspen- sion array was coupled with advanced data mining to identify sion array was coupled with advanced data mining to identify novel autoantibodies for SLE, SSc, and RA. Researchers also found that patients fell into one of two categories; either be- longing to clusters defined by particular characteristic markers, or phenotypically overlapping with one another. "Such technologies present a highly valuable tool for novel biomarker discovery, which will enable earlier diagnosis, differential diagnosis, and disease subgrouping," added Dr. Schulz-Knappe. n Autoantibody Discovery Using High-Throughput Screening GNS Healthcare's Reverse Engineering and Forward Simulation (REFS) platform is designed to predict patient care effectiveness as a function of patient characteristics and interventions. TRANSLATIONAL MEDICINE Continued from page 1