Leading the Way in Life Science Technologies

GEN Exclusives

More »

Feature Articles

More »
October 01, 2016 (Vol. 36, No. 17)

Dynamic Shifts in Precision Medicine

Data Never Rests. It Is Constantly Being Updated, Aggregated, Analyzed, and Reassessed in New Clinical Contexts

  • Pharmacogenomic precision medicine can now be incorporated into routine clinical practice according to Martin Dawes, M.D., professor and head of family medicine, University of British Columbia (UBC). TreatGX software, developed at UBC, integrates a patient’s current physical state, medications, and pharmacogenomics.

    “Within seconds, the software produces a list of safe and effective medication options for an individual patient,” exclaims Dr. Dawes.

    Abigail T. Berman, M.D., assistant professor at the University of Pennsylvania and associate clinical director at the Penn Center for Precision Medicine, reflects on precision medicine from the patients’ point of view: “If something has been proven, even if it was just proven yesterday, clinicians have the job of rolling it out very quickly into clinical care,” she insists. Alessandra Cesano, M.D., Ph.D., chief medical officer, NanoString Technologies, concurs: “The revolution of precision medicine 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 continuum and implementing precision medicine initiatives were key themes at the Global Engage and GTC Precision Medicine Conferences held in London and Boston, respectively. Highlights included strategies and technologies to exploit the vast and ever-expanding amount of scientific data and information. Some of the outstanding conference presentations are discussed herein.

  • Interrogative Systems Biology

    Traversing the translational research continuum has historically been a lengthy process. “BERG combines high-throughput molecular 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 Rodrigues, Ph.D., associate director, advanced analytics, BERG.

    “The BERG Interrogative Biology™ platform operates with any sample-related data including clinical information, treatment outcomes, and high-throughput molecular data such as proteomics, lipidomics, and metabolomics,” 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 output. “Our platform can be used for the discovery of novel therapeutics and biomarkers,” 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. Rodrigues. BPM 31510, a coenzyme Q10-containing proprietary formulation, may reverse the Warburg effect associated with altered lipid metabolism in cancer cells and elicit anticancer 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 Interrogative Biology platform. For example, patients’ biological samples such as blood, urine, and tissue, along with clinical information, 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

    Click Image To Enlarge +
    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.

    GNS Healthcare’s causal machine learning platform REFS™ (reverse engineering and forward simulation) turns healthcare data into actionable insights to guide precision medicine and scientific discovery. “We reverse engineer causal models from healthcare data,” explains Diane Wuest, Ph.D., associate director, precision medicine initiatives, GNS Healthcare. “Once these models 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, mobile, 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 interactions,” continues Dr. Wuest. “Then we conduct analyses to discern complex causal mechanisms and predictors across populations 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 progression 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 remote 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 Research 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. Berman has a particular perspective on the draft recommendations for lung cancer mutation testing that have been issued by the International Association for the Study of Lung Cancer (IASLC).

    “There is ongoing debate about what individual institutions should be doing about their depth of testing and exactly how mutation testing should be done,” comments Dr. Berman. “This is why IASLC opened a paper for public comment. Mutation testing is a constantly changing dynamic because the research framework on what mutations are actionable or prognostic is constantly changing.”

    The Penn Center for Precision Medicine’s next-generation sequencing (NGS) panel for lung cancer is based on a combination of actionable/druggable and prognostic mutations. Mutations have been identified in about 75% of over 800 lung cancer cases, with targeted drugs available for about 20% of the mutations. A retrospective study was done to find out if patients actually got available therapies. According to the study, not only did patients with common mutations get targeted drug therapy, but so did patients with more recently described mutations such as MET.

    “The study indicates that doing advanced testing and being ahead of the curve is really critical for patient care,” remarks Dr. Berman. “Appropriate testing opens doors for patients that would otherwise remain closed,” remarks Dr. Berman.

Related content