CAMBRIDGE, Mass., May 31, 2017 /PRNewswire/ -- Leading precision medicine company GNS Healthcare (GNS), today
This research, published in the American Association for Cancer Research's (AACR) journal Cancer Research, used in silico simulation of models developed from causal machine learning, which were then correctly validated to identify novel regulators of cell cycle progression and survival in cancer cells. The high rate of validated predictions underscores the power of simulation in REFS, a new approach for quickly generating, at scale, new biological hypotheses relevant for diagnosing and discovering novel treatments for complex diseases, including multiple myeloma, colorectal cancer, and Huntington's disease.
"This study, conducted at UCSF in collaboration with GNS Healthcare, demonstrates a critical role of TRIB1 in regulation of cell cycle and survival and provides important information on molecular responses to MEK inhibition in cancer cells," said W. Michael Korn, MD, Professor of Medicine, Division of Gastroenterology and Medical Oncology, University of California, San Francisco, and senior author of the study. "We are encouraged by these findings and the valuable role this discovery can play in the design of novel treatments for cancer."
"Going beyond the limits of existing human knowledge is critically important to make strides in the diagnosis and treatment of resistant diseases, like cancer," said Iya Khalil, PhD, a co-author of the paper and chief commercial officer and co-founder of GNS Healthcare. "This study provides a strong proof of concept for our unique causal machine learning approach, which allowed us to discover genes with previously unknown function in breast cancer. To put it simply, our platform allowed us to make a discovery that could have only otherwise been made by pure chance."
The paper, "Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells", was first published in the journal's January 2017 issue and was authored by Rina Gendelman, University of California, San Francisco; Heming Xing, Novartis Institutes for BioMedical Research, Inc.; Olga K. Mirzoeva, University of California, San Francisco; Preeti Sarde, Pharmacyclics; Christina Curtis, Stanford University; Heidi S. Feiler, Oregon Health and Sciences University; Paul McDonagh, Alexion Pharmaceuticals; Joe W. Gray, Oregon Health and Sciences University; Iya Khalil, GNS Healthcare; and W. Michael Korn, University of California, San Francisco. The data was collected by University of California, San Francisco and the study supported by NIH, National Cancer Institute.
About GNS HealthcareGNS Healthcare applies causal machine learning and simulation technology to predict which treatments will work for which patients, improving individual patient outcomes and the health of populations, while reducing the total cost of care. The GNS technology is based on its MeasureBase™ data integration architecture and patented REFS™ (Reverse Engineering and Forward Simulation) causal inference and simulation engine. Health plans, bio-pharmaceutical companies, healthcare providers, foundations, academic medical centers, and self-insured employers use GNS' cloud-based solutions to solve pressing and costly problems including those surrounding metabolic syndrome, medication adherence, end-of-life care, preterm birth, personalized care pathways in specialty care, oncology, and diabetes, new drug target discovery, patient stratification in clinical trials, and more. GNS solutions focus on reducing adverse events, slowing disease progression, and improving therapeutic effectiveness through precision matching that maximizes impact on individual patient health outcomes while reducing wasteful spending and downstream medical costs.
Discover what works. For whom.
Media ContactGNS HealthcareLauren KannryPublic [email protected]
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SOURCE GNS Healthcare
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