Results of GNS Healthcare Collaborations with UCSF, NCI Presented at AACR Annual Meeting in Chicago
CHICAGO, April 4, 2012 /PRNewswire/ — Results from two GNS Healthcare collaborations were presented this week at the American Association for Cancer Research (AACR) Annual Meeting in Chicago. In each, GNS researchers used supercomputer-powered data analytics to discover novel biological mechanisms in cancer. The predictions made using this computational approach were then validated experimentally.
In the first collaboration, the results of which were presented in a talk Sunday by Dr. Rina Gendelman of Dr. W. Michael Korn’s laboratory at University of California, San Francisco (UCSF), the GNS team used the REFS(TM) (Reverse Engineering and Forward Simulation) platform to identify novel signaling networks that slow the proliferation of breast cancer cells. REFS(TM) takes in massive amounts of biological data and uses machine learning to build a probabilistic model, known as a Bayesian network, of the signaling networks within the cell. It then uses computer simulations to identify genes that are drivers of a biological endpoint – in this case cell cycle arrest. “Using this approach, we are able to identify genes that are causally related to outcomes, not just correlated,” said Dr. Korn. “This allows us to predict the effect of molecular interventions.”
The team of GNS and UCSF researchers identified TRIB1 as a novel cell cycle regulator in breast cancer cells. This prediction was validated by experiments that showed that knocking down the expression of TRIB1 using short interfering RNA (siRNA) led to cell cycle arrest. The researchers then used REFS(TM) to identify downstream genes regulated by TRIB1. Using this information, Dr. Gendelman and colleagues showed experimentally that TRIB1, acting through the NFkB pathway, made cells more susceptible to apoptosis, the body’s own cell-killing mechanism.
In the second collaboration, the results of which were presented by Dr. Anne Monks in a poster session this morning, GNS researchers collaborated with Dr. James Doroshow, Director of the Division of Cancer Treatment and Diagnosis at the National Cancer Institute (NCI), to identify novel mechanisms of the well-known chemotherapy drug doxorubicin, which has been in use since the 1970s and is commonly used to treat a wide range of cancer types.
Doxorubicin binds to DNA, and thus can have effects on the cellular network that are quite complex. To better understand these effects, the researchers collected gene expression profiles from the human cancer cell lines that make up the NCI60 panel. The panel, a collection of 60 human cancer cell lines, has been used since the 1980s to screen potential anticancer compounds, and to discover their mechanisms of action. Now, through the use of high tech genetic and computational tools, the NCI60 panel is lending new insight into the mechanism of action of a well-known drug. The GNS and NCI research team used REFS(TM) to predict that peroxiredoxin II (PRDX2) was an important mediator of sensitivity to doxorubicin. Dr. Doroshow and colleagues then validated this prediction experimentally using siRNA knockdown.
“These results are exciting because they show the promise of hypothesis-free computational methods to help us understand disease, develop new drugs, and predict which drugs will work for which patients,” said Dr. Iya Khalil, Executive Vice President and co-founder of GNS Healthcare and a senior author on both studies.
About GNS Healthcare
GNS Healthcare is a big data analytics company that has developed a scalable approach for the discovery of what works in healthcare, and for whom. GNS’s analytics solutions are being applied across the healthcare industry: from pharmaceutical and biotechnology companies, health plans and hospitals, to integrated delivery systems, Pharmacy Benefits Managers (PBMs), and Accountable Care Organizations (ACOs). GNS Healthcare is helping healthcare leaders discover the knowledge they need to match patients with treatments that work.
SOURCE GNS Healthcare, Inc.