12 Jul 2011 Cambridge - GNS Healthcare Inc. (GNS) is collaborating with the National Cancer Institute (NCI) to accelerate lung cancer research with a supercomputing platform that can rapidly uncover cause-and-effect mechanisms hidden in huge data sets assembled from imaging, genetics, pathology, and other areas. The results could help predict which patients will respond to a given treatment.
GNS will analyze NCI data from the laboratory of Terry van Dyke, Ph.D., Director of the Center for Advanced Preclinical Research (CAPR) at NCI. These data were generated from genetically modified mouse models of non-small cell lung cancer (NSCLC).
This collaboration will utilize GNS's supercomputer-driven REFS platform to build computer models of NSCLC in a hypothesis-free, unbiased manner that will be simulated to identify key molecular mechanisms of NSCLC. The goal is to identify biomarkers and biological mechanisms that will lead to better matching of drugs to patients and new effective drugs in NSCLC.
GNS is excited to be deploying our supercomputer-driven REFS platform to enable the maximal extraction of actionable knowledge from the rich lung cancer datasets generated by the NCI", stated GNS Executive Vice President and Co-Founder Dr. Iya Khalil. "Combined with the expertise of our NCI colleagues in lung cancer biology and in designing powerful experiments to uncover its key mechanisms, we are creating the opportunity to provide better outcomes for lung cancer patients."
The data utilized in the collaboration will include data from the experimental assessment of transcriptomic and MRI data relating to NSCLC induction, regression and combination drug treatments. Starting from this data, GNS will utilize the REFS platform to reverse-engineer network models from the data that connect drug doses to transcriptional and imaging measurement networks to endpoints. The results from millions of in silico simulations of these models will provide unique insights into the fundamental mechanisms of NSCLC and its response to drug treatments, enabling the development of more effective treatments for NSCLC.
The initial phase of this project is also intended to help GNS and NCI develop standards for the exchange of data to conduct future collaborations in other relevant mouse model systems. From this starting point, the groups envision the possibility of a combined experimental and computational work flow aimed at rapidly enabling the generation of hypotheses, testing these hypothesis in silico and in vivo, generating new confirmatory data, and rapidly cycling back to additional computational modeling, with the goal of accelerating the conversion of knowledge into new clinical options for cancer patients.