GNS Healthcare 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 analyse NCI data from the laboratory of Terry van Dyke, director of the Center for Advanced Preclinical Research (CAPR) at NCI. This data was generated from genetically-modified mouse models of non-small-cell lung cancer (NSCLC). This collaboration will utilise GNS’s supercomputer-driven REFSTM 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.
Using data from the experimental assessment of transcriptomic and MRI data relating to NSCLC induction, regression and combination drug treatments, GNS will utilise the REFSTM platform to reverse-engineer network models that connect drug doses to transcriptional and imaging measurement networks, and to endpoints. The results from millions of in silico simulations of these models will provide insights into the fundamental mechanisms of NSCLC and its response to drug treatments, enabling the development of more effective treatments for NSCLC.
