Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of a specific drug, that predict the sensitivity of the same or similar compounds in different cell lines. Methodology invoked in this challenge will be useful not only in therapeutic decision-making but also understanding the basic mechanisms of drug mode of action and drug-drug interaction.
Sub challenge 1, The NCI-DREAM Drug Sensitivity Prediction Challenge
A community effort to assess and improve drug sensitivity prediction algorithms
James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-ud-din, Petteri Hintsanen, Suleiman A Khan, John-Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, NCI DREAM Community, James J Collins, Dan Gallahan, Dinah Singer,Julio Saez-Rodriguez, Samuel Kaski, Joe W Gray & Gustavo Stolovitzky
Nature Biotechnology (published online June 1, 2014)
Link to Nature Biotechnology