HPN-DREAM breast cancer network inference challenge Should you have any questions related to the challenge, please visit our Community Forum. Should you require help about Synapse itself, please see the dedicated Synapse Help Page. The HPN-DREAM challenge is closed. Thanks to all participants for helping to make this challenge a success! A manuscript summarizing the challenge results has been published in Nature Methods. Hill SM#, Heiser LM#, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ, The HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G+, Saez-Rodriguez J+ & Mukherjee S+. Inferring causal molecular networks: empirical assessment through a community-based effort. Nature Methods 13, 310?318 (2016). DOI:10.1038/nmeth.3773. #These authors contributed equally to this work. +Corresponding authors. SynopsisThe overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations. We propose three specific sub-challenges:(1) Network Inference Participants are asked to infer causal signaling networks from training data. (2) Time-course Prediction Participants are asked to use the training data to build models that can predict trajectories of protein levels following inhibitor perturbation(s) not seen in the training data. (3) Visualization Participants are asked to design a visualization strategy for high-dimensional molecular time-course data sets such as the ones used in this challenge. In sub-challenges (1) and (2), we also provide a parallel set of challenges based on in silico data. BackgroundCells respond to their environment by activating signaling networks that trigger processes such as growth, survival, apoptosis (cell death), and migration. Post-translational modifications, notably phosphorylation, play a key role in signaling. In cancer cells, signaling networks frequently become compromised, leading to abnormal behaviors and responses to external stimuli. Many current and emerging cancer treatments are designed to block nodes in signaling networks, thereby altering signalling cascades. Although there is a wealth of literature describing canonical cell signaling networks, little is known about exactly how these networks operate in different cancer cells. Advancing our understanding of how these networks are deregulated across cancer cells will ultimately lead to more effective treatment strategies for patients. MotivationThis challenge is motivated by the following observations: Causal signaling links and system dynamics can vary depending on lineage and (epi)genetic background, such that the same perturbation can lead to different signaling responses in different backgrounds. There is an urgent need for computational approaches that can characterize causal signaling networks using data acquired in a specific background or context of interest, for example a specific cell line under defined culture conditions. There is also a need to address the related task of predicting dynamical trajectories in specific contexts and under specific perturbations. Despite advances in this field, inference of causal networks in mammalian biology remains challenging. Equally, building dynamical models that can generalize beyond training data to predict trajectories under unseen system perturbations remains highly non-trivial. The set of challenges we propose, based on experimental and in silico data, are designed to assess ability to learn causal signaling networks, predict dynamical trajectories, and visualize complex time-course data. DataParticipants will be provided with an extensive training dataset comprised of proteomics time-courses from four breast cancer cell lines, acquired under different ligand stimuli, and under inhibition of network nodes, as well as an in silico dataset with similar characteristics. The data are explained in detail on the Data Description page, and the structure and content of the files is described on the Data Files page (the files can also be downloaded from this page). ChallengesThe challenge consists of the following three sub-challenges (click on the links for detailed descriptions): 1) Sub-challenge 1: Network Inference The aim is to infer causal signaling networks using time-course data with perturbations on network nodes. This sub-challenge is split into two independent parts: A - Breast cancer proteomic data. B - In silico data. 2) Sub-challenge 2: Time-course Prediction The aim is to build dynamical models that can predict trajectories of phospho-proteins. An important emphasis is on the ability of models to generalize beyond the training data by predicting trajectories under perturbations not seen in the training data. This sub-challenge is split into two independent parts: A - Breast cancer proteomic data. B - In silico data. 3) Sub-challenge 3: Visualization The aim is to propose novel strategies to visualize these high-dimensional molecular time-course data. For sub-challenges (1) and (2), a complete submission should include solutions to both parts A and B, but for the purpose of feedback to participants, performance on the two parts will be shown separately on the leaderboard. Both sub-challenges address the same question, but in one case with experimentally derived data, and in the other with data generated from a computational model. Participants are allowed to use any other source of information to solve the challenges, including (but not limited to) known signaling biology and information regarding the specific cell lines. Participants may find the following resources useful: KEGG, BioCarta, and Science Cell Signaling. AssessmentNetworks and predictions will be rigorously assessed using unseen test data, and in the case of in silico sub-challenges tested against gold-standard networks or trajectories. IncentivesIncentives for Sub-challenge 1: $15,000 to the top-performing team (provided by HPN) Development of the winning method as a Cytoscape Cyni App. The development will be contributed by B. Schwikowski's group in the context of The National Resource for Network Biology (PI: Trey Ideker) Invitation to present results at the 2013 RECOMB/ISCB Regulatory and Systems Genomics/DREAM Conference. Incentives for Sub-challenge 2: $15,000 to the top-performing team (provided by HPN) Invitation to present results at the 2013 RECOMB/ISCB Regulatory and Systems Genomics/DREAM Conference. Incentives for Sub-challenge 3: $5,000 to the top-performing team (provided by HPN) Implementation of the concept (provided by SageBionetworks) Invitation to present at the 2013 RECOMB/ISCB Regulatory and Systems Genomics/DREAM Conference. Travel support HPN has also donated $15,000 to help fund the travel of top scoring teams to attend the 2013 RECOMB/ISCB Regulatory and Systems Genomics/DREAM Conference (November 8-12 in Toronto, Canada) where the results and winners will be announced. Manuscript Nature Methods has agreed to consider for publication the submission of an overview paper describing the results and insights that arise from the HPN-DREAM Breast Cancer Network Inference Challenge. The challenge organizers will invite the best performing team to co-author the paper. The rest of the participants in the challenge will also be invited to co-sign the paper as part of the HPN-DREAM consortium. Publication is contingent on the outcome of the standard peer review process, embracing the ideas behind a blind challenge. Update (Feb 2016): The overview paper has now been published in Nature Methods (http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3773.html). CreditsThe NCI Division of Cancer Biology funded the generation of the experimental RPPA data in this challenge via an Integrative Cancer Biology CCSB grant to Gray/Spellman/Mukherjee/Mills, and these groups made the data available for this challenge. The Spellman and Gray labs at Oregon Health and Science University (OHSU) carried out the cell line experiments used for the challenge. The Mills lab at MD Anderson Cancer Center generated the proteomic data on their RPPA platform. The Mukherjee lab at the Netherlands Cancer Institute (NKI) led the analyses underlying formulation of the experimental data challenges. The Koeppl lab (ETH) led the development of the in silico challenges. The challenge organization, development and formulation was a tight collaboration of several DREAMers, including: Laura Heiser (OHSU), Heinz Koeppl and Michael Unger (ETH), Sach Mukherjee and Steven Hill (NKI), Thea Norman, Bruce Hoff, Jay Hodgson, and Mike Kellen (Sage Bionetworks), Julio Saez-Rodriguez and Thomas Cokelaer (EBI), all under the leadership of Gustavo Stolovitzky (IBM). Trey Ideker and Benno Schwikowski of The National Resource for Network Biology, will provide support to develop the best performing network inference method into a Cytoscape Cyni App. The Heritage Provider Network generously donated the funds for the challenge awards and for the logistics and organization of the challenge. ReferencesThe cell lines have been well-described in the following manuscripts: Neve et al. 2006, Cancer Cell Heiser et al. 2012, PNAS DataRail and the MIDAS format are described in the following manuscript: Saez-Rodriguez et al. 2008, Bioinformatics List of linksAt the top of the page you can find links to pages that describe the data, describe and provide the data files, and describe each sub-challenge. Should you have any questions related to the challenge, please visit our Community Forum. Should you require help about Synapse itself, please see the dedicated Synapse Help Page.