Taibo has recently been awarded a full MD/PhD scholarship to Johns Hopkins School of Medicine. Taibo started in the Lage Lab in April 2013 and worked on several projects including InWeb, GeNets, BINe, CanComSq. He looks forward to continuing working with the Lage Lab from his new position at Hopkins and we wish him the best of luck!
April and Edyta hosted a BroadE (E for Education) workshop on their newest web platform, Genoppi, which integrates experimental proteomics and genetic data. The workshop was oversubscribed and will likely be repeated at a later time to accommodate many people who couldn’t get a spot.
The presentation can be viewed by creating an account here: http://theleadingstrand.cshl.edu/activate/160970/2017/GENOME
Expanding discovery from cancer genomes by integrating network analyses with in vivo tumorigenesis experiments
Heiko Horn 1,2 , Michael Lawrence 2,3 , Candace Chouinard², Yashaswi Shresta², Jessica Hu 1,2 , Elizabeth Worstell 1,2 , Emily Shea², Nina Ilic 2,4 , Ejung Kim 2,4 , Atanas Kamburov 2,3 , Alireza Kashani 1,2 , William Hahn 2,4 , Joshua Campbell 2,5 , Jesse Boehm², Gad Getz 2,3 , Kasper Lage 1,2
1 Massachusetts General Hospital, Department of Surgery, Boston, MA, ²Broad Institute of MIT and Harvard, Cancer Program, Cambridge, MA, 3 Massachusetts General Hospital, Department of Pathology, Boston, MA, 4 Dana Farber Cancer Institute, Department of Medical Oncology, Boston, MA, 5 Boston University, Department of Medicine, Boston, MA
Gene-based statistical tests to find cancer genes look for increased rates of somatic mutations or genomic copy number changes in cancer genomes. However, considerable sample sizes are required to find driver genes with intermediate or low mutation frequencies, and additional cancer genes remain to be discovered. Previous analyses have shown that cancer mutations in some cases converge on specific functional genomics sub networks. This suggests that mutations in a genes’ functional network can be predictive of whether it is a cancer gene itself. However, this hypothesis has never been systematically explored across hundreds of known cancer genes, tens of tumor types, and thousands of cancer genomes. More importantly, analyses of cancer gene networks have not previously been coupled to systematic experimental validation assays and their predictive power to provide new insight into tumor biology remains unclear. We develop a statistic (NetSig) that combines molecular protein network information and existing cancer sequencing data to identify genes with a significantly mutated gene network (excluding data on the gene itself). We apply NetSig to data from 4,742 tumors spanning 21 tumor types and identify known and recently proposed driver genes in most (~ 60%) tumor types. NetSig also identifies 62 other genes with a significantly mutated gene network many suggesting new cancer biology. We test 25 known driver genes (positive controls), 33 NetSig candidates, and 79 random genes (random controls) in a massively parallel in vivo tumorigenesis cell assay. We demonstrate that the NetSig candidates induce tumors at rates that are comparable to the known driver genes and eightfold higher than random genes when injected into mouse models. Guided by the NetSig results and functional validation experiments, we looked for mutations and copy number changes in these genes that could explain 242 (out of a total of 660) lung adenocarcinomas without any known driver event; the analysis identified significant amplifications of several NetSig candidates in this patient subgroup. Overall, we present an integrated workflow that complements gene-based statistical tests by combining molecular network information, cancer sequencing data, and in vivo tumorigenesis assays to find and validate new driver genes in existing cancer genome data. The framework we describe is scalable to the rapid production of data and should become increasingly powerful as more tumors are sequenced in the future.
The Lage Lab and collaborators presented one talk and eight posters at the 2017 Annual Meeting of the Scientific Advisory Board of the Stanley Center
From models to mechanism: Neuronal protein networks perturbed by genetics in psychiatric diseases – Kasper Lage
#6. A unified web platform for network-based analyses of genomic data
Taibo Li, Johnathan Mercer, Joseph Rosenbluh, April Kim, Heiko Horn, Liraz Greenfeld, David An, Andrew Zimmer, Arthur Liberzon, Jon Bistline, Ted Natoli, Yang Li, Aviad Tsherniak, Rajiv Narayan, Aravind Subramanian, Ted Liefeld, Bang Wong, Dawn Thompson, Sarah Calvo, Steve Carr, Jesse Boehm, Jake Jaffe, Jill Mesirov, Nir Hacohen, Aviv Regev, Kasper Lage
#7. A scored human protein-protein interaction network to catalyze genomic interpretation
Taibo Li, Rasmus Wernersson, Rasmus B Hansen, Heiko Horn, Johnathan Mercer, Greg Slodkowicz, Christopher T Workman, Olga Rigina, Kristoffer Rapacki, Hans H Stærfeldt, Søren Brunak, Thomas S Jensen, Kasper Lage
#11. Complementary approaches for at scale generation of human neuronal material for proteomic analyses
Eugeniu Nacu, William Crotty, Alison O’Neil, Francesca, Rapino, Natalie Petrossian, Benjamin Tanenbaum, Edyta Malolepsza, April Kim, Monica Schenone, Jake Jaffe, Lee Rubin, Kasper Lage, Kevin Eggan
#12. Overview of human brain networks perturbed by genetics in psychiatric disorders
Edyta Malolepsza, Eugeniu Nacu, Natalie Petrossian, William Crotty, April Kim, Taibo Li, Benjamin Tanenbaum, Stephan Ripke, Taibo Li, Mark Daly, Kiki Lilliehook, Jake Jaffe, Monica Schenone, Kevin Eggan, Kasper Lage
#13. Time course analysis of CACNA1C protein interaction partners
Eugeniu Nacu, Edyta Małolepsza, April Kim, Taibo Li, Natalie Petrossian, Benjamin Tanenbaum, William Crotty, Stephan Ripke, Mark Daly, Kiki Lilliehook, Monica Schenone, Jake Jaffe, Kevin Eggan, Kasper Lage
#14. Genoppi: a web application for interactive integration of experimental proteomics results with genetic datasets
April Kim, Edyta Małolepsza, Justin Lim, Kasper Lage
#15. Bayesian multivariate analysis of RNA seq data to identify specific protein-protein interactions of the brain
Sandrine Muller, Taibo Li, April Kim, Edyta Małolepsza and Kasper Lage.
#16. BINe: selecting index genes for proteomics experiments and determining brain-relevant isoforms
April Kim, Taibo Li, Stephan Ripke, Eugene Nacu, Olli Pietilainen, Mark Daly, Kevin Eggan, Kasper Lage
April and Edyta were invited by Brett Tomson to present CanComSq and demo Genoppi, a web application for interactive integration of experimental proteomics results with genetics, to a group of enthusiastic Cancer Program postdocs.