February 8, 2018

Kasper spoke on his labs approaches in integrative analyses of genetic datasets in the session ‘Integrative Analysis, Dynamic Networks and Systems Immunology’ and chaired the session ‘Immuno-Oncology from Basic Science to Clinical Practice’

For full program click here.

Heiko Horn presenting his work ‘NetSig: Network-based discovery from cancer genomes’ at the weekly meeting of the GPP at the Broad Institute:

THE AUTHOR FILE by Vivien Marx:

Scoring genes in light of their ‘friends’, and a naval approach to science

The profile can be accessed here as a virtual pdf file and on the Nature Methods website here.

Kasper Lage was involved in a general discussion regarding a new Danish National Genome Center, featured on the front page of the well-respected Danish newspaper ‘Weekendavisen’, in two articles in Ingenioren and in an interview on Danish National Radio. Kasper was also featured in Nature Methods, in a profile titled “Scoring Genes in Light of Their ‘Friends’, a Naval Approach to Science”.

From the lab Heiko Horn led the development and implementation of network-based statistic that identifies cancer driver genes with high accuracy from cancer genomes. Results are validated using a massively parallel in vivo tumorigenesis assay in mice and by re-analyzing 660 lung adenocarcinoma patients where ~1/3 do not have mutations or copy number changes in known oncogenes identifying two new cancer-driving genes underlying this cancer type.

This project is a collaboration with Jesse Boehm and Gad Getz from the Broad Institute and MGH Cancer Center.

Paper can be found here:

NetSig: network-based discovery from cancer genomes

Heiko Horn, Michael S Lawrence, Candace R Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C Hahn, Joshua D Campbell, Jesse S Boehm, Gad Getz & Kasper Lage

Abstract:

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

Congratulations to Kasper on being promoted to Associate Professor at Harvard Medical School as of November 1st 2017!!!

For more information about the meeting click this link.

Title of Kasper’s talk:

Pathway modeling of genetic datasets in neurodegenerative and psychiatric diseases using
biological networks

More information on the conference can be found here: Click this link

Abstract:

Large-scale protein-protein interaction networks in human neurons coalesce schizophrenia risk loci into unexpected pathways

Eugene Nacu (1,3), April Kim (1,2), Edyta Malolepsza (1,2), Taibo Li (1,2) William Crotty (1,3), Natalie Petrossian (1,3), Benjamin Tanenbaum (4), Stephan Ripke(1,2), Jake Jaffe (4), Monica Schenone (4), Mark Daly (1,2), Kevin Eggann(1,3), Kasper Lage (1,2)

1) Stanley Center for Psychiatric Research at the Broad Institute. 2) Massachusetts General Hospital. 3) Harvard Stem Cell Institute. 4) Broad Institute.

The recent genome-wide association studies in schizophrenia have revealed many risk loci encoding genes likely to be involved in this disorder and exciting glimpses of molecular pathways have emerged from the data (e.g., chromatin remodeling, calcium signaling, synaptic pruning and synaptic transmission). Such examples illustrate how some genes associated with schizophrenia interact at the level of proteins to form networks involved in diverse areas of neurobiology. However, most of the identified genes do not connect with each other in well-defined cellular pathways and it is clear that the disease also includes largely uncharted and incomplete networks that are probably unique to the human brain. This is a key bottleneck towards biological insight and therapeutic intervention. Here, we describe a large-scale approach to overcome some of these challenges by executing systematic interaction experiments in human neurons of proteins encoded in schizophrenia risk loci. First, our approach capitalizes on unbiased genetic data to choose corresponding proteins as the starting point of the protein interaction experiments. Second, we developed several parallel workflows, using both manual production and automated approaches on robots, to generate human upper layer cortical excitatory neurons from embryonic stem cells at scale (meaning routinely producing billions of cells). Third, we exploited state-of-the-art proteomics technologies to map quantitative interaction networks of the index proteins at high resolution. Fourth, we developed a new analytical platform (Genoppi) to quality control and integrate cell-type-specific protein interaction experiments and genome-wide association data to identify unexpected pathways relationships between risk loci. Our analysis shows that a large fraction of the high-quality and reproducible protein interactions we identify are unique to human neurons meaning that the interactions have not earlier been reported in the literature and are not identified in non-brain tissues. These observations illustrate the importance of executing the experiments in a human cell type of relevance to the trait being analyzed. Importantly, we uncover many unexpected pathway connections between schizophrenia risk loci. For example, our analysis reveals neuron-specific protein-protein interactions between calcium channels and the classic complement cascade at three different time points of neuronal differentiation. This observation would have been missed in other cell types and provides an unexpected link between calcium signaling and synaptic pruning in schizophrenia. More generally the experimental and analytical approaches we develop here for a neuropsychiatric disease could potentially be applied to functionally annotate loci and provide new pathway insights into other common complex traits.

Title of Kasper’s talk: “Large-scale protein-protein interaction experiments of schizophrenia risk genes in human neurons coalesce GWAS loci into unexpected pathways”

The event will occur September 13 and 14: See more information here.