Anika Gupta accepted into the PhD programs at Harvard, MIT, and Stanford

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Anika Gupta, who has been an MIT Undergraduate Research Opportunity Program researcher in the lab for 2 years, was recently accepted into the PhD programs of Harvard (Bioinformatics and Integrative Genomics), Harvard-MIT (Health Sciences and Technology), and Stanford Medicine (Biomedical Informatics). Anika was co-supervised with Aviv Regev.

Congratulations Anika – we couldn’t be more proud of you and wish you the very best of luck as a graduate student!!!




Taibo Li, Heiko Horn, Eugene Nacu and April Kim presented their work on the weekly meeting of the Broad Institute’s Program in Medical and Population Genetics

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February 8, 2018

9:30 AM – 11:00 AM

415M-2-Monadnock (2040) (125) [desktop pc, microphone + speaker system, projector, touch panel phone], Medical and Population Genetics Program Talks

Medical and Population Genetics Program Meeting

Medical and Population Genetics Program

Organizer: Kasper Lage


Taibo Li: “A scored human protein interaction network to catalyze genomic interpretation”

Heiko Horn: “Expanding discovery from cancer genomes by coupling network analyses, massively parallel in vivo tumorigenesis experiments, and targeted patient-reanalysis”

Kevin Eggan: “Stem‐cell‐models to explore the molecular basis of complex traits”

Eugene Nacu/April Kim: “Human neuronal protein networks perturbed by genetics in psychiatric diseases”

#science #talks






Kasper Lage participates in debate about a Danish National Genome Center

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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”.



Paper on network-based discovery from cancer genomes published in Nature Methods

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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


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.






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