Vesteinn Thorsson

Principal Scientist

Send Emailvesteinn.thorsson@isbscience.org

(206) 732-1341

thorsson-shmulevich.isbscience.org/bio/vesteinn-thorsson/

Dr. Thorsson’s research encompasses mathematical modeling, gene regulatory networks, bioinformatics and data integration, cancer genomics and immunooncology. Dr. Thorsson worked on systems modeling of gene regulatory networks in model systems (yeast and halobacterium), and on molecular networks responsible for the activation of murine macrophages. More recently, as part of the Cancer Genome Atlas (TCGA) Research Network, Dr. Thorsson’s focus has been on human cancer genomics, contributing to the TCGA studies of gastrointestinal tumors – colorectal cancer, gastric cancer and esophageal cancer, serving as Data and Analysis Coordinator, and playing a key role in determining molecular subtypes. Dr. Thorsson also served as a co-chair of a working group dedicated to comprehensive analysis of all TCGA gastrointestinal tumor samples, and on another dedicated to characterizing immune response in the more than 10,000 TCGA tumor samples. Dr. Thorsson plays a lead role in the iAtlas portal for immunoncology and in the data coordinating center of the NCI Human Tumor Atlas Network.

Computational Biology, Immunooncology

PhD, Physics, Stony Brook University, 1992

[1]

C.-K. Mo et al., “Tumour evolution and microenvironment interactions in 2D and 3D space,” Nature, vol. 634, no. 8036, pp. 1178–1186, 2024, doi: 10.1038/s41586-024-08087-4. Cite Download

[1]

V. Thorsson et al., “927 Streamlining cancer immunotherapy research with the CRI iAtlas data resource and web portal,” J Immunother Cancer, vol. 10, no. Suppl 2, 2022, doi: 10.1136/jitc-2022-SITC2022.0927. Cite Download

[1]

R. W. Sayaman et al., “Analytic pipelines to assess the relationship between immune response and germline genetics in human tumors,” STAR Protoc, vol. 3, no. 4, p. 101809, 2022, doi: 10.1016/j.xpro.2022.101809. Cite Download

[1]

D. Bortone et al., “508 Generalizability of predictive versus prognostic indicators from published transcriptomic associations with tumor response to immune checkpoint inhibition,” J Immunother Cancer, vol. 10, no. Suppl 2, 2022, doi: 10.1136/jitc-2022-SITC2022.0508. Cite Download

[1]

L. Mok et al., “9 A pan-cancer multi-omic immune single-cell atlas for cancer immunotherapy: focus on CD4+ T cells,” J Immunother Cancer, vol. 10, no. Suppl 2, 2022, doi: 10.1136/jitc-2022-SITC2022.0009. Cite Download

[1]

A. J. Taylor et al., “Abstract 2131: Curating cartography: Enabling the harmonisation, visualisation, and reuse of diverse multiplexed imaging data through the Human Tumor Atlas Network Data Coordinating Center,” Cancer Research, vol. 82, no. 12_Supplement, p. 2131, 2022, doi: 10.1158/1538-7445.AM2022-2131. Cite

[1]

P. Middha, R. W. Sayaman, M. Saad, V. Thorsson, D. Bedognetti, and E. Ziv, “Multi-tissue Transcriptome-wide Association Study Identifies 12 Novel Candidate Genes Associated with the Immune Traits in Cancer,” in GENETIC EPIDEMIOLOGY, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2022, pp. 518–518. Cite

[1]

S. Sherif et al., “The immune landscape of solid pediatric tumors,” J Exp Clin Cancer Res, vol. 41, no. 1, p. 199, 2022, doi: 10.1186/s13046-022-02397-z. Cite

[1]

D. Schapiro et al., “MITI minimum information guidelines for highly multiplexed tissue images,” Nat Methods, vol. 19, no. 3, pp. 262–267, 2022, doi: 10.1038/s41592-022-01415-4. Cite Download

[1]

D. L. Gibbs, B. Aguilar, V. Thorsson, A. V. Ratushny, and I. Shmulevich, “Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer,” Front Genet, vol. 12, p. 667382, 2021, doi: 10.3389/fgene.2021.667382. Cite Download

[1]

R. W. Sayaman et al., “Germline genetic contribution to the immune landscape of cancer,” Immunity, vol. 54, no. 2, pp. 367-386.e8, 2021, doi: 10.1016/j.immuni.2021.01.011. Cite Download

[1]

J. A. Eddy et al., “CRI iAtlas: an interactive portal for immuno-oncology research,” F1000Res, vol. 9, p. 1028, 2020, doi: 10.12688/f1000research.25141.1. Cite Download

[1]

A. Cesano et al., “Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II-practical challenges,” J Immunother Cancer, vol. 8, no. 2, 2020, doi: 10.1136/jitc-2020-001472. Cite Download

[1]

S. Rutella et al., “Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I-conceptual challenges,” J Immunother Cancer, vol. 8, no. 2, 2020, doi: 10.1136/jitc-2020-001389. Cite Download

[1]

S. Derks et al., “Characterizing diversity in the tumor-immune microenvironment of distinct subclasses of gastroesophageal adenocarcinomas,” Ann. Oncol., 2020, doi: 10.1016/j.annonc.2020.04.011. Cite

[1]

V. Thorsson, “Multiplatform Integrative Analysis of Immunogenomic Data for Biomarker Discovery,” Methods Mol. Biol., vol. 2055, pp. 679–698, 2020, doi: 10.1007/978-1-4939-9773-2_30. Cite

[1]

V. Thorsson, “Multiplatform Integrative Analysis of Immunogenomic Data for Biomarker Discovery,” Methods Mol. Biol., vol. 2055, pp. 679–698, 2019, doi: 10.1007/978-1-4939-9773-2_30. Cite

[1]

K. Möller et al., “MITF has a central role in regulating starvation-induced autophagy in melanoma,” Sci Rep, vol. 9, no. 1, p. 1055, 2019, doi: 10.1038/s41598-018-37522-6. Cite

[1]

K. A. Hoadley et al., “Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer,” Cell, vol. 173, no. 2, pp. 291-304.e6, 2018, doi: 10.1016/j.cell.2018.03.022. Cite

[1]

J. Liu et al., “An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics,” Cell, vol. 173, no. 2, pp. 400-416.e11, 2018, doi: 10.1016/j.cell.2018.02.052. Cite Download

[1]

V. Thorsson et al., “The Immune Landscape of Cancer,” Immunity, vol. 48, no. 4, pp. 812-830.e14, 2018, doi: 10.1016/j.immuni.2018.03.023. Cite Download

[1]

J. Hmeljak et al., “Integrative Molecular Characterization of Malignant Pleural Mesothelioma,” Cancer Discov, 2018, doi: 10.1158/2159-8290.CD-18-0804. Cite

[1]

H. Shen et al., “Integrated Molecular Characterization of Testicular Germ Cell Tumors,” Cell Reports, vol. 23, no. 11, pp. 3392–3406, 2018, doi: 10.1016/j.celrep.2018.05.039. Cite Download

[1]

J. Saltz et al., “Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images,” Cell Reports, vol. 23, no. 1, pp. 181-193.e7, 2018, doi: 10.1016/j.celrep.2018.03.086. Cite Download

[1]

Y. Liu et al., “Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas,” Cancer Cell, vol. 33, no. 4, pp. 721-735.e8, 2018, doi: 10.1016/j.ccell.2018.03.010. Cite

[1]

L. Ding et al., “Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics,” Cell, vol. 173, no. 2, pp. 305-320.e10, 2018, doi: 10.1016/j.cell.2018.03.033. Cite

[1]

Z. Wang et al., “Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor,” Nat. Genet., vol. 49, no. 7, pp. 1141–1147, 2017, doi: 10.1038/ng.3879. Cite

[1]

V. Kytola et al., “Mutational Landscapes of Smoking-Related Cancers in Caucasians and African Americans: Precision Oncology Perspectives at Wake Forest Baptist Comprehensive Cancer Center,” Theranostics, vol. 7, no. 11, pp. 2914–2923, 2017, doi: 10.7150/thno.20355. Cite

[1]

M. C. Camargo et al., “Validation and calibration of next-generation sequencing to identify Epstein-Barr virus-positive gastric cancer in The Cancer Genome Atlas,” Gastric Cancer, vol. 19, no. 2, pp. 676–681, 2016, doi: 10.1007/s10120-015-0508-x. Cite Download

[1]

B. Bernard, V. Thorsson, H. Rovira, and I. Shmulevich, “Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology,” PloS one, vol. 7, no. 8, p. e42779, 2012. Cite

[1]

M. Nykter, H. Lahdesmaki, A. Rust, V. Thorsson, and I. Shmulevich, “A data integration framework for prediction of transcription factor targets,” Ann N Y Acad Sci, vol. 1158, pp. 205–14, Mar. 2009. Cite

[1]

N. T. Thuong et al., “Identification of tuberculosis susceptibility genes with human macrophage gene expression profiles,” PLoS Pathog, vol. 4, no. 12, p. e1000229, Dec. 2008. Cite

[1]

S. A. Ramsey et al., “Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics,” PLoS Comput Biol, vol. 4, no. 3, p. e1000021, Mar. 2008. Cite

[1]

G. W. Carter, V. Thorsson, and T. Galitski, “Integrated Network Modeling of Molecular and Genetic Interactions,” in Sourcebook of Models for Biomedical Research, Humana Press, 2008, pp. 67–74. Cite

[1]

M. Korb et al., “The Innate Immune Database (IIDB),” BMC Immunol, vol. 9, p. 7, 2008. Cite

[1]

R. Bonneau et al., “A predictive model for transcriptional control of physiology in a free living cell,” Cell, vol. 131, no. 7, pp. 1354–65, Dec. 2007. Cite

[1]

G. W. Carter et al., “Prediction of phenotype and gene expression for combinations of mutations,” Mol Syst Biol, vol. 3, p. 96, 2007. Cite

[1]

M. Gilchrist et al., “Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4,” Nature, vol. 441, no. 7090, pp. 173–8, May 2006. Cite

[1]

R. Bonneau et al., “The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo,” Genome Biol, vol. 7, no. 5, p. R36, May 2006. Cite

[1]

S. Ramsey et al., “Transcriptional noise and cellular heterogeneity in mammalian macrophages,” Philos Trans R Soc Lond B Biol Sci, vol. 361, no. 1467, pp. 495–506, Mar. 2006. Cite

[1]

V. Thorsson, M. Hornquist, A. F. Siegel, and L. Hood, “Reverse engineering galactose regulation in yeast through model selection,” Stat Appl Genet Mol Biol, vol. 4, p. Article28, 2005. Cite

[1]

D. J. Reiss, I. Avila-Campillo, V. Thorsson, B. Schwikowski, and T. Galitski, “Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection,” BMC Bioinformatics, vol. 6, p. 154, 2005. Cite

[1]

B. L. Drees et al., “Derivation of genetic interaction networks from quantitative phenotype data,” Genome Biol, vol. 6, no. 4, p. R38, 2005. Cite

[1]

Q. Tian et al., “Integrated genomic and proteomic analyses of gene expression in Mammalian cells,” Mol Cell Proteomics, vol. 3, no. 10, pp. 960–9, Oct. 2004. Cite

[1]

K. D. Smith and V. Thorsson, “Exploring Toll-Like Receptor Regulation of Innate Immunity with the Tools of Systems Biology,” Current Genomics, vol. 5, no. 7, pp. 545–557, 2004. Cite

[1]

C. Yoo, V. Thorsson, and G. F. Cooper, “Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data,” Pac Symp Biocomput, pp. 498–509, 2002. Cite

[1]

T. Ideker et al., “Integrated genomic and proteomic analyses of a systematically perturbed metabolic network,” Science, vol. 292, no. 5518, pp. 929–34, May 2001. Cite

[1]

W. V. Ng et al., “Genome sequence of Halobacterium species NRC-1,” Proc Natl Acad Sci U S A, vol. 97, no. 22, pp. 12176–81, Oct. 2000. Cite

Vesteinn Thorsson

at the Institute for Systems Biology

Vesteinn Thorsson

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