TY - JOUR
T1 - The Immune Landscape of Cancer
AU - The Cancer Genome Atlas Research Network
AU - Thorsson, Vésteinn
AU - Gibbs, David L.
AU - Brown, Scott D.
AU - Wolf, Denise
AU - Bortone, Dante S.
AU - Ou Yang, Tai Hsien
AU - Porta-Pardo, Eduard
AU - Gao, Galen F.
AU - Plaisier, Christopher L.
AU - Eddy, James A.
AU - Ziv, Elad
AU - Culhane, Aedin C.
AU - Paull, Evan O.
AU - Sivakumar, I. K.Ashok
AU - Gentles, Andrew J.
AU - Malhotra, Raunaq
AU - Farshidfar, Farshad
AU - Colaprico, Antonio
AU - Parker, Joel S.
AU - Mose, Lisle E.
AU - Vo, Nam Sy
AU - Liu, Jianfang
AU - Liu, Yuexin
AU - Rader, Janet
AU - Dhankani, Varsha
AU - Reynolds, Sheila M.
AU - Bowlby, Reanne
AU - Califano, Andrea
AU - Cherniack, Andrew D.
AU - Anastassiou, Dimitris
AU - Bedognetti, Davide
AU - Rao, Arvind
AU - Chen, Ken
AU - Krasnitz, Alexander
AU - Hu, Hai
AU - Malta, Tathiane M.
AU - Noushmehr, Houtan
AU - Pedamallu, Chandra Sekhar
AU - Bullman, Susan
AU - Ojesina, Akinyemi I.
AU - Lamb, Andrew
AU - Wilkerson, Matthew D.
AU - Deyarmin, Brenda
AU - Hu, Hai
AU - Kvecher, Leonid
AU - Somiari, Stella
AU - Fantacone-Campbell, J. Leigh
AU - Hooke, Jeffrey A.
AU - Kovatich, Albert J.
AU - Shriver, Craig D.
N1 - Publisher Copyright:
© 2018 The Authors
PY - 2018/4/17
Y1 - 2018/4/17
N2 - We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy.
AB - We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy.
KW - cancer genomics
KW - immune subtypes
KW - immuno-oncology
KW - immunomodulatory
KW - immunotherapy
KW - integrative network analysis
KW - tumor immunology
KW - tumor microenvironment
UR - http://www.scopus.com/inward/record.url?scp=85044934017&partnerID=8YFLogxK
U2 - 10.1016/j.immuni.2018.03.023
DO - 10.1016/j.immuni.2018.03.023
M3 - Article
C2 - 29628290
AN - SCOPUS:85044934017
SN - 1074-7613
VL - 48
SP - 812-830.e14
JO - Immunity
JF - Immunity
IS - 4
ER -