The Immune Landscape of Cancer

doi:10.1016/j.immuni.2018.03.023

The Immune Landscape of Cancer

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4155 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	812-830.e14
Journal	Immunity
Volume	48
Issue number	4
DOIs	https://doi.org/10.1016/j.immuni.2018.03.023
State	Published - Apr 17 2018

Bibliographical note

Publisher Copyright:
© 2018 The Authors

Funding

We are grateful to all the patients and families who contributed to this study. We also thank the Office of Cancer Genomics at the NCI for organizational and logistical support of this study. The high-throughput analyses in this study were performed on the Institute for Systems Biology-Cancer Genomics Cloud (ISB-CGC) under contract number HHSN261201400007C and on the Seven Bridges Cancer Genomics Cloud under contract HHSN261201400008C, with federal funds from the National Cancer Institute, NIH, Department of Health and Human Services. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from NCI through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, and P30 CA016672. The study was supported by W81XWH-12-2-0050, HU0001-16-2-0004 from the US Department of Defense through the Henry M. Jackson Foundation for the Advancement of Military Medicine. We thank Peter Hammerman and Yasin Şenbabaoğlu for contributions in early phases of this work.

Funders	Funder number
Henry M. Jackson Foundation
Office of Cancer Genomics
National Computational Infrastructure
National Institutes of Health (NIH)
U.S. Department of Health and Human Services
U.S. Department of Defense
National Childhood Cancer Registry – National Cancer Institute	P30CA016086, U24CA143858, P30CA124435, P30CA045508, ZIACP010212, R01CA163722, U24CA143882, R50CA221675, R35CA197745, U24CA143843, U24CA143866, U24CA143867, U24CA143845, P50CA058223, U24CA143883, U24CA143840, P30CA016672, U24CA143848, U24CA210949, R37CA214955, U54CA209997, U24CA210957, U24CA144025, U24CA210950, U24CA143799, K24CA169004, U24CA210990, U24CA143835, U24CA180924
NIH Office of the Director	S10OD021764, S10OD012351
ISB-CGC	HHSN261201400008C, HHSN261201400007C
National Institutes of Health/National Institute of Environmental Health Sciences	P30ES010126
National Human Genome Research Institute	U54HG003273, U54HG003079, U54HG003067
Children's Cancer Research Institute, Vienna	U24 CA143835, U24 CA143845, U24 CA143867, U24 CA143848, U24 CA143858, U54 HG003273, U54 HG003067, U54 HG003079, HU0001-16-2-0004, P30 CA016672, W81XWH-12-2-0050, U24 CA143866, U24 CA143799, U24 CA143843, U24 CA143882, U24 CA143840, U24 CA143883, U24 CA144025
U.S. National Library of Medicine	R01LM009239

Keywords

cancer genomics
immune subtypes
immuno-oncology
immunomodulatory
immunotherapy
integrative network analysis
tumor immunology
tumor microenvironment

ASJC Scopus subject areas

Immunology and Allergy
Immunology
Infectious Diseases

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.immuni.2018.03.023

Cite this

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title = "The Immune Landscape of Cancer",

abstract = "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.",

keywords = "cancer genomics, immune subtypes, immuno-oncology, immunomodulatory, immunotherapy, integrative network analysis, tumor immunology, tumor microenvironment",

author = "V{\'e}steinn Thorsson and Gibbs, \{David L.\} and Brown, \{Scott D.\} and Denise Wolf and Bortone, \{Dante S.\} and \{Ou Yang\}, \{Tai Hsien\} and Eduard Porta-Pardo and Gao, \{Galen F.\} and Plaisier, \{Christopher L.\} and Eddy, \{James A.\} and Elad Ziv and Culhane, \{Aedin C.\} and Paull, \{Evan O.\} and Sivakumar, \{I. K.Ashok\} and Gentles, \{Andrew J.\} and Raunaq Malhotra and Farshad Farshidfar and Antonio Colaprico and Parker, \{Joel S.\} and Mose, \{Lisle E.\} and Vo, \{Nam Sy\} and Jianfang Liu and Yuexin Liu and Janet Rader and Varsha Dhankani and Reynolds, \{Sheila M.\} and Reanne Bowlby and Andrea Califano and Cherniack, \{Andrew D.\} and Dimitris Anastassiou and Davide Bedognetti and Arvind Rao and Ken Chen and Alexander Krasnitz and Hai Hu and Malta, \{Tathiane M.\} and Houtan Noushmehr and Pedamallu, \{Chandra Sekhar\} and Susan Bullman and Ojesina, \{Akinyemi I.\} and Andrew Lamb and Wanding Zhou and Hui Shen and Choueiri, \{Toni K.\} and Weinstein, \{John N.\} and Justin Guinney and Joel Saltz and Holt, \{Robert A.\} and Rabkin, \{Charles E.\} and Therese Bocklage",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors",

year = "2018",

month = apr,

day = "17",

doi = "10.1016/j.immuni.2018.03.023",

language = "English",

volume = "48",

pages = "812--830.e14",

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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 - Zhou, Wanding

AU - Shen, Hui

AU - Choueiri, Toni K.

AU - Weinstein, John N.

AU - Guinney, Justin

AU - Saltz, Joel

AU - Holt, Robert A.

AU - Rabkin, Charles E.

AU - Bocklage, Therese

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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

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VL - 48

SP - 812-830.e14

JO - Immunity

JF - Immunity

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ER -

The Immune Landscape of Cancer

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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