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Genome Biol. 2016 Aug 22;17(1):174. doi: 10.1186/s13059-016-1028-7.

Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.

Author information

1
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, 450 Brookline Ave., Boston, MA, 02215, USA.
2
Department of Statistics, Harvard University, 1 Oxford St., Cambridge, MA, 02138, USA.
3
Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St., Boston, MA, 02215, USA.
4
State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, 14 Renmin South Rd 3rd Section, Wuhou, Chengdu, Sichuan, 610041, China.
5
Center for Epigenetics, Van Andel Research Institute, 333 Bostwick Ave N.E., Grand Rapids, MI, 49503, USA.
6
Department of Statistics, Harvard University, 1 Oxford St., Cambridge, MA, 02138, USA. jliu@stat.harvard.edu.
7
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, 450 Brookline Ave., Boston, MA, 02215, USA. xsliu@jimmy.harvard.edu.

Abstract

BACKGROUND:

Understanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor-immune interactions in cancers.

RESULTS:

We analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER .

CONCLUSIONS:

We develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.

KEYWORDS:

Cancer immunity; Cancer immunotherapies; Cancer vaccine; Checkpoint blockade; Tumor immune infiltration

PMID:
27549193
PMCID:
PMC4993001
DOI:
10.1186/s13059-016-1028-7
[Indexed for MEDLINE]
Free PMC Article

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