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Oncotarget. 2017 Jul 7;8(34):57121-57133. doi: 10.18632/oncotarget.19078. eCollection 2017 Aug 22.

Data-driven analysis of immune infiltrate in a large cohort of breast cancer and its association with disease progression, ER activity, and genomic complexity.

Author information

1
Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.
2
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
3
Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
4
Department of Clinical Molecular Oncology, Division of Medicine, Akershus University Hospital, Ahus, Norway.
5
Lady Davis Institute for Medical Research, McGill University, Montreal, Quebec, Canada.
6
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
7
Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway.
8
Department of Oncology, Division for Surgery, Cancer, and Transplantation, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway.
9
Flatiron Institute, Simons Foundation, New York, New York, United States of America.

Abstract

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.

KEYWORDS:

breast cancer; immune infiltration; immune profiling; lymphocyte infiltration; normal breast tissue

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