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.

Highlights

  • Cancer cells develop in the extracellular matrix (ECM) surrounded by a variety of non-malignant cells, such as fibroblasts, vascular cells, leukocytes, and bioactive substances such as chemokines and cytokines

  • We used an in silico method to find genes that are preferentially expressed in four lymphocyte cell types: B cells, CD8+ T cells (CTL), CD4+ T helper 1 (Th1), and CD4+ T helper 2 (Th2) cells (Figure 1)

  • The top significantly enriched terms are predominantly immune related and are representative of the action of each lymphocyte subset (Figure 2A, Supplementary Table 3), with B cell activation enriched for the B cell marker, cell defense terms such as cytolysis for CD8+ cytotoxic -T lymphocytes (CTL), and many T cell activation terms enriched for the T-helper cells

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Summary

Introduction

Cancer cells develop in the extracellular matrix (ECM) surrounded by a variety of non-malignant cells, such as fibroblasts, vascular cells, leukocytes, and bioactive substances such as chemokines and cytokines. Together these cells and substances of the host form an environment conducive to carcinogenesis [1,2,3]. Lymphocytes, while generally having a positive effect, exert pro-tumor or anti-tumor functions in a tissue and cancer specific manner [13] This effect is largely due to their plasticity [14,15]

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