PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Epigenetic quantification of tumor-infiltrating T-lymphocytes
Jalid Sehouli, Christoph Loddenkemper, Tatjana Cornu, Tim Schwachula, Ulrich Hoffmüller, Andreas Grützkau, Philipp Lohneis, Thorsten Dickhaus, Jörn Gröne, Martin Kruschewski, Alexander Mustea, Ivana Turbachova, Udo Baron and Sven Olek
Epigenetics Volume 6, Number 2, pp. 236-246, 2011.

Abstract

The immune system plays a pivotal role in tumor establishment. However, the role of T-lymphocytes within the tumor microenvironment as major cellular component of the adaptive effector immune response and their counterpart, regulatory T-cells (Treg), responsible for suppressive immune modulation, is not completely understood. This is partly due to the lack of reliable technical solutions for specific cell quantification in solid tissues. Previous reports indicated that epigenetic marks of immune cells, such as the Treg specifically demethylated region (TSDR) within the FOXP3 gene, may be exploited as robust analytical tool for Treg-quantification. Here, we expand the concept of epigenetic immunophenotyping to overall T-lymphocytes (oTL). This tool allows cell quantification with at least equivalent precision to FACS and is adoptable for analysis of blood and solid tissues. Based on this method, we analyze the frequency of Treg, oTL and their ratio in independent cohorts of healthy and tumorous ovarian, colorectal and bronchial tissues with 616 partly donor-matched samples. We find a shift of the median ratio of Treg-to-oTL from 3–8% in healthy tissue to 18–25% in all tumor entities. Epigenetically determined oTL frequencies correlate with the outcome of colorectal and ovarian cancers. Together, our data show that the composition of immune cells in tumor microenvironments can be quantitatively assessed by epigenetic measurements. This composition is disturbed in solid tumors, indicating a fundamental mechanism of tumor immune evasion. Epigenetic quantification of T-lymphocytes serves as independent clinical parameter for outcome prognosis.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:7864
Deposited By:Thorsten Dickhaus
Deposited On:17 March 2011