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Updated: Aug 2, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA
W de Koning1,2, F F Feenstra3, F G J Calkoen3
1Tumor Immuno-Pathology Laboratory, Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Background:
Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.
Results:
Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the PTPRC gene, known as CD45, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.
Conclusions:
The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells.
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