Evaluation of Orbital Lymphoproliferative and Inflammatory Disorders by Gene Expression Analysis

Abstract

Non-specific orbital inflammation (NSOI) and IgG4-related orbital disease (IgG4-ROD) are often challenging to differentiate. Furthermore, it is still uncertain how chronic inflammation, such as IgG4-ROD, can lead to mucosa-associated lymphoid tissue (MALT) lymphoma. Therefore, we aimed to evaluate the diagnostic value of gene expression analysis to differentiate orbital autoimmune diseases and elucidate genetic overlaps. First, we established a database of NSOI, relapsing NSOI, IgG4-ROD and MALT lymphoma patients of our orbital center (2000−2019). In a consensus process, three typical patients of the above mentioned three groups (mean age 56.4 ± 17 years) at similar locations were selected. Afterwards, RNA was isolated using the RNeasy FFPE kit (Qiagen) from archived paraffin-embedded tissues. The RNA of these 12 patients were then subjected to gene expression analysis (NanoString nCounter®), including a total of 1364 target genes. The most significantly upregulated and downregulated genes were used for a machine learning algorithm to distinguish entities. This was possible with a high probability (p < 0.0001). Interestingly, gene expression patterns showed a characteristic overlap of lymphoma with IgG4-ROD and NSOI. In contrast, IgG4-ROD shared only altered expression of one gene regarding NSOI. To validate our potential biomarker genes, we isolated the RNA of a further 48 patients (24 NSOI, 11 IgG4-ROD, 13 lymphoma patients). Then, gene expression pattern analysis of the 35 identified target genes was performed using a custom-designed CodeSet to assess the prediction accuracy of the multi-parameter scoring algorithms. They showed high accuracy and good performance (AUC ROC: IgG4-ROD 0.81, MALT 0.82, NSOI 0.67). To conclude, genetic expression analysis has the potential for faster and more secure differentiation between NSOI and IgG4-ROD. MALT-lymphoma and IgG4-ROD showed more genetic similarities, which points towards progression to lymphoma.

Keywords: IgG4-ROD; IgG4-related orbitopathy; MALT; idiopathic orbital inflammation; lymphoma; non-specific orbital inflammation; pseudotumor orbitae.

Figure 1

Clinical symptoms present in our index population stratified for each disease entity and subtype of disease.

Figure 2

Venn diagram showing the significantly expressed genes for each group and the overlap between the entities. Whereas NSOI and lymphoma as well as IgG4 and lymphoma showed many overlaps, only one overlap could be identified for IgG4 and NSOI.

Figure 3

Decision-tree-based analysis using the “conditional inference tree” (CIT) machine learning algorithm regarding the different groups revealed a three-tier system based on (1) PLA2G2A (p = 0.017), (2) RBM47 (p = 0.024), and (3) AQP1 (p = 0.026) expression levels.

Figure 4

Decision-tree-based analysis using the “conditional inference tree” (CIT) machine learning algorithm revealed significant differences between the three groups. Based on these differently up- and downregulated genes, a logistic regression model was used to differentiate IgG4-ROD (A), MALT lymphoma (C), and NSOI (D) with a high probability. Volcano plots for IgG4-ROD (B), MALT lymphoma (E), and NSOI (F) shows the most significantly differentially expressed genes.

Figure 5

Validation of our logistic regression model based on the differently up- and downregulated genes. The validation cohort compromises 48 independent samples. ROC analysis of group-prediction for IgG4-ROD (A) and MALT lymphoma (B). Synopsis of all three scores (C).