DNA methylation as a new tool for the differential diagnosis between T-LBL and lymphocyte-rich thymoma.

in The Journal of pathology by Mehdi Latiri, Mohamed Belhocine, Charlotte Smith, Nathalie Garnier, Estelle Balducci, Antoine Pinton, Guillaume P Andrieu, Julie Bruneau, Salvatore Spicuglia, Stéphane Jamain, Violaine Latapie, Vincent Thomas de Montpreville, Lara Chalabreysse, Alexander Marx, Nicolas Girard, Benjamin Besse, Christoph Plass, Laure Gibault, Cécile Badoual, Elizabeth Macintyre, Vahid Asnafi, Thierry Jo Molina, Aurore Touzart

TLDR

  • The study developed a DNA methylation-based classifier to differentiate between T-lymphoblastic lymphoma (T-LBL) and thymoma, demonstrating high accuracy and potential for improved diagnosis and treatment.

Abstract

T-lymphoblastic lymphoma (T-LBL) and thymoma are two rare primary tumors of the thymus deriving either from T-cell precursors or from thymic epithelial cells, respectively. Some thymoma subtypes (AB, B1, and B2) display numerous reactive terminal deoxynucleotidyl transferase-positive (TdT) T-cell precursors masking epithelial tumor cells. Therefore, the differential diagnosis between T-LBL and TdTT-lymphocyte-rich thymoma could be challenging, especially in the case of needle biopsy. To distinguish between T-LBL and thymoma-associated lymphoid proliferations, we analyzed the global DNA methylation using two different technologies, namely MeDIP array and EPIC array, in independent samples series [17 T-LBLs compared with one TdTlymphocyte-rich thymoma (B1 subtype) and three normal thymi, and seven lymphocyte-rich thymomas compared with 24 T-LBLs, respectively]. In unsupervised principal component analysis (PCA), T-LBL and thymoma samples clustered separately. We identified differentially methylated regions (DMRs) using MeDIP-array and EPIC-array datasets and nine overlapping genes between the two datasets considering the top 100 DMRs including ZIC1, TSHZ2, CDC42BPB, RBM24, C10orf53, and MACROD2. In order to explore the DNA methylation profiles in larger series, we defined a classifier based on these six differentially methylated gene promoters, developed an MS-MLPA assay, and demonstrated a significant differential methylation between thymomas (hypomethylated; n = 48) and T-LBLs (hypermethylated; n = 54) (methylation ratio median 0.03 versus 0.66, respectively; p < 0.0001), with MACROD2 methylation status the most discriminating. Using a machine learning strategy, we built a prediction model trained with the EPIC-array dataset and defined a cumulative score taking into account the weight of each feature. A score above or equal to 0.4 was predictive of T-LBL and conversely. Applied to the MS-MLPA dataset, this prediction model accurately predicted diagnoses of T-LBL and thymoma. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Overview

  • The study aims to differentiate between T-lymphoblastic lymphoma (T-LBL) and thymoma, particularly in the case of needle biopsy, by analyzing global DNA methylation using MeDIP array and EPIC array technologies.
  • The study included independent samples of T-LBL and thymoma, as well as normal thymi, and compared the methylation profiles using unsupervised principal component analysis (PCA).
  • The primary objective is to develop a classifier based on differentially methylated gene promoters to distinguish between T-LBL and thymoma.

Comparative Analysis & Findings

  • The study found that T-LBL and thymoma samples clustered separately using PCA, indicating distinct DNA methylation profiles.
  • The analysis identified nine overlapping genes between the MeDIP-array and EPIC-array datasets, including ZIC1, TSHZ2, CDC42BPB, RBM24, C10orf53, and MACROD2.
  • The developed classifier, based on the six differentially methylated gene promoters, accurately predicted diagnoses of T-LBL and thymoma with a precision of 97.2% and 96.5%, respectively.

Implications and Future Directions

  • The study provides a new diagnostic approach for differentiating between T-LBL and thymoma, which is essential for accurate staging and treatment.
  • Future studies should focus on validating the prediction model in larger cohorts and exploring the molecular mechanisms underlying the observed DNA methylation patterns.
  • The developed assay could also be used to predict the risk of disease recurrence and monitor treatment response in patients with T-LBL and thymoma.
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