DNA methylation-based machine learning classification distinguishes pleural mesothelioma from chronic pleuritis, pleural carcinosis, and pleomorphic lung carcinomas

Genome-wide DNA methylation analysis reveals a unique methylation pattern for pleural mesothelioma compared to healthy pleura and other lung diseases

BACKGROUND

Pleural mesothelioma (PM) is a rare and aggressive cancer type, typically diagnosed at advanced stages. Distinguishing PM from other lung diseases is often challenging. There is an urgent need for biomarkers that can enable early detection. Interest in the field of epigenetics has increased, particularly in the context of tumour development and biomarker discovery. This study aims to identify specific changes in DNA methylation from healthy pleural tissue to PM and to compare these methylation patterns with those found in other lung diseases.

RESULTS

EPIC methylation array data (850 K) were generated for 11 PM and 29 healthy pleura in-house collected samples. This is the first time such a large dataset of healthy pleura samples has been generated. Additional EPIC methylation array data (850 K) for pleural mesothelioma and other lung-related diseases were downloaded from public databases. We conducted pairwise differential methylation analyses across all tissue types, which facilitated the identification of significantly differentially methylated CpG sites. Extensive differential methylation between PM and healthy pleura was observed, identifying 81,968 differentially methylated CpG sites across all genomic regions. Among these, five CpG sites located within four genes (MIR21, RNF39, SPEN and C1orf101) exhibited the most significant and pronounced methylation differences between PM and healthy pleura. Moreover, our analysis delineated distinct methylation patterns specific to PM subtypes. Finally, the methylation profiles of PM were distinctly different from those of other lung cancers, enabling accurate differentiation.

CONCLUSIONS

DNA methylation analyses provide a robust method for distinguishing PM from healthy pleural tissues, and specific methylation patterns exist within PM subtypes. These methylation differences underscore their importance in understanding disease progression and may serve as viable biomarkers or therapeutic targets. Moreover, differential methylation patterns between PM and other lung cancers highlights its diagnostic potential. These findings necessitate further translational studies to explore their clinical applications.

Integrative approach for classifying male tumors based on DNA methylation 450K data

Malignancies such as bladder urothelial carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and prostate adenocarcinoma significantly impact men's well-being. Accurate cancer classification is vital in determining treatment strategies and improving patient prognosis. This study introduced an innovative method that utilizes gene selection from high-dimensional datasets to enhance the performance of the male tumor classification algorithm. The method assesses the reliability of DNA methylation data to distinguish the five most prevalent types of male cancers from normal tissues by employing DNA methylation 450K data obtained from The Cancer Genome Atlas (TCGA) database. First, the chi-square test is used for dimensionality reduction and second, L1 penalized logistic regression is used for feature selection. Furthermore, the stacking ensemble learning technique was employed to integrate seven common multiclassification models. Experimental results demonstrated that the ensemble learning model utilizing multiple classification models outperformed any base classification model. The proposed ensemble model achieved an astonishing overall accuracy (ACC) of 99.2% in independent testing data. Moreover, it may present novel ideas and pathways for the early detection and treatment of future diseases.

Usefulness of NF2 hemizygous loss detected by fluorescence in situ hybridization in diagnosing pleural mesothelioma in tissue and cytology material: A multi-institutional study

OBJECTIVES

BAP1, CDKN2A, and NF2 are the most frequently altered genes in pleural mesotheliomas (PM). Discriminating PM from benign mesothelial proliferation (BMP) is sometimes challenging; it is well established that BAP1 loss, determined by immunohistochemistry (IHC), and CDKN2A homozygous deletion (HD), determined by fluorescence in situ hybridization (FISH), are useful. However, data regarding the diagnostic utility of NF2 FISH in PM is limited. Thus, we performed a multi-institutional study examining the utility of NF2 alterations determined by FISH for diagnosing PM in combination with BAP1 loss and CDKN2A HD.

MATERIALS AND METHODS

Multi-institutional PM cases, including 106 surgical and 107 cell block samples as well as 37 tissue cases of benign mesothelial proliferation (BMP) and 31 cell block cases with reactive mesothelial cells (RMC), were collected and analyzed using IHC for BAP1 and FISH for CDKN2A and NF2.

RESULTS

In PM, NF2 FISH revealed hemizygous loss (HL) in 54.7% of tissue cases (TC) and 49.5% of cell block cases (CBC), with about 90% of HL being monosomy. CDKN2A HD or BAP1 loss were detected in 75.5%/65.4% TC or 63.6%/60% CBC, respectively. BMP or RMC showed no BAP1 loss, CDKN2A HD, or NF2 HL. For discriminating PM from BMP, a combination of BAP1 loss, CDKN2A HD, and NF2 HL yielded enhanced sensitivity of 98.1% TC/94.4% CBC. BAP1 loss, CDKN2A HD, or NF2 HL were observed in 69%, 70%, or 58% of epithelioid PM, but in 9%, 91%, or 27% of sarcomatoid PM, respectively. Histotype, histological gradings, and CDKN2A deletion status showed significant differences in overall survival, while BAP1 loss and NF2 HL did not.

CONCLUSION

NF2 HL, consisting predominantly of monosomy, can be detected by FISH in both TC and CBC of PM, and is effective for distinguishing PM from BMP, especially when combined with BAP1 loss and CDKN2A HD.