DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours.

BACKGROUND: Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. METHODS: We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier. FINDINGS: On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. INTERPRETATION: We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. FUNDING: This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

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

OBJECTIVES: Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. MATERIAL AND METHODS: DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we performed differential methylation analysis and used a deconvolution method to estimate the composition of the tumor microenvironment. RESULTS: T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. In a nested cross validation on the training cohort, both machine learning algorithms achieved the same accuracies (94.8%). On the validation cohort, we observed high accuracies for the support vector machine (97.8%) while the random forest performed considerably worse (89.5%), especially in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. CONCLUSION: DNA methylation in combination with machine learning classifiers is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

Comparative investigation of cell cycle and immunomodulatory genes in mucosal and cutaneous melanomas: Preliminary data suggest a potential promising clinical role for p16 and the PD-1/PD-L1 axis.

Mucosal melanomas arise from the mucosal lining of various organs. Their etiology is currently unknown and there are no tissue-based methods to differentiate it from cutaneous melanomas. Furthermore, prognostic and predictive markers (e.g. for immune checkpoint inhibition) are lacking. In this study, we aimed to assess the protein expression levels of cell cycle-associated proteins and immune checkpoint markers in a cohort of mucosal melanomas in comparison to cutaneous melanomas and evaluated the effect of potential regulatory mechanisms. We performed immunohistochemistry, DNA methylation analysis and copy number profiling of 47 mucosal and 28 cutaneous melanoma samples. Protein expression of CD117, Ki67 and p16 was higher in mucosal melanomas, while BCL2, Cyclin D1, PD-1 and PD-L1 were overexpressed in cutaneous melanomas. CDKN2A deletions were the most prevalent numeric chromosomal alterations in both mucosal and cutaneous melanoma and were associated with decreased p16 expression. KIT was frequently amplified in mucosal melanomas, but not associated with CD117 expression. On the other hand, amplification of CCND1 lead to Cyclin D1 overexpression. In mucosal melanoma patients high PD-1 expression and high PD-L1 promoter methylation levels were associated with improved survival. PD-L1 expression correlated with response to immune checkpoint inhibitor therapy in the combined group of melanoma patients. Mucosal and cutaneous melanomas show different expression levels of cell cycle-associated and immunomodulatory proteins that are partially regulated by DNA methylation and copy number alterations. PD-1 expression and PD-L1 promoter methylation levels might be a prognostic marker for mucosal melanomas.

Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location-dependent patterns of genetic and epigenetic alterations.

Cutaneous, ocular, and mucosal melanomas are histologically indistinguishable tumors that are driven by a different spectrum of genetic alterations. With current methods, identification of the site of origin of a melanoma metastasis is challenging. DNA methylation profiling has shown promise for the identification of the site of tumor origin in various settings. Here we explore the DNA methylation landscape of melanomas from different sites and analyze if different melanoma origins can be distinguished by their epigenetic profile. We performed DNA methylation analysis, next generation DNA panel sequencing, and copy number analysis of 82 non-cutaneous and 25 cutaneous melanoma samples. We further analyzed eight normal melanocyte cell culture preparations. DNA methylation analysis separated uveal melanomas from melanomas of other primary sites. Mucosal, conjunctival, and cutaneous melanomas shared a common global DNA methylation profile. Still, we observed location-dependent DNA methylation differences in cancer-related genes, such as low frequencies of RARB (7/63) and CDKN2A promoter methylation (6/63) in mucosal melanomas, or a high frequency of APC promoter methylation in conjunctival melanomas (6/9). Furthermore, all investigated melanomas of the paranasal sinus showed loss of PTEN expression (9/9), mainly caused by promoter methylation. This was less frequently seen in melanomas of other sites (24/98). Copy number analysis revealed recurrent amplifications in mucosal melanomas, including chromosomes 4q, 5p, 11q and 12q. Most melanomas of the oral cavity showed gains of chromosome 5p with TERT amplification (8/10), while 11q amplifications were enriched in melanomas of the nasal cavity (7/16). In summary, mucosal, conjunctival, and cutaneous melanomas show a surprisingly similar global DNA methylation profile and identification of the site of origin by DNA methylation testing is likely not feasible. Still, our study demonstrates tumor location-dependent differences of promoter methylation frequencies in specific cancer-related genes together with tumor site-specific enrichment for specific chromosomal changes and genetic mutations. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.