Eilís Perez’s research while affiliated with Charité Universitätsmedizin Berlin and other places

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Publications (15)


Upgrading the classifier. A Overview of the patient datasets used to develop, validate and test the classifier. B Overview of the classification pipeline. C Confusion matrix with the classifier results after applying the anomaly detection filter and the specific thresholds for the biological validation samples (n = 3579)
Off-label use of the classifier. A The two-dimensional representation using the t-SNE method based on the DNA methylation profiles of the reference cohort used to develop the classifier, to which the PAAD metastasis positive control samples were added (n = 415). B The two-dimensional representation using the t-SNE method based on the DNA methylation profiles of the reference cohort to which we added all in-house PAAD samples and in-house iCCA samples from the biological validation and the positive control samples (n = 487). C Classifier results of the positive control samples. D Confusion matrix with the classifier results after applying the anomaly detection filter and the specific thresholds for the negative control samples (n = 124, from three independent cohorts: BRCA metastases from 18 anatomical sites, n = 96; brain metastases extern, n = 13; brain metastases intern, n = 15). E Classifier results of the organoid models
DNA methylation-associated organotropism of pancreatic adenocarcinoma metastases. A The two-dimensional representation using the t-SNE method, based on the DNA methylation profiles of in-house primary PAAD (n = 16), PAAD met.PC (n = 11), and PAAD met.Liv. (n = 21). B Venn diagram comparing differentially methylated CpGs (log FC > 0.2, and adj. p value < 0.01) between primary PAAD and PAAD met.Liv. and PAAD met.PC Comparison of enhancer- (left) and promoter-associated (right) DNA methylation levels (beta values) in C PAAD met.Liv. versus PAAD met.PC, D PAAD met.PC versus primary PAAD, and E PAAD met.Liv. versus primary PAAD. Volcano plots showing significantly activated pathways of genes linked to enhancer-associated CpGs (hypomethylated) in F PAAD met.Liv. versus PAAD met.PC, G PAAD met.PC versus primary PAAD, and H PAAD met.Liv. versus primary PAAD
DNA methylation workflow for the PAAD-iCCA-Classifier. Proposed workflow for the PAAD-iCCA-Classifier for PAAD samples in a CUP-like scenario
DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites
  • Article
  • Full-text available

November 2024

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57 Reads

Clinical Epigenetics

Teodor G. Calina

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Eilís Perez

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Elena Grafenhorst

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[...]

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Background We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites. Methods For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain. Results The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition. Conclusions The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.

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DNA methylation-array interlaboratory comparison trial demonstrates highly reproducible paediatric CNS tumour classification across 13 international centres

October 2024

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28 Reads

Neuropathology and Applied Neurobiology

Aims DNA methylation profiling, recently endorsed by the World Health Organisation (WHO) as a pivotal diagnostic tool for brain tumours, most commonly relies on bead arrays. Despite its widespread use, limited data exist on the technical reproducibility and potential cross‐institutional differences. The LOGGIC Core BioClinical Data Bank registry conducted a prospective laboratory comparison trial with 12 international laboratories to enhance diagnostic accuracy for paediatric low‐grade gliomas, focusing on technical aspects of DNA methylation data generation and profile interpretation under clinical real‐time conditions. Methods Four representative low‐grade gliomas of distinct histologies were centrally selected, and DNA extraction was performed. Participating laboratories received a DNA aliquot and performed the DNA methylation‐based classification and result interpretation without knowledge of tumour histology. Additionally, participants were required to interpret the copy number profile derived from DNA methylation data and conduct DNA sequencing of the BRAF hotspot p.V600 due to its relevance for low‐grade gliomas. Results had to be returned within 30 days. Results High technical reproducibility was observed, with a median pairwise correlation of 0.99 (range 0.94–0.99) between coordinating laboratory and participants. DNA methylation‐based tumour classification and copy number profile interpretation were consistent across all centres, and BRAF mutation status was accurately reported for all cases. Eleven out of 12 centres successfully reported their analysis within the 30‐day timeframe. Conclusion Our study demonstrates remarkable concordance in DNA methylation profiling and profile interpretation across 12 international centres. These findings underscore the potential contribution of DNA methylation analysis to the harmonisation of brain tumour diagnostics.



Patient cohort flowchart with details concerning included and excluded cases
t-SNE showing the methylation families as determined by the brain tumor classifier v12.5. Enlarged dots represent the grade 2 meningiomas analyzed in this study. Small dots show a reference cohort of 148 cases previously analyzed for better visualization and grouping [9]
Alluvial plot for the assessment of the integrated molecular-morphological risk group using the brain tumor classifier v12.5. The starting layer (bottom) is represented by the WHO grading based on the histopathological examination. All analyzed tumors underwent methylation analyses with subsequent allocation to the three methylation families (benign, intermediate, and malignant (Mal), second layer). To calculate the risk score, CNV assessment of 1p, 6q, and 14 q was additionally required (third layer). After calculation of the risk score (fourth layer), the final risk group assessment was done (fifth layer)
Local control rates based on the integrated molecular-morphological risk score utilizing the brain tumor classifier v12.5. Intermediate and high risk tumors show a significantly elevated risk for local failures while low risk meningiomas display a durable local control
Clinical implications of DNA methylation-based integrated classification of histologically defined grade 2 meningiomas

May 2024

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65 Reads

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4 Citations

Acta Neuropathologica Communications

The combination of DNA methylation analysis with histopathological and genetic features allows for a more accurate risk stratification and classification of meningiomas. Nevertheless, the implications of this classification for patients with grade 2 meningiomas, a particularly heterogeneous tumor entity, are only partially understood. We correlate the outcomes of histopathologically confirmed grade 2 meningioma with an integrated molecular-morphologic risk stratification and determine its clinical implications. Grade 2 meningioma patients treated at our institution were re-classified using an integrated risk stratification involving DNA methylation array-based data, copy number assessment and TERT promoter mutation analyses. Grade 2 meningioma cases according to the WHO 2021 criteria treated between 2007 and 2021 (n = 100) were retrospectively analyzed. The median clinical and radiographic follow-up periods were 59.8 and 54.4 months. A total of 38 recurrences and 17 deaths were observed. The local control rates of the entire cohort after 2-, 4-, and 6-years were 84.3%, 68.5%, and 50.8%, with a median local control time of 77.2 months. The distribution of the integrated risk groups were as follows: 31 low, 54 intermediate, and 15 high risk cases. In the multivariable Cox regression analysis, integrated risk groups were significantly associated with the risk of local recurrence (hazard ratio (HR) intermediate: 9.91, HR high-risk: 7.29, p < 0.01). Gross total resections decreased the risk of local tumor progression (HR gross total resection: 0.19, p < 0.01). The comparison of 1p status and integrated risk groups (low vs. intermediate/high) revealed nearly identical local control rates within their respective subgroups. In summary, only around 50% of WHO 2021 grade 2 meningiomas have an intermediate risk profile. Integrated molecular risk stratification is crucial to guide the management of patients with grade 2 tumors and should be routinely applied to avoid over- and undertreatment, especially concerning the use of adjuvant radiotherapy. Supplementary Information The online version contains supplementary material available at 10.1186/s40478-024-01739-6.


Abstract 4414: Genome-wide DNA methylation classifier diagnoses pancreatic ductal adenocarcinoma in CUP setting

March 2024

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11 Reads

Cancer Research

Introduction: The metastasis of a pancreatic ductal adenocarcinoma (PAAD) is a diagnosis of exclusion and one of the most common causes of cancer of unknown primary (CUP). We have recently developed a genome-wide DNA methylation-based neural network classifier that can accurately differentiate between liver metastasis of a PAAD and intrahepatic cholangiocarcinoma (iCCA) (PAAD-iCCA-Classifier). Therefore, the aim of our study was to test whether our PAAD-iCCA-Classifier can be extended to be used to correctly diagnose PAAD metastases from other sites in CUP setting. Methods: For this purpose, we enhanced the anomaly detection layer of the classifier by incorporating ten mimicker carcinomas to be excluded by this layer. We used a validation set 1 (n=3786) including primary PAAD (n=242), PAAD liver metastases (n=20), iCCA (n=151) and 10 other mimicker carcinomas (n=3373) and a validation set 2 (n=26) including primary PAAD from a real-life clinical cohort from an independent institution to validate the classifier. Next, we tested the classifier on 16 PAAD initially considered CUP samples (test set) from different sites: peritoneum, lung, liver, and lymph node. The clinical history and diagnostic imaging of these samples were used to confirm PAAD as the most probable origin. We further performed differentially methylated probes (DMP) and copy number alterations (CNA) analysis of primary PAAD and metastatic PAAD from different locations. Results: The improved version of the PAAD-iCCA-Classifier achieved an accuracy of 98.43% on the validation set 1 and was able to exclude most of the mimicker carcinomas. On validation set 2, the classifier achieved an accuracy of 88.46%. Medical history, imaging and immunohistochemical analysis of the test set samples confirmed the diagnosis of PAAD. The DNA methylation classifier correctly labeled 15/16 PAAD metastatic samples as PAAD (93.75% accuracy). We observed that the classifier performance was negatively affected by a high CD3+ immune infiltrate and positively affected by high tumor purity and high proliferation rate. CNA revealed that PAAD liver metastases have a distinct CNA profile characterized by chromosome 6, 9 (CDKN2A/B) and 18q (SMAD4) deletions. DMP analysis showed that PAAD liver metastases have global hypomethylation of both promoter- and enhancer-associated CpGs compared to primary PAAD and PAAD peritoneal carcinomatosis. Finally, gene ontology analysis revealed that different epithelial-mesenchymal transition pathways are activated in PAAD liver metastases compared to PAAD peritoneal carcinomatosis. Conclusion: Our tool performs well in classifying metastatic PAAD samples and could be of great clinical use when a PAAD origin is suspected in the case of a CUP. DMP and CNA profiles show that PAAD liver metastases may have a distinct DNA methylation and copy number profile compared to primary and peritoneal carcinomatosis PAAD. Citation Format: Teodor G. Calina, Eilís Perez, Simon Schallenberg, Horst David, Erik Knutsen, David Capper, Mihnea P. Dragomir. Genome-wide DNA methylation classifier diagnoses pancreatic ductal adenocarcinoma in CUP setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4414.


Figure 1. Illustration of the conumee 2.0 workflow. Data objects and commands are indicated. Gray boxes illustrate key steps of the workflow. During data preparation, summed signal intensities for query and control samples are calculated separately. The CNV analysis itself comprises tangent normalization, a genomic binning step, circular binary segmentation, and segmented block bootstrapping.
Figure 3. Performance of the revised approach. (a) Heatmap shows the pairwise correlation between SNP array-derived (x-axis) and DNA methylation array-derived (y-axis) CNVs across all 367 TCGA LUSC tumors. Highest correlation coefficients are observed for data generated from the same tumor on the diagonal. (b) Density heatmap (kernel density estimation) shows the correlation between both array types for a single query. (c) qplots generated using GISTIC 2.0 from conumee 2.0 output illustrate recurrent amplifications (left) and deletions (right), as analyzed from DNA methylation and SNP arrays. Known hallmark alterations of LUSC are indicated in red.
Figure 4. Detection of high-level alterations. Genome plot shows CNVs across a single query sample from the TCGA LUSC cohort. Indicated regions harbor a high-level amplification of EGFR and a homozygous deletion of the CDKN2A/B locus. Density plots illustrate the segmented block bootstrapping approach which allows for the calculation of empirical P-values to assess the statistical significance of identified focal CNVs.
Figure 5. Overview of plotting functionality in conumee 2.0. (a) Genome plots show CNVs for a single query sample across the whole genome (top), chromosome 10 (middle), and two detail regions (bottom). (b) Interactive genome plot generated using the CNV.plotly function which can be used to identify genes within bins of interest. (c) Summary genome plot generated using the CNV.summaryplot function illustrates the percentage of samples exhibiting CNVs across the genome for the TCGA LUSC cohort. (d) Summary genome heatmap generated using the CNV.heatmap function shows binlevel intensities across the genome for the TCGA LUSC cohort (367 samples). (e) Genome plot showing a CNV profile of a mouse sample.
Conumee 2.0: Enhanced copy-number variation analysis from DNA methylation arrays for humans and mice

January 2024

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338 Reads

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68 Citations

Bioinformatics

Motivation Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated us to create a software tool to infer CNVs from DNA methylation arrays which are often generated as part of clinical routines and in research settings. Results We present our R package, conumee 2.0, that combines tangent normalization, an adjustable genomic binning heuristic, and weighted circular binary segmentation to utilize DNA methylation arrays for CNV analysis and mitigate technical biases and batch effects. Segmentation results were validated in a lung squamous cell carcinoma dataset from TCGA (n = 367 samples) by comparison to segmentations derived from genotyping arrays (Pearson’s correlation coefficient of 0.91). We further introduce a segmented block bootstrapping approach to detect focal alternations that achieved 60.9% sensitivity and 98.6% specificity for deletions affecting CDKN2A/B (60.0% and 96.9% for RB1, respectively) in a low-grade glioma cohort from TCGA (n = 239 samples). Finally, our tool provides functionality to detect and summarize CNVs across large sample cohorts. Availability and implementation Conumee 2.0 is available under open-source license at: https://github.com/hovestadtlab/conumee2. Supplementary information Supplementary data are available at Bioinformatics online.


A Novel Type of IDH Wild-type Glioma Characterized by Gliomatosis Cerebri-like Growth Pattern, TERT Promoter Mutation, and Distinct Epigenetic Profile

September 2023

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142 Reads

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9 Citations

The American Journal of Surgical Pathology

Diffuse gliomas in adults encompass a heterogenous group of central nervous system neoplasms. In recent years, extensive (epi-)genomic profiling has identified several glioma subgroups characterized by distinct molecular characteristics, most importantly IDH1/2 and histone H3 mutations. A group of 16 diffuse gliomas classified as “adult-type diffuse high-grade glioma, IDH-wildtype, subtype F (HGG-F)” was identified by the DKFZ v12.5 Brain Tumor Classifier . Histopathologic characterization, exome sequencing, and review of clinical data was performed in all cases. Based on unsupervised t -distributed stochastic neighbor embedding and clustering analysis of genome-wide DNA methylation data, HGG-F shows distinct epigenetic profiles separate from established central nervous system tumors. Exome sequencing demonstrated frequent TERT promoter (12/15 cases), PIK3R1 (11/16), and TP53 mutations (5/16). Radiologic characteristics were reminiscent of gliomatosis cerebri in 9/14 cases (64%). Histopathologically, most cases were classified as diffuse gliomas (7/16, 44%) or were suspicious for the infiltration zone of a diffuse glioma (5/16, 31%). None of the cases demonstrated microvascular proliferation or necrosis. Outcome of 14 patients with follow-up data was better compared to IDH-wildtype glioblastomas with a median progression-free survival of 58 months and overall survival of 74 months (both P <0.0001). Our series represents a novel type of adult-type diffuse glioma with distinct molecular and clinical features. Importantly, we provide evidence that TERT promoter mutations in diffuse gliomas without further morphologic or molecular signs of high-grade glioma should be interpreted in the context of the clinicoradiologic presentation as well as epigenetic profile and may not be suitable as a standalone marker for glioblastoma, IDH-wildtype.


Fig. 1 T-distributed stochastic neighbor embedding (t-SNE) analysis of DNA methylation data of the eight cases of diffuse paediatrictype high-grade glioma, H3-wildtype and IDH-wildtype in this series together with a reference cohort of 19 different molecular tumor classes (n = 650). Reference methylation classes: a IDH HG astrocytoma, IDH-mutant, high grade (41 cases); A IDH LG astrocytoma, IDH-mutant, low grade (81 cases); AG MYB angiocentric glioma, MYB/MYBL1-altered (14 cases); DLGNT 1 diffuse leptomeningeal glioneuronal tumor, subtype 1 (6 cases); DMG K27 diffuse midline glioma, H3 K27M mutant (74 cases); GBM MES ATYP glioblastoma, IDH-wildtype, mesenchymal subtype, subclass B (novel) (3 cases); GBM MES TYP Glioblastoma, IDH-wildtype, mesenchymal subtype (52 cases); GBM RTK1 glioblastoma, IDH-wild-type, subclass RTK1 (69 cases); GBM RTK2 glioblastoma IDH-wildtype, sub-
Characteristics of eight patients with diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype
Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype: case series of a new entity

August 2023

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273 Reads

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11 Citations

Brain Tumor Pathology

Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype (pHGG) is a rare and aggressive brain tumor characterized by a specific DNA methylation profile. It was recently introduced in the 5th World Health Organization classification of central nervous system tumors of 2021. Clinical data on this tumor is scarce. This is a case series, which presents the first clinical experience with this entity. We compiled a retrospective case series on pHGG patients treated between 2015 and 2022 at our institution. Data collected include patients' clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging and adjuvant therapy. Eight pHGG were identified, ranging in age from 8 to 71 years. On MRI tumors presented with an unspecific intensity profile, T1w hypo- to isointense and T2w hyperintense, with inhomogeneous contrast enhancement, often with rim enhancement. Three patients died of the disease, with overall survival of 19, 28 and 30 months. Four patients were alive at the time of the last follow-up, 4, 5, 6 and 79 months after the initial surgery. One patient was lost to follow-up. Findings indicate that pHGG prevalence might be underestimated in the elderly population.



Fig. 1: The methylation landscape of pancreato-biliary tumours. (a) Graphical representation of the pancreato-biliary tumours and normal tissues represented in the t-SNE plot. (b) The two-dimensional representation of pancreato-biliary tumours analysed using the t-SNE method based on DNA methylation profiles. The colour of the samples represents the tumour type and their origin: iCCA-intrahepatic cholangiocarcinoma; iIPNB-intrahepatic intraductal papillary neoplasia of the bile duct; iITPN-intrahepatic intraductal tubulopapillary neoplasia of the bile duct; pCCA-perihilar cholangiocarcinoma; pIPNB-perihilar intraductal papillary neoplasia of the bile duct; pITPN-perihilar intraductal tubulopapillary neoplasia of the bile duct; eCCA-extrahepatic cholangiocarcinoma (samples that are pCCA/dCCA but no further details were available); dCCA-distal cholangiocarcinoma; dIPNB-distal intraductal papillary neoplasia of the bile duct; PAAD-pancreatic adenocarcinoma; ITPN-P-intraductal tubulopapillary neoplasia of the pancreas; normal bile; and normal pancreas. (c) The design of the methylation-based classifier that can distinguish between iCCA, PAAD liver metastases and normal bile duct samples. (d) Representative H&E images for an iCCA and a PAAD liver metastasis.
Fig. 2: An in-depth biological analysis of the reference cohort. (a) A detailed overview of the samples composing the reference cohort including the study name, number of samples, material, array, and data type. (b) The two-dimensional representation of the reference cohort samples (n = 399) using the t-SNE method based on DNA methylation profiles. The colour of the samples represents their tissue of origin. (c-i) The same t-SNE in which the colour of the samples represents: (c) tumour purity, (d) study sets, (e) IDH1/2 status, (f) KRAS status, (g) TP53 status, (h) SMAD4 status, and (i) Fluke status.
Fig. 3: Classification results of three machine learning models (random forest, support vector machine, and neural networks) on an independent validation cohort. (a) A detailed overview of the samples composing the validation cohort (n = 361) including the study name, number of samples, material, array, data type, and processing method. (b-d) Accuracy and predictable cases for different thresholds (0.5-0.95) for the random forest, support vector machine and neural networks classifiers. (e-g) Confusion matrices without filters (upper panel) and with filters (lower panel) for the three classifiers: random forest (e), support vector machine (f), and neural networks (g). (h-j) The probability score of the correct class for the classifiers (random forest (h), support vector machine (i), and neural network (j)) for three different variables: tissue of origin (iCCA, normal bile, and PAAD, left), material type (FFPE versus frozen, middle), and study set (right).
Fig. 4: Testing the classifier on our in-house clinical samples. (a) The two-dimensional representation of the reference cohort samples (n = 399) using the t-SNE method based on DNA methylation profiles to which we added the samples from the clinical test cohort (n = 72). (b) Confusion matrices with no filters (upper panel) and with filters (lower panel) for the clinical test cohort. (c) A heatmap overview of the neural networks, support vector machine, and random forest results corroborated with clinical, pathological and molecular data.
DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

June 2023

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128 Reads

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14 Citations

EBioMedicine

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).


Citations (9)


... copy number profiles were generated from the DNA methylation data and evaluated manually with consideration of the histological tumor cell content. 32,33 In general, changes were considered potentially relevant if the intensity ratio of a segment deviated from the baseline by more than 0.1 in high quality samples. 34 A profile was considered of lower quality if general bin intensity ratios dispersed around the baseline by more than 0.6 ("noise"). ...

Reference:

VGLL-fusions define a new class of intraparenchymal CNS schwannoma
Conumee 2.0: Enhanced copy-number variation analysis from DNA methylation arrays for humans and mice

Bioinformatics

... Leveraging the robust and well-validated Cell2location algorithm, combined with an extensive GB reference dataset (GBMap), we predicted the abundance of myeloid, T cell and stromal subpopulations across all methylation classes (Fig. 4a). Although cell type distributions in GB were recently delineated 15 , less common subgroups, such as high-grade astrocytoma with piloid features or diffuse high-grade neuroepithelial tumors (adult-type, subgroup F) 16 , remain understudied. Although the tumor cell state distribution across GB subtypes was relatively equal with enrichment of the mesenchymal (Mes)-like states in the methylation group Mes, high-grade astrocytoma with piloid features lacked the astrocyte (AC)-like cell populations with significant enrichment of the Mes-like, oligodendrocyte-progenitor-like and neural-progenitor-like states (Fig. 4a). ...

A Novel Type of IDH Wild-type Glioma Characterized by Gliomatosis Cerebri-like Growth Pattern, TERT Promoter Mutation, and Distinct Epigenetic Profile
  • Citing Article
  • September 2023

The American Journal of Surgical Pathology

... One case was indicative of a new tumour entity. diagnoses based on their methylome profile, demonstrating that DNA methylation analysis is essential for the classification of these tumour types (Diffuse hemispheric glioma, H3.G34-mutant (n = 5); CNS highgrade astrocytoma with piloid features, MAPK pathway altered (n = 1) [19]; Diffuse pediatric-type HGG, H3-wildtype and IDH-wildtype (n = 7) [20]; Infant type hemispheric glioma (IHG, n = 1) [21], Diffuse Leptomeningeal Glioneuronal Tumour (DLGNT, n = 3) [22]; EPN-PFA (n = 3) and EPN-PFB (n = 1) [23]; CNS tumour with BCOR internal tandem duplication (n = 2) [24], and LGG with MYB alteration (n = 1) [25]) (Fig. 3, Suppl. Table 1). ...

Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype: case series of a new entity

Brain Tumor Pathology

... Preclinical studies provide complementary evidence on the effect of sleep behavior on cancer progression and treatment efficacy. 25,26 The impact of sleep behavior on the progression of HCC is primarily related to the regulatory effects of the circadian clock on immune escape. 27, 28 Wu J et al reported that the circadian clock gene BMAL1 was associated with bevacizumab resistance and that inhibiting the expression of BMAL1 may prevent resistance to anti-angiogenic therapy in patients with colorectal cancer 29 Additionally, another study showed that the circadian clock component RORA suppressed programed cell death 1 ligand 1 (PD-L1) expression and was significantly positively correlated with T-cell infiltration and recruitment in patients with melanoma. ...

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

EBioMedicine

... The genome-wide DNA methylation pattern in cancer likely reflects both the cell type of origin and the modifications acquired over the course of tumor development [15,16]. This concept resulted in the development of a classifier based on the methylation profiles of 1,077 bone and soft tissue sarcomas as well as their normal counterparts [17], further validated by other studies [17][18][19]. In this first version of the sarcoma classifier, 75% of the samples received a molecular class, with an agreement of 91% with the original histological diagnosis, while 9% of the classifier predictions resulted in a revised diagnosis in favor of the predicted methylation class after histological review and confirmation by other molecular techniques [17]. ...

Sarcoma classification by DNA methylation profiling in clinical everyday life: the Charité experience

Clinical Epigenetics

... Whole chromosomes were classified as gained or lost if at least 90% of probe signals from both arms were above or below the threshold. For acrocentric chromosomes (13,14,15,21,22), only changes in the q-arm were considered. ...

Expanded analysis of high-grade astrocytoma with piloid features identifies an epigenetically and clinically distinct subtype associated with neurofibromatosis type 1

Acta Neuropathologica

... Additionally, one study found no differences between GHPAs and NFPAs, but did find genes C7orf50, GNG7, and BAHCC1 to be associated with postoperative progression [114]. Tumors from the posterior pituitary lobe showed only minor methylation differences [115]. ...

Genetic and epigenetic characterization of posterior pituitary tumors

Acta Neuropathologica

... Sınırlı tip astrositik tümörlerin alt grubu olarak yeni sınıflamada eklenen "piloid özellik-ler gösteren yüksek dereceli astrositom", spinal kordda izlenebilen agresif karakterde bir astrositik tümördür. Genellikle erişkinlerde izlenen bu tümör grubu tipik çocukluk çağı pilositik astrositomlarından farklı bir deoksiriboz nükleik asit metilasyon profiline sahip olup, CDK-N2A/B delesyonu göstermektedirler [27]. ...

High-grade astrocytoma with piloid features (HGAP): the Charité experience with a new central nervous system tumor entity

Journal of Neuro-Oncology

... DNA Methylation consists of the addition of a methyl group ( CH 3 ) to a cytosine, forming a 5mC link [3,4]. DNA methylation inhibits the expression of certain genes by preventing the proteins responsible for DNA transcription to initiate this process [5], and it has become one of the main epigenetic markers of cancer, due to its strong correlation with the abnormal activity of genes [6][7][8][9]. However, obtaining the DNA methylation analysis at a single-base (nucleotide) resolution is a very complex and costly process, since human DNA is composed by a double helix of 3 * 10 9 nucleotides each. ...

DNA‐Methylation‐based Classification of Paediatric Brain Tumours

Neuropathology and Applied Neurobiology