scholarly journals Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

2022 ◽  
Vol 12 ◽  
Author(s):  
Pengfei Liu
Keyword(s):  
Dna Methylation ◽  
Regulatory Network ◽  
Metastatic Cancer ◽  
Methylation Status ◽  
Unknown Primary ◽  
Aberrant Methylation ◽  
Omics Data ◽  
Driver Genes ◽  
Tumor Origin ◽  
Cancer Types

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

scholarly journals Hierarchical Classification of Cancers of Unknown Primary Using Multi-Omics Data

2019 ◽  
Vol 18 ◽  
pp. 117693511987216 ◽  
Author(s):  
Elham Bavafaye Haghighi ◽  
Michael Knudsen ◽  
Britt Elmedal Laursen ◽  
Søren Besenbacher
Keyword(s):  
Metastatic Cancer ◽  
Hierarchical Classification ◽  
Molecular Data ◽  
Unknown Primary ◽  
Cancer Type ◽  
Omics Data ◽  
Data Types ◽  
Cancer Types ◽  
Hierarchical Classifier

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset.

Progressive alteration of DNA methylation of Alu, MGMT, MINT2, and TFPI2 genes in colonic mucosa during colorectal cancer development

2021 ◽  
pp. 1-6
Author(s):  
Ben Kang ◽  
Hyun Seok Lee ◽  
Seong Woo Jeon ◽  
Soo Yeun Park ◽  
Gyu Seog Choi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Healthy Subjects ◽  
Large Scale ◽  
Methylation Status ◽  
Clinical Information ◽  
Field Cancerization ◽  
Aberrant Methylation ◽  
Clinical Value ◽  
Mortality And Morbidity

BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is characterized by different pathways of carcinogenesis and is a heterogeneous disease with diverse molecular landscapes that reflect histopathological and clinical information. Changes in the DNA methylation status of colon epithelial cells have been identified as critical components in CRC development and appear to be emerging biomarkers for the early detection and prognosis of CRC. OBJECTIVE: To explore the underlying disease mechanisms and identify more effective biomarkers of CRC. METHODS: We compared the levels and frequencies of DNA methylation in 11 genes (Alu, APC, DAPK, MGMT, MLH1, MINT1, MINT2, MINT3, p16, RGS6, and TFPI2) in colorectal cancer and its precursor adenomatous polyp with normal tissue of healthy subjects using pyrosequencing and then evaluated the clinical value of these genes. RESULTS: Aberrant methylation of Alu, MGMT, MINT2, and TFPI2 genes was progressively accumulated during the normal-adenoma-carcinoma progression. Additionally, CGI methylation occurred either as an adenoma-associated event for APC, MLH1, MINT1, MINT31, p16, and RGS6 or a tumor-associated event for DAPK. Moreover, relatively high levels and frequencies of DAPK, MGMT, and TFPI2 methylation were detected in the peritumoral nonmalignant mucosa of cancer patients in a field-cancerization manner, as compared to normal mucosa from healthy subjects. CONCLUSION: This study identified several biomarkers associated with the initiation and progression of CRC. As novel findings, they may have important clinical implications for CRC diagnostic and prognostic applications. Further large-scale studies are needed to confirm these findings.

scholarly journals SUN-115 Distinct DNA Methylation Signature in Neuroendocrine Tumors of Different Primary Sites and Hereditary Predisposition

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Amit Tirosh ◽  
Jonathan Keith Killian ◽  
Petersen David ◽  
Yuelin Jack Zhu ◽  
Jenny Blau ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Neuroendocrine Tumors ◽  
Methylation Status ◽  
Principal Component ◽  
Circulating Tumor Dna ◽  
Adenomatous Polyposis Coli Gene ◽  
Unknown Primary ◽  
Cpg Sites ◽  
Genome Wide ◽  
Cpg Hypermethylation

Abstract Objective There is scant data of the genome-wide methylome alterations in neuroendocrine tumors (NET). Thus, the goal of this study was to compare the DNA methylation signature of NETs with respect to various primary sites and inherited genetic predisposition syndromes including von Hippel-Lindau (VHL) and multiple endocrine neoplasia type 1 (MEN1). Methods Genome-wide DNA methylation analysis of 96 NETs (primary and metastatic) was performed by using the Illumina Infinium EPIC Array. Principal component analysis (PCA) and unsupervised clustering analyses were performed to identify distinct methylome signatures. The methylation status of genetic drivers such as APC were assessed by primary site. Results A total of 835,424 CpGs methylation sites were quantified. Hypermethylated CpG sites were detected more frequently in sporadic vs. MEN1-related vs. VHL-related NETs, respectively (p < 0.001 for all comparisons), while hypomethylated CpGs sites were more common in VHL-related NETs vs. sporadic and MEN1-related NETs (p<0.001 for both comparisons). Small-intestinal NETs (SINETs) had the most differences at CpGs with the highest number of hyper- and hypomethylated CpG sites, followed by duodenal NETs (DNETs) and pancreatic NETs (PNETs, p<0.001 for all comparisons). PCA showed distinct clustering of SINETs and three NETs of unknown primary. Sporadic, VHL-related and MEN1-related PNETs formed distinct groups on PCA. VHL-related NETs clustered separately showing pronounced CpG hypomethylation, while sporadic and MEN1-related NETs clustered together showing relative CpG hypermethylation. In a subgroup analysis, MEN1-related SINETs, DNETs and gastric NETs had distinct methylome signatures, respectively, with complete separation by PCA and unsupervised hierarchical clustering. Furthermore, we found CpG hypermethylation in the APC (adenomatous polyposis coli) gene, specifically in the 1A promoter, with higher methylation levels in gastric- and DNETs vs. SINETs, PNETs and NETs of unknown primary (p < 0.001 for all comparisons). Conclusion Various primary NET sites and genetically predisposed MEN1-related NETs have distinct DNA CpG methylation signatures. The methylome signatures identified in this study may be useful for non-invasive molecular characterization of NETs, through DNA methylation profiling of biopsy samples or circulating tumor DNA.

scholarly journals Multiple gene promoter methylation and clinical stage in adjacent normal tissues: Effect on prognosis of colorectal cancer in Taiwan

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chih-Hsiung Hsu ◽  
Cheng-Wen Hsiao ◽  
Chien-An Sun ◽  
Wen-Chih Wu ◽  
Tsan Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Tumor Tissue ◽  
Clinical Stage ◽  
Methylation Status ◽  
Aberrant Methylation ◽  
Event Free Survival ◽  
Free Survival ◽  
Normal Tissues ◽  
Colorectal Cancer Patients

AbstractThis study provide an insight that the panel genes methylation status in different clinical stage tended to reflect a different prognosis even in matched normal tissues, to clinical recommendation. We enrolled 153 colorectal cancer patients from a medical center in Taiwan and used the candidate gene approach to select five genes involved in carcinogenesis pathways. We analyzed the relationship between DNA methylation with different cancer stages and the prognostic outcome. There were significant trends of increasing risk of 5-year time to progression and event-free survival of subjects with raising number of hypermethylation genes both in normal tissue and tumor tissue. The group with two or more genes with aberrant methylation in the advanced cancer stages (Me/advanced) had lower 5-year event-free survival among patients with colorectal cancer in either normal or tumor tissue. The adjusted hazard ratios in the group with two or more genes with aberrant methylation with advanced cancer stages (Me/advanced) were 8.04 (95% CI, 2.80–23.1; P for trend <0.01) and 8.01 (95% CI, 1.92–33.4; P for trend <0.01) in normal and tumor tissue, respectively. DNA methylation status was significantly associated with poor prognosis outcome. This finding in the matched normal tissues of colorectal cancer patients could be an alternative source of prognostic markers to assist clinical decision making.

scholarly journals Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3768
Author(s):  
Vijayachitra Modhukur ◽  
Shakshi Sharma ◽  
Mainak Mondal ◽  
Ankita Lawarde ◽  
Keiu Kask ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Metastatic Cancer ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cancer Type ◽  
Data Repositories ◽  
Extreme Gradient Boosting ◽  
Cancer Types ◽  
Tissue Of Origin

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.

scholarly journals Significant associations between driver gene mutations and DNA methylation alterations across many cancer types

2017 ◽  
Author(s):  
Yun-Ching Chen ◽  
Valer Gotea ◽  
Gennady Margolin ◽  
Laura Elnitski
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Thyroid Cancer ◽  
Gene Mutations ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractRecent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. We identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver-gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to their underlying molecular characteristics, which could improve treatment efficacy.Author summaryMutations that alter the function of driver genes by changing DNA nucleotides have been recognized as a key player in cancer progression. Recent evidence showed that DNA methylation, a molecular signature that is used for controlling gene expression and that consists of cytosine residues with attached methyl groups in the context of CG dinucleotides, is also highly dysregulated in cancer and contributes to carcinogenesis. However, whether those methylation alterations correspond to mutated driver genes in cancer remains unclear. In this study, we analyzed 4,302 tumors from 18 cancer types and demonstrated that driver gene mutations are inherently connected with the aberrant DNA methylation landscape in cancer. We showed that those driver gene-associated methylation patterns can classify heterogeneous tumors in a cancer type into homogeneous subtypes and have the potential to influence the genes that contribute to tumor growth. This finding could help us to better understand the fundamental connection between driver gene mutations and DNA methylation alterations in cancer and to further improve the cancer treatment.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches

2021 ◽  
Author(s):  
M. W. Wojewodzic ◽  
J. P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites.We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis.The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types.These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

TP73 DNA methylation and upregulation of ΔNp73 are associated with an adverse prognosis in breast cancer

2017 ◽  
Vol 71 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Laura C Gomez ◽  
Mayra L Sottile ◽  
Martin E Guerrero-Gimenez ◽  
Felipe C M Zoppino ◽  
Analia L Redondo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Histological Grade ◽  
Methylation Status ◽  
Prognostic Significance ◽  
Inverse Correlation ◽  
Positive Association ◽  
Immunohistochemical Analysis ◽  
Aberrant Methylation ◽  
Breast Cancer Patients

AimAccumulated evidence suggests that aberrant methylation of the TP73 gene and increased levels of ΔNp73 in primary tumours correlate with poor prognosis. However, little is known regarding the transcriptional and functional regulation of the TP73 gene in breast cancer. The aim of the present study was to determine the expression of the ΔNp73 isoform, its relationship with DNA methylation of TP73 and their clinical prognostic significance in breast cancer patients.MethodsTP73 gene methylation was studied in TCGA datasets and in 70 invasive ductal breast carcinomas (IDCs). The expression of p73 isoforms was evaluated by immunohistochemistry (IHC) and Western blot and correlated with clinicopathological variables and clinical outcome.ResultsWe observed that the methylation of diverse CpG islands of TP73 differed significantly between molecular subtypes. An inverse correlation was found between p73 protein expression and the methylation status of the TP73 gene. The expression of exon 3’ of p73 (only expressed in ΔNp73) was significantly higher in patients with wild-type p53. Immunohistochemical analysis revealed that all p73 isoforms were localised in both the nuclear and cytoplasmic compartments. We confirmed a positive association between the expression of ∆Np73 and high histological grade.ConclusionsOur findings suggest that high expression of ΔNp73 could be used to determine the aggressiveness of IDCs and could be incorporated in the pathologist’s report.

Epigenetic Profiling of Primary CLL Reveals Novel DNA Methylation-Based Clusters and Novel Mechanisms of Leukemogenesis

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3877-3877
Author(s):  
Fong Fong ◽  
Haim Y Bar ◽  
Kerby Shedden ◽  
Kamlai saiya-Cork ◽  
Peter Ouillette ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
B Cells ◽  
Conflicts Of Interest ◽  
Critical Role ◽  
Methylation Status ◽  
Snp Array ◽  
Aberrant Methylation ◽  
Unequal Variance ◽  
Genomic Deletions

Abstract Abstract 3877 Introduction: Chronic Lymphocytic Leukemia (CLL) is the most common leukemia in the Western world with nearly 15,000 new cases diagnosed every year in the USA. The characterization of CLL has resulted in the identification of important disease biomarkers: these include the recurrent genomic deletions del17p and del11q, genomic complexity, TP53 mutations, the expression level of ZAP70 and the mutational status of IgVH. While genomic and transcriptional profiling of CLL identified clinically and biologically relevant markers, there is still significant uncertainty about the pathobiology and the origin of CLL. It is increasingly clear that epigenetic deregulation plays an important role in the biology of all lymphomas/leukemias including CLL. Methods: We hypothesized that DNA methylation profiling would allow us to identify new, biologically significant CLL subtypes and yield greater insight into the biology of this disease. We therefore examined the DNA methylation of over 240 patients with CLL using the HELP assay and hybridization to high density custom microarray that reports on the methylation status of more than 250,000 CpGs corresponding to 20,401 genes. Gene expression profiling and SNP array-based copy number assessments and targeted gene resequencing were available on most of these cases. We performed unsupervised analysis on the most variable probesets (standard deviation > 1.3) using K-means consensus clustering. Results: The experimental approach reproducibly identified three robust CLL subtypes based on epigenetic profiles. To identify the genes that define these three subtypes we next performed unequal variance t-test of the CLL subtypes comparing them to Peripheral Blood CD19+ B cells as a normal control, and identified that clusters are defined by differential methylation of 3719, 6145 and 3349 genes (selected probes displayed changes in methylation of at least 30% and FDR corrected p-value < 0.05), The three clusters featured respectively i) aberrant methylation of MYC and WNT target genes, ii) aberrant methylation of NOTCH1 targets and iii) aberrant methylation of bcl6 and inflammatory cytokines. There was inverse correlation between gene expression and cytosine methylation, suggesting that DNA methylation had an impact on the transcriptional programming of these CLL cases. Strikingly the CLL MYC/WNT cluster displayed poorer prognosis as opposed to the CLL BCL6 cluster (HR=0.14 95% CI: 0.07–0.30). The CLL NOTCH1 cluster had an intermediate prognosis. It was also notable that all CLL patients exhibited deregulation of the B-cell receptor pathway as compared to normal CD19+ B-cells, consistent with the notion that this pathway plays a critical role in CLL pathogenesis. Finally, we divided the cohort into training and testing cohorts and used a machine learning BDVAL algorithm to identify DNA methylation outcome classifiers. This procedure identified a 40-probeset classifier that accurately predicted outcome (Area Under the ROC Curve of 0.77; performance was assessed with 10 fold cross-validation in a training set with 76 patients; validation on an independent set of 105 samples). Conclusion: This large epigenetic profiling study in CLL identifies aberrant epigenetic regulation as a core part of the pathobiology of CLL and identifies novel CLL clusters with distinct effects on survival. MYC-WNT pathway inhibitors are warranted for use in clinical trials for patients belonging to this aggressive epigenetically defined subtype. Disclosures: No relevant conflicts of interest to declare.

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