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Recurrent chromosomal and epigenetic alterations in oral squamous cell carcinoma and its putative premalignant condition oral lichen planus
research-article
Author(s):
Christopher G. Németh
1 ,
Christoph Röcken
2 ,
Reiner Siebert
3
,
4 ,
Jörg Wiltfang
1 ,
Ole Ammerpohl
3
,
4
,
* ,
Volker Gassling
1
,
*
Publication date (Electronic):
9 April 2019
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
Head and neck squamous cell carcinoma (HNSCC) affects about 700.000 individuals per year worldwide with oral squamous cell carcinoma (OSCC) as a major subcategory. Despite a comprehensive treatment concept including surgery, radiation, and chemotherapy the 5-year survival rate is still only about 50 percent. Chronic inflammation is one of the hallmarks of carcinogenesis. Until now, little is known about the premalignant status of oral lichen planus (OLP) and molecular alterations in OLP are still poorly characterized. Our study aims to delineate differential DNA methylation patterns in OLP, OSCC, and normal oral mucosa.
By applying a bead chip approach, we identified altered chromosomal patterns characteristic for OSCC while finding no recurrent alterations in OLP. In contrast, we identified numerous alterations in the DNA methylation pattern in OLP, as compared to normal controls, that were also present in OSCC. Our data support the hypothesis that OLP is a precursor lesion of OSCC sharing multiple epigenetic alterations with OSCC.
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Most cited references48
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Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1.
N. Agrawal, M Frederick, C. R. Pickering … (2011)
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. To explore the genetic origins of this cancer, we used whole-exome sequencing and gene copy number analyses to study 32 primary tumors. Tumors from patients with a history of tobacco use had more mutations than did tumors from patients who did not use tobacco, and tumors that were negative for human papillomavirus (HPV) had more mutations than did HPV-positive tumors. Six of the genes that were mutated in multiple tumors were assessed in up to 88 additional HNSCCs. In addition to previously described mutations in TP53, CDKN2A, PIK3CA, and HRAS, we identified mutations in FBXW7 and NOTCH1. Nearly 40% of the 28 mutations identified in NOTCH1 were predicted to truncate the gene product, suggesting that NOTCH1 may function as a tumor suppressor gene rather than an oncogene in this tumor type.
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Discovering Motifs in Ranked Lists of DNA Sequences
Eran Eden, Doron Lipson, Sivan Yogev … (2007)
Introduction Background This paper examines the problem of discovering “interesting” sequence motifs in biological sequence data. A widely accepted and more formal definition of this task is: given a target set and a background set of sequences (or a background model), identify sequence motifs that are enriched in the target set compared with the background set. The purpose of this paper is to extend this formulation and to make it more flexible so as to enable the determination of the target and background set in a data driven manner. Discovery of sequences or attributes that are enriched in a target set compared with a background set (or model) has become increasingly useful in a wide range of applications in molecular biology research. For example, discovery of DNA sequence motifs that are overabundant in a set of promoter regions of co-expressed genes (determined by clustering of expression data) can suggest an explanation for this co-expression. Another example is the discovery of DNA sequences that are enriched in a set of promoter regions to which a certain transcription factor (TF) binds strongly, inferred from chromatin immuno-precipitation on a microarray (ChIP–chip) [1] measurements. The same principle may be extended to many other applications such as discovery of genomic elements enriched in a set of highly methylated CpG island sequences [2]. Due to its importance, this task of discovering enriched DNA subsequences and capturing their corresponding motif profile has gained much attention in the literature. Any approach to motif discovery must address several fundamental issues. The first issue is the way by which motifs are represented. There are several strategies for motif representation: using a k-mer of IUPAC symbols where each symbol represents a fixed set of possible nucleotides at a single position (examples of methods that use this representation include REDUCE [3], YMF [4,5], ANN-SPEC [6], and a hypergeometric-based method [7]) or using a position weight matrix (PWM), which specifies the probability of observing each nucleotide at each motif position (for example MEME [8], BioProspector [9], MotifBooster [10], DME-X [11], and AlignACE [12]). Both representations assume base position independence. Alternatively, higher order representations that capture positional dependencies have been proposed (e.g., HMM and Bayesian networks motif representations [13]). While these representations circumvent the position independence assumption, they are more vulnerable to overfitting and lack of data for determining model parameters. The method described in this paper uses the k-mer model with symbols above IUPAC. The second issue is devising a motif scoring scheme. Many strategies for scoring motifs have been suggested in the literature. One simple yet powerful approach uses the hypergeometric distribution for identifying enriched motif kernels in a set of sequences and then expanding these motifs using an EM algorithm [7]. The framework described in this paper is a natural extension of the approach of [7]. YMF [4,5] is an exhaustive search algorithm which associates each motif with a z-score. AlignACE [12] uses a Gibbs sampling algorithm for finding global sequence alignments and produces a MAP score. This score is an internal metric used to determine the significance of an alignment. MEME [8] uses an expectation maximization strategy and outputs the log-likelihood and relative entropy associated with each motif. Once a scoring scheme is devised, a defined motif search space is scanned (either heuristically or exhaustively) and motifs with significantly high scores are identified. To determine the statistical significance of the obtained scores, many methods resort to simulations or ad hoc thresholds. Several excellent reviews narrate the different strategies for motif detection and use quantitative benchmarking to compare their performance [14–18]. A related aspect of motif discovery, which is outside the scope of this paper, focuses on properties of clusters and modules of TF binding sites (TFBS). Examples of approaches that search for combinatorial patterns and modules underlying TF binding and gene expression include [19–23]. Open Challenges in Motif Discovery One issue of motif discovery that is often overlooked concerns the partition of the input set of sequences into target and background sets. Many methods rely on the user to provide these two sets and search for motifs that are overabundant in the former set compared with the latter. The question of how to partition the data into target and background sets is left to the user. However, the boundary between the sets is often unclear and the exact choice of sequences in each set arbitrary. For example, suppose that one wishes to identify motifs within promoter sequences that constitute putative TFBS. An obvious strategy would be to partition the set of promoter sequences into target and background sets according to the TF binding signal (as measured by ChIP–chip experiments). The two sets would contain the sequences to which the TF binds “strongly” and “weakly,” respectively. A motif detection algorithm could then be applied to find motifs that are overabundant in the target set compared with the background set. In this scenario, the positioning of the cutoff between the strong and weak binding signal is somewhat arbitrary. Obviously, the final outcome of the motif identification process can be highly dependent on this choice of cutoff. A stringent cutoff will result in the exclusion of informative sequences from the target set while a promiscuous cutoff will cause inclusion of nonrelevant sequences—both extremes hinder the accuracy of motif prediction. This example demonstrates a fundamental difficulty in partitioning most types of data. Several methods attempt to circumvent this hurdle. For example, REDUCE [3] uses a regression model on the entire set of sequences. However, it is difficult to justify this model in the context of multiple motif occurrence (as explained below). In other work, a variant of the Kolmogorov-Smirnov test was used for motif discovery [24]. This approach successfully circumvents arbitrary data partition. However, it has other limitations such as the failure to address multiple motif occurrences in a single promoter, and the lack of an exact characterization of the null distribution. Overall, the following four major challenges in motif discovery still require consideration: (c1) the cutoff used to partition data into a target set and background set of sequences is often chosen arbitrarily; (c2) lack of an exact statistical score and p-value for motif enrichment. Current methods typically use arbitrarily set thresholds or simulations, which are inherently limited in precision and costly in terms of running time; (c3) a need for an appropriate framework that accounts for multiple motif occurrences in a single promoter. For example, how should one quantify the significance of a single motif occurrence in a promoter against two motif occurrences in a promoter? Linear models [3] assume that the weight of the latter is double that of the former. However, it is difficult to justify this approach since biological systems do not necessarily operate in such a linear fashion. Another issue related to motif multiplicity is low complexity or repetitive regions. These regions often contain multiple copies of degenerate motifs (e.g., CA repeats). Since the nucleotide frequency underlying these regions substantially deviates from the standard background frequency, they often cause false-motif discoveries. Consequently, most methods mask these regions in the preprocessing stage and thereby lose vital information that might reside therein; (c4) criticism has been made over the fact that motif discovery methods tend to report presumably significant motifs even when applied on randomly generated data [25]. These motifs are clear cases of false positives and should be avoided. Data Lends Itself to Ranking in a Natural Manner In this paper we describe a novel method that attempts to solve the above-mentioned four challenges in a principled manner. It exploits the following observation: data often lends itself to ranking in a natural manner, e.g., ranking sequences according to TF binding signal: ranking according to CpG methylation signal, ranking according to distance in expression space from a set of co-expressed genes, ranking according to differential expression, etc. We exploit this inherent ranking property of biological data in order to circumvent the need for an arbitrary and difficult-to-justify data partition. Consequently, we propose the following formulation of the motif finding task: given a list of ranked sequences, identify motifs that are overabundant at either end of the list. Our solution employs a statistical score termed mHG (minimal hypergeometric) [26]. It is related to the concept of rank-imbalanced motifs, which are sequence motifs that tend to appear at either end of a ranked sequence list. In previous work [26], the authors used mHG to identify sequence motifs in expression data. We use this simple yet powerful approach as the starting point for our study. Overview The rest of this paper is divided into two main parts, each of which is self-contained: in the Results we briefly outline our method and describe new biological findings that were obtained by applying this method to biological data. We address challenge (c4) by testing the algorithm on randomly ranked real genomic sequences. In the Methods, we describe the mHG probabilistic and algorithmic framework and explain how we deal with challenges (c1)–(c3). Results Statistics and Algorithms in a Nutshell Based on the mHG framework, we developed a software tool termed DRIM (discovery of rank imbalanced motifs) for motif identification in DNA sequences. A flow chart of DRIM is provided in Figure 1. The formal introduction and details of the mHG statistics are given in Methods. However, to facilitate the explanation and interpretation of our biological results, we begin with a brief description of the method. Figure 1 DRIM Flow Chart DRIM receives a list of DNA sequences as input and a criterion by which the sequences should be ranked, for example, TF binding signals as measured by ChIP ChIP–chip: (i) The sequences are ranked according to the criterion. (ii) A “blind search” is performed over all the motifs that reside in the restricted motif space (in this study the restricted motif space contains ∼100,000 motifs, see Methods, The DRIM software). For each motif an occurrence vector is generated. Each position in the vector is the number of motif occurrences in the corresponding sequence, (the figure shows the vector for the motif CACGTGW). (iii) The motif significance is computed using the mHG scheme, and the optimal partition into target and background sets in terms of motif enrichment is identified. The promising motif seeds are passed as input to the heuristic motif search model and the rest are filtered out. (iv,v) The motif seeds are expanded in an iterative manner (the mHG is computed in each lap), until a local optimum motif is found. (vi) The exact mHG p-value of the motif is computed. If it has a p-value p. The (0,0) → (N,B) path representing λ visits N distinct grid points (excluding the point (0,0)), representing the N different HGT scores that are considered when calculating its mHG score: mHG(λ) = min1≤n
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Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort
Laura Perna, Yan-Yan Zhang, Ute Mons … (2016)
Background Previous studies have developed models predicting methylation age from DNA methylation in blood and other tissues (epigenetic clock) and suggested the difference between DNA methylation and chronological ages as a marker of healthy aging. The goal of this study was to confirm and expand such observations by investigating whether different concepts of the epigenetic clocks in a population-based cohort are associated with cancer, cardiovascular, and all-cause mortality. Results DNA methylation age was estimated in a cohort of 1863 older people, and the difference between age predicted by DNA methylation and chronological age (Δage) was calculated. A case-cohort design and weighted proportional Cox hazard models were used to estimate associations of Δage with cancer, cardiovascular, and all-cause mortality. Hazard ratios for Δage (per 5 years) calculated using the epigenetic clock developed by Horvath were 1.23 (95 % CI 1.10–1.38) for all-cause mortality, 1.22 (95 % CI 1.03–1.45) for cancer mortality, and 1.19 (95 % CI 0.98–1.43) for cardiovascular mortality after adjustment for batch effects, age, sex, educational level, history of chronic diseases, hypertension, smoking status, body mass index, and leucocyte distribution. Associations were similar but weaker for Δage calculated using the epigenetic clock developed by Hannum. Conclusions These results show that age acceleration in terms of the difference between age predicted by DNA methylation and chronological age is an independent predictor of all-cause and cause-specific mortality and may be useful as a general marker of healthy aging. Electronic supplementary material The online version of this article (doi:10.1186/s13148-016-0228-z) contains supplementary material, which is available to authorized users.
Author and article information
Contributors
Christopher G. Németh:
ORCID: http://orcid.org/0000-0003-4613-7873
Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole:
MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing
– review & editing
Christoph Röcken: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole:
ResourcesRole: Validation
Reiner Siebert: Role: Formal analysisRole: InvestigationRole: ResourcesRole: Software
Jörg Wiltfang: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole:
Project administrationRole: ResourcesRole: SoftwareRole: Supervision
Ole Ammerpohl: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole:
ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original
draftRole: Writing – review & editing
Volker Gassling: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole:
InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole:
ValidationRole: Writing – original draftRole: Writing – review & editing
Hiromu Suzuki: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Electronic):
1932-6203
Publication date
(Electronic):
9
April
2019
Publication date Collection: 2019
Volume: 14
Issue: 4
Electronic Location Identifier: e0215055
Affiliations
Author notes
* E-mail:
gassling@
123456mkg.uni-kiel.de
(VG);
ole.ammerpohl@
123456uni-ulm.de
(OA)
Author information
Christopher G. Németh
http://orcid.org/0000-0003-4613-7873
Article
Publisher ID: PONE-D-18-25820DOI: 10.1371/journal.pone.0215055
PMC ID: 6456184
PubMed ID: 30964915
SO-VID: 65e3664b-0864-4223-ab1e-4fbb9f00c1aa
Copyright © © 2019 Németh et al
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 3
September
2018
Date
accepted
: 26
March
2019
Page count
Figures: 6,
Tables: 1,
Pages: 17
Funding
The authors received no specific funding for this work.
Categories
Subject:
Research Article
Subject:
Biology and life sciences
Subject:
Cell biology
Subject:
Chromosome biology
Subject:
Chromatin
Subject:
Chromatin modification
Subject:
DNA methylation
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Biology and life sciences
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Genetics
Subject:
Epigenetics
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Chromatin
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Chromatin modification
Subject:
DNA methylation
Subject:
Biology and life sciences
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Genetics
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Gene expression
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Chromatin
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Chromatin modification
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DNA methylation
Subject:
Biology and life sciences
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Genetics
Subject:
DNA
Subject:
DNA modification
Subject:
DNA methylation
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Biology and life sciences
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Biochemistry
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Nucleic acids
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DNA
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DNA modification
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DNA methylation
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Biology and life sciences
Subject:
Genetics
Subject:
Epigenetics
Subject:
DNA modification
Subject:
DNA methylation
Subject:
Biology and life sciences
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Genetics
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Gene expression
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DNA modification
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DNA methylation
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Biology and Life Sciences
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Genetics
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Genetic Loci
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Biology and Life Sciences
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Gene Expression
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Gene Regulation
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Locus Control Region
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Medicine and Health Sciences
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Oncology
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Cancers and Neoplasms
Subject:
Carcinomas
Subject:
Squamous Cell Carcinomas
Subject:
Medicine and Health Sciences
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Oncology
Subject:
Cancers and Neoplasms
Subject:
Head and Neck Cancers
Subject:
Head and Neck Tumors
Subject:
Head and Neck Squamous Cell Carcinoma
Subject:
Medicine and Health Sciences
Subject:
Oncology
Subject:
Cancers and Neoplasms
Subject:
Carcinomas
Subject:
Squamous Cell Carcinomas
Subject:
Head and Neck Squamous Cell Carcinoma
Subject:
Biology and Life Sciences
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Genetics
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Epigenetics
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Medicine and Health Sciences
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Diagnostic Medicine
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Principal Component Analysis
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