Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice

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.

Signal sequence receptor subunit 3: A novel indicator of immunosuppressive tumor microenvironment and clinical benefits from immunotherapy

BACKGROUND

Signal sequence receptor subunit 3 (SSR3), a translocation-associated protein complex, plays a vital role in various diseases. However, its involvement in human cancers remains unclear.

METHODS

We conducted a comprehensive analysis by integrating data from multiple sources, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI. Additionally, we incorporated data from a clinical trial, two immunotherapy cohorts, and in vitro experiments to investigate SSR3's impact on cancer prognosis and immune response.

RESULTS

Our findings revealed a significant correlation between elevated SSR3 expression and unfavorable prognosis across various cancer types. Amplification is the most common genetic alteration in SSR3. Furthermore, functional enrichment analysis highlighted SSR3's regulatory role in promoting proliferation. In addition, SSR3 also serves as a pivotal mediator bridging the innate and adaptive immune systems and several related signaling pathways. Moreover, the correlation of SSR3 expression with tumor mutation burden in five cancer types, as well as with microsatellite instability in nine cancer types, suggests the potential of SSR3 as a predictive marker for immunotherapy response. To validate this hypothesis, we examined data from patients who underwent immunotherapy treatment. Our analysis revealed that individuals with low SSR3 expression demonstrated higher response rates to immune checkpoint inhibitors and longer overall survival compared to those with high SSR3 expression.

CONCLUSIONS

Our study identifies SSR3 as a potential oncogene in humans, implicated in both tumorigenesis and cancer immunity. Elevated SSR3 expression is indicative of an immunosuppressive tumor microenvironment. Therefore, SSR3 holds promise as a potential prognostic biomarker and a target for immunotherapy in cancer treatment.

Evaluation of the Oncomine Comprehensive Assay v3 panel for the detection of 1p/19q codeletion in oligodendroglial tumours

AIMS

Accurate assessment of 1p/19q codeletion status in diffuse gliomas is of paramount importance for diagnostic, prognostic and predictive purposes. While targeted next generation sequencing (NGS) has been widely implemented for glioma molecular profiling, its role in detecting structural chromosomal variants is less well established, requiring supplementary informatic tools for robust detection. Herein, we evaluated a commercially available amplicon-based targeted NGS panel (Oncomine Comprehensive Assay v3) for the detection of 1p/19q losses in glioma tissues using an Ion Torrent platform and the standard built-in NGS data analysis pipeline solely.

METHODS

Using as little as 20 ng of DNA from formalin-fixed paraffin-embedded tissues, we analysed 25 previously characterised gliomas for multi-locus copy number losses (CNLs) on 1p and 19q, including 11 oligodendrogliomas (ODG) and 14 non-oligodendroglial (non-ODG) controls. Fluorescence in-situ hybridisation (FISH) was used as a reference standard.

RESULTS

The software confidently detected combined contiguous 1p/19q CNLs in 11/11 ODGs (100% sensitivity), using a copy number cut-off of ≤1.5 and a minimum of 10 amplicons covering the regions. Only partial non-specific losses were identified in non-ODGs (100% specificity). Copy number averages of ODG and non-ODG groups were significantly different (p<0.001). NGS was concordant with FISH and was superior to it in distinguishing partial from contiguous losses indicative of whole-arm chromosomal deletion.

CONCLUSIONS

This commercial NGS panel, along with the standard Ion Torrent algorithm, accurately detected 1p/19q losses in ODG samples, obviating the need for specialised custom-made informatic analyses. This can easily be incorporated into routine glioma workflow as an alternative to FISH.

Methylation microarray-based detection of clinical copy-number aberrations in CLL benchmarked to standard FISH analysis

Copy-number aberrations (CNAs) are assessed using FISH analysis in diagnostics of chronic lymphocytic leukemia (CLL), but CNAs can also be extrapolated from Illumina BeadChips developed for genome-wide methylation microarray screening. Increasing numbers of microarray data-sets are available from diagnostic samples, making it useful to assess the potential in CNA diagnostics. We benchmarked the limitations of CNA testing from two Illumina BeadChips (EPIC and 450k) and using two common packages for analysis (conumee and ChAMP) to FISH-based assessment of 11q, 13q, and 17p deletions in 202 CLL samples. Overall, the two packages predicted CNAs with similar accuracy regardless of the microarray type, but lower than FISH-based assessment. We showed that the bioinformatics analysis needs to be adjusted to the specific CNA, as no general settings were identified. Altogether, we were able to predict CNAs using methylation microarray data, however, with limited accuracy, making FISH-based assessment of deletions the superior diagnostic choice.

DNA Methylation Episignatures in Neurodevelopmental Disorders Associated with Large Structural Copy Number Variants: Clinical Implications

Large structural chromosomal deletions and duplications, referred to as copy number variants (CNVs), play a role in the pathogenesis of neurodevelopmental disorders (NDDs) through effects on gene dosage. This review focuses on our current understanding of genomic disorders that arise from large structural chromosome rearrangements in patients with NDDs, as well as difficulties in overlap of clinical presentation and molecular diagnosis. We discuss the implications of epigenetics, specifically DNA methylation (DNAm), in NDDs and genomic disorders, and consider the implications and clinical impact of copy number and genomic DNAm testing in patients with suspected genetic NDDs. We summarize evidence of global methylation episignatures in CNV-associated disorders that can be used in the diagnostic pathway and may provide insights into the molecular pathogenesis of genomic disorders. Finally, we discuss the potential for combining CNV and DNAm assessment into a single diagnostic assay.

DNA methylation as a diagnostic tool

DNA methylation of cytosines in CpG sites throughout the genome is an epigenetic mark contributing to gene expression regulation. DNA methylation patterns are specific to tissue type, conserved throughout life and reflect changes during tumorigenesis. DNA methylation recently emerged as a diagnostic tool to classify tumors based on a combination of preserved developmental and mutation induced signatures. In addition to the tumor classification, DNA methylation data can also be used to evaluate copy number variation, assess promoter methylation status of specific genes, such as MGMT or MLH1, and deconvolute the tumor microenvironment, assessing the tumor immune infiltrate as a potential biomarker for immunotherapy. Here we review the role for DNA methylation in tumor diagnosis.

MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions

DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing routines. We employed the Affymetrix 6.0 SNP Chip results as a gold standard against which to compare large-scale event recall. As there are disparities within the software calling algorithms themselves, no single software is likely to perform best for all samples and all combinations of parameters. The employment of a standardized software framework via creating a Docker image and its subsequent deployment as a Docker container allows researchers to efficiently compare algorithms and lends itself to the development of modified workflows such as the custom workflow we have developed. Researchers can now use the MethylMasteR software for their methylation-based CNV calling needs and follow our software deployment framework. We will continue to refine our methodology in the future with a specific focus on identifying large-scale chromosomal rearrangements in cancer methylation data.

DNA methylation epi-signature is associated with two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome

Background

Phelan-McDermid syndrome is characterized by a range of neurodevelopmental phenotypes with incomplete penetrance and variable expressivity. It is caused by a variable size and breakpoint microdeletions in the distal long arm of chromosome 22, referred to as 22q13.3 deletion syndrome, including the SHANK3 gene. Genetic defects in a growing number of neurodevelopmental genes have been shown to cause genome-wide disruptions in epigenomic profiles referred to as epi-signatures in affected individuals.

Results

In this study we assessed genome-wide DNA methylation profiles in a cohort of 22 individuals with Phelan-McDermid syndrome, including 11 individuals with large (2 to 5.8 Mb) 22q13.3 deletions, 10 with small deletions (< 1 Mb) or intragenic variants in SHANK3 and one mosaic case. We describe a novel genome-wide DNA methylation epi-signature in a subset of individuals with Phelan-McDermid syndrome.

Conclusion

We identified the critical region including the BRD1 gene as responsible for the Phelan-McDermid syndrome epi-signature. Metabolomic profiles of individuals with the DNA methylation epi-signature showed significantly different metabolomic profiles indicating evidence of two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome.

Copy number alterations are associated with metastatic-lethal progression in prostate cancer

BACKGROUNDS

Aside from Gleason score few factors accurately identify the subset of prostate cancer (PCa) patients at high risk for metastatic progression. We hypothesized that copy number alterations (CNAs), assessed using CpG methylation probes on Illumina Infinium® Human Methylation450 (HM450K) BeadChip arrays, could identify primary prostate tumors with potential to develop metastatic progression.

METHODS

Epigenome-wide DNA methylation profiling was performed in surgically resected primary tumor tissues from two cohorts of PCa patients with clinically localized disease who underwent radical prostatectomy (RP) as primary therapy and were followed prospectively for at least 5 years: (1) a Fred Hutchinson (FH) Cancer Research Center-based cohort (n = 323 patients); and (2) an Eastern Virginia (EV) Medical School-based cohort (n = 78 patients). CNAs were identified using the R package ChAMP. Metastasis was confirmed by positive bone scan, MRI, CT or biopsy, and death certificates confirmed cause of death.

RESULTS

We detected 15 recurrent CNAs were associated with metastasis in the FH cohort and replicated in the EV cohort (p < 0.05) without adjusting for Gleason score in the model. Eleven of the recurrent CNAs were associated with metastatic progression in the FH cohort and validated in the EV cohort (p < 0.05) when adjusting for Gleason score.

CONCLUSIONS

This study shows that CNAs can be reliably detected from HM450K-based DNA methylation data. There are 11 recurrent CNAs showing association with metastatic-lethal events following RP and improving prediction over Gleason score. Genes affected by these CNAs may functionally relate to tumor aggressiveness and metastatic progression.

Overexpression of TWA1 predicts poor prognosis in patients with gastric cancer

TWA1 is associated with microtubule dynamics, cell migration, nucleokinesis and chromosome segregation. However, the role of TWA1 in gastric cancer (GC) remains unclear. In this study, Cosmic database revealed that the expression level of TWA1 ranks in the top 20 of overexpressed genes in GC. Further bioinformatic analysis revealed that the expression level of TWA1 was not in connection with the infection status of HP or EB. IHC and IF showed that TWA1 protein was present in both the cytoplasm and nucleus, but mainly in the cytoplasm. The high expression level of TWA1 was also related to tumor size, depth of invasion, lymph node metastasis, TNM stage, cancerous node and vascular invasion. Furthermore, higher TWA1 expression was also associated with shorter PFS and OS in GC. The univariate and multivariate analysis suggested the expression of TWA1 was an independent poor prognostic factor in GC. DNA copy number gain contributes to TWA1 overexpression and promoter methylation of TWA1 predicts profitable prognosis. Co-expression showed that TAF4 may function as a transcription factor (TF) regulates TWA1 expression, which further to mediate tumor invasion and metastasis. These findings revealed that TWA1 plays an important role in the development of GC and is expected to become an important biomarker and therapeutic target of tumors.