Breast cancer associated germline structural variants harboring small noncoding RNAs impact post-transcriptional gene regulation

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

Automated prediction of the clinical impact of structural copy number variations

Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned "uncertain" significance in predictions. We believe that ISV's prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion

Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example, gene expressions (GEs) by copy number variations (CNVs), and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation as well as sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is "guided" by a known biomarker. We develop a multivariate sparse fusion (MSF) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA data leads to interesting findings.

Genetic variations in 3´UTRs of SMUG1 and NEIL2 genes modulate breast cancer risk, survival and therapy response

Breast cancer (BC) is the most frequent malignancy in women accounting for approximately 2 million new cases worldwide annually. Several genetic, epigenetic and environmental factors are known to be involved in BC development and progression, including alterations in post-transcriptional gene regulation mediated by microRNAs (miRNAs). Single nucleotide polymorphisms (SNPs) located in miRNA binding sites (miRSNPs) in 3'-untranslated (UTR) regions of target genes may affect miRNA-binding affinity and consequently modulate gene expression. We have previously reported a significant association of miRSNPs in the SMUG1 and NEIL2 genes with overall survival in colorectal cancer patients. SMUG1 and NEIL2 are DNA glycosylases involved in base excision DNA repair (BER). Assuming that certain genetic traits are common for solid tumours, we have investigated wherever variations in SMUG1 and NEIL2 genes display an association with BC risk, prognosis, and therapy response in a group of 673 BC patients and 675 healthy female controls. Patients with TC genotype of NEIL2 rs6997097 and receiving only hormonal therapy displayed markedly shorter overall survival (OS) (HR=4.15, 95% CI=1.7-10.16, P= 0.002) and disease-free survival (DFS) (HR=2.56, 95% CI=1.5-5.7, P= 0.02). Our results suggest that regulation of base excision repair glycosylases operated by miRNAs may modulate the prognosis of hormonally treated BC.

Copy number variation: Characteristics, evolutionary and pathological aspects

Copy number variants (CNVs) were the subject of extensive research in the past years. They are common features of the human genome that play an important role in evolution, contribute to population diversity, development of certain diseases, and influence host–microbiome interactions. CNVs have found application in the molecular diagnosis of many diseases and in non-invasive prenatal care, but their full potential is only emerging. CNVs are expected to have a tremendous impact on screening, diagnosis, prognosis, and monitoring of several disorders, including cancer and cardiovascular disease. Here, we comprehensively review basic definitions of the term CNV, outline mechanisms and factors involved in CNV formation, and discuss their evolutionary and pathological aspects. We suggest a need for better defined distinguishing criteria and boundaries between known types of CNVs.

Copy Number Variation: Methods and Clinical Applications

Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress of sequencing methods has enabled precise detection of CNVs, determining their functional effect on cellular and whole-body physiology remains a challenge. Here, we provide a comprehensive list of databases and bioinformatics tools that may serve as useful assets for researchers, laboratory diagnosticians, and clinical geneticists facing the challenge of CNV detection and interpretation.

Small nucleolar RNA and its potential role in breast cancer - A comprehensive review

Small Nucleolar RNAs (snoRNAs) are known for their canonical functions, including ribosome biogenesis and RNA modification. snoRNAs act as endogenous sponges that regulate miRNA expression. Thus, precise snoRNA expression is critical for fine-tuning miRNA expression. snoRNAs processed into miRNA-like sequences play a crucial role in regulating the expression of protein-coding genes similar to that of miRNAs. Recent studies have linked snoRNA deregulation to breast cancer (BC). Inappropriate snoRNA expression contributes to BC pathology by facilitating breast cells to acquire cancer hallmarks. Since snoRNAs show significant differential expression in normal and cancer conditions, measuring snoRNA levels could be useful for BC prognosis and diagnosis. The present article provides a comprehensive overview of the role of snoRNAs in breast cancer pathology. More specifically, we have discussed the regulation, biological function, signaling pathways, and clinical utility of abnormally expressed snoRNAs in BC. Besides, we have also discussed the role of snoRNA host genes in breast tumorigenesis and emerging and future research directions in the field of snoRNA and cancer.

A HPV16-related prognostic indicator for head and neck squamous cell carcinoma

Background

The human papillomavirus (HPV) is emerging as an important risk factor in head and neck squamous cell carcinoma (HNSCC) patients. This has been observed particularly in the case of HPV16. The HPV16+ HNSCC subtype has distinct pathological, clinical, molecular, and prognostic characteristics. This study aimed to identify potential microRNAs (miRNAs) and their roles in HPV16+ HNSCC progression.

Method

miRNA, mRNA and the clinical data of 519 HNSCC and 44 HNSCC-negative samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DEMs) in HPV16-related HNSCC tissues with prognostic value were selected. DEM levels were assessed based on clinicopathological parameters and overall survival (OS). Target genes were also predicted and functional analysis based on Gene Set Enrichment Analysis (GSEA) were then performed.

Results

In HPV16+ HNSCC tissues, miR-99a-3p and miR-4746-5p were significantly upregulated. In contrast, miR-411-5p was shown to be downregulated. miR-99a-3p^highmiR-411-5p^lowmiR-4746-5p^high expression could estimate improved OS and low frequent perineural invasion (PNI). Predicted target genes were enriched in cell growth, neuroepithelial cell differentiation, MAPK and FoxO signaling pathways. Epithelial mesenchymal transition (EMT) gene set and invasion related genes were downregulated in miR-99a-3p^highmiR-411-5p^lowmiR-4746-5p^high HNSCC patients.

Conclusion

miR-99a-3p, miR-411-5p and miR-4746-5p might participate in HPV16+ HNSCC progression through EMT related pathways and affect prognosis.

BARD1 is A Low/Moderate Breast Cancer Risk Gene: Evidence Based on An Association Study of the Central European p.Q564X Recurrent Mutation

In addition to several well-established breast cancer (BC) susceptibility genes, the contribution of other candidate genes to BC risk remains mostly undefined. BARD1 is a potentially predisposing BC gene, however, the rarity of its mutations and an insufficient family/study size have hampered corroboration and estimation of the associated cancer risks. To clarify the role of BARD1 mutations in BC predisposition, a comprehensive case-control association study of a recurring nonsense mutation c.1690C>T (p.Q564X) was performed, comprising ~14,000 unselected BC patients and ~5900 controls from Polish and Belarusian populations. For comparisons, two BARD1 variants of unknown significance were also genotyped. We detected the highest number of BARD1 variants in BC cases in any individual BARD1-specific study, including 38 p.Q564X mutations. The p.Q564X was associated with a moderately increased risk of BC (OR = 2.30, p = 0.04). The estimated risk was even higher for triple-negative BC and bilateral BC. As expected, the two tested variants of unknown significance did not show significant associations with BC risk. Our study provides substantial evidence for the association of a deleterious BARD1 mutation with BC as a low/moderate risk allele. The p.Q564X was shown to be a Central European recurrent mutation with potential relevance for future genetic testing.