High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification

Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia

In our work, the associations of GWAS (genome-wide associative studies) impact for sex-hormone-binding globulin (SHBG)-level SNPs with the risk of breast cancer (BC) in the cohort of Caucasian women of Russia were assessed. The work was performed on a sample of 1498 women (358 BC patients and 1140 control (non BC) subjects). SHBG correlated in previously GWAS nine polymorphisms such as rs780093 GCKR, rs17496332 PRMT6, rs3779195 BAIAP2L1, rs10454142 PPP1R21, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs440837 ZBTB10, rs12150660 SHBG, and rs8023580 NR2F2 have been genotyped. BC risk effects of allelic and non-allelic SHBG-linked gene SNPs interactions were detected by regression analysis. The risk genetic factor for BC developing is an SHBG-lowering allele variant C rs10454142 PPP1R21 ([additive genetic model] OR = 1.31; 95%CI = 1.08-1.65; pperm = 0.024; power = 85.26%), which determines 0.32% of the cancer variance. Eight of the nine studied SHBG-related SNPs have been involved in cancer susceptibility as part of nine different non-allelic gene interaction models, the greatest contribution to which is made by rs10454142 PPP1R21 (included in all nine models, 100%) and four more SNPs-rs7910927 JMJD1C (five models, 55.56%), rs17496332 PRMT6 (four models, 44.44%), rs780093 GCKR (four models, 44.44%), and rs440837 ZBTB10 (four models, 44.44%). For SHBG-related loci, pronounced functionality in the organism (including breast, liver, fibroblasts, etc.) was predicted in silico, having a direct relationship through many pathways with cancer pathophysiology. In conclusion, our results demonstrated the involvement of SHBG-correlated genes polymorphisms in BC risk in Caucasian women in Russia.

Genetic Variation and Recurrent Haplotypes on Chromosome 6q23-25 Risk Locus in Familial Lung Cancer

Although lung cancer is known to be caused by environmental factors, it has also been shown to have genetic components, and the genetic etiology of lung cancer remains understudied. We previously identified a lung cancer risk locus on 6q23-25 using microsatellite data in families with a history of lung cancer. To further elucidate that signal, we performed targeted sequencing on nine of our most strongly linked families. Two-point linkage analysis of the sequencing data revealed that the signal was heterogeneous and that different families likely had different risk variants. Three specific haplotypes were shared by some of the families: 6q25.3-26 in families 42 and 44, 6q25.2-25.3 in families 47 and 59, and 6q24.2-25.1 in families 30, 33, and 35. Region-based logarithm of the odds scores and expression data identified the likely candidate genes for each haplotype overlap: ARID1B at 6q25.3, MAP3K4 at 6q26, and UTRN (6q24.1) and PHACTR2 (6q24.2). Further annotation was used to zero in on potential risk variants in those genes. All four genes are good candidate genes for lung cancer risk, having been linked to either lung cancer specifically or other cancers. However, this is the first time any of these genes has been implicated in germline risk. Functional analysis of these four genes is planned for future work. SIGNIFICANCE: This study identifies four genes associated with lung cancer risk, which could help guide future lung cancer prevention and treatment approaches.

Germline microsatellite genotypes differentiate children with medulloblastoma

BACKGROUND

The germline genetic events underpinning medulloblastoma (MB) initiation, and therefore the ability to determine who is at risk, are still unknown for the majority of cases. Microsatellites are short repeated sequences that make up ~3% of the genome. Repeat lengths vary among individuals and are often nonrandomly associated with disease, including several cancers such as breast, glioma, lung, and ovarian. Due to their effects on gene function, they have been called the "tuning knobs of the genome."

METHODS

We have developed a novel approach for identifying a microsatellite-based signature to differentiate MB patients from controls using germline DNA.

RESULTS

Analyzing germline whole exome sequencing data from a training set of 120 MB subjects and 425 controls, we identified 139 individual microsatellite loci whose genotypes differ significantly between the groups. Using a genetic algorithm, we identified a subset of 43 microsatellites that distinguish MB subjects from controls with a sensitivity and specificity of 92% and 88%, respectively. This microsatellite signature was validated in an independent dataset consisting of 102 subjects and 428 controls, with comparable sensitivity and specificity of 95% and 90%, respectively. Analysis of the allele genotypes of those 139 informative loci demonstrates that their association with MB is a consequence of individual microsatellites' genotypes rather than their hypermutability. Finally, an analysis of the genes harboring these microsatellite loci reveals cellular functions important for tumorigenesis.

CONCLUSION

This study demonstrates that MB-specific germline microsatellite variations mark those at risk for MB development and suggests mechanisms of predisposition.

Whole-Exome Sequencing Identifies Somatic Mutations Associated With Mortality in Metastatic Clear Cell Kidney Carcinoma

Clear cell renal cell carcinoma (ccRCC) is among the most aggressive histologic subtypes of kidney cancer, representing about 3% of all human cancers. Patients at stage IV have nearly 60% of mortality in 2–3 years after diagnosis. To date, most ccRCC studies have used DNA microarrays and targeted sequencing of a small set of well-established, commonly altered genes. An exception is the large multi-omics study of The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), which identified new ccRCC genes based on whole exome-sequencing (WES) data, and molecular prognostic signatures based on transcriptomics, epigenetics and proteomics data. Applying WES to simultaneously interrogate virtually all exons in the human genome for somatic variation, here we analyzed the burden of coding somatic mutations in metastatic ccRCC primary tumors, and its association with patient mortality from cancer, in patients who received VEGF receptor-targeting drugs as the first-line therapy. To this end, we sequenced the exomes of ten tumor–normal pairs of ccRCC patient tissues from primary biopsies at >100× mean depth and called somatic coding variation. Mutation burden analysis prioritized 138 genes linked to patient mortality. A gene set enrichment analysis evidenced strong statistical support for the abundance of genes involved in the development of kidney cancer (p = 2.31 × 10⁻⁹) and carcinoma (p = 1.22 × 10⁻⁵), with 49 genes having direct links with kidney cancer according to the published records. Two of these genes, SIPA1L2 and EIF3A, demonstrated independent associations with mortality in TCGA-KIRC project data. Besides, three mutational signatures were found to be operative in the tumor exomes, one of which (COSMIC signature 12) has not been previously reported in ccRCC. Selection analysis yielded no detectable evidence of overall positive or negative selection, with the exome-wide number of nonsynonymous substitutions per synonymous site reflecting largely neutral tumor evolution. Despite the limited sample size, our results provide evidence for candidate genes where somatic mutation burden is tentatively associated with patient mortality in metastatic ccRCC, offering new potential pharmacological targets and a basis for further validation studies.

Conserved microsatellites may contribute to stem-loop structures in 5', 3' terminals of Ebolavirus genomes

Microsatellites (SSRs) are ubiquitous in coding and non-coding regions of the Ebolavirus genomes. We synthetically analyzed the microsatellites in whole-genome and terminal regions of 219 Ebolavirus genomes from five species. The Ebolavirus sequences were observed with small intraspecies variations and large interspecific variations, especially in the terminal non-coding regions. Only five conserved microsatellites were detected in the complete genomes, and four of them which well base-paired to help forming conserved stem-loop structures mainly appeared in the terminal non-coding regions. These results suggest that the conserved microsatellites may be evolutionary selected to form conserved secondary structures in 5′, 3′ terminals of Ebolavirus genomes. It may help to understand the biological significance of microsatellites in Ebolavirus and also other virus genomes.

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Conserved microsatellites mainly occurred in 5′, 3′ terminal non-coding regions.

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Conserved microsatellites may contribute to conserved stem-loop structures.

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Conserved microsatellites might be preserved under greater evolutionary pressure.

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.

CAGm: a repository of germline microsatellite variations in the 1000 genomes project

The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): a database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: a mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/.

ZDHHC3 as a Risk and Mortality Marker for Breast Cancer in African American Women

African American woman are 43% more likely to die from breast cancer than white women and have increased the risk of tumor recurrence despite lower incidence. We investigate variations in microsatellite genomic regions-a type of repetitive DNA-and possible links to the breast cancer mortality gap. We screen 33 854 microsatellites in germline DNA of African American women with and without breast cancer: 4 are statistically significant. These are located in the 3' UTR (untranslated region) of gene ZDHHC3, an intron of transcribed pseudogene INTS4L1, an intron of ribosomal gene RNA5-8S5, and an intergenic region of chromosome 16. The marker in ZDHHC3 is interesting for 3 reasons: (a) the ZDHHC3 gene is located in region 3p21 which has already been linked to early invasive breast cancer, (b) the Kaplan-Meier estimator demonstrates that ZDHHC3 alterations are associated with poor breast cancer survival in all racial/ethnic groups combined, and (c) data from cBioPortal suggest that ZDHHC3 messenger RNA expression is significantly lower in African Americans compared with whites. These independent lines of evidence make ZDHHC3 a candidate for further investigation.