Over the last twenty-five years, breast cancer genetics has moved from linkage in high-risk families to association in population-based studies. Accordingly, the genetic variants that have been identified range from rare high-penetrance mutations to common low-penetrance markers. We summarize current knowledge and consider whether understanding how these that variants influence risk could help to refine risk prediction and develop targeted therapies.

by Dr Olivia Fletcher and Dr Syed Haider

Rare high-penetrance mutations
The earliest evidence for genetic susceptibility to cancer came from epidemiological studies in the 1940s and 1950s showing increased cancer risk in the relatives of cancer patients. It was not until the 1990s that linkage analysis, i.e. the genotyping of genetic markers in large family pedigrees, led to the identification of the first breast cancer susceptibility gene, BRCA1, at 17q21 [1]. Identification of the second breast cancer susceptibility gene, BRCA2, at 13q12-13 followed relatively quickly [2]. Mutations in BRCA1 and BRCA2 are present at a frequency of approximately 1 in 800 for BRCA1 and 1 in 500 for BRCA2, they confer high relative risks of breast cancer in carriers (more than tenfold) and are associated with early onset disease [3, 4].
Moderate-risk variants
The next milestone in breast cancer genetics came in 2002 with the discovery of frameshift alteration in the checkpoint kinase 2 gene, CHEK2*1100delC. This variant was discovered using a combination of linkage and mutation screening in a large multiple-case breast cancer family from the Netherlands, followed by analysis of the CHEK2*110delC variant in high-risk breast cancer families, ‘unselected’ breast cancer cases and controls [5]. The CHEK2*1100delC variant occurs on a single haplotype indicating that all CHEK2*1100delC-carrying chromosomes arise from a single founder; this variant is confined to Northern European populations with a prevalence in controls that varies significantly between Northern European populations. Compared to truncating mutations in BRCA1 and BRCA2, the relative risk associated with CHEK2*1100delC is moderate – approximately twofold.

Subsequent to the discovery of CHEK2*1100delC, additional moderate-risk variants were identified in candidate genes including ataxia telangiectasia mutated (ATM), partner and localiser of BRCA2 (PALB2) and BRCA1 interacting protein C-terminal helicase 1 (BRIP1). These variants were discovered by sequencing of exons and exon/intron boundaries of DNA damage repair genes in breast cancer cases from high- and moderate-risk families. Variants in these genes occur in the population at combined frequencies (per gene) of around 1% and are predominantly protein-truncating mutations.

Common low-penetrance variants
It was not until 2007 that the first genome-wide association study (GWAS) of breast cancer successfully identified five common low-penetrance variants; minor allele frequencies of these variants ranged from 25 to 40% and they were associated with relative risks of 1.07 to 1.26 [6]. Detecting relative risks of this magnitude required three stages of genotyping and a total of 26 258 cases and 26 894 controls. This study was an order of magnitude larger than any previous study marking the beginning of the era of GWAS as well as large consortia. Since 2007 many more breast cancer GWASs have been published, but the major advances in identifying and cataloguing additional low-penetrance variants have come from large collaborative efforts led by the Breast Cancer Association Consortium (http://bcac.ccge.medschl.cam.ac.uk/); in particular two large analyses – the Collaborative Oncological Gene-environment study (COGS) and OncoArray [7, 8]. To date, more than 150 low-penetrance variants conferring relative risks of approximately 0.81–1.35 have been identified. Not surprisingly, the more common variants with the (relatively) more extreme breast cancer odds ratios were identified first, by the GWASs (shown in deep blue, Fig. 1); less common variants and variants with less extreme odds ratios were identified most recently, by the largest pooled analysis, OncoArray (shown in green, Fig. 1).

Contribution to the excess familial relative risk
Breast cancer, like most common cancers, shows familial aggregation; the risk of breast cancer in the first-degree relative of a breast cancer case is about twice that of the risk in the general population [3]. The proportion of this ‘familial relative risk’ that is explained by one or more variants is the metric used to quantify the relative contributions of the different classes of variants – and to estimate the number of variants that have not yet been identified. Relative proportions of all three types of variants are shown in Figure 2; mutations in BRCA1 and BRCA2 account for approximately the same proportion of the familial relative risk as the sum of the common low-penetrance variants.
Differences between coding variants and non-coding variants
One fundamental difference between the high-penetrance mutations in BRCA1 and BRCA2, the moderate-risk variants in DNA damage repair genes and the low-penetrance variants identified by GWAS is that the vast majority of low-penetrance GWAS variants map to non-coding DNA. Estimating the risk of breast cancer for individual BRCA1 and BRCA2 mutation carriers is not trivial; there is some evidence that breast cancer risks differ according to the position of the mutation within the gene [4] and for BRCA2, there is evidence of effect modification by common low-penetrance variants [9]. For the low-penetrance GWAS variants, however, the problem is rather different; while the relative risks associated with the marker single nucleotide polymorphisms (SNPs) are fairly precisely estimated, the underlying ‘causal’ variants and the genes that these variants influence remain – largely – unknown. Approaches to the functional characterisation of GWAS risk loci include fine-scale mapping of potentially large genomic regions, the analysis of SNP genotypes in relation to expression of nearby genes (eQTL) and the use of chromatin association methods [chromosome conformation capture (3C) and Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET)] of regulatory regions to determine the identities of target genes. Regulatory elements have been shown to form physical interactions with the genes that they regulate, often over long distances and frequently ‘skipping over’ proximal genes; chromatin association methods capture these interactions and use them to infer likely target genes. We have recently carried out a high-throughput, high resolution analysis of 63 breast cancer risk loci using Capture Hi-C [10]. We were able to identify 110 putative target genes mapping to 33 risk loci. Although some of these putative target genes were well-known cancer genes others were not; in depth follow-up studies will be required to determine which of these putative target genes truly influence breast cancer risk and the mechanisms by which they do so.

Causal variants and target genes can inform risk prediction and therapy
NICE guidelines for the classification, care and management of breast cancer, based on an individual’s family history of breast and other cancers, are used to classify women into three categories: population risk (<17% lifetime risk), moderate risk (17–30% lifetime risk) and high risk (≥30% lifetime risk; https://www.nice.org.uk/guidance/CG164). The options that are available to a woman – increased surveillance, genetic testing, chemoprevention and prophylactic surgery – depend on which category she falls within. A longer-term aim of GWAS is the development of polygenic risk scores (PRS) that can be incorporated into risk prediction algorithms to refine risk estimates. A recent analysis based on 77 breast cancer-associated SNPs, estimated lifetime risks of breast cancer for women in the lowest and highest quintiles of the PRS as 5.3% (population risk) and 17.2% (moderate risk), respectively [11]. Inclusion of larger numbers of SNPs and incorporating causal variants rather than tag SNPs should improve the discriminatory power of the PRS.

In this era of stratified medicine, identifying the genes that underlie GWAS associations and hence – presumably – contribute to defining disease subgroups, also offers the potential for targeted therapies. For instance, metastatic breast cancer patients with germline BRCA1 or BRCA2 mutations who also lack HER2 expression are eligible for Olaparib [a targeted cancer drug that inhibits poly-ADP ribose polymerase (PARP)] as of January 2018. A recent study demonstrated that Olaparib-treated patients have significantly improved progression-free survival (PFS) compared to patients treated with standard-therapy (median PFS of 7 months vs 4.2 months respectively) [12]. Breast cancer patients with germline BRCA1 or BRCA2 mutations already have a defect in their DNA repair mechanisms; by blocking PARP proteins, Olaparib acts to exacerbate DNA damage and trigger cell death, specifically in cancer cells (synthetic lethality). Although defects in DNA repair can be a consequence of germline BRCA mutations, some breast cancer patients manifest defects in DNA repair in the absence of germline BRCA mutations; these patients are also regarded as BRCA deficient – a characteristic often termed as ‘BRCAness’ [13]. Scientists are actively searching for biomarkers of BRCAness in order to assess the suitability of existing PARP inhibitors for patients exhibiting BRCAness [14]. Additional clinical trials on studying efficacy of PARP inhibitors for treating other breast cancer subgroups are underway.

The associations between GWAS SNPs and disease are very modest, and this is often cited as a disadvantage when it comes to considering the genes that map to these loci as putative drug targets. However, an individual non-coding ‘causal’ SNP will usually explain only a small proportion of variation in expression of the gene(s) that it regulates; chemically targeting these genes could have a much more profound effect on disease incidence or outcome. In support of this prediction, a recent investigation by scientists from GlaxoSmithKline estimated that selecting genetically supported targets (including those identified by GWAS) could double the success rate of drugs in clinical development. Although this estimate may be less applicable to cancer drugs, where the somatic genome is as important – or more important – than the germline genome [15,] it leaves open the possibility of new therapies targeting the genes that underlie GWAS associations.

Acknowledgements
We thank Breast Cancer Now for funding this work as part of Programme Funding to the Breast Cancer Now Toby Robins Research Centre.

References
1. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 1994; 266(5182): 66–71.
2. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Gregory S, Gumbs C, et al. Identification of the breast cancer susceptibility gene BRCA2. Nature 1995; 378(6559): 789–792.
3. Easton DF. How many more breast cancer predisposition genes are there? Breast Cancer Res 1999; 1(1): 14–17.
4. Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, Jervis S7, van Leeuwen FE5, Milne RL, et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA 2017; 317(23): 2402–2416.
5. Meijers-Heijboer H, van den Ouweland A, Klijn J, Wasielewski M, de Snoo A, Oldenburg R, Hollestelle A, Houben M, Crepin E, et al. Low-penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet 2002; 31(1): 55–59.
6. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007; 447(7148): 1087–1093.
7. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 2013; 45(4): 353–361e2.
8. Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, Lemaçon A, Soucy P, Glubb D, et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017; 551(7678): 92–94.
9. Gaudet MM, Kirchhoff T, Green T, Vijai J, Korn JM, Guiducci C, Segrè AV, McGee K, McGuffog L, et al. Common genetic variants and modification of penetrance of BRCA2-associated breast cancer. PLoS Genet 2010; 6(10): e1001183.
10. Baxter JS, Leavy OC, Dryden NH, Maguire S, Johnson N, Fedele V, Simigdala N, Martin LA, Andrews S, et al. Capture Hi-C identifies putative target genes at 33 breast cancer risk loci. Nat Commun 2018; 9(1): 1028.
11. Mavaddat N, Pharoah PD, Michailidou K, Tyrer J, Brook MN, Bolla MK, Wang Q, Dennis J, Dunning AM, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 2015; 107(5): pii: djv036.
12. Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, Delaloge S, Li W, Tung N, et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Eng J Med 2017; 377(6): 523–533.
13. Lord CJ, Ashworth A. BRCAness revisited. Nat Rev Cancer 2016; 16(2): 110–120.
14. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, Ramakrishna M, Martin S, Boyault S, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017; 23(4): 517–525.
15. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nat Genet 2015; 47(8): 856–860.

The authors
Olivia Fletcher* PhD, Syed Haider PhD
Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK

*Corresponding author
E-mail: Olivia.fletcher@icr.ac.uk

Age-related macular degeneration is a late-onset disease of the eye macula that can result in blindness and in a significant deterioration of quality of life. Genetics and oxidative stress from light exposure and smoking are major risk factors. In this brief report, we discuss genetic and plasma epigenetic biomarkers that are examined for their association with the disease.

by Prof. Christos Kroupis, Prof. George Kitsos, Prof. Marilita M. Moschos and Prof. Michael B. Petersen

Introduction
Age-related macular degeneration (AMD) is a slow and progressive disease of the macula, i.e. the central part of the retina, and the leading cause of irreversible visual loss in the Western world. Globally, AMD accounts for 8.7% of all blindness and is predicted to affect 196 million people by 2020; it is more prevalent in populations of European descent than those of Asian and African descent [1]. With the loss of central vision frequently involving both eyes, AMD is a debilitating condition affecting daily tasks such as reading and driving, and ultimately having severe consequences on independence and quality of life. AMD is a late-onset disease with a complex etiology. Major risk factors contributing to susceptibility include age, family history (genetics), light exposure and smoking [2–4].

AMD can be considered a multifactorial dysfunction of the retinal photoreceptor cells and their support system, which includes the retinal pigment epithelium (RPE), Bruch’s membrane (BrM), and the choroidal vasculature. The fundamental cause of vision loss in AMD is the progressive damage to photoreceptors, which can be triggered by RPE dysfunction and atrophy, impaired transport of oxygen, nutrients and metabolites between vessels and outer retinal cells and leakage from choroidal capillaries that invade the retina through the RPE [5].

Light entering the eye is focused on the retina, where delicately specialized rod and cone photoreceptors allow its transduction into chemical signals to visual centers in the brain. Photoreceptors are metabolically active neurons with oxygen requirements that are among the highest in the human body. In humans, rods and cones exhibit a distinct topography; the macula (6-mm diameter) contains a cone-dominated fovea (0.8-mm diameter) that is associated with high-acuity vision [5] (Fig. 1a). Just posterior to the photoreceptors, the RPE consists of polarized epithelial cells located at the base of the retina as a single layer of hexagonal cells that are densely packed with pigment granules (melanosomes). The RPE is firmly attached to the underlying basement membrane (BrM). The RPE provides the nutrients needed to maintain visual function by light-sensitive outer segments of the photoreceptors. RPE melanosomes absorb excess incoming light, which protects the retina from light damage. Other critical roles for the RPE involve phagocytosing shed outer retinal segments and scavenging photoreceptor debris, thus, serving as part of the waste-disposal system for the retina. The RPE is known to produce and to secrete a variety of growth factors to help build and sustain the choroid and photoreceptors [6]. The choroid is an extensive vascular meshwork of capillaries lining the posterior part of the eye that supplies nutrients utilized by the retina and acts as a conduit for the by-products of photoreceptor and RPE metabolism [5]. The inner aspect of the choroid, next to the RPE, is the BrM, a laminar extracellular matrix composed mainly of collagen and elastin. Accumulating evidence suggests that the molecular, structural and functional properties of the BrM are dependent on age, genetics, environmental factors, retinal location and disease state. As a result, some properties of the BrM are unique to each human individual at a given age and, therefore, affect uniquely the progression of AMD [6].

AMD pathology
There are two AMD forms: dry (in 90% of patients) and wet (in 10%). In the dry form of AMD, apoptosis of the RPE, neuroretina and choriocapillaris progresses slowly and causes permanent central vision loss. Initially, the BrM exhibits increased deposition of cholesterol and calcium with age. Drusen genesis is a sign of AMD progression (Fig. 1b). Drusen are amorphic extracellular deposits of lipids, proteins, inflammatory molecules in the space between RPE and BrM. The alternative complement path is activated by lipofuscin constituents (which are mostly by-products of the retinal vision cycle) as a response to the inflammatory process connected with drusen genesis. Unfortunately, as we age, mitochondrial function decreases (and mtDNA mutations accumulate) and, therefore, oxidative damage increases. In parallel, antioxidant capacity decreases and the efficiency of repair systems and cytoprotective ubiquitin proteolytic system become impaired [4]. Environmental factors associated with increased production of reactive oxygen species (ROS), such as increased light exposure and cigarette smoking, are additive and have been linked with AMD risk. Collectively, these factors create an environment in which proteins, DNA and lipids become oxidatively damaged. The combination of inadequately neutralized oxidized proteins in the drusen and inflammation associated with OSEs (oxidative specific epitopes) induce focal loss of RPE cells, degeneration of the overlying photoreceptors and vision loss as described in Figure 2 [4].

In the advanced dry form of AMD, geographic atrophy (GA) develops from large, confluent drusen proceeds to hyperpigmentation and then, to cell apoptosis. At present, there is no effective treatment of the dry form. In the wet form, the cause of potential central vision loss is choroidal neovascularization (CNV). An inflammatory reaction initiates pathological angiogenesis that penetrates through defects in the BrM and the RPE layers to the subretinal space, where exudation and bleeding destroy photoreceptors. Commonly used anti-VEGF factors given in repeated intravitreal injections inhibit neovascularization and can stabilize vision acuity in most wet AMD patients.

Genetic biomarkers in AMD
Identification of associated genetic variants can help uncover disease mechanisms and provide entry points for therapy. Linkage of AMD families to 1q32 and the complement factor H (CFH) gene by many groups in 2005, led to the identification of the first common genome-wide significant risk variant, Y402H (rs1061170, g.43097C>T) with variable frequencies across various populations. This SNP (single nucleotide polymorphism) results in an impaired alternative complement pathway inactivation. This discovery propagated numerous genetic and genomic studies that have contributed to our understanding of the pathological mechanisms contributing to AMD. Notably, the subsequent association of common and rare alleles at or near several additional complement genes (CFH, C2/CFB, C3, CFI and C9) has led to the ‘inflammation hypotheses’, with cumulative evidence from genetics and histopathological studies [3]. Another major non-complement pathway AMD-associated locus lies on chromosome 10q26 (LOC387715) and many studies have demonstrated a strong association between AMD and the ARMS2 gene that encodes for a small 107-amino acid protein. ARMS2 A69S SNP (rs10490924, g.5270G>T) is a mutation associated with subsequent mitochondrial dysfunction, ROS generation and accumulation of somatic mitochondrial DNA mutations. These initial promising findings prompted world-wide efforts and culminated in the AMD Gene consortium 2013 study where 19 common variants were associated with the disease in a large number of patients with the use of SNP microarrays; still the two aforementioned SNPs possessed the highest odds ratios (OR) for AMD development (between 2.4 and 2.7) with some differences in their effect according to their different allele frequencies in various populations. It was estimated that these 19 variants can explain ~45% of the genetic heterogeneity in AMD patients above 85 years old; the two main AMD associations with CFH and ARMS2 genes account for a significant 25% of the total cases [5]. Therefore, we and other groups have developed fast, high-throughput robust and accurate genotyping assays for their accurate detection (Fig. 3) [7–9]. Early identification of individuals at risk provides an opportunity to prevent or attenuate the AMD disease. Homozygosity for both CFH and ARMS2 risk alleles increases the progression to advanced AMD stages (GA or CNV) to 48% compared to 5% for those carrying wild-type alleles in both genes [10]. Models incorporating these alleles and/or an expanded variant panel along with smoking and body mass index have been the basis for various commercial tests estimating AMD risk, such as RetnaGene (Nicox), Macula/Vita Risk (ArcticDx), Asper Ophthalmics, etc. Potential nutrigenetic antioxidant interventions have been proposed based on CFH and ARMS2 genotypes [11, 12]. In dry AMD where no therapy exists, anti-complement antibodies are in clinical trials right now (eculizumab, lampalizumab) and genetic tests providing information for complement polymorphisms could select appropriate patients that could benefit from such therapy.

The largest and latest 2016 AMD Gene Consortium study identified additional loci by using an Illumina human core exome array for >12 million variants in 16,144 advanced AMD patients versus 17,832 controls; 52 independently associated common and rare variants were distributed across 34 loci [13]. Now that technological advances permit – with the advent of next-generation sequencing platforms – it would be extremely useful to validate AMD-specific gene-panels for these patients.
Plasma epigenetic biomarkers in AMD
A small, non-coding micro(mi)RNA (18–24 nt) binds to specific mRNAs – depending on its sequence – and results in their degradation by cleavage, translational repression and/or polyA-deadenylation. One miRNA can target many mRNAs but also one mRNA can be targeted by many miRNAs. Emerging evidence arising from tissue studies suggest that beside environmental and genetic factors, epigenetic mechanisms (such as miRNA regulation of gene expression) are relevant to AMD and are providing an exciting new avenue for research and therapy. Sera and plasma (which are easily collected non-invasively) contain cell free DNA, RNA and circulating nucleic acids that can serve as potential biomarkers. The miRNAs identified in human plasma are known to be relatively stable, as they have been found to be resistant to RNase degradation. A recent study has identified a plasma miRNA expression profile specific for AMD patients [14]. Plasma miRNA expression was first screened for multiple miRNAs and then, those showing differences between patients and healthy controls were further explored with individual, specific RT-qPCR assays in a larger number of samples. In another study exploring wet and dry AMD differences in plasma, the miRNA expression analysis revealed increased expression of miR661 and miR3121 in dry AMD patients and miR4258, miR889 and let7 in wet AMD patients compared to controls [15].

References
1. Wong WL, Su X, Li X, Cheung CM, Klein R, Cheng CY, Wong TY. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health 2014; 2: e106–e116.
2. Kokotas H, Grigoriadou M, Petersen MB. Age-related macular degeneration: genetic and clinical findings. Clin Chem Lab Med 2011; 49: 601–616.
3. Tan PL, Bowes RC, Katsanis N. AMD and the alternative complement pathway: genetics and functional implications. Hum Genomics 2016; 10: 23.
4. Chiras D, Kitsos G, Petersen MB, Skalidakis I, Kroupis C. Oxidative stress in dry age-related macular degeneration and exfoliation syndrome. Crit Rev Clin Lab Sci 2015; 52: 12–27.
5. Fritsche LG, Fariss RN, Stambolian D, Abecasis GR, Curcio CA, Swaroop A. Age-related macular degeneration: genetics and biology coming together. Annu Rev Genomics Hum Genet 2014; 15: 151–171.
6. Bhutto I, Lutty G. Understanding age-related macular degeneration (AMD): relationships between the photoreceptor/retinal pigment epithelium/Bruch’s membrane/choriocapillaris complex. Mol Aspects Med 2012; 33: 295–317.
7. Velissari A, Skalidakis I, Oliveira SC, Koutsandrea C, Kitsos G, Petersen MB, Kroupis C. Novel association of FCGR2A polymorphism with age-related macular degeneration (AMD) and development of a novel CFH real-time genotyping method. Clin Chem Lab Med 2015; 53: 1521–15219.
8. Sarli A, Skalidakis I, Velissari A, Koutsandrea C, Stefaniotou M, Petersen MB, Kroupis C, Kitsos G, Moschos MM. Investigation of associations of ARMS2, CD14, and TLR4 gene polymorphisms with wet age-related macular degeneration in a Greek population. Clin Ophthalmol 2017; 11: 1347–1358.
9. Xu Y, Guan N, Xu J, Yang X, Ma K, Zhou H, Zhang F, Snellingen T, Jiao Y, et al. Association of CFH, LOC387715, and HTRA1 polymorphisms with exudative age-related macular degeneration in a northern Chinese population. Mol Vis 2008; 14: 1373–1381.
10. Seddon JM, Francis PJ, George S, Schultz DW, Rosner B, Klein ML. Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA 2007; 297: 1793–1800.
11. Awh CC, Hawken S, Zanke BW. Treatment response to antioxidants and zinc based on CFH and ARMS2 genetic risk allele number in the Age-Related Eye Disease Study. Ophthalmology 2015; 122: 162–169.
12. Vavvas DG, Small KW, Awh CC, Zanke BW, Tibshirani RJ, Kustra R. CFH and ARMS2 genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation. Proc Natl Acad Sci U S A 2018; 115: E696–E704.
13. Fritsche LG, Igl W, Bailey JN, Grassmann F, Sengupta S, Bragg-Gresham JL, Burdon KP, Hebbring SJ, Wen C, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet 2016; 48: 134–143.
14. Ertekin S, Yıldırım O, Dinç E, Ayaz L, Fidancı SB, Tamer L. Evaluation of circulating miRNAs in wet age-related macular degeneration. Mol Vis 2014; 20: 1057–1066.
15. Szemraj M, Bielecka-Kowalska A, Oszajca K, Krajewska M, Goś R, Jurowski P, Kowalski M, Szemraj J. Serum microRNAs as potential biomarkers of AMD. Med Sci Monit 2015; 21: 2734–2742.

The authors
Christos Kroupis*¹ MSc, PhD; George Kitsos² MD, PhD; Marilita M. Moschos³ MD, PhD; Michael B. Petersen⁴ MD, PhD

*Corresponding author
E-mail: ckroupis@med.uoa.gr

Dermatomycoses are extremely widespread, and are characteristically long-lasting, recurring and very difficult to cure. Early and accurate identification of the causative agent is essential for targeted therapy. A new DNA microarray provides direct detection and differentiation of the most important dermatomycosis pathogens in one reaction. The assay simultaneously detects up to 50 dermatophyte species, and provides species identification for 23 of these, as well as 6 yeasts and moulds. The microarray analysis aids differential diagnosis of dermatomycoses from other dermatoses (e.g. psoriasis), and specifically identifies mixed infections with yeasts and moulds. The dermatomycosis microarray is part of the established EUROArray platform, which also includes microarrays for multiplex identification of sexually transmitted infections (STI) and complete detection and typing of human papillomaviruses (HPV).

by Dr Jacqueline Gosink

Dermatomycosis
Dermatomycoses are infections of the skin, hair and nails which are typically caused by dermatophytes and in rarer cases by yeasts and moulds. Fungal infections of the skin are the most frequently occurring infectious diseases globally with high and growing relapse rates. Elderly people and immunocompromised patients are especially at risk. Worldwide, around 20 to 25% of the population is affected by fungal skin diseases.

Infections which are caused exclusively by dermatophytes are referred to as dermatophytoses or tinea. Tinea pedis, which occurs on the soles of the feet and between the toes, is one of the most frequent forms worldwide, followed by tinea unguium, which affects the nails, and tinea corporis, which affects the neck, back or trunk. Further forms, for example, on the face, legs, beard area, arms and hands, are rarer. Nail infections caused by dermatophytes and/or yeasts/moulds are called onychomycoses. They are typically accompanied by deformation of the nail.

Pathogens of dermatomycosis
Dermatophytes encompass fungi of the genera Trichophyton, Epidermophyton, Nannizzia, Paraphyton, Lophophyton, Microsporum and Arthroderma. Individual species are classified as anthropophilic, zoophilic or geophilic according to their main occurrence. Human pathogenic yeasts and moulds include Candida spp., Scopulariopsis brevicaulis, Fusarium spp. and Aspergillus fumigatus.

Around 70% of human dermatophyte infections are caused by anthropophilic species. Trichophyton rubrum, in particular, is the most frequent cause of fungal skin infections worldwide. Infections can spread easily to other areas of the body or to other persons, for example, via showers, bathtubs or floors. Zoophilic dermatophytes are transmitted to humans by close contact with animals, especially pets, which are often asymptomatic. They can cause severe inflammatory reactions in humans. Geophilic dermatophytes cause disease less frequently in humans. Infections typically occur in gardeners and farm workers or children who play outside. Moulds and yeasts often cause opportunistic infections, benefitting from damage to the skin or nail caused by an existing dermatophyte infection. In immunocompromised individuals, local fungal infections may develop into systemic mycosis.

Clinical picture
Dermatomycoses are clinically heterogeneous and cannot always be differentiated from other dermatoses, such as eczema, psoriasis, erysipelas, or autoimmune diseases such as Lichen ruber planus. Furthermore, 5 to 15% of onychomycosis cases comprise mixed infections of dermatophytes with yeasts and moulds. Simultaneous bacterial infection of the damaged skin, pretreatment with corticosteroid-containing preparations, or secondary contact allergy can also hinder diagnosis.

Dermatomycoses must always be treated. This is generally undertaken using various topical antifungal drugs, with severe cases sometimes requiring oral medication. Each drug has a limited activity spectrum. Positive pathogen identification prior to treatment enables targeted selection of the most suitable drug and optimal planning of the oftentimes lengthy therapy. In multiple infections, a change of the primary pathogenic agent may occur during the therapy and it may seem like the therapy is failing. This must be taken into account in the treatment of fungal infections of the nails, which may only yield first success after months. Identification of the causative pathogen also helps to determine the source of the infection. In the case of zoophilic pathogens, for example, this is usually a pet.

Laboratory diagnostics
Laboratory diagnostic methods for identifying dermatomycosis pathogens include microscopic detection and an attempt at culturing from clinical material. Successful culture in most cases enables species assignment based on micro- and macromorphological presentation of the fungus. Culturing is, however, time-consuming and is not possible for all dermatophytes. In mixed infections, false diagnoses may occur since slowly growing species may be overgrown or overlooked. Furthermore, antifungal therapy started before the sampling can hinder the culture.

Direct detection of pathogen genomic material by DNA microarray enables secure and accurate identification of the causative agent. Microarray analysis offers a significant time advantage over detection by culturing, and is especially useful for detecting dermatophytes that are difficult to cultivate. It provides higher sensitivity and specificity, even in patients already undergoing treatment. The EUROArray Dermatomycosis analysis includes a universal dermatophyte detection encompassing 50 species of the genera Trichophyton, Epidermophyton, Microsporum, Nannizzia, Arthoderma, Lophophyton, as well as species identification for the 23 dermatophyte and 6 yeast and mould species listed in Table 1.

EUROArray procedure
The EUROArray procedure (Figure 1) is performed on DNA samples isolated from skin scales, nail shavings or hair stubs. Defined gene sections of the pathogens are first amplified by multiplex polymerase chain reaction (PCR). The fluorescently labelled PCR products are then incubated with biochip microarray slides containing immobilized complementary probes. Specific binding (hybridization) of the PCR products to their corresponding oligonucleotide probes is detected using a special microarray scanner. The signals are evaluated and interpreted automatically by the EUROArrayScan software (Figure 2). A detailed result report is produced for each patient and all data are documented and archived. Meticulously designed primers and probes, ready-to-use PCR components and integrated controls all contribute to the reliability of the analysis. The entire EUROArray procedure from sample arrival to report release is IVD-validated and CE-registered, supporting quality management in diagnostic laboratories.

Specifications and evaluation
The lower detection limit of the test system depends on the pathogen and lies between 50 and 600 DNA copies per reaction, in individual cases also higher. Evaluation studies verified that template DNA in concentrations ranging from the lower detection limit to 50 ng can be used in the PCR without generating any false positive results. Furthermore, potential cross reactivity with 37 microorganisms of the resident and transient skin flora was excluded experimentally.

In an evaluation study with 409 clinical samples, the EUROArray Dermatomycosis yielded a good agreement with the precharacterization. In many cases additional pathogens that were not included in the precharacterization were detected. The additional findings were confirmed by further independent tests or sequencing. Thus, the microarray provides reliable results and broad detection capabilities.

STI detection
The EUROArray STI is based on the same technology and provides parallel direct detection of the pathogenic agents of eleven sexually transmitted infections (STIs) in one reaction, namely Chlamydia trachomatis, Neisseria gonorrhoea, Mycoplasma genitalium, Mycoplasma hominis, Ureaplasma urealyticum, Ureaplasma parvum, Haemophilus ducreyi, Treponema pallidum, Trichomonas vaginalis and herpes simplex viruses 1 and 2.
STIs are often asymptomatic, but can nevertheless lead to serious sequelae, for example infertility, fetal damage during pregnancy, and severe postnatal infections in newborns. The PCR-based detection allows identification of both manifest and silent infections, and is thus suitable for diagnosis of symptomatic patients as well as for general screening. It offers a huge time advantage over cultivation and is especially useful for detecting sexually transmitted pathogens that are difficult or impossible to cultivate, e.g. C. trachomatis, Mycoplasma, Ureaplasma, T. pallidum. Due to amplification of the pathogen DNA, infections with a reduced pathogen number can also be reliably detected. A broad screening for STI pathogens is particularly important in asymptomatic or clinically ambiguous cases and for detecting multiple infections, which are often missed during single-parameter testing.

HPV detection and typing
The EUROArray HPV provides detection and typing of all 30 genitally relevant HPV subtypes in one test. HPV are involved in the development of cervical carcinoma, and HPV testing plays a central role in risk assessment and early diagnosis of this cancer. In contrast to Pap examinations, HPV detection is not dependent on subjective evaluation and it offers very high sensitivity even in the early stages of infection.
The EUROArray HPV is based on detection of the viral oncogenes E6 and E7, which provides the highest possible sensitivity. Using an extensive panel of specific primers and probes, the EUROArray detects and distinguishes between 18 high-risk subtypes that may trigger cancer (16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82) and 12 low-risk subtypes that cause benign genital warts (6, 11, 40, 42, 43, 44, 54, 61, 70, 72, 81, 89). Multiple infections are reliably identified, and primary and persistent infections can be differentiated. A positive result for a high-risk subtype indicates an increased risk for cervical carcinoma, which can then be minimised by more frequent follow-up examinations to detect morphological cell changes at an early stage. Based on the recommendations of the respective professional societies, HPV-negative women can forgo subsequent HPV tests and Pap smears for a longer time interval.

Conclusions
Molecular diagnostic tests such as the EUROArray are an important tool for identifying the precise pathogenic agent in various infectious diseases, supporting decision-making on specific treatment. The new EUROArray Dermatomycosis provides the most comprehensive direct detection of dermatomycosis pathogens currently available commercially, and complements existing assays for STI and HPV. The EUROArray procedure is easy to perform and does not require extensive expertise in molecular biology. Moreover, the fully automated evaluation ensures objective, accurate and reproducible results. Further infectious disease microarrays based on the same technology are in development.

The author
Jacqueline Gosink, PhD
EUROIMMUN AG, Seekamp 31,
23560 Luebeck, Germany

www.euroimmun.com