Open access
1. Introduction
Although malignant melanoma (MM) is mainly a sporadic disease, about 3 to 15% of the cases may show familial aggregation [1, 2]. The diagnosis of melanoma in different members of the same families does indeed suggest there is a genetically-based hereditary predisposition in a significant percentage of the cases. However, this predisposition has proven to be genetically heterogeneous. Only two high-penetrance genes had been described so far:
Other putative low-penetrance genes involved in melanoma predisposition are DNA repair genes belonging to the base excision repair (BER) and the nucleotide excision repair (NER) mechanisms. BER and NER pathways eliminate a wide variety of DNA damage, including ultraviolet (UV) photoproducts. Therefore, the ability of each individual to repair DNA damage following different causes might explain at least in part the variability in melanoma susceptibility. Although several studies have investigated the association between polymorphisms in NER genes and risk of melanoma, most of the study sizes were relatively small, and the results were not consistent [18-24]. On the other hand, genetic polymorphisms have been identified in several BER genes and studies suggest that some of these polymorphisms may be associated with cancer risk [25-29].
Further candidate low-penetrance genes allegedly linked to melanoma predisposition are members of the glutathione S-transferase (
In recent years, several genome-wide association studies (GWAS) have identified novel genomic
What follows is a summary of the results obtained in our laboratory after the screening of genes belonging to three different genetic pathways: pigmentation, DNA repair and oxidative stress. For the past few years, our group has been studying melanoma candidate genes in Spain, a southern European population, displaying a considerably darker skin than most of the other well-studied Caucasian populations, including Australian, North American and Northern Europe populations.
2. Research methods
2.1. Study subjects and data collection
A case-control collection of 946 non-related MM cases from several Spanish Hospitals and 353 volunteer cancer-free controls were recruited from 1 September 2004 to January 2011. All participants were Caucasians of Spanish origin. A standardized questionnaire was used to collect information on pigmentation characteristics (eye, hair and skin color, number of nevi, presence of solar lentigines, sun exposure habits and presence of childhood sunburns), Fitzpatrick’s classification of skin type (extracted from the medical record of cases only), tumor localization, Breslow index (depth index), and personal or family history of cancer. All study subjects gave informed consent and the study was approved by the Ethics Committee of Gregorio Marañón General University hospital and Clínico University Hospital.
Genomic DNA from cases and controls was isolated from peripheral blood lymphocytes and diluted to a final solution of 50ng/ml. MagNA Pure LC Instrument DNA extracción was used according to the manufacturer’s protocol (Roche Applied Science, Mannheim, Germany); the DNAzol procedure (Invitrogen, Eugene, OR, USA) or traditional saline method was used. DNA concentration was quantified in samples prior to genotyping by using Quant-iT PicoGreen dsDNA Reagent (Invitrogen, Eugene, OR, USA).
2.2. SNP genotyping
2.2.1. MC1R sequencing
2.2.2. Gene and SNP selection
The rest of the genes in the study were analyzed by genotyping selected SNPs. Public databases were used to collect information about single nucleotide polymorphisms (SNPs): NCBI (http://www.ncbi.nlm.nih.gov), Ensembl (http://www.ensembl.org/index.html) and HapMap (http://www.hapmap.org). SNPs selected were located in exons, in putative promoter regions or had been reported to be associated with cancer in previous studies. All SNPs had a minor allele frequency (MAF) greater than or equal to 5%. As a quality control measure we included two sample duplicates and a non-template sample per 96-well plate. For some high-throughput platforms three DNA duplicates (two intra-assays and one inter-assay) were added. For all the studies performed genotypes were scored by two different personnel in the laboratory and no discrepancies were observed.
2.2.3. Taqman and kaspar assays
The PCR primers and probes were designed by Life Technology (Foster City, CA) using their Custom Taqman SNP genotyping assays or KASPAR SNP Genotyping System KBiosciences (Hoddesdon, Herts UK). The primer and allele-specific probe sequences for Taqman as well as those used for Kaspar assays are detailed elsewhere [6, 7, 14, 16, 17, 27].
PCR conditions used were according to the manufacturer’s protocol (Life technology, Foster City, CA). After PCR, the genotype of each sample was automatically determined by measuring allele-specific final fluorescence in the ABI Prism 7900HT Detection System, using the SDS 2.1 software for allele discrimination (Life technology, Foster City, CA).
2.2.4. Sequenom
Genotyping assays were designed according to the Sequenom MassARRAY Assay Design software (version 3.0.0; Sequenom Inc., San Diego, CA, USA). Assay primers are detailed elsewhere [15, 27]. One duplicate sample, one father–mother–child trio and two negative controls were included across the plates to assess the accuracy of genotyping. SNPs were genotyped using iPLEXTM chemistry on a MALDI-TOF Mass Spectrometer (Sequenom Inc, San Diego, CA, USA). PCR reactions were carried out according to their own instructions (Sequenom Inc.).
2.2.5. Illumina
A total of 384 SNPs were genotyped using the GoldenGate Genotyping Assay system according to the manufacturer’s protocol (Illumina, San Diego, CA, USA) [16]. Genotyping was carried out using 350 ng of DNA per reaction. In addition, cases and control samples were always included in the same run. Genotypes were called using the proprietary software supplied by Illumina (BeadStudio, version 3.1.3.).
2.2.6. Taqman quantitative real-time PCR
The
2.3. Statistical analysis
Associations between
To study the effect of combined protective and risk genotypes, we reduced the sample set to 528 samples successfully genotyped for all the associated SNPs. We used a 2x2 contingency table and a t-student test between
3. Results
3.1. MC1R
Of the 946 individuals studied, 559 (59.15%) carried at least one
Among these, 25 variants were non-synonymous changes, 20 of which had been described previously [5] and 5 were identified for the first time: S41F, M128T, P268R, A285V and N281S. Six variants of the receptor have been traditionally associated with red hair color (RHC): D84E, R142H, R151C, R160W, I155T and D294H.
Similarly, another three variants have not been associated with RHC phenotype and have been designated as NRHC (V60L, V92M and R163Q). These amino acid changes have been studied in different populations because their frequency is greater than 1%.
The other variants detected in the
Among the 36 changes detected, five were individually associated with melanoma risk: V60L, R151C, I155T, R160W and D294H (P< 0.05). The highest OR was estimated for I155T (OR 3.65, 95% CI: 1.40–9.52; P=0.006). The estimated OR associated with carrying one non-synonymous variant was 1.58 (95% CI: 1.19–2.097; P =0.0013); however, the OR for carrying two non-synonymous variants was 4.38 (95% CI: 2.72–7.05; P =1.33x 10-9). The MM associated OR among those Spanish patients carrying one RHC variant was 2.36 (95% CI: 1.71–3.26; P =1.86 x10-7). However if we consider individuals homozygous or compound heterozygous for two RHC variants, the OR increased to 12.76 (95% CI: 3.06–53.29; P =1.9 x10-5).
We considered blue ⁄green eye color, blond ⁄red hair color, solar lentigines and childhood sunburns as confounders in a multivariate model.
Figure 1.
a) Summary of all
3.2. Other genes from pathways associated to melanoma
Several studies have been performed in order to evaluate other pigmentation-related genes and their relationship to MM susceptibility. The first results generated by Fernandez and cols. [4] analyzed the oculocutaneous albinism (OCA) genes:
Allele frequencies for each SNP and the P-value for their comparisons between case and control subjects are detailed in Figure 2. After discarding two of the selected SNPs, one in the
Figure 2.
Odds ratios (OR), 95% confidence interval (95% CI), P-values and Minor Allele Frequency (MAF) for the best seven genetic variants in genes belonging to pigmentation, DNA repair and oxidative stress pathways. Grey bars represent control data while black bars represent MM cases. Dark circles denote statistically significant values. Exact P-values are indicated in number.
In a second study we genotyped 384 SNPs from 65 genes belonging mainly to the pigmentation pathway [16]. Ten SNPs located on six individual chromosomes (one in each of
After analysis of genes in the pigmentation pathway, we conducted two studies where 16 genes belonging to both base excision repair (BER) and nucleotide excision repair (NER) pathways, as well as 14 genes involved in oxidative stress, including
First of all, a novel variant in the
3.3. Phenotypic characteristics
If we take into account
The number of
We assessed whether
Evidence of association with phenotypic characteristics for two
Two different SNPs in
3.4. Gene-gene interactions
We explored the combined effects of the individually associated SNPs located in the six relevant genes studied:
3.4.1. Interaction between protective alleles
Two SNPs, rs16891982 and rs35414, located in the
We observed some degree of epistatic protective interaction between rs35414 (
In addition, we observed some degree of epistatic protective interaction between rs35414 (
The interaction analyses between
Figure 3.
Interaction between protective allele variants and their effect on MM susceptibility. a)
Finally, when the effect of the interaction between the three
3.4.2. Interaction between risk alleles
In order to show the distinct combinations of MM risk alleles we performed two different analyses. The first comparison studied the effect in MM susceptibility by taking together four genotypes: rs17793678 (
Some degree of epistatic risk interaction was seen between rs17793678 (
Figure 4.
Interactions between risk allele variants and their effect on MM susceptibility. a)
A similar effect was observed when we compared rs1695 (
An additional comparison with rs17793678 (
For the second type of analyses we included the
Figure 5.
Interactions between risk allele variants on
The last group of comparisons was done taking into consideration three and four genes at the same time. Firstly, we showed that combining
Figure 6.
Interactions between risk variants on
3.4.3. Complex interactions
The role of
A great reduction of risk was detected when the rare protective alleles at
4. Discussion
Since
RHC variants have been consistently associated with MM in Northern European populations [3, 10, 12] and also in the Northern French population [9]. In Spain, we detected statistically significant individual associations for R151C, R160W and D294H. These three variants have been detected in the Northern French and Central Italian populations [9, 54]. We did not observe any MM risk associated with the rare RHC variant D84E (OR: 1.63, 95% CI: 0.02–128, P= 0.99), as detected in Northern Europeans [32, 55, 56], probably due to its low prevalence in Spain (0.28% in controls). The I155T variant has not been associated with MM in other populations to date, but this may also be due to its low frequency. However, our results clearly suggest that this rare variant increases risk of MM, at least in the Spanish population (OR: 3.51, 95% CI: 1.35–9.12, P= 0.006). While the associations of RHC with MM were expected, the case of V60L (an NRHC variant) was more intriguing, since its involvement in MM pathology has been generally unclear in Caucasian populations. However, V60L could play a role in MM susceptibility only in darker skinned populations since it has been found associated with MM in other Mediterranean populations such as France and Greece [9, 11]. The fact that NRHC variants could be important in MM risk is also supported by our finding that risk increased with the number of non-synonymous changes carried, regardless of whether they were RHC or NRHC. The presence of two non-synonymous changes implies that both copies of the MC1R protein are compromised. In addition, the presence of two NRHC increases by more than five times the risk of only one non-synonymous variant (P=1.9x10-5). All these results taken together strongly support the role of the
In recent years,
The
Despite not being able to detect association between MM and the absence of either
It seemed biologically plausible that genetic interactions would be detected between several of the SNPs identified in our studies, and for that reason we grouped protective alleles located in the genes
In order to determine possible genetic interactions between
Finally, we tried to see whether protective alleles were going to be able to reduce the risk induced by the accumulation of risk alleles. Some comparisons were not possible due to the absence of individuals in some of the categories. Therefore, we present only the results between
In summary, we found that five
Acknowledgements
All these works were supported by several grants from the Ministerio de Educación y Ciencia (MEC) (SAF2007-65542-C02-01), Fundación Mutua Madrileña (FMMA 2009), and Ministerio Salud Carlos III (CP08/00069 and FI10_0405). LPF was funded by the Ministerio de Ciencia y Tecnología (MCT) and a grant from the Fondo de Investigación Sanitaria (FIS) FI05/00918; MI-V and MP-Ch were funded by the Spanish Ministerio de Educación y Ciencia under a grant FPI (BES-2008-009234) and by Generalitat Valenciana ValI+D under a grant (ACIF[2011/207) respectively. GR is funded by Ministerio de Salud Carlos III (MS08/00069). We thank the staff at the Spanish National Genotyping Centre in Santiago de Compostela and Madrid for their expert technical support with Sequenom and Illumina. Quantitative real-time PCR and Taqman was performed at the Unidad Central de Investigacion Medica (UCIM) of the Faculty of Medicine at the University of Valencia. Sequencing was done at the Sequencing Unit at CNIO and the Diagnostic and Genotyping Unit at UCIM, Faculty of Medicine at the University of Valencia.
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