a INSERM Unité 358, Hôpital Saint-Louis, Paris, France.
b Department of Dermatology, Institut Gustave Roussy, Villejuif, France.
c INSERM Unité 155, Paris, France.
d Unité des Marqueurs Génétiques des Cancers, Institut Gustave Roussy, Villejuif, France.
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Abstract |
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Methods Familial aggregation of GNN, LP and HDSE was investigated in 66 French families with at least two CMM cases and was measured by the asssociation of the relatives' traits with the probands' traits, using the generalized estimating equations approach. The probands were the melanoma cases leading to ascertainment of the families, subdivided into cases (with the trait studied) and controls (without the trait).
Results We found significant evidence for familial aggregation of GNN only among sibs (OR = 3.7, 95% CI : 1.410.5, P = 0.01), of LP among blood relatives (OR = 3.8, 95% CI : 1.88.0, P = 0.004) and of HDSE among blood relatives (OR = 4.5, 95% CI : 2.19.9, P < 0.001) and spouses (OR = 44.3, 95% CI : 5.1382.2, P < 103). These results suggest that genetic factors might account for the clustering of GNN and LP and shared environment for the aggregation of HDSE. The GNN clustering was lower in families with increasing numbers of CMM (3 cases) or presence of p16 mutations, the opposite being observed for LP and HDSE. Moreover, the familial aggregation of LP was significantly lower in families with highly sun-exposed members.
Conclusion Melanoma might not only result from specific genetic and environmental factors but also from those underlying melanoma-associated traits involving complex gene-gene and gene-environment interactions.
Keywords Melanoma, familial aggregation, naevus, phototype, sun exposure, generalized estimating equations
Accepted 2 December 1999
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Introduction |
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To better understand the effects and interactions of genetic and environmental factors in CMM aetiology, a family study was conducted at Institut Gustave Roussy (IGR), Villejuif, France, that led to the collection of 295 families ascertained by 295 CMM probands during the period 19861989. A family history of CMM was reported in 22 cases and was found to be associated significantly with red hair and presence of atypical moles, and to a lesser extent with a great number of naevi, a young age at diagnosis and multiple primary melanomas.15 Segregation analysis of CMM in this sample showed evidence for the transmission of a rare dominant gene interacting with age and propensity to sunburn,16 while the transmission of a great number of naevi appeared to involve more complex genetic mechanisms.17 To investigate the part of familial clustering of CMM that might be due to familial aggregation of melanoma risk factors, our first series of 22 familial CMM probands was extended to a total of 100 probands with at least one affected relative with CMM. Clinical and epidemiological data in probands and relatives were obtained in a total of 66 families. The method of generalized estimating equations (GEE) was used to assess the patterns of family resemblance of three melanoma risk factors: great number of naevi (GNN), light phototype (LP) and high degree of sun exposure (HDSE).
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Subjects and Methods |
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Statistical methods
The familial case-control approach19 was used to assess the familial aggregation. The probands were the melanoma cases leading to ascertainment of the families and subdivided into cases if they had the studied trait (GNN, LP or HDSE) and controls if they did not have it. Familial aggregation was measured by the association of the relatives' trait with the probands' trait. In family k, the association of the ith relative's trait, yik, with the proband's trait, ck (ck = 1 if k is a case, ck = 0 if k is a control), was modelled by a logistic function, where the logit, ik, is:
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Models
From the general model given in (1), different models were considered to test various patterns of familial aggregation.
Model 1
This model was used to measure the association of the probands' traits with those observed in two types of relatives respectively: blood relatives (first, second and third-degree relatives) and spouses, in order to distinguish genetic from environmental sources of familial aggregation.
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Model 2
Model 2 was similar to model 1, but the relationships to probands were specified as parents, children, sibs, a pooled category of second- and third-degree relatives, and spouses:
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Model 3
Model 3 derived the familial aggregation dependent on the number of CMM cases in the family, mel3k, where mel3k was equal to 1 if the family included three or more melanoma cases and was equal to 0 if it had only two cases.
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Model 4
Model 4 derived the familial aggregation dependent on the presence of p16 mutations in the family, p16k, where p16k was equal to 1 if a p16 mutation was detected in the family and was equal to 0 otherwise (the p16 symbol was used instead of the usual CDKN2A symbol for sake of simplicity).
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Model 5
Model 5 derived the familial aggregation varying according to the relatives' sun exposure (expoik) and was used when the traits studied were GNN and LP.
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Model 6
The familial aggregation of either one of the three traits (trait1) was measured while adjusting for the possible confounding effects of the two other traits in probands (trait2k, trait3k) and in probands' relatives (trait2ik, trait3ik), that were included as covariates in the model:
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Parameter estimation
Since the traits studied were correlated within families, we used the GEE approach to estimate the regression coefficients in the logistic model to obtain valid standard errors.20,21 The GEE method does not need the specification of the joint distribution of each trait in a family, but only requires specifying the traits' means, variances and correlations among family members. The mean µik for relative i in family k with trait yik is µik = exp(ik)/[1 + exp(
ik)] which depends on the parameters measuring familial aggregation,
' = (
1, ...,
n), and the ß coefficients for covariates, the expected variance is
ik = µik(1µik) and the correlation between each pair of relative (ij) is assumed to be function of a constant term,
, with
ijk =
ik
jk
.
Estimates of the parameters (, ß,
) were obtained by solving a system of p equations (p being the number of parameters estimated):
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The parameter estimates obtained from the GEE approach have an asymptotic normal distribution. For example, under model 1, the null hypothesis of interest is H0: exp(1) = 1, i.e. there is no familial aggregation among blood relatives. The test statistic T1 =
1/
1 has an asymptotically standardized normal distribution under the null hypothesis. So, an absolute value of T1 >1.96 at the 5% level indicates a significant familial aggregation among blood relatives. Another null hypothesis of interest, considered in model 2, is whether the familial aggregation among blood relatives is significantly different from the one among spouses, i.e. H0:
1 =
2. The test statistic is T2 = (
1
2)/
12 with
122 =
12 +
22
12, the estimated variance of (
1
2).
The computer program QGE (EE: Estimating Equations, Fred Hutchinson Cancer Research Center, Quantitative Genetic Epidemiology, Technical Report 126) was used to perform all computations. This program incorporates the Newton-Raphson algorithm to estimate the parameters.
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Results |
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Discussion |
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Familial aggregation of increased numbers of naevi and atypical naevi was first described in melanoma-prone families, and recognized as the familial atypical multiple mole melanoma (FAMMM) syndrome or dysplastic naevus syndrome (DNS).2224 One study suggested that the co-segregation of melanoma and DNS might result from the pleiotropic effect of the same gene.25 However, the difficulty in reaching a clear consensus on the DNS definition hampered further studies to clarify its genetic determinism. Twin studies and segregation analyses applied to more objective naevus phenotypes, naevus count and/or naevus density, suggested that these phenotypes were under genetic control,2629 but their familial transmission appeared more complex than the Mendelian transmission of a single major gene.17,28,30 A recent twin study found a substantial contribution of genetic influences to the colour and size of naevi and also a significant environmental contribution to colour.31 In our present series of 66 French families, familial aggregation of GNN was only significant among siblings. Although this might be due to the influence of recessive-like genetic factors, as also suggested by segregation analysis of GNN in our first series of 295 melanoma families,17 the lack of significant results in other relatives might be due to the use of a binary phenotype instead of a more informative quantitative measure which could not be obtained in relatives not seen at the hospital. The familial aggregation of GNN, lower in families with at least three CMM cases and in those with p16 mutations, suggests that distinct genetic factors might be involved in GNN and melanoma. This result is in agreement with a recent study of 20 American melanoma-prone families32 where the presence of dysplastic naevi (DN) was found to interact significantly with the p16 mutation-carrier status in melanoma risk, with DN being a stronger risk factor for CMM subjects without p16 mutations versus those with mutations. However, a combined linkage and association analysis in Australian twins has recently shown that a CDKN2A-linked gene influences the number of flat moles but has no effect on raised moles or atypical moles.33 No distinction between the different types of moles, flat or raised, was made in our study.
Familial aggregation of LP has been little studied and was suggested by a twin study of skin reflectance.34 Our results indicate the possible influence of genetic determinants. This is supported by the recent finding of a significant association between variants of the melanocortin 1 receptor (MCR1) gene with red hair and propensity to sunburn,35 these variants being also associated with melanoma independent of skin type.36 We found that the familial aggregation of LP was higher in families with three or more melanoma cases and in those with p16 mutations, suggesting possible common genetic determinants underlying LP and melanoma and/or interactions between their respective determinants. A significant interaction between a putative melanoma gene and propensity to sunburn was found in our first series of 295 French melanoma families ascertained through one melanoma proband.16 Moreover, in Dutch melanoma families sharing the same p16 mutation,37 polymorphisms of the MCR1 gene associated with LP were suggested to be modifiers of the risk of melanoma.
Familial clustering of melanoma might also be due to shared environmental factors. The pattern of familial aggregation of HDSE in our families strongly suggests a common behaviour with respect to sun exposure that appears to be shared by spouses more strongly than among blood relatives. The higher association of children's HDSE (OR = 11.6, P < 0.001) than sibs' HDSE (OR = 4.7, P = 0.01) with the probands' HDSE supports this view since the probands, their spouses and children, living in the same household, are more likely to share the same type of sun exposure. The clustering of HDSE increasing with the number of CMM cases or presence of p16 mutation suggests that sun exposure may enhance the expression of melanoma genes. Putative p16 gene carriers that develop melanoma38 or subjects belonging to melanoma high risk families14 were also reported to have been more exposed to sun.
Interactions between the traits studied were also observed. The familial aggregation of LP was significantly lower in families with highly sun-exposed members, the same trend being observed for GNN although it was not significant. This might be explained by protective behaviours of individuals with a fair skin at higher risk for melanoma who cluster more in families with limited sun exposure. Moreover, while adjusting each of these traits for the effect of the other two, the familial aggregation of LP and HDSE was twice as high than without adjustment, the opposite trend being observed for GNN. These results underline the complex confounding relationships among LP, HDSE and GNN. As shown before, there is a negative confounding relationship between LP and HDSE, which may explain the higher familial aggregation of each of these traits when adjusting for the other. On the other hand, the positive association of a high number of naevi with a light phototype and high sun exposure4 suggests that clustering of GNN might be partly accounted for by clustering of LP and HDSE. However, as mentioned earlier, there might be different genetic factors with complex interactive effects underlying these traits. Further combined segregation-linkage analysis, considering simultaneously the transmission of these phenotypes, may help in disentangling the mechanisms that are common or specific to these traits.
In conclusion, this study underlines the importance of taking into account melanoma risk factors to dissect the complex mechanisms causing the development of CMM. Melanoma may not only result from specific genetic and environmental determinants but also from those underlying melanoma-associated phenotypes with complex gene-gene and gene-environment interactions. Further genetic and epidemiological studies directed towards these melanoma-associated phenotypes, especially the phototype and number of naevi, may help in unravelling the multiple factors causing this cancer.
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Acknowledgments |
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References |
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