Affiliations of authors: D. T. Bishop, J. Newton Bishop, M. Harland, Genetic Epidemiology Division, Cancer Research UK Clinical Centre, Leeds, U.K.; F. Demenais, Institut National de la Santé et de la Recherche Médicale EMI 0006, Evry, France; A. M. Goldstein, M. A. Tucker, Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; W. Bergman, N. Gruis, Department of Dermatology and Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands; B. Bressac-de Paillerets, A. Chompret, Institut Gustave Rousy, Villejuif, France; P. Ghiorzo, M. Mantelli, Department of Oncology, Biology, and Genetics, University of Genova, Genova, Italy; J. Hansson, A. Platz, Department of OncologyPathology, Karolinska Hospital, Stockholm, Sweden; N. Hayward, D. Nancarrow, Queensland Cancer Fund Research Unit, Brisbane, Australia; E. A. Holland, G. J. Mann, Westmead Institute for Cancer Research, Sydney, Australia.
Correspondence to: D. Timothy Bishop, Ph.D., Genetic Epidemiology Division, Cancer Research UK Clinical Centre, Cancer Genetics Building, St. James's University Hospital, Beckett Street, Leeds LS9 7TF, U.K. (e-mail: tim.bishop{at}cancer.org.uk).
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Germline CDKN2A mutations have been identified in melanoma-prone families from Australia, Europe, and North America. Most CDKN2A mutations described to date are missense mutations scattered throughout exons 1 and 2 (17). Overall, CDKN2A mutations have been observed in approximately 20% (range <5% to >50% in individual studies) of tested melanoma families (18,19). In addition, linkage studies suggest that approximately one half of families with three or more cases of melanoma show evidence of linkage to the 9p21 region (2023).
Epidemiologic studies suggest that exposure to sunlight is the major environmental risk factor associated with melanoma, although the exposureresponse relationship appears complex [for review, see (24)]. The major host factors associated with melanoma are increased numbers of melanocytic nevi, both clinically banal and atypical (dysplastic) (24,25). Other host factors associated with melanoma include hair color, eye color, extent of freckling, and skin type (26,27). Differences in the amount of exposure to ultraviolet (UV) radiation by geographic latitude and variation in host characteristics may contribute to the wide geographic variation in melanoma incidence rates. Within Australia, annual age-standardized melanoma incidence rates per 100 000 people from 1992 through 1996 varied with geographic latitude, from 63 for males and 46 for females in Queensland to 33 for males and 28 for females in Victoria (28). For the U.S. Surveillance, Epidemiology and End Results Program of the National Cancer Institute (SEER),1 the overall annual age-standardized incidence rate per 100 000 people was 19.6 for males and 12.6 for females in 1994 (29); the U.K. rates were 3.9 for males and 6.8 for females from 1983 through 1987 (30). Rates within Western Europe are generally similar, except in Scandinavian countries, where the rates are more in line with those in the United States (30).
The Melanoma Genetics Consortium was founded in 1997 to address issues related to inherited melanoma susceptibility. One of its first projects has been the examination of the penetrance of CDKN2A mutations for melanoma. All of the groups participating in this consortium have been involved in identifying families with multiple cases of melanoma and in investigating CDKN2A mutations. The interest in evaluating CDKN2A penetrance arises for a number of reasons, most notably that the clinical application of this information is being discussed (17). In addition, studies of melanoma etiology will be enhanced by clearer documentation of the contribution of the CDKN2A gene to melanoma incidence.
The goals of this study were, therefore, to determine the penetrance of CDKN2A mutations and to test whether any of three covariatesgender, the effect of the CDKN2A mutation on the p14ARF protein, or the population melanoma incidence rate relevant to the geographic location of residence of each familyinfluences penetrance. Families are identified for such genetic studies because their family history of melanoma is sufficiently strong that it is brought to the attention of the medical profession; however, not all families with a similar extent of family history will be identified. As with other disease susceptibility genes identified through family studies, the estimation of penetrance is, therefore, not straightforward because of the difficulty in defining the particular reason that each family is identified (termed "ascertainment"). Families with large numbers of affected individuals are presumably more likely to be identified than families with only a few affected individuals; failure to correctly allow for this factor in the calculations would overestimate the risk of melanoma among mutation carriers within such families. To overcome the uncertainty of the precise reason that a specific family is identified, the statistical approach of preference is to perform the penetrance calculations under the assumption that the family was identified because of the exact configuration of affected persons actually observed.
![]() |
SUBJECTS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Eight groups from the Melanoma Genetics Consortium contributed information concerning the genealogical structure of families with multiple cases of melanoma and a CDKN2A mutation, the melanoma status of family members, the CDKN2A mutation carrier status of family members, the age of onset for those family members with melanoma, the age at which each individual was last examined (or known to be unaffected) or the age at death for family members, and the CDKN2A mutation segregating in each family to these analyses (Table 1).
|
Families submitted from the Queensland Institute of Medical Research, Brisbane, Australia: To be eligible for participation, families needed to have had at least three living family members with melanoma willing to donate blood for linkage and DNA analysis (31,32). Ten of the 15 mutation-positive families were ascertained in two population-based studies of melanoma (33,34). The remaining five families were referred by local surgeons in an ad hoc manner.
Families submitted from Westmead Institute for Cancer Research, Sydney, Australia: Individuals with a self-reported family history of melanoma were identified through several large participating clinics, notably the Sydney Melanoma Unit, Sydney, and the Victorian Melanoma Service, Melbourne, and by other referring clinicians throughout southeastern Australia since 1985. The cancer history of each case patient and his or her relatives was confirmed from medical records and extended as far as possible, essentially until no cases of melanoma were reported within two degrees of relationship to the last confirmed case ascertained. For the families reported in this study, an attempt was made to trace back the CDKN2A mutation as far as possible and then to identify all descendents of the most ancestral couple thought to carry the CDKN2A mutation. The disease status of all families reported here was updated within the year before this analysis. The collection of these families and the precise mutations identified is described in more detail elsewhere (3538). Blood samples have been routinely collected for DNA analysis, but phenotypic examination of skin has not routinely been performed.
Families submitted from Cancer Research UK, Leeds, U.K.: Melanoma families were identified by contacting dermatologists, surgeons, and clinical geneticists throughout the United Kingdom and outlining the criteria for participation; these criteria included that there be at least two cases of melanoma in the family and that at least one living case patient be willing to provide a blood sample for DNA analysis. Each family was visited by a dermatologist involved in the research (usually Dr. J. Newton Bishop). A blood sample was taken and a skin examination performed. Family members with melanoma and those without melanoma were examined. Attempts have been made to identify all branches of the families in this study independent of the presence or absence of melanoma. More details about the families can be found elsewhere (36,39,40).
Families submitted from Institut Gustave Roussy, Villejuif, France: Melanoma families were identified by contacting dermatologists and geneticists throughout a French clinician network co-coordinated at the Institut Gustave Roussy. The criteria for inclusion in the study included the presence of at least two cases of melanoma in the family, confirmed by pathological reports or medical records. A blood sample was taken for DNA analysis and a skin examination was performed on family members with melanoma and on those without melanoma. The families are described in more detail elsewhere (18).
Families submitted from University of Genova, Genova, Italy: Families were ascertained predominantly through patients presenting to Italian medical genetics services after their having been identified by dermatologists or oncologists as having multiple family members with melanoma; the dermatologists and oncologists had been informed of the activity of the melanoma research group at national conferences. The selection criteria were for families to have at least two cases of melanoma and at least one living case patient willing to provide a blood sample for DNA analysis. For expediency, some family members were visited by dermatologists or surgeons not involved in the research program. Blood samples have been taken for DNA analysis and the skin examined for as many family members as possible. The families are described in more detail elsewhere (41).
Families submitted from Leiden University Medical Center, The Netherlands: The identification of Dutch families with multiple cases of melanoma began with a cohort of melanoma patients attending the surgical oncology department of the Leiden University Medical Center, Leiden, The Netherlands, between 1982 and 1984. This cohort of case patients, which were being followed up, and more recently diagnosed case patients were interviewed on two separate occasions about the presence of melanoma in their relatives. In the case of a positive response of any relative with a confirmed diagnosis of melanoma, all first-degree and second-degree relatives of the initial case patient were ascertained for inclusion in this study. All family members were interviewed about their family and personal medical history, had their skin examined, and had a blood sample taken for DNA analysis by the same dermatologist (Dr. W. Bergman). The families are described in more detail elsewhere (42,43).
Families submitted from Karolinska Hospital, Stockholm, Sweden: Melanoma families were identified in a national program aimed at detecting and monitoring melanoma families that was started by the Swedish Melanoma Study Group in 1987. All case patients with cutaneous melanoma were questioned with respect to their family history of melanoma, and the diagnosis of melanoma in other family members was verified by histopathology reports. Criteria for inclusion of the families in the study was that they had to have two or more family members with verified cutaneous melanoma. Family members including all case patients and unaffected relatives were followed clinically at specialized outpatient clinics at several centers in all health care regions of Sweden. The families are described in more detail elsewhere (4446).
Families submitted by the Genetic Epidemiology Branch, National Cancer Institute, Bethesda, USA. Melanoma families were referred to the National Cancer Institute by healthcare professionals or through self-referral. Families were eligible for inclusion in the study if there were at least two living first-degree relatives with invasive melanoma. Clinical examination of family members and spouses included complete skin examination (by Dr. M A Tucker), routine medical history, and a blood sample for DNA analysis. Both affected case patients and unaffected relatives were examined. The families are described in more detail elsewhere (11,19).
Confirmation of diagnoses. Melanoma cases were verified as far as possible from cancer registries, pathology laboratories, or clinical records. We have not summarized the extent of verification, as the procedures differ by center and family; however, every effort was made to confirm diagnoses, and only those family members with convincing evidence (i.e., pathology confirmation or medical record or, in a few exceptional circumstances, death certificate) were included as affected in these analyses.
Inclusion criteria for families. In an attempt to introduce more consistency among the varied ascertainment approaches of the participating groups, families were only included in the analysis if each had at least two cases of cutaneous malignant melanoma in first-degree relatives and a proven germline mutation in CDKN2A present in at least one family member. Several groups had identified families with CDKN2A mutations that did not satisfy this relatedness criterion. These few families were not included in this analysis. CDKN2A mutations were considered as causal if they cosegregated with melanoma in multiple-case families or if a binding assay of p16INK4A activity confirmed a functionally deficient p16INK4A protein with impaired binding to one or more cyclin-dependent kinases (47).
Statistical analysis. Family data were sent in a standard format to the Analysis Team (D. T. Bishop, F. Demenais, and A. Goldstein). The data included the genetic relationships of all individuals within each family, a phenotypic record for each family member indicating melanoma status and age at first diagnosis for those individuals diagnosed with melanoma, age at examination or age last known to be unaffected with melanoma for unaffected family members and demographic information (birth year, age at death or current age, gender), and CDKN2A mutation status of all tested individuals. For each family, the country of residence was recorded, as were the CDKN2A mutation segregating in the family and whether this mutation affected the reading frame of the p14ARF protein or the p14ARF protein sequence. These family data were stored in a common database (held by D. T. Bishop). For this analysis of penetrance, only individuals with a diagnosis of cutaneous malignant melanoma were considered to be "affected." Family members with a diagnosis of cancer other than melanoma (and without a diagnosis of melanoma) were considered as "unaffected with melanoma" for the penetrance analysis. Pancreatic cancer has been reported in some melanoma families to be observed at increased frequency, but for the purposes of this study such diagnoses were ignored (19,41). The penetrance estimates, therefore, relate to the risk of developing melanoma rather than to the risk of developing any cancer.
The penetrance of the CDKN2A gene mutation was estimated with a logistic regression model (48) extended to take into account variable age at diagnosis of disease (49) and linked marker loci (50). The regression model is formally known as a Class D regressive model, which in its generality allows the construction of patterns of correlations within families to include both genetic and nongenetic factors. For this analysis, in which we assume that the CDKN2A mutation is the only cause for the number of cases of melanoma within the family, the regression model is constructed by specifying a regression relationship between each individual's phenotype (i.e., affected or unaffected with melanoma) and a set of explanatory variables, including the individual's CDKN2A genotype and covariates such as the individual's age, gender, and geographic location of residence. Under this model, the probability of observing a family with a particular configuration of affected and unaffected individuals is written as the product of the probability of the vector of genotypes at the disease-causing locus multiplied by the penetrance function summed over all unobserved disease genotypes in family members. In this analysis, the summation is only over individuals with an unknown CDKN2A genotype. For those individuals, the probability of unobserved genotypes is expressed in terms of the frequency of the CDKN2A mutation in the general population if that individual has no ancestors in the pedigrees (founders of the pedigrees and spouses) and in terms of Mendelian probabilities if that individual does have ancestors recorded within the family. The frequency of the CDKN2A mutation was set at 0.0001 (Demenais, F: unpublished data), which is believed to be the approximate frequency of all CDKN2A mutations combined in the general population; that is, the source of the spouses of case patients.
The penetrance function (probability of the disease phenotype, Y, given the vector of genotypes, g, at the CDKN2A locus and covariates, X) over n individuals in the family, is decomposed into a product of penetrance functions for each individual i:
![]() |
where gi is the ith individual's genotype and Xi is the vector of covariates for i. Survival analysis concepts were introduced to take into account a censored age at diagnosis of melanoma (48). Age at diagnosis is regarded as a failure time, and age at examination (for unaffected family members) as a censored failure time, where the scale for measuring time is age. We began counting the follow-up time at 15 years of age because experience with this and other unpublished studies (Demenais F, Goldstein A: unpublished observation) suggests that the fit of the model is improved with this offset (in fact, in this dataset, there were two individuals with age at diagnosis under the age of 15 years, so we recoded their ages of diagnosis to 15 years). The period of follow-up was taken from 15 years of age to age at diagnosis for affected case patients, age at examination for unaffected subjects (or affected case patients with unknown age at diagnosis), or age at death for deceased subjects (there were three individuals with convincing diagnoses of melanoma in the dataset but for which ages at diagnosis were not available; for these individuals, their age at examination were taken as their age at diagnosis). This follow-up period was partitioned into K mutually exclusive intervals of one year each (1 . . . K). In each interval, we computed the hazard function, (k), which is the probability of being affected in the kth interval given not being affected before. The penetrance function is then derived from the hazard function
(k). For affected cases, the penetrance function is a density function evaluated at that individual's actual age of diagnosis (k):
![]() |
For unaffected family members, the penetrance function is the probability of being unaffected at all ages up to the current age (k):
![]() |
For the three case patients with an unknown age at diagnosis but a known age at examination (k), the penetrance becomes F(k) = 1 S(k). The penetrance function is defined as one for individuals with unknown disease status. The hazard function i(k), for the ith individual in the kth interval, is then a logistic function:
, where
i(k), the logit of the hazard function, is
![]() |
The parameter g is the genotype-specific baseline parameter;
g is the row vector of genotype-specific regression coefficients for covariates X(k) that can be time dependent; ug(k) is a function of k that represents the variation of the logit of the hazard function with time and can be genotype dependent. Because the disease-associated CDKN2A mutations are assumed to have a dominant mode of inheritance, the model includes two genotype-specific baseline risks:
Aa for mutation carriers (
AA =
Aa) and
aa for mutation noncarriers. The hazard function can be assumed to be constant over time (ug[k] = 0) or varying with time using different parametric functions of k; k increases from 1 in the first interval (corresponding here to 15 years of age for each individual) to K, K being equal to the individuals' age at diagnosis (or age at examination) minus 15 years. We found that the function ug(k) =
gln(k) fit the data better (Demenais F, Goldstein A: unpublished observation) than a polynomial function of k (linear, quadratic or cubic). Moreover, the variation of the hazard function with time did not differ in mutation carriers and noncarriers. The function, u(k) =
ln(k) was then used in all analyses.
The above calculation produces the probability of a particular configuration of individuals with defined melanoma status, ages (at diagnosis and at last examination), and CDKN2A mutation status (termed the "likelihood"). To estimate penetrance, we needed to take into account that we did not ascertain these families by chance; rather, we found them because of the large number of melanoma cases within each family. The likelihood on which to base the penetrance estimation is, therefore, the likelihood as described above conditional on the disease phenotypes (affected or unaffected with melanoma) in all individuals. This conditional likelihood represents an assumption-free method of ascertainment and is known to yield unbiased parameter estimates (51). Parameter estimation and tests of hypotheses were carried out using maximum-likelihood methods as implemented in the computer program REGRESS (52), which performs such likelihood calculations for family data. The analysis (performed in REGRESS) considered two distinct loci, a disease locus and a marker locus (CDKN2A) with equal allele frequencies but without linkage disequilibrium and assuming complete linkage between these two loci in the numerator of the likelihood and no linkage in the denominator. The effects of covariates on the penetrance function were tested by a likelihood-ratio test that compares a submodel where the regression coefficient, , of a given covariate is set to zero with a model where it is estimated.
The covariates included in the model were gender, the effect of the CDKN2A mutation on the p14ARF protein, and geographic location. Gender was coded 0 for females and 1 for males. The p14ARF variable was coded 0 if the CDKN2A mutation was predicted not to affect the p14ARF coding sequence and 1 otherwise. We adopted two approaches to the examination of the effect of geographic location. The geographically separated groups of families represent a wide range of melanoma population incidence rates. Because of the limited statistical information from each group, we dichotomized the geographic locations of each group into low-incidence countries (European countries except Sweden) and high-incidence countries (Sweden, the United States, and Australia). With the comparison of low-incidence and high-incidence countries, we could examine the effect of baseline melanoma incidence rates on CDKN2A mutation penetrance. In addition, to relate the penetrance of the CDKN2A gene mutation to more readily interpretable geographic and population units, we estimated the penetrance separately in three geographic areas: Europe (excluding Sweden), the United States, and Australia. The only problem with respect to this categorization was that, on the basis of baseline melanoma incidence rates, Sweden should not be pooled with Europe for the geographic comparisons of CDKN2A gene mutation penetrance. However, there were too few Swedish families to allow separate estimation of the penetrance. In addition, although baseline incidence rates for melanoma are similar between the United States and Sweden, substantial differences in major melanoma risk factors, such as UV exposure, tanning ability, skin complexion, and eye and hair color precluded conducting a meaningful analysis of penetrance when combining families from the United States and Sweden. Hence, for the analyses by geographic region, the Swedish families were excluded.
We varied the CDKN2A allele frequency from the value used in the reported analyses (0.0001) and have found that the estimates of penetrance are robust to reasonable changes in this value (data not shown) so that only the estimates for the one allele frequency are included here.
Laboratory Methods
CDKN2A gene mutations were identified either as a one-stage process involving direct sequencing of CDKN2A using the PCR technique described previously (39) or as a two-stage process involving an initial screen for heterozygous bases with single-stranded conformational polymorphism followed by sequencing of PCR products identified as containing heterozygosity [as, for instance, in (34)] based on PCR products as described previously (11,12).
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The penetrance estimation derives most information from the carrier status (i.e., mutation carrier or noncarrier) of unaffected relatives of melanoma case patients and especially from the elderly unaffected relatives. A large number of elderly unaffected mutation carriers would indicate a lower penetrance of a CDKN2A mutation than would a paucity of such individuals. Among the unaffected family members, 713 were mutation tested, and 194 of them carried the CDKN2A mutation. The current average ages of the unaffected family members who carried the CDKN2A mutation ranged from 33.0 years in the United States to 51.6 years in Queensland, Australia (Table 1). Overall, the mean current age of unaffected noncarriers of the family mutation was 5.6 years older than the mean current age of unaffected carriers (data not shown).
Table 2 shows the CDKN2A mutations in the study families in the standard convention for presenting mutations (53) together with the number of families carrying each mutation. Of the 37 distinct mutations, only nine occurred in more than one family. Of these mutations, three were founder mutations within the respective populations: 225-243del19 mutation from the Netherlands (43) which was seen in seven families, 112-113insArg mutation from Sweden (in five families) (54), and Gly101Trp mutation from Southeastern Europe (in 16 families) (55,56). Overall, approximately half of the mutations were in exon 1
and half were in exon 2. Table 2
also indicates the effect of the CDKN2A mutation on the reading frame of p14ARF and the p14ARF protein coding sequence. Mutations in exon 1
will have no effect on p14ARF protein, whereas some mutations in exon 2 will have an effect on p14ARF. No mutations were identified that modified the p14ARF protein and not CDKN2A.
|
|
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The estimation of penetrance requires a precise statement for each family of the reason that family was ascertained. Failure to correctly specify the reason for the ascertainment will lead to biased estimates of penetrance. The statistical methodology we used to estimate the penetrance of CDKN2A mutations takes a conservative approach by assuming that the reason for the ascertainment of each particular family cannot be defined explicitly. Our approach assumes that the family is ascertained because of the combination of all the affected family members. This approach should lead to an appropriate estimate of penetrance; however, a statistical price is paid in that the confidence intervals of these estimates are broad.
We found no evidence of an effect on CDKN2A mutation penetrance by gender or by whether the CDKN2A mutation altered the predicted p14ARF protein. The former issuethat is, of the potential effect of gender on riskwas suggested by the apparent discrepancy [e.g., (30) and Table 1] in gender-specific incidence rates. As for the second issue, we postulated that CDKN2A mutations that had an effect on both p16INK4A and p14ARF might have a more extreme phenotype because such mutations would affect both the retinoblastoma and p53 pathways. However, even though there was not a statistically significant effect of the type of CDKN2A mutation, the analysis suggested a trend toward a higher penetrance among mutation carriers with a mutation that creates both a p16INK4A and a p14ARF coding mutation, with those individuals having an estimated risk 1.8 times that of carriers of a mutation that affects only p16INK4A (Table 3
). Our study however, may have had limited statistical power to address this particular issue; larger studies will be needed for a more definitive answer.
We wished to investigate two additional issues relating to inherited melanoma risk, notably, the change in risk in CDKN2A mutation penetrance over time and the contribution of nevi and/or dysplastic nevi to risk. The incidence of melanoma throughout the Western world has been increasing dramatically during the past century, with earlier birth cohorts having lower incidence rates than more recent birth cohorts (5759). Although it would have been interesting to examine the effect of birth cohort on penetrance, we could not do so because of confounding between age, birth cohort, and availability of mutation information. That is, most family members from earlier birth cohorts were deceased at ascertainment of their families and, therefore, unavailable for CDKN2A mutation testing. For example, only 397 (38%) of the 1032 family members who were born before 1945 had undergone CDKN2A mutation testing. In contrast, 643 (68%) of the 948 family members who were born since 1945 had undergone CDKN2A testing, and most of them have not yet reached the age where they are at highest risk for melanoma.
We were also not able to examine the effect of nevi on CDKN2A penetrance in this analysis. Whereas the consortium groups had clinically examined many of the family members, the disparity in recording this phenotype precluded comparative analyses. The Melanoma Genetics Consortium is currently developing a standardized scoring system to allow such comparisons.
Despite these limitations, the analyses presented here show that modifying factors of CDKN2A penetrance are to be expected (whether genetic or environmental) and, therefore, that families with many cases of melanoma may share both the CDKN2A mutation as well as other exposures. For this reason, the comparison of penetrance estimates derived from multiple-case families and those derived from population-based studies may well prove to be a fruitful area of investigation. Substantial differences between the two estimates are suggestive of the importance of modifying genetic and/or lifestyle factors. From the evidence produced in this analysis, the estimates of CDKN2A mutation penetrance obtained are, therefore, the appropriate estimates for families with a strong family history of melanoma; they may, however, not be appropriate for individuals without a family history or with a weak family history.
![]() |
NOTES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
D. T. Bishop, F. Demenais, and A. M. Goldstein contributed equally to the work.
The groups from the Melanoma Genetics Consortium participating in this research are: Wilma Bergman, Rune Frants, Nelleke Gruis, and Pieter van der Velden, Department of Dermatology and Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands; Giovanna Bianchi-Scarrà, Paola Ghiorzo, and Michela Mantelli, Department of Oncology, Biology, and Genetics, University of Genova, Genova, Italy; Johan Hansson, Anton Platz, and Jamileh Hashemi, Department of Oncology-Pathology, Radiumhemmet , Karolinska Hospital, S-171 76 Stockholm, Sweden; Jenny Barrett, Julia Newton Bishop, Timothy Bishop, and Mark Harland, Genetic Epidemiology Division, Cancer Research UK Clinical Centre, Leeds, U.K.; Marie-Françoise Avril, Brigitte Bressac de Paillerets, and Agnès Chompret, Institut Gustave Rousy, Villejuif, France; Florence Demenais, Institut National de la Santé et de la Recherche Médicale, Evry, France; Alisa M. Goldstein, Margaret A. Tucker, Jeffery P. Struewing, and Mary C. Fraser, Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; Nicholas Hayward, Derek Nancarrow, Jane Palmer, Nicholas Martin, Adele Green, Joanne Aitken, and David Whiteman, Queensland Cancer Fund Research Unit, Joint Experimental Oncology Programme of the Queensland University and Queensland Institute of Medical Research, Brisbane, Australia; and Richard F. Kefford, Graham J. Mann, Helen Schmid, and Elizabeth A. Holland, Westmead Institute for Cancer Research, University of Sydney at Westmead Millenium Institute, Sydney, Australia.
Supported in part by Cancer Research UK, National Health and Medical Research Council of Australia, Melanoma and Skin Cancer Research Institute, the Association pour la Recherce sur le Cancer (ARC), Fondation de France, and the Stockholm Cancer Society, Sweden.
We are grateful to clinicians, in particular Rod McLeod and Mark Smithers (Queensland, Australia) and the clinicians of the Sydney Melanoma Unit and John Kelly (Victorian Melanoma Service), for referring families. We are grateful to the French Hereditary Melanoma Study Group for referring families. P. Ghiorzo is the recipient of a fellowship from the Italian foundation for Cancer Research.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
1 Bergman W, Palan A, Went LN. Clinical and genetic studies in six Dutch kindred with the Dysplastic Naevus Syndrome. Ann Hum Genet 1986;50:24958.[Medline]
2 Clark WH, Reimer RR, Greene M, Ainsworth AM, Mastranelo MJ. Origin of familial malignant melanoma from heritable melanocytic lesions. Arch Dermatol 1978;114:7328.[Abstract]
3 Lynch HT, Fusaro RM, Kimberling WJ, Lynch JF, Danes BS. Familial atypical multiple mole-melanoma (FAMMM) syndrome: segregation analysis. J Med Genet 1983;20:3424.[Abstract]
4 Newton JA. Familial Melanoma. Clin Exp Dermatol 1993;18:511.[Medline]
5 Cowan JM, Halaban R, Francke U. Cytogenetic analysis of melanocytes from premalignant nevi and melanomas. J Natl Cancer Inst 1988;80:115964.[Abstract]
6 Dracopoli NC, Alhadeff B, Houghton AN, Old LJ. Loss of heterozygosity at autosomal and X-linked loci during tumor progression in a patient with melanoma. Cancer Res 1987;47:39954000.[Abstract]
7 Fountain JW, Karayiorgou M, Ernstoff MS, Kirkwood JM, Vlock DR, Titus-Ernstoff L, et al. Homozygous deletions within human chromosome band 9p21 in melanoma. Proc Natl Acad Sci U S A 1992;89:1055761.[Abstract]
8 Nancarrow DJ, Mann GJ, Holland EA, Walker GJ, Beaton SC, Walters MK, et al. Confirmation of chromosome 9p linkage in familial melanoma. Am J Hum Genet 1993;53:93642.[Medline]
9 Pederson MI, Wang N. Chromosomal evolution in the progression and metastasis of human malignant melanoma. Cancer Genet. Cytogenet. 1989;41:185201.
10 Petty EM, Gibson LH, Fountain JW, Bolognia JL, Yang-Feng TL, Housman DE, et al. Molecular definition of a chromosome 9p21 germ-line deletion in a woman with multiple melanomas and a plexiform neurofibroma: implications for 9p tumor-suppressor gene(s). Am J Hum Genet 1993;53:96104.[Medline]
11 Hussussian CJ, Struewing JP, Goldstein AM, Higgins PA, Ally DS, Sheahan MD, et al. Germline p16 mutations in familial melanoma. Nat Genet 1994;8:1521.[Medline]
12 Kamb A, Shattuck-Eidens D, Eeles R, Liu Q, Gruis NA, Ding W, et al. Analysis of the p16 gene (CDKN2) as a candidate for the chromosome 9p melanoma susceptibility locus. Nat Genet 1994;8:236.[Medline]
13 Serrano M, Hannon GJ, Beach D. A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4. Nature 1993;366:7047.[Medline]
14 Serrano M, Gomez-Lahoz E, DePinho RA, Beach D, Bar-Sagi D. Inhibition of ras-induced proliferation and cellular transformation by p16INK4. Science 1995;267:24952.[Medline]
15 Zhang Y, Xiong Y, Yarbrough WG. ARF promotes MDM2 degradation and stabilizes p53: ARF-INK4a locus deletion impairs both the Rb and p53 tumor suppression pathways. Cell 1998;92:72534.[Medline]
16 Pomerantz J, Schreiber-Agus N, Liegeois NJ, Silverman A, Alland L, Chin L, et al. The Ink4a tumor suppressor gene product, p19Arf, interacts with MDM2 and neutralizes MDM2's inhibition of p53. Cell 1998;92:71323.[Medline]
17
Kefford RF, Newton Bishop JA, Bergman W, Tucker MA. Counseling and DNA testing for individuals perceived to be genetically predisposed to melanoma: A consensus statement of the Melanoma Genetics Consortium. J Clin Oncol 1999;17:324551.
18
Soufir N, Avril M-F, Chompret A, Demenais F, Bombled J, Spatz A, et al. Prevalence of p16 and CDK4 germline mutations in 48 melanoma-prone families in France. Hum Molec Genet 1998;7:20916.
19
Goldstein AM, Struewing JP, Chidambaram A, Fraser MC, Tucker MA. Genotype-phenotype relationships in U.S. melanoma-prone families with CDKN2A and CDK4 mutations. J Natl Cancer Inst 2000;92:100610.
20 Dracopoli NC, Fountain JW. CDKN2 mutations in melanoma. Cancer Surv 1996;26:11532.[Medline]
21 Hayward NK. The current situation with regard to human melanoma and genetic inferences. Current Opinion in Oncology 1996;8:13642.[Medline]
22 Zuo L, Weger J, Yang Q, Goldstein AM, Tucker MA, walker GJ, et al. Germline mutations in the p16INK4a binding domain of CDK4 in familial melanoma. Nat Genet 1996;12:9799.[Medline]
23 Foulkes W, Flanders TY, Pollock PM, Hayward NK. The CDKN2A (p16) gene and human cancer. Mol Med 1997;3:520.[Medline]
24 Armstrong BK, English DR: Cutaneous Malignant Melanoma. In Shottenfeld D, Fraumeni JFJ, editors. Cancer Epidemiology and Prevention. New York (NY): Oxford University Press; 1996 p. 1282312.
25 Tucker MA, Halpern A, Holly EA, Hartge P, Elder DE, Sagebiel RW, et al. Clinically recognized dysplastic nevi. A central risk factor for cutaneous melanoma. JAMA 1997;277:143944.[Abstract]
26 Elwood JM, Whitehead SM, Davison J, Stewart M, Galt M. Malignant melanoma in England: risks associated with naevi, freckles, social class, hair color, and sunburn. Int J Epidemiol 1990;19:80110.[Abstract]
27 Osterlind A, Tucker MA, Hou-Jensen K, Stone BJ, Engholm G, Jensen OM. The Danish case-control study of cutaneous malignant melanoma. I. Importance of host factors. Int J Cancer 1988;42:2006.[Medline]
28 Australian Institute of Health and Welfare (AIHW) and Australasian Association of Cancer Registries (AICR): Cancer in Australia 1996: incidence and mortality data for 1996 and selected data for 1997 and 1998. Canberra (Australia): AIHW (Cancer Series); 1999.
29 Ries LAG, Eisner MP, Kosary CL, Hankey BF, Miller BA, Clegg L, et al. (editors). SEER Cancer Statistics Review, 19731999. Bethesda (MD): National Cancer Institute; 2002, http://seer.cancer.gov/csr/1973-1999.
30 Cancer incidence in five continents. Age-standardized incidence rates, four-digit rubrics, and age-standardized and cumulative incidence rates, three-digit rubrics. Lyon (France): IARC Scientific Publications, 1992. p. 8711011.
31 Walker GJ, Hussussian CJ, Flores JF, Glendening JM, Haluska FG, Dracopoli NC, et al. Mutations of the CDKN2/p16INK4 gene in Australian melanoma kindreds. Hum Mol Genet 1995;4(Part 10):184552.[Abstract]
32 Flores JF, Pollock PM, Walker GJ, Glendening JM, Lin AH, Palmer JM, et al. Analysis of the CDKN2A, CDKN2B and CDK4 genes in 48 Australian melanoma kindreds. Oncogene 1997;15:29993005.[Medline]
33
Whiteman DC, Milligan A, Welch J, Green AC, Hayward NK. Germline CDKN2A mutations in childhood melanoma. J Natl Cancer Inst 1997;89:1460.
34
Aitken J, Welch J, Duffy D, Milligan A, Green A, Martin N, et al. CDKN2A variants in a population-based sample of Queensland families with melanoma. J Natl Cancer Inst 1999;91:44652.
35 Holland EA, Schmid H, Kefford RF, Mann GJ. CDKN2A (P16(INK4a)) and CDK4 mutation analysis in 131 Australian melanoma probands: effect of family history and multiple primary melanomas. Genes Chromosomes Cancer 1999;25:33948.[Medline]
36 Harland M, Holland EA, Ghiorzo P, Mantelli M, Bianchi-Scarra G, Goldstein AM, et al. Mutation screening of the CDKN2A promoter in melanoma families. Genes Chromosomes Cancer 2000;28:4557.[Medline]
37 Holland EA, Beaton SC, Becker TM, Grulet OM, Peters BA, Rizos H, et al. Analysis of the p16 gene, CDKN2, in 17 Australian melanoma kindreds. Oncogene 1995;11:228994.[Medline]
38 Rizos H, Becker TM, Holland EA, Kefford RF, Mann GJ. Differential expression of p16INK4a and p16beta transcripts in B-lymphoblastoid cells from members of hereditary melanoma families without CDKN2A exon mutations. Oncogene 1997;15:51523.[Medline]
39 Newton Bishop JA, Harland M, Bennett DC, Bataille V, Goldstein AM, Tucker MA, et al. Mutation testing in melanoma families INK4A, CDK4 and INK4D. Br J Cancer 1999;80:295399.[Medline]
40
Newton Bishop JA, Wachsmuth RC, Harland M, Bataille V, Pinney E, Mack P, et al. Genotype/phenotype and penetrance studies in melanoma families with germline CDKN2A mutations. J Invest Dermatol 2000;114:2833.
41 Ghiorzo P, Ciotti P, Mantelli M, Heouaine A, Queirolo P, Rainero ML, et al. Characterization of ligurian melanoma families and risk of occurrence of other neoplasia. Int J Cancer 1999;83:4418.[Medline]
42 Bergman W, Gruis NA, Frants RR. The Dutch FAMMM family material: clinical and genetic data. Cytogenet Cell Genet 1992;59:1614.[Medline]
43 Gruis NA, van der Velden PA, Sandkuijl LA, Prins DE, Weaver-Feldhaus J, Kamb A, et al. Homozygotes for CDKN2 (p16) germline mutation in Dutch familial melanoma kindreds. Nat Genet 1995;10:35153.[Medline]
44 Platz A, Hansson J, Ringborg U. Screening of germline mutations in the CDK4, CDKN2C and TP53 genes in familial melanoma: a clinic-based population study. Int J Cancer 1998;78:135.[Medline]
45
Platz A, Hansson J, Mansson-Brahme E, Lagerlof B, Linder S, Lundqvist E, et al. Screening of germline mutations in the CDKN2A and CDKN2B genes in Swedish families with hereditary cutaneous melanoma. J Natl Cancer Inst 1997;89:697702.
46 Hashemi J, Linder S, Platz A, Hansson J. Melanoma development in relation to non-functional p16/INK4A protein and dysplastic naevus syndrome in Swedish melanoma kindreds. Melanoma Res 1999;9:2130.[Medline]
47 Ruas M, Brookes S, N.Q. M, Peters G. Functional evaluation of tumour-specific variants of p16INK4a/CDKN2A: correlation with protein structure information. Oncogene 1999;18:542334.[Medline]
48 Bonney GE. Regressive logistic models for familial disease and other binary traits. Biometrics 1986;42:61125.[Medline]
49 Abel L, Bonney GE. A time-dependent logistic hazard function for modeling variable age of onset in analysis of familial diseases. Genet Epidemiol 1990;7:391407.[Medline]
50 Bonney GE, Lathrop GM, Lalouel JM. Combined linkage and segregation analysis using regressive models. Am J Hum Genet 1988;43:2937.[Medline]
51 Hodge SE, Elston RC. Lods, wrods, and mods: the interpretation of lod scores calculated under different models. Genet Epidemiol 1994;11:32942.[Medline]
52 Demenais FM, Lathrop GM. REGRESS: a computer program including the regressive approach into the LINKAGE programs. Genet Epidemiol 1994;11:291.
53 Antonarakis SE. Recommendations for a nomenclature system for human gene mutations. Nomenclature Working Group. Hum Mutat 1998;11:13.[Medline]
54 Borg A, Johannsson U, Johannsson O, Hakansson S, Westerdahl J, Masback A, et al. Novel germline p16 mutation in familial malignant melanoma in southern Sweden. Cancer Res 1996;56:2497500.[Abstract]
55 Auroy S, Avril MF, Chompret A, Pham D, Goldstein AM, Bianchi-Scarra G, et al. Sporadic multiple primary melanoma cases: CDKN2A germline mutations with a founder effect. Genes Chromosomes Cancer 2001;32:195202.[Medline]
56 Ciotti P, Struewing JP, Mantelli M, Chompret A, Avril MF, Santi PL, et al. A single genetic origin for the G101W CDKN2A mutation in 20 melanoma-prone families. Am J Hum Genet 2000;67:3119.[Medline]
57 Bulliard JL, Cox B, Semenciw R. Trends by anatomic site in the incidence of cutaneous malignant melanoma in Canada, 196993. Cancer Causes Control 1999;10:40716.[Medline]
58 Chen YT, Zheng T, Holford TR, Berwick M, Dubrow R. Malignant melanoma incidence in Connecticut (US): time trends and age-period-cohort modeling by anatomic site. Cancer Causes Control 1994;5:34150.[Medline]
59 Dennis LK, White E, Lee JA. Recent cohort trends in malignant melanoma by anatomic site in the United States. Cancer Causes Control 1993;4:93100.[Medline]
Manuscript received November 21, 2001; revised April 18, 2002; accepted May 1, 2002.
This article has been cited by other articles in HighWire Press-hosted journals:
![]() |
||||
|
Oxford University Press Privacy Policy and Legal Statement |