Hum Genet (2011) 129:663–673 DOI 10.1007/s00439-011-0957-1
ORIGINAL INVESTIGATION
Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li–Fraumeni syndrome Chih-Chieh Wu • Ralf Krahe • Guillermina Lozano • Baili Zhang • Charmaine D. Wilson • Eun-Ji Jo • Christopher I. Amos • Sanjay Shete • Louise C. Strong
Received: 30 September 2010 / Accepted: 19 January 2011 / Published online: 9 February 2011 Springer-Verlag 2011
Abstract Li–Fraumeni syndrome (LFS) is a rare familial cancer syndrome characterized by early cancer onset, diverse tumor types, and multiple primary tumors. Germline TP53 mutations have been identified in most LFS families. A high-frequency single-nucleotide polymorphism, SNP309 (rs2279744), in MDM2 was recently confirmed to be a modifier of cancer risk in several case-series studies: substantially earlier cancer onset was observed in SNP309 G-allele carriers than in wild-type individuals by 7–16 years. However, cancer risk analyses that jointly account for measured hereditary TP53 mutations and MDM2 SNP309 have not been systematically investigated in familial cases. Here, we determined the combined effects of measured TP53 mutations, MDM2 SNP309, and gender and their interactions simultaneously in LFS families. We used the method that is designed for extended pedigrees and structured for age-specific risk models based on Cox proportional hazards regression. We analyzed the cancer incidence in 19 extended pedigrees with germ-line TP53 mutations ascertained through the clinical LFS phenotype. The dataset consisted of 463 individuals with 129 TP53 mutation carriers. Our analyses showed that the TP53 germ-line mutation and its interaction with gender were C.-C. Wu (&) E.-J. Jo C. I. Amos S. Shete Department of Epidemiology, Unit 1340, The University of Texas M. D. Anderson Cancer Center, 1155 Pressler Street, Houston, TX 77030, USA e-mail:
[email protected] R. Krahe G. Lozano B. Zhang C. D. Wilson L. C. Strong Department of Genetics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA E.-J. Jo Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
strongly associated with familial cancer incidence and that the association between MDM2 SNP309 and increased cancer risk was modest. In contrast with several case-series studies, the interaction between MDM2 SNP309 and TP53 mutation was not statistically significant in our LFS family cohort. Our results showed that SNP309 G-alleles were associated with accelerated tumor formation in both carriers and non-carriers of germ-line TP53 mutations.
Introduction Li–Fraumeni syndrome (LFS) is one of the most devastating familial cancer syndromes; its major hallmarks include early cancer onset, a wide diversity of tumor types, and a high frequency of multiple primary tumors (Li and Fraumeni 1969a, b). LFS was initially characterized by aggregation of various tumor types within families, including soft-tissue sarcomas (STS), osteosarcomas (OST), female breast cancer, brain tumors, leukemias, and adrenocortical carcinomas (Birch et al. 1994a, b; Hwang et al. 2003b; Nichols et al. 2001; Strong et al. 1987). However, substantially increased risks for many other common cancers have been observed in subsequent studies, such as lung and colon cancers (Bougeard et al. 2008; Hwang et al. 2003a, b; Wong et al. 2006). Mutations in TP53 are among the most common tumor-specific genetic alterations in human neoplasms, observed in [50% of various human cancers. Germ-line TP53 mutations have been identified in most families with LFS (Malkin et al. 1990; Srivastava et al. 1990; Varley et al. 1997). Recently, a high-frequency single-nucleotide polymorphism, SNP309 (rs2279744), in MDM2, which encodes a direct negative regulator of p53, was found to be a genetic risk modifier of cancer incidence in TP53 mutation carriers (Bond et al.
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2004). Those authors proposed a model that the G-allele of SNP309 is associated with high levels of MDM2 mRNA and protein and attenuates the p53 stress response pathway, resulting in accelerated tumor formation in both sporadic and hereditary cancers (Bond et al. 2004). Several independent reports in case-series studies confirmed that MDM2 SNP309 is associated with increased cancer risk; these studies also revealed a significantly earlier mean age of cancer onset in SNP309 G-allele carriers than in wild-type (T/T) individuals by 7–16 years in TP53 mutation carriers (Bougeard et al. 2006; Ruijs et al. 2007). Our group recently analyzed the combined cancer patients who are de novo carriers or familial hereditary carriers of TP53 germ-line mutations (Fang et al. 2010). They used the standard Kaplan–Meier method that does not take account of intra-familial correlations in hereditary mutation distributions among relatives. Distributions of hereditary mutations are inter-dependent in families (e.g., the offspring of a mutation carrier generally has 50% chances to become a carrier). Recently, we developed a statistical method to evaluate cancer risk attributable to a measured hereditary susceptibility gene in family studies (Wu et al. 2010). It was particularly designed for risk analyses of families and extended pedigrees, in which measured risk genotypes are segregated within the family. Our method (1) accounts for measured hereditary susceptibility genotypes of the proband and each relative in a family, (2) assigns to relatives with missing genotypes possible genotypes conditional on the known genotypes of others, (3) enables the measured susceptibility genotypes to follow Mendelian transmission patterns among relatives, and (4) accounts for genetic heterogeneity in TP53 mutation carrier status. These key features lead to robust and reliable estimates of main and interaction effects in family studies because the proband and each relative, regardless of affection status, are all taken into likelihood calculations under the family structure. We previously assessed the associations of increased cancer risk with germ-line TP53 mutations and gender in 6 TP53 families of LFS (Wu et al. 2006, 2010). In this report, we evaluated not only the main component effects but also the gene–gene interaction effects on cancer risk simultaneously among germ-line TP53 mutations, MDM2 SNP309, and gender using the novel statistical method that we recently developed for family studies (Wu et al. 2010). A study population that comprised 19 extended pedigrees of LFS with multiple germ-line TP53 mutations provides us a unique opportunity to perform this cancer risk analysis in a quantitative way. The dataset consisted of 463 individuals with 129 germ-line TP53 mutation carriers. Our analyses showed that TP53 germ-line mutations and their interaction with gender were strongly associated with familial cancer incidence and that the association between
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MDM2 SNP309 and increased cancer risk was modest. In contrast with outcomes in case-series analyses (Bougeard et al. 2006; Fang et al. 2010; Ruijs et al. 2007), the interaction between MDM2 and TP53 mutations was not statistically significant in this family LFS cohort. Our results show that SNP309 G-alleles were associated with accelerated tumor formation in both carriers and non-carriers, but that the effect of MDM2 SNP309 on cancer risk in our family study was not as strong as that reported in caseseries analyses.
Materials and methods Study population The study population consisted of 19 extended pedigrees with multiple germ-line TP53 mutations, ascertained through systematic surveys and cancer genetics clinics. All met the clinical LFS phenotype. Of these kindreds, 11 were ascertained through systematic studies of sequential childhood STS (6 families) and OST (5 families) patients without regard to cancer family history (Brown et al. 2005; Hwang et al. 2003b), and 8 were ascertained due to LFS phenotypes referred through research and clinical programs. Once an individual was identified as having a TP53 germ-line mutation, all available adult relatives at risk were invited to participate including undergoing TP53 genotyping without regard to affection status. The kindreds were extended in that way for each additional mutation carrier identified. Because extension was based on relationship and not phenotype, this approach to extending the family should not introduce an ascertainment bias during the segregation analyses. As the goal of the study was to assess the simultaneous effects of germ-line TP53 mutation, MDM2 SNP309, and gender in the context of the family, we included only those kindreds with at least two generations of mutation carriers. We present the detailed characteristics of TP53 germ-line mutations in 19 LFS kindreds in Table 1. We included all invasive cancers as a single combined phenotype, excluding non-melanoma skin cancers and in situ carcinoma. These disease criteria are broader than the classic LFS component tumors, but are based on observations of diverse cancer types occurring in excess in TP53 germ-line mutation carriers (Hwang et al. 2003a, b; Nichols et al. 2001). Medical records or death certificates were used to confirm all cancers included in the analysis. Individuals were considered at risk from their date of birth to their date of cancer diagnosis, death, loss to follow-up, study termination, or age 75 years, whichever came first. The evaluation of cancer incidence was truncated at age 75 years because of the limited reliability of cancer rates at
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Table 1 Characteristics of germ-line TP53 mutations in 19 LFS kindreds Kindred no.
Gene bank ref seq X54156a
Coding DNA sequenceb
1
g.12066
c.142delG
2
g.12113
c.189delTinsAGA
3
g.13053
c.376-2A[G
4
g.13077
c.398T[C
5
g.13117
c.437G[A
6
g.13116
c.437G[A
7 8
g.13203 g.13231
c.524G[A c.552delT
9
g.13240
c.559 ? 2T[G
10
g.14048
c.721delT
11
g.14069
c.742C[T
12
g.14069
c.742C[T
13
g.14070
c.743G[A
14
g.14078
c.751A[C
15
g.14487
c.818G[A
16
g.14511
c.842A[C
17
g.14513
c.844C[T
18
g.14561
c.892G[T
19
g.14680
c.920-1G[A
a
Accession X54156
Version X54156 .1 Gl:35213 b
Accession X02469 M60950
Version X02469 .1 Gl:35209 Source: http://www.ncbi.nlm.nih.gov/nuccore/x54156; http://www.ncbi.nlm.nih.gov/nuccore/x02469
older ages. The data collection methods, overall cancer incidence, germ-line TP53 mutation identification, and frequencies of site-specific cancers in the systematic studies have been described elsewhere (Brown et al. 2005; Hwang et al. 2003a, b; Lustbader et al. 1992; Strong et al. 1987, 1992). Figure 1 shows segregation of a germ-line TP53 mutations in nine individuals in one kindred of the
study population. The digit below each individual indicates the age of first cancer onset for the affected or the age at last examination for unaffected relatives. We present the spectrum of first-tumor for affected relatives with TP53 germ-line mutations by MDM2 SNP309 genotype in 19 LFS kindreds and the corresponding frequencies in Table 2. The final dataset for these 19 extended kindreds consisted of 463 individuals with 76 men and 77 women affected with cancers. One hundred twenty-nine were germ-line TP53 mutation carriers, 169 were wild-type, and 165 were at risk for being a TP53 mutation carrier but had unknown genotypes; 105 were MDM2 SNP309 G-allele carriers and 89 were MDM2 SNP309 wild-type (T/T) individuals. All probands were carriers of TP53 germ-line mutations. The overall mean age of first cancer onset of probands was 13.7 years of age; it was 12.7 for males and 14.5 for females. Across the families, the frequency distributions of age of first cancer onset for affected relatives by TP53 mutation, MDM2 SNP309 G-allele genotype and gender are shown in Table 3. Similarly, the frequency distributions of age at last examination for unaffected relatives by MDM2 SNP309 genotype and gender are shown in Table 4. We also present the frequency distributions of MDM2 SNP309 genotypes by affection and germ-line TP53 mutation status in Table 5. Female breast cancer and STS are the two most common tumors in this study population. Specifically, the mean age of female breast cancer diagnosis is 32.5 years old for ten SNP309 G-allele carriers and 37.8 years old for eight SNP309 T/T individuals with TP53 mutations across 19 LFS kindreds. The mean age of STS diagnosis is 12.0 years old for 12 G-allele carriers and 26.7 for 7 T/T individuals with TP53 mutations. A recent report in case-series studies found that SNP309 G alleles are associated with early ages of onset in STS patients with mutant TP53, but not with breast cancer patients (Marcel et al. 2009).
Fig. 1 One sample kindred in the study. The digit below each individual indicates the age of first cancer onset for the affected or the age at last examination for unaffected relatives
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Table 2 Spectrum and frequencies of first tumor for affected relatives with germ-line TP53 mutations by MDM2 SNP309 genotype in 19 LFS kindreds Tumor type
Table 4 Age at last examination for unaffected relatives across families by MDM2 SNP309 genotype and gender MDM2 SNP309
G/G or T/G (n = 61)
T/T (n = 56)
MDM2 SNP309 Age group G/G or G/T
T/T
0–15
4
2
Female breast
10
8
15–30
12
10
Soft-tissue sarcomaa
12
7
30–45
10
15
4
3
45–60
19
16
60–75
16
13
30
32
31
24
Bone sarcoma Brain tumor
1
3
Hematologic system
4
2
Adrenocortical tumor
2
1
Oral cavity
0
1
Respiratory system
1
1
Gastrointestinal system
1
3
Overall
45.24 (20.15)
46.27 (19.01)
Genitourinary system
0
1
Male
45.17 (20.38)
47.52 (19.32)
Female
45.31 (20.26)
44.60 (18.88)
a
Sarcomas of organ systems are included as sarcomas, not tumors of the organ system; e.g. rhabdomyosarcoma of the lung is included here as sarcoma Table 3 Age of first cancer diagnosis for affected relatives across families by TP53 mutation, MDM2 SNP309 G-allele genotype, and gender Parameter
Patients with TP53 mutation G/G or T/G (n = 24)
Patients with no TP53 mutation G/G or T/G (n = 9)
0–15
8
0
15–30
8
0
30–45
6
1
45–60
2
1
60–75
0
7
Male
11
4
Female
13
5
22.50 (16.17)
65.13 (11.28)
Male
23.40 (18.20)
68.95 (6.35)
Female
21.34 (14.90)
62.08 (14.06)
MDM2 SNP309 Age group
Sex
Mean age of onset Overall
Statistical analysis models We recently developed a statistical method that uses the measured susceptibility genotype (instead of the ordinary genetic allele marker) as the linked marker to the putative major gene in joint segregation and linkage analyses to estimate the cancer risk attributable to a measured susceptibility gene in family studies. We further proposed to test the significance of linkage disequilibrium (LD) between the putative gene and measured susceptibility gene and to account for LD in the model (Wu et al. 2010). The method was proven reliable and robust that accounts for measured
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Sex Male Female Mean age at last contact
hereditary susceptibility genotypes of the proband and each relative in a family. Gauderman and Faucett (1997) developed the complex joint segregation and linkage analysis model based on Cox proportional hazards regression and the program package of Genetic Analysis Package (GAP) for the model (Genetic Analysis Package 1997; Gauderman and Faucett 1997). Our method extended the usual model and program GAP that they developed. Segregation analysis is a statistical method for family studies that is frequently used to evaluate and compare various modes of inheritance for a trait and test for evidence of age modification of genetic relative risks in association with epidemiological and environmental factors. Most segregation analysis programs assume that a putative major gene is segregating within a family and allow the measured covariates, adjusted for epidemiological and environmental risk factors, to be tested for significance and accounted for in models. Adding a linked marker to the putative major gene could provide additional characterization of the putative major gene in joint segregation and linkage analyses, resulting in greater statistical power than segregation analyses alone (Gauderman and Faucett 1997). On the basis of Cox proportional hazards regression, the age-specific risk model that uses the hazard scale to account for variability in age of onset for right-censored traits is expressed as a function of a vector of measured covariates (z), a covariate (G) for a putative major gene, and their interactions (G 9 z): kðtjz; g; XÞ ¼ k0 ðtÞ exp bT z þ cG þ gT G z : ð1Þ Let g denote the diallelic genotype at the putative major gene that has high-risk allele A with frequency qA and normal allele a; G is a covariate that depends on this
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Table 5 The frequency distributions of MDM2 SNP309 Genotypes by affection and TP53 mutation status across 19 families Number (%)
Affected G/G or G/T
Unaffected T/T
Total
G/G or G/T
T/T
Total
TP53?
35 (45.45)
30 (38.96)
65 (84.41)
20 (17.09)
6 (5.13)
26 (22.22)
TP53-
9 (11.69)
3 (3.90)
12 (15.59)
41 (35.04)
50 (42.74)
91 (77.78)
Total
44 (67.14)
33 (42.86)
77 (100)
61 (52.13)
56 (47.87)
117 (100)
genotype and assumed inheritance mode. Under the assumption of dominant inheritance, G is coded as 1 for genotype g = AA or Aa and 0 for g = aa. The letter d is a disease status indicator, and t represents the age of onset for diseased subjects (d = 1) or the last known disease-free age for unaffected subjects (d = 0). b, c, and g are regression coefficients to be estimated; g measures the degree of departure from a purely multiplicative hazards model. The function k0(t) describes the age-specific incidence rate for baseline groups; it is often expressed as a step function on a pre-determined set (e.g., 5 equal age intervals of 0–75: k0(t) = kk for tk-1 \ t B tk, k = 1,…,5, with t0 = 0 and tk = 15 9 k). The set of hazard model parameters is denoted by X = {b, c, g, kk}. The parameters b, c, and g represent the respective log-relative risks for a vector of measured covariates (z), a covariate (G) for a putative gene, and their interactions (G 9 z), compared to baseline group. For example, this model constrains the genetic relative risk for carriers (G = 1) to non-carriers (G = 0) at time t to be exp(c). Suppressing subscripts, the penetrance function for a given individual is expressed as f(d, t|z, g, X) = k(t|z, g, Rt X)dS(t|z, g, X), where Sðtjz; g; XÞ ¼ exp 0 kðsjz; g; XÞ dsÞ is the survival function (the probability of remaining disease-free up to time t). In joint segregation and linkage analysis models, the total likelihood for all pedigrees is the product of the family-specific likelihoods. Details of the methods and applications of this model have been described elsewhere (1997, Bonney et al. 1988; Gauderman and Faucett 1997; Wu et al. 2010). Modeling measured TP53 as a linked marker and MDM2 SNP309 as a measured covariate In this study, we modeled the measured TP53 genotype as the linked marker to the putative major gene in joint segregation and linkage analyses. We further included a genetic covariate to adjust for the measured MDM2 SNP309 genotype. With this novel design, the main component effects and gene–gene interaction effect of the TP53 and MDM2 SNP309 genotypes were tested for significance and accounted for in the model simultaneously. This is an important feature in our study because gene–gene and
gene-environment interactions play important roles in complex diseases and because current statistical approaches based on case-series and case–control designs have limited power for detecting such interactions (Altshuler et al. 2008). In this model setting, the effect of measured TP53 mutations on cancer risk can be assessed through the regression coefficient c of the putative major gene in equation [1]. Similarly, the main component effect of MDM2 SNP309 can be estimated through b of the measured covariate z and the interaction effect between the TP53 and MDM2 SNP309 genotypes, through g of the interaction covariate G 9 z. We used the magnitude of LD between the measured TP53 gene and putative major gene as a measure to determine to what extent the putative major gene serves as a proxy of the measured TP53 susceptibility gene in cancer risk estimation. The stronger the LD magnitude, the more the putative major gene represents the TP53 susceptibility gene in association with cancer risk (Balding 2006). We used GAP software to perform the analyses in this report. Hypothesis testing The logarithm ln(L) of the maximum likelihood of the data was computed for each model. The likelihood ratio test (LRT) was used to test a specific model against the baseline model to identify a better fit to the data. The specific model serves as the null model, and the baseline model as the alternative model. The LRT is computed as follows: LRT ¼ 2 ln Lspecific lnðLbaseline Þ ; where LRT approximately follows a v2 distribution with degrees of freedom (DF) equal to the difference in the numbers of independent parameters estimated between these two models. We also used Akaike’s Information Criteria (AIC) [AIC = -2 ln(L) ? 2 (number of independent parameters estimated)] to compare non-nested models. The model with the lowest AIC value and fewest estimated parameters is generally considered the most parsimonious. The LRT is also used to test the significance of covariate(s) when the model that includes additional covariate(s) is used as the baseline model.
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Results Because the measured TP53 germ-line mutant allele is dominant to the wild-type allele, we assumed the underlying inheritance mode for the putative major gene to be dominant in the model setting of joint segregation and linkage analyses. It indicates the effect of genotype AA equal to that for genotype Aa on cancer risk. Using the germ-line TP53 gene as a linked marker to the putative major gene in joint segregation and linkage analyses, we obtained estimates of the trait model parameters and recombination fraction ^ hp53 : between the TP53 gene and putative major gene. We used a 5-step baseline proportional hazard model k0(t) for the age-specific incidence rate, in which t0 = 0, tk = 15 9 k for k = 1,…,5 and k0(t) = kk for tk-1 \ t B tk. We obtained the highest LODscore of 22.64 at ^ hp53 ¼ 0:0 and cAA ¼ cAa ¼ 3:29; indicating that the TP53 and putative major genes were tightly linked and that the relative risk (RR) for cancer was 26:84 ¼ ðe3:29 Þ for individuals with a TP53 mutant allele as defined in Eq. 1. Testing linkage disequilibrium We tested the significance of LD between the TP53 and putative major genes and accounted for LD in the model. The highest LOD-score was 34.18 at ^ hp53 ¼ 0:0 and Table 6 Testing for significance of linkage disequilibrium between the putative major gene and TP53 gene
cAA ¼ cAa ¼ 2:84: Letting qB denote the frequency of highrisk allele B for the linked marker (the germ-line TP53 mutant allele in this case) and qAB denote the frequency of haplotype A and B, we obtained the LD measure of D0 1 and qAB qB ; suggesting that nearly perfect LD exists between the TP53 and putative major genes and that the haplotype of high-risk allele A and TP53 mutant allele was transmitted intact through generations within families (Devlin and Risch 1995). Using the LRT to compare the two models shown in Table 6, the v2 value was 35.24 that gives a p value of 2:91 109 with 1 DF, suggesting that the model that accounted for LD was significantly better than the one that did not account for LD. This was further supported by the difference in LOD-scores. An LOD-score difference of 1.5 is considered evidence of a better model; we found an LOD-score difference of 11.54 (=34.18–22.64), which also provides strong evidence that the model that accounted for LD was a far better fit to the data (Greenberg 1989). Thus, our investigation revealed that germ-line TP53 mutations are directly associated with cancer phenotype and play a causal role that is nearly identical to that of the putative major gene in cancer incidence in this application. The analyses for the models that did and did not account for LD are summarized in Table 6. The parameter k represents the estimates of the baseline annual age-specific cancer incidence rate per 100,000 persons for the age
Parameter
Maximum-likelihood estimate (standard error) Model without LD
Model with LD
0–15 15–30
43.3 49.6
64.1 71.5
30–45
139.7
193.2
45–60
316.9
368.8
60–75
898.5
666.7
-5
Baseline risk kk (10 ) Age group
Putative major gene g: c (g = Aa, AA)
3.29 (0.27)
2.84 (0.25)
Relative risk (=exp(c))
26.84
17.12
qA
5.40 9 10-2 (1.81 9 10-2) -14
qB
6.25 9 10
qAB
N.A.
(0.0)
3.16 9 10-4 (6.85 9 10-6) 8.08 9 10-12 (1.08 9 10-5) 8.08y 9 10-12 (1.08 9 10-5)
Linkage analysis Highest LOD-score ^ hp53 The parameter kk represents the estimates of the baseline annual age-specific cancer incidence rate per 100,000 persons for age groups of 0–15, 15–30, 30–45, 45–60, and 60–75 years, respectively
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-2 Log-likelihood
22.64
34.18
0.00
0.00
1667.14
1631.90
Likelihood ratio test (LRT) Degree of freedom (DF)
1
v2 value
35.24
p value
2.91 9 10-9
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groups of 0–15, 15–30, 30–45, 45–60, and 60–75 years, respectively. Testing interactions among TP53 mutations, MDM2 SNP309, and gender Because of recent observations that MDM2 SNP309 is associated with significantly earlier ages of cancer onset in TP53 mutation carriers in case-series studies (Bond et al. 2004; Bougeard et al. 2006; Ruijs et al. 2007) and that female TP53 mutation carriers have a higher cancer risk than males in family studies (Chompret et al. 2000; Wu et al. 2006), we not only evaluated the main component effects but also the interaction effects on cancer risk simultaneously among germ-line TP53 mutations, MDM2 SNP309, and gender in family studies. We used the model that accounted for LD as the base model (rather than the one that ignores LD) to further evaluate the joint contributions of these risk factors combined to cancer incidence. Letting the model that includes covariate MDM2 serve as the baseline model and the model with no covariates serve as the null model, the v2 value for the LRT with 1 DF was 1.57, which gives a p value of 0.21. The null model is not rejected at a 0.05 nominal significance level, indicating that MDM2 SNP 309 is not significant. The null and baseline models for LRT testing are presented in the second and third columns of Table 7, respectively. Note that MDM2 is coded as 1 for SNP309 G-allele carriers and 0 for wild-type T/T individuals. We tested the significance of the interaction between MDM2 SNP309 and TP53 mutations by letting the covariate MDM2 depend on the putative major gene (denoted by G in Eq. 1) in the model. We had MDM2 9 TP53 & MDM2 9 G because of nearly perfect LD with D0 1 between the putative major gene and TP53 gene in our previous analysis. The interaction covariate MDM2 9 TP53 was used to estimate the excess of cancer risk in MDM2 SNP309 G/G or G/T over wild-type genotypes among TP53 mutation carriers. Using the LRT to compare the model with MDM2 9 TP53 (baseline model) and that with no covariates (null model), the v2 value with 1 DF was 0.68, indicating that the interaction covariate MDM2 9 TP53 was not significant. We also analyzed the model that included both covariates MDM2 9 TP53 and MDM2 and found that it was not superior. The results of the models with MDM2 9 TP53 alone and with MDM2 9 TP53 and MDM2 are presented in the fourth and fifth columns of Table 7, respectively. Next, we tested the significance of gender difference on cancer risk in germ-line TP53 mutation carriers by letting the covariate Gender depend on the TP53 status. That is, the interaction covariate Gender 9 TP53 (&Gender 9 G)
669
was used to estimate the excess of cancer risk in female carriers over male carriers. Note that Gender is coded as 1 for females and 0 for males. Using the LRT to compare the model with covariate Gender 9 TP53 (baseline model) and that with no covariates (null model), the v2 value with 1 DF was 6.18, giving a p value of 1:29 102 and rejecting the null model at a 0.05 nominal significance level. It indicates the significance of sex difference on cancer risk in TP53 mutation carriers. To test the main effect of Gender in the presence of the interaction effect of TP53 mutation and Gender, we calculated the likelihood of the model with covariates Gender 9 TP53 and Gender and found little improvement in likelihood over the model with covariate Gender 9 TP53 alone but with one extra DF. This comparison indicated that the main effect of Gender is not needed when the interaction effect was included to the model. The results of the models with Gender 9 TP53 and with Gender and Gender 9 TP53 are shown in the sixth and seventh columns of Table 7, respectively. We further analyzed the data by including both covariates Gender 9 TP53 and MDM2 in the model; the result is shown in the eighth column of Table 7. This model (baseline model) is significantly better than that with no covariate (null model); the v2 value with 2 DF was 9.33, which gives a p value of 9:42 103 and rejects the null model at a 0.01 nominal significance level. This finding suggests that Gender 9 TP53 and MDM2 are both associated with increased cancer risk. In comparison with the model with covariate MDM2 alone (the third column of Table 7), it is noteworthy that the contribution of MDM2 to cancer risk was substantially enhanced when both germline TP53 and gender were incorporated into the model in our analyses. Because a substantial correlation of 0.20 was found between covariates MDM2 and Gender 9 TP53 in the maximization likelihood estimation, it is appropriate to compare the model with Gender 9 TP53 and MDM2 to that with no covariates, rather than that with covariate Gender 9 TP53 alone. It is further evidenced by the fact that the model with Gender 9 TP53 and MDM2 had a lower p value (9:42 103 ) than that with Gender 9 TP53 alone (1:29 102 ) in comparison with the model with no covariate (serves as null model). If we ignored this correlation and used the LRT to compare the model with Gender 9 TP53 and MDM2 to that with Gender 9 TP53, the v2 value was 3.15 with 1 DF, which gives a p value of 7:59 102 ; and MDM2 is not a significant risk modifier. We calculated the likelihood of the model with covariates Gender 9 TP53, Gender, and MDM2 and found that its likelihood was nearly identical to the one of the model with covariates Gender 9 TP53 and MDM2 (shown in the ninth column of Table 7). It indicated that the main effect of Gender is not needed when its interaction effect with
123
123
165.4
511.8 1083.4
30–45
45–60 60–75
5.58 9 10-11 (1.55 9 10-4)
1.78 9 10-10 (3.00 9 10-4)
qAB
859.76
AIC
0.21
0.41
0.68
1
861.08
839.08
0.21 (0.30)
2.25 9 10-12 (3.64 9 10-6)
2.25 9 10-12 (3.64 9 10-6)
2.46 9 10-5 (2.38 9 10-6)
20.49
3.02 (0.38)
516.2 1124.3
162.9
60.3
54.8
MDM2 9 TP53
0.32
2.26
2
861.50
837.50
-0.64 (0.65)
0.84 (0.60)
1.57 9 10-11 (2.91 9 10-5)
1.57 9 10-11 (2.91 9 10-5)
1.71 9 10-3 (2.21 9 10-5)
36.60
3.60 (0.59)
290.8 628.4
92.4
34.3
31.1
MDM2 ? MDM2 9 TP53
0.0129
6.18
1
855.58
833.58
0.75 (0.31)
3.66 9 10-10 (4.89 9 10-4)
3.66 9 10-10 (4.89 9 10-4)
3.21 9 10-3 (4.37 9 10-4)
16.78
2.82 (0.40)
495.0 1218.6
150.0
52.2
48.2
Gender 9 TP53
0.60
0.28
1
857.30
833.30
0.12 (0.50)
0.64 (0.57)
3.63 9 10-11 (3.70 9 10-5)
3.63 9 10-11 (3.70 9 10-5)
4.72 9 10-3 (4.82 9 10-5)
17.64
2.87 (0.46)
472.8 1158.0
143.5
50.0
46.2
0.0094
9.33
2
854.43
830.43
0.86 (0.31)
0.47 (0.27)
1.22 9 10-10 (3.53 9 10-4)
1.22 9 10-10 (3.53 9 10-4)
9.91 9 10-4 (1.42 9 10-5)
18.54
2.92 (0.41)
372.3 965.7
103.8
34.1
31.0
Gender ? Gender 9 MDM2? TP53 Gender 9 TP53
Note that MDM2 is coded as 1 for SNP309 G-allele carriers and 0 for wild-type T/T individuals; Gender is coded as 1 for females and 0 for males
1.57
P value
1
860.19
838.19
v2 value
Likelihood ratio test (LRT) Degree of freedom (DF)
839.76
-2 Log-likelihood
Gender
Gender 9 TP53
MDM2 9 TP53
MDM2
0.32 (0.26)
5.58 9 10-11 (1.55 9 10-4)
1.78 9 10-10 (3.00 9 10-4)
qB
Covariate z
4.31 9 10-4 (8.66 9 10-5)
2.37 9 10-3 (2.51 9 10-4)
24.05
3.18 (0.37)
424.3 937.6
132.3
48.6
44.0
MDM2
qA
Relative risk (=exp(c)) 22.42
3.11 (0.36)
62.2
TP53 major gene: c (g = Aa, AA)
57.0
15–30
No covariate
Covariates included in the models
0–15
Age group
Baseline risk kk (10-5)
Parameter
Table 7 Outcomes of complex joint segregation and linkage analyses
0.84
0.04
1
856.39
830.39
0.06 (0.49)
0.80 (0.56)
0.47 (0.27)
5.90 9 10-12 (6.81 9 10-6)
5.90 9 10-12 (6.81 9 10-6)
6.81 9 10-4 (5.57 9 10-6)
19.11
2.95 (0.47)
362.9 940.4
101.2
33.2
30.2
MDM2 ? Gender ? Gender 9 TP53
670 Hum Genet (2011) 129:663–673
Hum Genet (2011) 129:663–673
TP53 was included to the model. The AIC criterion confirmed this comparison as the model with covariates Gender 9 TP53 and MDM2 had a lower AIC value than the model with covariate Gender 9 TP53 alone and the model with covariates Gender 9 TP53, Gender, and MDM2. Parsimonious model We analyzed the models that included various covariate combinations, such as MDM2 9 Gender ? Gender 9 TP53, MDM2 ? Gender ? Gender 9 TP53, and Gender ? Gender 9 TP53. They were not superior to that with Gender 9 TP53 and MDM2. We also investigated the three-way interaction among germ-line TP53 mutations, MDM2, and gender. The numerical convergence in the maximization procedure was not satisfactory; the corresponding outcomes were, thus, not trustworthy (data not shown). In conclusion, the model that accounted for LD between the TP53 and putative major genes and included the covariate MDM2 and interaction covariate Gender 9 TP53 was most plausible. The AIC criterion confirmed this result, as shown in Table 7. Our analyses showed that both TP53 and Gender 9 TP53 were strongly associated with familial cancer incidence and that MDM2 had a modest contribution to incidence. The results of our investigation relative to the causal role of MDM2 in family studies were consistent with those of the original caseseries study, in which SNP309 G-alleles were associated with early age of cancer onset in both carriers and noncarriers of germ-line TP53 mutations (Bond et al. 2004), but the marginal effect of MDM2 SNP309 on cancer risk was not as large as those in the case-series study. According to the parameter estimation in our parsimonious model (the eighth column of Table 7), the coefficient of covariate MDM2 was 0.47, indicating that the RR for developing cancer was 1:6 ¼ ðe0:47 Þ for SNP309 G-allele carriers. The estimated coefficient cAA ¼ cAa was 2.92, indicating that the RR for developing cancer was 18:5 ¼ ðe2:92 Þ for men with germ-line TP53 mutations. The covariate coefficient of Gender 9 TP53 was 0.86, indicating that women with TP53 mutations had a 43:8 ¼ 2:92þ0:86 e -fold higher RR of developing cancer than did those without mutations and a 2:4 ¼ e0:86 -fold higher RR than did men with mutations. Men with both TP53 mutations and G-allele had a 29:7 ¼ ðe2:92þ0:47 Þ-fold higher RR of developing cancer than did those with neither. Women with both TP53 mutations and G-allele had a 70:1 ¼ e2:92þ0:86þ0:47 -fold higher RR than did those with neither. We calculated the associated 95% confidence intervals (CIs) by inverting the Fisher Information Matrix, which was obtained as a part of the maximum likelihood estimation of independent variables. The 95% CIs of the RR
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for developing cancer in individuals with TP53 mutations were (18.5, 103.5) and (8.3, 41.3) for women and men, respectively. The 95% CI of the RR in individuals with MDM2 G-allele was (0.9, 2.7). The 95% CIs of the RR for developing cancer in individuals with both TP53 mutations and G-allele were (22.4, 219.2) for women and (10.5, 83.9) for men. The 95% CI of the RR for the difference in women over men with TP53 mutations was (1.3, 4.4). We also calculated cancer-free survival curves by TP53 mutation, MDM2 SNP309 G-allele, gender, and age on the basis of the assumption of Cox proportional hazards model. These age-specific survival plots are shown in Fig. 2. The estimated cancer-free survival probabilities for the highest risk group, female carriers of TP53 mutations and MDM2 SNP309 G-allele, were 50.5, 16.9, and 0.4% at ages 30, 45, and 60 years, respectively. The corresponding survival rates for male carriers were 74.8, 47.2, and 9.0%. The estimated cancer-free survival probabilities for female TP53 mutation carriers with no SNP309 G-allele were 65.2, 33.0, and 2.9% at ages 30, 45, and 60 years, respectively. The corresponding survival rates for male carriers were 83.4, 62.5, and 22.2%. The estimated cancerfree survival probabilities for SNP309 G-allele carriers with wild-type TP53 were 98.4, 96.0, and 87.8% at ages 30, 45, and 60 years, respectively. In contrast, the estimated cancer-free survival rates for those with neither risk allele were 99.0, 97.5, and 92.2%.
Discussion Since the original study that reported the high-frequency genetic variant, SNP309 in MDM2, to be a genetic risk modifier of cancer incidence in TP53 mutation carriers (Bond et al. 2004), several independent reports in caseseries studies have shown that MDM2 SNP309 is
Fig. 2 Cancer-free survival curves by TP53 mutation, MDM2 SNP309, sex, and age
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associated with attenuation of the p53 pathway, an enhanced early age of cancer onset, and an increased frequency of multiple primary tumors (Bougeard et al. 2006; Ruijs et al. 2007). However, there is paucity in family studies that systematically investigate the causal role of MDM2 SNP309 in cancer incidence in the existing literature. In response, we designed a study to evaluate the causal role of MDM2 SNP309 in association with germline TP53 mutations and gender, including main component and interaction effects, on cancer risk simultaneously in our extended familial cases. We extended our statistical method by modeling the measured TP53 genotype as the linked marker to the putative major gene and including a measured covariate to adjust for the MDM2 SNP309 genotype in joint segregation and linkage analyses. With this novel design, we provided robust estimates of main component and gene–gene interaction effects of the TP53 and MDM2 SNP309 genotypes and gender simultaneously. The method is particularly designed for risk analyses of families and extended pedigrees. Our method accounts for intra-familial correlations in hereditary TP53 mutation distributions among the proband and each relative in a family, regardless of affection status. This is an important feature in our study because eight LFS kindreds contained more than 5 germ-line TP53 mutation carriers and 3 families even have more than 13. We illustrated in Fig. 1 the segregation of a germ-line TP53 mutation among relatives in a kindred, including nine mutation carriers. Unlike existing approaches, our method does not assume distributional independence of mutations among relatives. Furthermore, inappropriate management of missing genotypes in relatives could lead to substantial power loss and distorted estimates of risk factors’ effects. Modeling the measured TP53 genotypes as the linked marker enables all possible genotypes to be assigned to individuals with missing genotypes at the susceptibility locus with corresponding probabilities conditional on the known genotypes of others in the family. TP53 mutations constitute the major susceptibility component effect on cancer risk under study. Information on TP53 genotypes was missing for 37% of the 444 relatives in these 19 pedigrees. We previously demonstrated that this approach would lead to robust and reliable estimates of main and interaction effects of a measured susceptibility gene (Wu et al. 2010). Previously, our group used the standard Kaplan–Meier method that does not take account of inter-dependence in hereditary mutation distributions among relatives although several pedigrees are large, including more than 10 TP53 mutation carriers (Fang et al. 2010). Our analyses showed that the TP53 germ-line mutation and its interaction with gender were strongly associated with familial cancer incidence and that there was modest
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Hum Genet (2011) 129:663–673
evidence of an association between MDM2 SNP309 and increased cancer risk. Although the contribution of MDM2 SNP309 to cancer incidence became substantially greater when both germ-line TP53 and gender were incorporated into the model, the interaction between MDM2 SNP309 and TP53 mutations was not statistically significant in this study. The role of MDM2 SNP309 in our family study was consistent with the finding that SNP309 G-alleles were associated with the attenuated TP53 tumor suppressor pathway and led to accelerated tumor formation in both carriers and non-carriers of germ-line TP53 mutations (Bond et al. 2004); however, the marginal effect of MDM2 SNP309 on cancer risk was not as large as those estimates in case studies. We also evaluated a three-way interaction among germ-line TP53 mutations, MDM2 SNP309, and gender. Because the numerical convergence in the maximization likelihood procedure was not satisfactory, we believe the corresponding outcomes were less trustworthy (data not shown). In conclusion, the germ-line TP53 mutation, its interaction with gender, and MDM2 SNP309 were associated with increased cancer risk in our family study. The quantitative analyses of this family LFS cohort study found that SNP309 G-alleles were associated with early age of first cancer diagnosis in those who had no known germ-line TP53 mutations. This result is different from several studies in the literature (Bougeard et al. 2008; Marcel et al. 2009; Ruijs et al. 2007). But it is consistent with the finding by Bond et al. (2004) of significantly earlier age of STS diagnosis in adult patients with no known TP53 germ-line mutation. However, the marginal effect of SNP309 on cancer risk for those non-carriers was not as strong in this family study as that estimated by Bond et al. (2004). It is noteworthy that the studies by Bougeard et al. (2006), Ruijs et al. (2007), and Marcel et al. (2009) assumed that the case subjects were unrelated and used standard statistical methods for analyses, such as t test, chisquared test, and rank-sum test. The cancer patients in their studies actually consist of a substantial portion of familial cases, leading to genetic dependence among patients. In contrast, the method that we developed and used in this study is particularly designed for risk analyses of familial cases. The difference in analytic approaches may account for some of the variation in findings across studies, in addition to differences in ascertainment. Accurate age-specific assessment of cancer risk for known risk factors (the germ-line TP53 mutation, gender, MDM2 SNP309, and their interactions in this application) in family studies would provide valuable inference for clinical and genetic counseling. In addition, this study provides a greater understanding of the effects of genetic and non-genetic variants and their interactions on cancer incidence, which could ultimately lead to better risk
Hum Genet (2011) 129:663–673
prediction, prevention and clinical management of cancer incidence (Bond and Levine 2007). Acknowledgments This research was supported by the US National Cancer Institute grants R03-CA128103 to Wu CC and P01CA034936 to Strong LC.
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