Effects of measured susceptibility genes on cancer risk in family studies

Spatially varying effects of measured confounding variables on disease risk

BACKGROUND

The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence.

METHODS

We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina.

RESULTS

The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender.

CONCLUSION

The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature.

Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts

Li-Fraumeni syndrome (LFS) is a rare hereditary cancer syndrome associated with an autosomal-dominant mutation inheritance in the TP53 tumor suppressor gene and a wide spectrum of cancer diagnoses. The previously developed R package, LFSPRO, is capable of estimating the risk of an individual being a TP53 mutation carrier. However, an accurate estimation of the penetrance of different cancer types in LFS is crucial to improve the clinical characterization and management of high-risk individuals. Here, we developed a competing risk-based statistical model that incorporates the pedigree structure efficiently into the penetrance estimation and corrects for ascertainment bias while also increasing the effective sample size of this rare population. This enabled successful estimation of TP53 penetrance for three LFS cancer types: breast (BR), sarcoma (SA), and others (OT), from 186 pediatric sarcoma families collected at MD Anderson Cancer Center (Houston, TX). Penetrance validation was performed on a combined dataset of two clinically ascertained family cohorts with cancer to overcome internal bias in each (total number of families = 668). The age-dependent onset probability distributions of specific cancer types were different. For breast cancer, the TP53 penetrance went up at an earlier age than the reported BRCA1/2 penetrance. The prediction performance of the penetrance estimates was validated by the combined independent cohorts (BR = 85, SA = 540, and OT = 158). Area under the ROC curves (AUC) were 0.92 (BR), 0.75 (SA), and 0.81 (OT). The new penetrance estimates have been incorporated into the current LFSPRO R package to provide risk estimates for the diagnosis of breast cancer, sarcoma, or other cancers. SIGNIFICANCE: These findings provide specific penetrance estimates for LFS-associated cancers, which will likely impact the management of families at high risk of LFS.See related article by Shin et al., p. 347.

Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective

Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with TP53 germline mutations and an increased lifetime risk of multiple primary cancers (MPC). Penetrance estimation of time to first and second primary cancer within LFS remains challenging because of limited data and the difficulty of characterizing the effects of a primary cancer on the penetrance of a second primary cancer. Using a recurrent events survival modeling approach that incorporates a family-wise likelihood to efficiently integrate the pedigree structure, we estimated the penetrance for both first and second primary cancer diagnosis from a pediatric sarcoma cohort at MD Anderson Cancer Center [MDACC, Houston, TX; number of families = 189; single primary cancer (SPC) = 771; and MPC = 87]. Validation of the risk prediction performance was performed using an independent MDACC clinical cohort of TP53 tested individuals (SPC = 102 and MPC = 58). These findings showed that an individual diagnosed at a later age was more likely to be diagnosed with a second primary cancer. In addition, TP53 mutation carriers had a HR of 1.65 (95% confidence interval, 1.1-2.5) for developing a second primary cancer versus SPC. The area under the ROC (AUC) curve for predicting individual outcomes of MPC versus SPC was 0.77. In summary, we provide the first set of penetrance estimates for first and second primary cancer for TP53 germline mutation carriers and demonstrate its accuracy for cancer risk assessment. SIGNIFICANCE: These findings present an open-source R package LFSPRO that could be used for genetic counseling and health management of individuals with LFS as it estimates the risk of both first and second primary cancer diagnosis.See related article by Shin et al., p. 354.

Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a high-risk group develop different cancers, and accommodate family data using family-wise likelihoods. We tackle the ascertainment bias arising when family data are collected through probands in a high-risk population in which disease cases are more likely to be observed. We apply the proposed method to a cohort of 186 families with Li-Fraumeni syndrome identified through probands with sarcoma treated at MD Anderson Cancer Center from 1944 to 1982.

Estimating TP53 Mutation Carrier Probability in Families with Li-Fraumeni Syndrome Using LFSPRO

Background: Li-Fraumeni syndrome (LFS) is associated with germline TP53 mutations and a very high lifetime cancer risk. Algorithms that assess a patient's risk of inherited cancer predisposition are often used in clinical counseling. The existing LFS criteria have limitations, suggesting the need for an advanced prediction tool to support clinical decision making for TP53 mutation testing and LFS management.Methods: Based on a Mendelian model, LFSPRO estimates TP53 mutation probability through the Elston-Stewart algorithm and consequently estimates future risk of cancer. With independent datasets of 1,353 tested individuals from 867 families, we evaluated the prediction performance of LFSPRO.Results: LFSPRO accurately predicted TP53 mutation carriers in a pediatric sarcoma cohort from MD Anderson Cancer Center in the United States, the observed to expected ratio (OE) = 1.35 (95% confidence interval, 0.99-1.80); area under the receiver operating characteristic curve (AUC) = 0.85 (0.75-0.93); a population-based sarcoma cohort from the International Sarcoma Kindred Study in Australia, OE = 1.62 (1.03-2.55); AUC = 0.67 (0.54-0.79); and the NCI LFS study cohort, OE = 1.28 (1.17-1.39); AUC = 0.82 (0.78-0.86). LFSPRO also showed higher sensitivity and specificity than the classic LFS and Chompret criteria. LFSPRO is freely available through the R packages LFSPRO and BayesMendel.Conclusions: LFSPRO shows good performance in predicting TP53 mutations in individuals and families in varied situations.Impact: LFSPRO is more broadly applicable than the current clinical criteria and may improve clinical management for individuals and families with LFS. Cancer Epidemiol Biomarkers Prev; 26(6); 837-44. ©2017 AACR.

Cytochrome P450 and matrix metalloproteinase genetic modifiers of disease severity in Cerebral Cavernous Malformation type 1

BACKGROUND

Familial Cerebral Cavernous Malformation type 1 (CCM1) is an autosomal dominant disease caused by mutations in the Krev Interaction Trapped 1 (KRIT1/CCM1) gene, and characterized by multiple brain lesions. CCM lesions manifest across a range of different phenotypes, including wide differences in lesion number, size and susceptibility to intracerebral hemorrhage (ICH). Oxidative stress plays an important role in cerebrovascular disease pathogenesis, raising the possibility that inter-individual variability in genes related to oxidative stress may contribute to the phenotypic differences observed in CCM1 disease. Here, we investigated whether candidate oxidative stress-related cytochrome P450 (CYP) and matrix metalloproteinase (MMP) genetic markers grouped by superfamilies, families or genes, or analyzed individually influence the severity of CCM1 disease.

METHODS

Clinical assessment and cerebral susceptibility-weighted magnetic resonance imaging (SWI) were performed to determine total and large (≥5mm in diameter) lesion counts as well as ICH in 188 Hispanic CCM1 patients harboring the founder KRIT1/CCM1 'common Hispanic mutation' (CCM1-CHM). Samples were genotyped on the Affymetrix Axiom Genome-Wide LAT1 Human Array. We analyzed 1,122 genetic markers (both single nucleotide polymorphisms (SNPs) and insertion/deletions) grouped by CYP and MMP superfamily, family or gene for association with total or large lesion count and ICH adjusted for age at enrollment and gender. Genetic markers bearing the associations were then analyzed individually.

RESULTS

The CYP superfamily showed a trend toward association with total lesion count (P=0.057) and large lesion count (P=0.088) in contrast to the MMP superfamily. The CYP4 and CYP8 families were associated with either large lesion count or total lesion count (P=0.014), and two other families (CYP46 and the MMP Stromelysins) were associated with ICH (P=0.011 and 0.007, respectively). CYP4F12 rs11085971, CYP8A1 rs5628, CYP46A1 rs10151332, and MMP3 rs117153070 single SNPs, mainly bearing the above-mentioned associations, were also individually associated with CCM1 disease severity.

CONCLUSIONS

Overall, our candidate oxidative stress-related genetic markers set approach outlined CYP and MMP families and identified suggestive SNPs that may impact the severity of CCM1 disease, including the development of numerous and large CCM lesions and ICH. These novel genetic risk factors of prognostic value could serve as early objective predictors of disease outcome and might ultimately provide better options for disease prevention and treatment.

Li-Fraumeni syndrome: report of a clinical research workshop and creation of a research consortium

Li-Fraumeni syndrome (LFS) is a rare dominantly inherited cancer predisposition syndrome that was first described in 1969. In most families, it is caused by germline mutations in the TP53 gene and is characterized by early onset of multiple specific cancers and very high lifetime cumulative cancer risk. Despite significant progress in understanding the molecular biology of TP53, the optimal clinical management of this syndrome is poorly defined. We convened a workshop on November 2, 2010, at the National Institutes of Health in Bethesda, Maryland, bringing together clinicians and scientists, as well as individuals from families with LFS, to review the state of the science, address clinical management issues, stimulate collaborative research, and engage the LFS family community. This workshop also led to the creation of the Li-Fraumeni Exploration (LiFE) Research Consortium.

Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li-Fraumeni syndrome

Li-Fraumeni syndrome (LFS) is a rare familial cancer syndrome characterized by early cancer onset, diverse tumor types, and multiple primary tumors. Germ-line 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 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.