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ABSTRACT: Personalized cancer risk assessment remains an essential imperative in postgenomic cancer medicine. In hereditary melanoma, germline CDKN2A mutations have been reproducibly identified in melanoma-prone kindreds worldwide. However, genetic risk counseling for hereditary melanoma remains clinically challenging. To address this challenge, we developed and validated MelaPRO, an algorithm that provides germline CDKN2A mutation probabilities and melanoma risk to individuals from melanoma-prone families. MelaPRO builds on comprehensive genetic information, and uses Mendelian modeling to provide fine resolution and high accuracy. In an independent validation of 195 individuals from 167 families, MelaPRO exhibited good discrimination with a concordance index (C) of 0.86 [95% confidence intervals (95% CI), 0.75-0.97] and good calibration, with no significant difference between observed and predicted carriers (26; 95% CI, 20-35, as compared with 22 observed). In cross-validation, MelaPRO outperformed the existing predictive model MELPREDICT (C, 0.82; 95% CI, 0.61-0.93), with a difference of 0.05 (95% CI, 0.007-0.17). MelaPRO is a clinically accessible tool that can effectively provide personalized risk counseling for all members of hereditary melanoma families.
Cancer Research 01/2010; 70(2):552-9. · 7.86 Impact Factor
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ABSTRACT: Genomic changes such as copy number alterations are one of the major underlying causes of human phenotypic variation among normal and disease subjects. Array comparative genomic hybridization (CGH) technology was developed to detect copy number changes in a high-throughput fashion. However, this technology provides only a >30-kb resolution, which limits the ability to detect copy number alterations spanning small regions. Higher resolution technologies such as single nucleotide polymorphism (SNP) microarrays allow detection of copy number alterations at least as small as several thousand base pairs. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively, thus greatly reducing resolution. Recently, regression-type models that account for probe effects have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution, specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314-sample database, to motivate and fit models for the conditional distribution of the observed intensities given allele-specific copy number. We can then compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo package (www.bioconductor.org).
Journal of computational biology: a journal of computational molecular cell biology 09/2008; 15(7):857-66. · 1.69 Impact Factor
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ABSTRACT: The rapid fatality of pancreatic cancer is, in large part, the result of an advanced stage of diagnosis for the majority of patients. Identification of individuals at high risk of developing pancreatic cancer is a first step towards the early detection of this disease. Individuals who may harbor a major pancreatic cancer susceptibility gene are one such high-risk group. The goal of this study was to develop and validate PancPRO, a Mendelian model for pancreatic cancer risk prediction in individuals with familial pancreatic cancer, to identify high-risk individuals.
PancPRO was built by extending the Bayesian modeling framework developed for BRCAPRO, trained using published data, and validated using independent prospective data on 961 families enrolled onto the National Familial Pancreas Tumor Registry, including 26 individuals who developed incident pancreatic cancer during follow-up.
We developed a risk prediction model, PancPRO, and free software for the estimation of pancreatic cancer susceptibility gene carrier probabilities and absolute pancreatic cancer risk. Model validation demonstrated an observed to predicted pancreatic cancer ratio of 0.83 (95% CI, 0.52 to 1.20) and high discriminatory ability, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.68 to 0.81) for PancPRO.
PancPRO is the first risk prediction model for pancreatic cancer. When we validated our model using the largest registry of familial pancreatic cancer, our model provided accurate risk assessment. Our findings highlight the importance of detailed family history for clinical cancer risk assessment and demonstrate that accurate genetic risk assessment is possible even when the causative genes are not known.
Journal of Clinical Oncology 05/2007; 25(11):1417-22. · 18.37 Impact Factor
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Research in Computational Molecular Biology, 11th Annual International Conference, RECOMB 2007, Oakland, CA, USA, April 21-25, 2007, Proceedings; 01/2007
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Sining Chen, Wenyi Wang,
Shing Lee,
Khedoudja Nafa,
Johanna Lee,
Kathy Romans,
Patrice Watson,
Stephen B Gruber,
David Euhus,
Kenneth W Kinzler,
Jeremy Jass,
Steven Gallinger,
Noralane M Lindor,
Graham Casey,
Nathan Ellis,
Francis M Giardiello,
Kenneth Offit,
Giovanni Parmigiani
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ABSTRACT: Identifying families at high risk for the Lynch syndrome (ie, hereditary nonpolyposis colorectal cancer) is critical for both genetic counseling and cancer prevention. Current clinical guidelines are effective but limited by applicability and cost.
To develop and validate a genetic counseling and risk prediction tool that estimates the probability of carrying a deleterious mutation in mismatch repair genes MLH1, MSH2, or MSH6 and the probability of developing colorectal or endometrial cancer.
External validation of the MMRpro model was conducted on 279 individuals from 226 clinic-based families in the United States, Canada, and Australia (referred between 1993-2005) by comparing model predictions with results of highly sensitive germline mutation detection techniques. MMRpro models the autosomal dominant inheritance of mismatch repair mutations, with parameters based on meta-analyses of the penetrance and prevalence of mutations and of the predictive values of tumor characteristics. The model's prediction is tailored to each individual's detailed family history information on colorectal and endometrial cancer and to tumor characteristics including microsatellite instability.
Ability of MMRpro to correctly predict mutation carrier status, as measured by operating characteristics, calibration, and overall accuracy.
In the independent validation, MMRpro provided a concordance index of 0.83 (95% confidence interval, 0.78-0.88) and a ratio of observed to predicted cases of 0.94 (95% confidence interval, 0.84-1.05). This results in higher accuracy than existing alternatives and current clinical guidelines.
MMRpro is a broadly applicable, accurate prediction model that can contribute to current screening and genetic counseling practices in a high-risk population. It is more sensitive and more specific than existing clinical guidelines for identifying individuals who may benefit from MMR germline testing. It is applicable to individuals for whom tumor samples are not available and to individuals in whom germline testing finds no mutation.
JAMA The Journal of the American Medical Association 10/2006; 296(12):1479-87. · 30.03 Impact Factor
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ABSTRACT: Several important syndromes are caused by deleterious germline mutations of individual genes. In both clinical and research applications it is useful to evaluate the probability that an individual carries an inherited genetic variant of these genes, and to predict the risk of disease for that individual, using information on his/her family history. Mendelian risk prediction models accomplish these goals by integrating Mendelian principles and state-of-the-art statistical models to describe phenotype/genotype relationships. Here we introduce an R library called BayesMendel that allows implementation of Mendelian models in research and counseling settings. BayesMendel is implemented in an object-oriented structure in the language R and distributed freely as an open source library. In its first release, it includes two major cancer syndromes: the breast-ovarian cancer syndrome and the hereditary non-polyposis colorectal cancer syndrome, along with up-to-date estimates of penetrance and prevalence for the corresponding genes. Input genetic parameters can be easily modified by users. BayesMendel can also serve as a generic tool for genetic epidemiologists to flexibly implement their own Mendelian models for novel syndromes and local subpopulations, without reprogramming complex statistical analyses and prediction tools.
Statistical Applications in Genetics and Molecular Biology 02/2004; 3:Article21. · 1.52 Impact Factor
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Sining Chen, Wenyi Wang,
Shing Lee,
Khedoudja Nafa,
Johanna Lee,
Kathy Romans,
Patrice Watson,
Stephen B. Gruber,
David Euhus,
Kenneth W. Kinzler,
Jeremy Jass,
Steven Gallinger,
Noralane M. Lindor,
Graham Casey,
Nathan Ellis,
Francis M. Giardiello,
Kenneth Offit,
Giovanni Parmigiani,
for the Colon Cancer Family Registry
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ABSTRACT: Context
Identifying families at high risk for the Lynch syndrome (ie, hereditary nonpolyposis colorectal cancer) is critical for both genetic counseling and cancer prevention. Current clinical guidelines are effective but limited by applicability and cost.Objective
To develop and validate a genetic counseling and risk prediction tool that estimates the probability of carrying a deleterious mutation in mismatch repair genes MLH1, MSH2, or MSH6 and the probability of developing colorectal or endometrial cancer.Design, Setting, and Patients
External validation of the MMRpro model was conducted on 279 individuals from 226 clinic-based families in the United States, Canada, and Australia (referred between 1993-2005) by comparing model predictions with results of highly sensitive germline mutation detection techniques. MMRpro models the autosomal dominant inheritance of mismatch repair mutations, with parameters based on meta-analyses of the penetrance and prevalence of mutations and of the predictive values of tumor characteristics. The model's prediction is tailored to each individual's detailed family history information on colorectal and endometrial cancer and to tumor characteristics including microsatellite instability.Main Outcome Measure
Ability of MMRpro to correctly predict mutation carrier status, as measured by operating characteristics, calibration, and overall accuracy.Results
In the independent validation, MMRpro provided a concordance index of 0.83 (95% confidence interval, 0.78-0.88) and a ratio of observed to predicted cases of 0.94 (95% confidence interval, 0.84-1.05). This results in higher accuracy than existing alternatives and current clinical guidelines.Conclusions
MMRpro is a broadly applicable, accurate prediction model that can contribute to current screening and genetic counseling practices in a high-risk population. It is more sensitive and more specific than existing clinical guidelines for identifying individuals who may benefit from MMR germline testing. It is applicable to individuals for whom tumor samples are not available and to individuals in whom germline testing finds no mutation.
Figures in this Article
The Lynch syndrome (ie, hereditary nonpolyposis colorectal cancer [HNPCC]) is the most common familial colorectal cancer (CRC).1- 2 It can be caused by germline deleterious mutations of DNA mismatch repair (MMR) genes, including MLH1,3- 4MSH2,5MSH6,6 and several others.7 Screening for individuals likely to carry a deleterious mutation of these genes has traditionally relied on examination of family history, as per the Amsterdam Criteria,8- 10 and has recently moved toward multistep algorithms combining family history with molecular tumor characteristics such as microsatellite instability (MSI),11 as per the Bethesda Guidelines.2,12
The latter were developed to help recognize those individuals who would potentially benefit from a more detailed molecular diagnostic workup, including MSI and subsequent germline testing. This approach is useful, but not without important limitations. First, MSI tests can only be performed on affected patients for whom a tumor block is available. This limits the preventive usefulness of the approach. Second, the algorithm calls for MSI testing on a large population with colorectal cancer, which increases the cost per mutation carrier detected. Third, for individuals tested with commercial germline testing techniques that have imperfect sensitivity, this approach cannot offer guidance for decision making when no mutation is found.
Extensive knowledge is now available about the Lynch syndrome: the mode of inheritance of the genes is autosomal dominant,13 prevalence and penetrance have been studied, and their clinical/molecular manifestation in tumors is well characterized. The translation of such knowledge into clinically useful algorithms can benefit greatly from a systematic, quantitative, and objective approach. Application of such an approach to a specific family should use as much clinical and biological information about the pedigree as possible, to provide an individualized assessment of risks. Insurance companies may demand an objective assessment of mutation probability when considering reimbursement for genetic testing, and they often accept a statistical estimate of mutation probability as a justification.
A step in this direction is the Leiden model,14 which estimates the combined probability of carrying a mutation of an MMR gene using a logistic regression based on 3 variables: fulfillment of the Amsterdam Criteria, mean age of CRC diagnoses, and presence of any endometrial cancers in the family. This model is useful but does not use all available biological knowledge and does not provide risk predictions.
The translational goals described above are more fully achieved by formulating an explicit genetic model,15 as was done successfully for the BRCA genes.16- 19 To this end, we developed MMRpro, a model that estimates the probability that an individual carries a deleterious mutation in an MMR gene. This probability is evaluated on the basis of detailed family history of colorectal and endometrial cancer, including information for the individual being counseled and each of his or her first- and second-degree relatives, as described in the Box. In addition, MMRpro provides estimates of future cancer risks for unaffected individuals, including known mutation carriers, untested individuals, and individuals in whom no mutation is found. MMRpro is appropriate for prediction in both population-based and clinic-based families. It is useful to clinical geneticists in parallel with clinical criteria, especially when current guidelines are inadequate to address the particularity of a given family and when the possibility of a heritable defect cannot be directly addressed in the laboratory.
BOX. FAMILY HISTORY AS INPUTS TO THE MMRPRO MODEL AND RESULTING OUTPUT
ABSTRACT
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BOX. FAMILY HISTORY AS INPUTS TO THE MMRPRO MODEL AND RESULTING OUTPUT
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METHODS
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RESULTS
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COMMENT
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AUTHOR INFORMATION
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REFERENCES
Input (for the counselee and each first- or second-degree relative)*Exact relation to the counseleeColorectal cancer status (affected or unaffected) Age at diagnosis (in years) of colorectal cancer if affectedEndometrial cancer status (affected or unaffected) Age at diagnosis (in years) of endometrial cancer if affected Current age or age at last follow-up (in years) if unaffectedResult of microsatellite instability testing (instability present or not present) or immunohistochemical staining (loss of expression or present) if tumor availableResult of previous germline testing of MLH1, MSH2, or MSH6 (positive or not found)Output (for the counselee)†Probability, by gene, that the counselee carries a deleterious mutation of MLH1, MSH2, and MSH6Probability, in yearly intervals, that the counselee, if asymptomatic, will develop colorectal or endometrial cancers in the future
*Any input can be left unspecified if not available, with the exception of relation to the counselee.
†Prediction can be made for any member in the family by designating that member as the counselee.
JAMA The Journal of the American Medical Association 296(12):1479-1487. · 30.03 Impact Factor
