|Grant Number:||7R01CA105090-05 Interpret this number|
|Primary Investigator:||Parmigiani, Giovanni|
|Organization:||Dana-Farber Cancer Inst|
|Project Title:||Statistical Methods for Cancer Genes|
DESCRIPTION (provided by applicant): Identifying individuals at high risk of cancer because of inherited genetic susceptibility is complex and increasingly important. Probabilistic prediction algorithms that exploit domain knowledge of Mendelian inheritance and other biological characteristics of susceptibility genes successfully contribute to improved screening, prevention, and genetic testing, and to the design and analysis of cancer studies. The investigators have developed, validated, applied and disseminated the widely used Mendelian model BRCAPRO. Based on their experience they have identified the need for a new generation of Mendelian prediction models in cancer genetics. The first aim will develop statistical approaches that generalize Mendelian models currently used in clinical genetic counseling practice. Innovation will focus on five areas: A) accounting for errors in reported pedigrees; B) accounting for dependencies in time-to-event distributions for multiple cancer sites; C) accounting for familial correlations arising from shared environmental factors or other sources; D) accounting for multiallelic syndromes; and E) incorporating information on covariates and on biomarkers related to the genes' activity. The second aim will introduce a novel class of multi-syndrome models to simultaneously identify cancer syndromes and predict mutation carrier status. These will enable clinicians and researchers to address the emerging challenges posed by the overlap in phenotype for cancer susceptibility genes, and by the high frequency of sporadic families with multiple sites. The third aim will develop flexible user-friendly software for the application of the methods in both research and clinical settings. The aims of this proposal will overcome pressing practical limitations of tools currently used in clinical genetic counseling, and thus contribute to improved screening, prevention and decision making about genetic testing.
Estimating CDKN2A carrier probability and personalizing cancer risk assessments in hereditary melanoma using MelaPRO.
Authors: Wang W, Niendorf KB, Patel D, Blackford A, Marroni F, Sober AJ, Parmigiani G, Tsao H
Source: Cancer Res, 2010 Jan 15;70(2), p. 552-9.
EPub date: 2010 Jan 12.
The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.
Authors: Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D
Source: BMC Bioinformatics, 2009 Aug 20;10, p. 256.
EPub date: 2009 Aug 20.
Tailoring BRCAPRO to Asian-Americans.
Authors: Chen S, Blackford AL, Parmigiani G
Source: J Clin Oncol, 2009 Feb 1;27(4), p. 642-3; author reply 643-4.
EPub date: 2008 Dec 15.
Estimating genome-wide copy number using allele-specific mixture models.
Authors: Wang W, Carvalho B, Miller ND, Pevsner J, Chakravarti A, Irizarry RA
Source: J Comput Biol, 2008 Sep;15(7), p. 857-66.
Classification of Missense Mutations of Disease Genes.
Authors: Zhou X, Iversen ES Jr, Parmigiani G
Source: J Am Stat Assoc, 2005;100, p. 51-60.
Population-Calibrated Gene Characterization: Estimating Age at Onset Distributions Associated With Cancer Genes.
Authors: Iversen ES Jr, Chen S
Source: J Am Stat Assoc, 2005;100, p. 399-409.
Multiple diseases in carrier probability estimation: accounting for surviving all cancers other than breast and ovary in BRCAPRO.
Authors: Katki HA, Blackford A, Chen S, Parmigiani G
Source: Stat Med, 2008 Sep 30;27(22), p. 4532-48.
Breast cancer risk among male BRCA1 and BRCA2 mutation carriers.
Authors: Tai YC, Domchek S, Parmigiani G, Chen S
Source: J Natl Cancer Inst, 2007 Dec 5;99(23), p. 1811-4.
EPub date: 2007 Nov 27.
Validity of models for predicting BRCA1 and BRCA2 mutations.
Authors: Parmigiani G, Chen S, Iversen ES Jr, Friebel TM, Finkelstein DM, Anton-Culver H, Ziogas A, Weber BL, Eisen A, Malone KE, Daling JR, Hsu L, Ostrander EA, Peterson LE, Schildkraut JM, Isaacs C, Corio C, Leondaridis L, Tomlinson G, Amos CI, Strong LC, Berry DA, Weitzel JN, Sand S, Dutson D, Kerber R, Peshkin BN, Euhus DM
Source: Ann Intern Med, 2007 Oct 2;147(7), p. 441-50.
A multidimensional analysis of genes mutated in breast and colorectal cancers.
Authors: Lin J, Gan CM, Zhang X, Jones S, Sjöblom T, Wood LD, Parsons DW, Papadopoulos N, Kinzler KW, Vogelstein B, Parmigiani G, Velculescu VE
Source: Genome Res, 2007 Sep;17(9), p. 1304-18.
EPub date: 2007 Aug 10.
PancPRO: risk assessment for individuals with a family history of pancreatic cancer.
Authors: Wang W, Chen S, Brune KA, Hruban RH, Parmigiani G, Klein AP
Source: J Clin Oncol, 2007 Apr 10;25(11), p. 1417-22.
Meta-analysis of BRCA1 and BRCA2 penetrance.
Authors: Chen S, Parmigiani G
Source: J Clin Oncol, 2007 Apr 10;25(11), p. 1329-33.
Incorporating medical interventions into carrier probability estimation for genetic counseling.
Authors: Katki HA
Source: BMC Med Genet, 2007 Mar 22;8, p. 13.
EPub date: 2007 Mar 22.
Prediction of germline mutations and cancer risk in the Lynch syndrome.
Authors: Chen S, Wang W, Lee S, Nafa K, Lee J, Romans K, Watson P, Gruber SB, Euhus D, Kinzler KW, Jass J, Gallinger S, Lindor NM, Casey G, Ellis N, Giardiello FM, Offit K, Parmigiani G, Colon Cancer Family Registry
Source: JAMA, 2006 Sep 27;296(12), p. 1479-87.
A recessive Mendelian model to predict carrier probabilities of DFNB1 for nonsyndromic deafness.
Authors: González JR, Wang W, Ballana E, Estivill X
Source: Hum Mutat, 2006 Nov;27(11), p. 1135-42.
Effect of misreported family history on Mendelian mutation prediction models.
Authors: Katki HA
Source: Biometrics, 2006 Jun;62(2), p. 478-87.
BayesMendel: an R environment for Mendelian risk prediction.
Authors: Chen S, Wang W, Broman KW, Katki HA, Parmigiani G
Source: Stat Appl Genet Mol Biol, 2004;3, p. Article21.
EPub date: 2004 Sep 17.
Characterization of BRCA1 and BRCA2 mutations in a large United States sample.
Authors: Chen S, Iversen ES, Friebel T, Finkelstein D, Weber BL, Eisen A, Peterson LE, Schildkraut JM, Isaacs C, Peshkin BN, Corio C, Leondaridis L, Tomlinson G, Dutson D, Kerber R, Amos CI, Strong LC, Berry DA, Euhus DM, Parmigiani G
Source: J Clin Oncol, 2006 Feb 20;24(6), p. 863-71.
A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans.
Authors: Favorov AV, Andreewski TV, Sudomoina MA, Favorova OO, Parmigiani G, Ochs MF
Source: Genetics, 2005 Dec;171(4), p. 2113-21.
EPub date: 2005 Aug 22.
Accuracy of MSI testing in predicting germline mutations of MSH2 and MLH1: a case study in Bayesian meta-analysis of diagnostic tests without a gold standard.
Authors: Chen S, Watson P, Parmigiani G
Source: Biostatistics, 2005 Jul;6(3), p. 450-64.
EPub date: 2005 Apr 14.