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Validity of Models for Predicting BRCA1 and BRCA2 Mutations

Profile image of Sining ChenSining Chen

2007, Annals of Internal Medicine

https://doi.org/10.7326/0003-4819-147-7-200710020-00002
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23 pages

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Abstract

Background-Deleterious mutations of the BRCA1 and BRCA2 genes confer susceptibility to breast and ovarian cancer. At least 7 models for estimating the probabilities of having a mutation are used widely in clinical and scientific activities; however, the merits and limitations of these models are not fully understood. Objective-To systematically quantify the accuracy of the following publicly available models to predict mutation carrier status: BRCAPRO, family history assessment tool, Finnish, Myriad

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Estimate of the penetrance of BRCA mutation and the COS software for the assessment of BRCA mutation probability
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Accuracy of Risk Prediction Models for Breast Cancer and BRCA1/BRCA2 Mutation Carrier Probabilities in Israel
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Bayesian predictive model to assess BRCA2 mutational status according to clinical history: Early onset, metastatic phenotype or family history of breast/ovary cancer
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Patient consent statement All patients selected for this study were recruited in the PROGENE study (FWA00006032), and provided written informed consent in order to participate in the study. This paper does not contain any individual person's data in any form. Permission to reproduce material from other sources Not applicable Clinical trial registration Not applicable

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Evaluation of BRCA1 and BRCA2 mutation prevalence, risk prediction models and a multistep testing approach in French-Canadian families with high risk of breast and ovarian cancer
Jocelyne Chiquette, Francine Durocher

Journal of Medical Genetics, 2006

Background and objective: In clinical settings with fixed resources allocated to predictive genetic testing for high-risk cancer predisposition genes, optimal strategies for mutation screening programmes are critically important. These depend on the mutation spectrum found in the population under consideration and the frequency of mutations detected as a function of the personal and family history of cancer, which are both affected by the presence of founder mutations and demographic characteristics of the underlying population. The results of multistep genetic testing for mutations in BRCA1 or BRCA2 in a large series of families with breast cancer in the French-Canadian population of Quebec, Canada are reported. Methods: A total of 256 high-risk families were ascertained from regional familial cancer clinics throughout the province of Quebec. Initially, families were tested for a panel of specific mutations known to occur in this population. Families in which no mutation was identified were then comprehensively tested. Three algorithms to predict the presence of mutations were evaluated, including the prevalence tables provided by Myriad Genetics Laboratories, the Manchester Scoring System and a logistic regression approach based on the data from this study. Results: 8 of the 15 distinct mutations found in 62 BRCA1/BRCA2-positive families had never been previously reported in this population, whereas 82% carried 1 of the 4 mutations currently observed in >2 families. In the subset of 191 families in which at least 1 affected individual was tested, 29% carried a mutation. Of these 27 BRCA1-positive and 29 BRCA2-positive families, 48 (86%) were found to harbour a mutation detected by the initial test. Among the remaining 143 inconclusive families, all 8 families found to have a mutation after complete sequencing had Manchester Scores >18. The logistic regression and Manchester Scores provided equal predictive power, and both were significantly better than the Myriad Genetics Laboratories prevalence tables (p,0.001). A threshold of Manchester Score >18 provided an overall sensitivity of 86% and a specificity of 82%, with a positive predictive value of 66% in this population. Conclusion: In this population, a testing strategy with an initial test using a panel of reported recurrent mutations, followed by full sequencing in families with Manchester Scores >18, represents an efficient test in terms of overall cost and sensitivity.

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References (68)

  1. Struewing JP, Hartge P, Wacholder S, Baker SM, Berlin M, McAdams M, et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 1997;336:1401-1408. [PubMed: 9145676]PMID: 9145676
  2. Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet 2003;72:1117-1130. [PubMed: 12677558]PMID: 12677558
  3. King MC, Marks JH, Mandell JB. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003;302:643-646. [PubMed: 14576434]PMID: 14576434
  4. Newman B, Millikan RC, King MC. Genetic epidemiology of breast and ovarian cancers. Epidemiol Rev 1997;19:69-79. [PubMed: 9360904]PMID: 9360904
  5. Ford D, Easton DF, Stratton M, Narod S, Goldgar D, Devilee P, et al. Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1998;62:676-689. [PubMed: 9497246]PMID: 9497246
  6. Weber BL. Genetic testing for breast cancer. Sci Med 1996;3:12-21.
  7. American Society of Clinical Oncology policy statement update: genetic testing for cancer susceptibility. J Clin Oncol 2003;21:2397-2406. [PubMed: 12692171]PMID: 12692171
  8. Trepanier A, Ahrens M, McKinnon W, Peters J, Stopfer J, Grumet SC, et al. Genetic cancer risk assessment and counseling: recommendations of the national society of genetic counselors. J Genet Couns 2004;13:83-114. [PubMed: 15604628]PMID: 15604628
  9. Nelson HD, Huffman LH, Fu R, Harris EL. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 2005;143:362-379. [PubMed: 16144895]PMID: 16144895
  10. Claus EB, Risch N, Thompson WD. Genetic analysis of breast cancer in the cancer and steroid hormone study. Am J Hum Genet 1991;48:232-242. [PubMed: 1990835]PMID: 1990835
  11. Couch FJ, DeShano ML, Blackwood MA, Calzone K, Stopfer J, Campeau L, et al. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 1997;336:1409- 1415. [PubMed: 9145677]PMID: 9145677
  12. Shattuck-Eidens D, Oliphant A, McClure M, McBride C, Gupte J, Rubano T, et al. BRCA1 sequence analysis in women at high risk for susceptibility mutations. Risk factor analysis and implications for genetic testing. JAMA 1997;278:1242-1250. [PubMed: 9333265]PMID: 9333265
  13. Berry DA, Parmigiani G, Sanchez J, Schildkraut J, Winer E. Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. J Natl Cancer Inst 1997;89:227-238. [PubMed: 9017003]PMID: 9017003
  14. Parmigiani G, Berry D, Aguilar O. Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 1998;62:145-158. [PubMed: 9443863]PMID: 9443863
  15. Hartge P, Struewing JP, Wacholder S, Brody LC, Tucker MA. The prevalence of common BRCA1 and BRCA2 mutations among Ashkenazi Jews. Am J Hum Genet 1999;64:963-970. [PubMed: 10090881]PMID: 10090881
  16. Gilpin CA, Carson N, Hunter AG. A preliminary validation of a family history assessment form to select women at risk for breast or ovarian cancer for referral to a genetics center. Clin Genet 2000;58:299-308. [PubMed: 11076055]PMID: 11076055
  17. Vahteristo P, Eerola H, Tamminen A, Blomqvist C, Nevanlinna H. A probability model for predicting BRCA1 and BRCA2 mutations in breast and breast-ovarian cancer families. Br J Cancer 2001;84:704-708. [PubMed: 11237395]PMID: 11237395
  18. Frank TS, Deffenbaugh AM, Reid JE, Hulick M, Ward BE, Lingenfelter B, et al. Clinical characteristics of individuals with germline mutations in BRCA1 and BRCA2: analysis of 10,000 individuals. J Clin Oncol 2002;20:1480-1490. [PubMed: 11896095]PMID: 11896095
  19. Bluman LG, Rimer BK, Berry DA, Borstelmann N, Iglehart JD, Regan K, et al. Attitudes, knowledge, and risk perceptions of women with breast and/or ovarian cancer considering testing for BRCA1 and BRCA2. J Clin Oncol 1999;17:1040-1046. [PubMed: 10071299]PMID: 10071299
  20. Skinner CS, Schildkraut JM, Berry D, Calingaert B, Marcom PK, Sugarman J, et al. Pre-counseling education materials for BRCA testing: does tailoring make a difference? Genet Test 2002;6:93-105. [PubMed: 12215248]PMID: 12215248
  21. Schwartz MD, Peshkin BN, Hughes C, Main D, Isaacs C, Lerman C. Impact of BRCA1/BRCA2 mutation testing on psychologic distress in a clinic-based sample. J Clin Oncol 2002;20:514-520. [PubMed: 11786581]PMID: 11786581
  22. Hartman AR, Daniel BL, Kurian AW, Mills MA, Nowels KW, Dirbas FM, et al. Breast magnetic resonance image screening and ductal lavage in women at high genetic risk for breast carcinoma. Cancer 2004;100:479-489. [PubMed: 14745863]PMID: 14745863
  23. Domchek SM, Eisen A, Calzone K, Stopfer J, Blackwood A, Weber BL. Application of breast cancer risk prediction models in clinical practice. J Clin Oncol 2003;21:593-601. [PubMed: 12586794] PMID: 12586794
  24. Euhus DM, Smith KC, Robinson L, Stucky A, Olopade OI, Cummings S, et al. Pretest prediction of BRCA1 or BRCA2 mutation by risk counselors and the computer model BRCAPRO. J Natl Cancer Inst 2002;94:844-851. [PubMed: 12048272]PMID: 12048272
  25. Richards CS, Ward PA, Roa BB, Friedman LC, Boyd AA, Kuenzli G, et al. Screening for 185delAG in the Ashkenazim. Am J Hum Genet 1997;60:1085-1098. [PubMed: 9150156]PMID: 9150156
  26. Weitzel JN, McCaffrey SM, Nedelcu R, MacDonald DJ, Blazer KR, Cullinane CA. Effect of genetic cancer risk assessment on surgical decisions at breast cancer diagnosis. Arch Surg 2003;138:1323- 1328. [PubMed: 14662532]discussion 1329. [PMID: 14662532]
  27. Miron A, Schildkraut JM, Rimer BK, Winer EP, Sugg Skinner C, Futreal PA, et al. Testing for hereditary breast and ovarian cancer in the southeastern United States. Ann Surg 2000;231:624-634. [PubMed: 10767783]PMID: 10767783
  28. Malone KE, Daling JR, Thompson JD, O'Brien CA, Francisco LV, Ostrander EA. BRCA1 mutations and breast cancer in the general population: analyses in women before age 35 years and in women before age 45 years with first-degree family history. JAMA 1998;279:922-929. [PubMed: 9544766] PMID: 9544766
  29. Friebel TM, Beutler RA, Lee SM, Bernhardt BA, Helzlsouer KJ, Griffin CA. Active recruitment increased enrollment in a hereditary cancer registry. J Clin Epidemiol 2004;57:1172-1176. [PubMed: 15567634]PMID: 15567634
  30. Martin AM, Blackwood MA, Antin-Ozerkis D, Shih HA, Calzone K, Colligon TA, et al. Germline mutations in BRCA1 and BRCA2 in breast-ovarian families from a breast cancer risk evaluation clinic. J Clin Oncol 2001;19:2247-2253. [PubMed: 11304778]PMID: 11304778
  31. Anton-Culver H, Cohen PF, Gildea ME, Ziogas A. Characteristics of BRCA1 mutations in a population-based case series of breast and ovarian cancer. Eur J Cancer 2000;36:1200-1208. [PubMed: 10882857]PMID: 10882857
  32. Barcenas CH, Hosain GM, Arun B, Zong J, Zhou X, Chen J, et al. Assessing BRCA carrier probabilities in extended families. J Clin Oncol 2006;24:354-360. [PubMed: 16421416]PMID: 16421416
  33. Kristensen VN, Kelefiotis D, Kristensen T, Børresen-Dale AL. High-throughput methods for detection of genetic variation. Biotechniques 2001;30:318-322. 324, 326. [PubMed: 11233601] passim. [PMID: 11233601]
  34. Andrulis IL, Anton-Culver H, Beck J, Bove B, Boyd J, Buys S, et al. Comparison of DNA-and RNA- based methods for detection of truncating BRCA1 mutations. Hum Mutat 2002;20:65-73. [PubMed: 12112659]PMID: 12112659
  35. Myriad Genetics Laboratories. Salt Lake City, UT: Myriad Genetics Laboratories; [updated 4 May 2006]. BRACAnalysis technical specifications. Accessed at www.myriadtests.com/provider/doc/BRACAnalysis-Technical-Specifications.pdf on 26 July 2007.
  36. Yan H, Kinzler KW, Vogelstein B. Tech.sight. Genetic testing-present and future. Science 2000;289:1890-1892. [PubMed: 11012364]PMID: 11012364
  37. Murphy EA, Mutalik GS. The application of Bayesian methods in genetic counseling. Hum Hered 1969;19:126-151.
  38. Elston RC, Stewart J. A general model for the genetic analysis of pedigree data. Hum Hered 1971;21:523-542. [PubMed: 5149961]PMID: 5149961
  39. Szolovits, P.; Pauker, SP. Pedigree analysis for genetic counseling. In: Lun, KC.; Degoulet, P.; Piemme, TE.; Rienhoff, O., editors. MEDINFO 92: Proceedings of the Seventh Conference on Medical Informatics. New York: Elsevier; 1992. p. 679-683.
  40. Offit K, Brown K. Quantitating familial cancer risk: a resource for clinical oncologists. J Clin Oncol 1994;12:1724-1736. [PubMed: 8040684]PMID: 8040684
  41. Antoniou AC, Gayther SA, Stratton JF, Ponder BA, Easton DF. Risk models for familial ovarian and breast cancer. Genet Epidemiol 2000;18:173-190. [PubMed: 10642429]PMID: 10642429
  42. Iversen ES Jr, Parmigiani G, Berry DA, Schildkraut JM. Genetic susceptibility and survival: application to breast cancer. J Am Stat Assoc 2000;95:28-42.
  43. Claus EB, Risch NJ, Thompson WD. Age at onset as an indicator of familial risk of breast cancer. Am J Epidemiol 1990;131:961-972. [PubMed: 2188501]PMID: 2188501
  44. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994;73:643-651. [PubMed: 8299086]PMID: 8299086
  45. Apicella C, Andrews L, Hodgson SV, Fisher SA, Lewis CM, Solomon E, et al. Log odds of carrying an ancestral mutation in BRCA1 or BRCA2 for a defined personal and family history in an Ashkenazi Jewish woman (LAMBDA). Breast Cancer Res 2003;5:R206-R216. [PubMed: 14580256]PMID: 14580256
  46. de la Hoya M, Díez O, Pérez-Segura P, Godino J, Fernández JM, Sanz J, et al. Pre-test prediction models of BRCA1 or BRCA2 mutation in breast/ovarian families attending familial cancer clinics. J Med Genet 2003;40:503-510. [PubMed: 12843322]PMID: 12843322
  47. Evans DG, Eccles DM, Rahman N, Young K, Bulman M, Amir E, et al. A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRCAPRO. J Med Genet 2004;41:474-480. [PubMed: 15173236]PMID: 15173236
  48. Antoniou AC, Pharoah PP, Smith P, Easton DF. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer 2004;91:1580-1590. [PubMed: 15381934]PMID: 15381934
  49. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004;23:1111-1130. [PubMed: 15057881]PMID: 15057881
  50. Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA 1982;247:2543-2546. [PubMed: 7069920]PMID: 7069920
  51. Harrell, FE, Jr. New York: Springer; 2001. Regression Modeling Strategies with Application to Linear Models, Logistic Regression, and Survival Analysis.
  52. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 2003;95:14-18. [PubMed: 12509396] PMID: 12509396
  53. Berry DA, Iversen ES Jr, Gudbjartsson DF, Hiller EH, Garber JE, Peshkin BN, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. J Clin Oncol 2002;20:2701-2712. [PubMed: 12039933]PMID: 12039933
  54. James PA, Doherty R, Harris M, Mukesh BN, Milner A, Young MA, et al. Optimal selection of individuals for BRCA mutation testing: a comparison of available methods. J Clin Oncol 2006;24:707-715. [PubMed: 16446345]PMID: 16446345
  55. Marroni F, Aretini P, D'Andrea E, Caligo MA, Cortesi L, Viel A, et al. Evaluation of widely used models for predicting BRCA1 and BRCA2 mutations [Letter].
  56. J Med Genet 2004;41:278-285. [PubMed: 15060102]PMID: 15060102
  57. Antoniou AC, Durocher F, Smith P, Simard J, Easton DF. BRCA1 and BRCA2 mutation predictions using the BOADICEA and BRCAPRO models and penetrance estimation in high-risk French- Canadian families. Breast Cancer Res 2006;8:R3. [PubMed: 16417652]PMID: 16417652
  58. Capalbo C, Ricevuto E, Vestri A, Sidoni T, Buffone A, Cortesi E, et al. Improving the accuracy of BRCA1/2 mutation prediction: validation of the novel country-customized IC software. Eur J Hum Genet 2006;14:49-54. [PubMed: 16288312]PMID: 16288312
  59. Nanda R, Schumm LP, Cummings S, Fackenthal JD, Sveen L, Ademuyiwa F, et al. Genetic testing in an ethnically diverse cohort of high-risk women: a comparative analysis of BRCA1 and BRCA2 mutations in American families of European and African ancestry. JAMA 2005;294:1925-1933. [PubMed: 16234499]PMID: 16234499
  60. Vogel KJ, Atchley DP, Erlichman J, Broglio KR, Ready KJ, Valero V, et al. BRCA1 and BRCA2 genetic testing in Hispanic patients: mutation prevalence and evaluation of the BRCAPRO risk assessment model. J Clin Oncol. [Forthcoming]
  61. Myriad Genetics Laboratories. Salt Lake City, UT: Myriad Genetics Laboratories; 2006. Myriad Genetics 2006 annual report. Accessed at www.myriad.com/downloads/MYGN_AR2006.pdf on 26 July 2007.
  62. Frank TS, Manley SA, Olopade OI, Cummings S, Garber JE, Bernhardt B, et al. Sequence analysis of BRCA1 and BRCA2: correlation of mutations with family history and ovarian cancer risk. J Clin Oncol 1998;16:2417-2425. [PubMed: 9667259]PMID: 9667259
  63. Gross E, Arnold N, Goette J, Schwarz-Boeger U, Kiechle M. A comparison of BRCA1 mutation analysis by direct sequencing, SSCP and DHPLC. Hum Genet 1999;105:72-78. [PubMed: 10480358]PMID: 10480358
  64. Warner E, Foulkes W, Goodwin P, Meschino W, Blondal J, Paterson C, et al. Prevalence and penetrance of BRCA1 and BRCA2 gene mutations in unselected Ashkenazi Jewish women with breast cancer. J Natl Cancer Inst 1999;91:1241-1247. [PubMed: 10413426]PMID: 10413426
  65. Peshkin BN, Lerman C, Isaacs C, Brown KM, de Leon A, Abbaszadegan MR. A detection panel of prevalent mutations in BRCA1/2 genes is sensitive and cost-effective in an initial screen of high risk patients [Abstract]. Proc Am Assoc Cancer Res 1998;39:3232.
  66. Chen S, Wang W, Broman KW, Katki HA, Parmigiani G. BayesMendel: an R environment for Mendelian risk prediction. Stat Appl Genet Mol Biol 2004;3Article21. [PMID: 16646800]
  67. Myriad Genetics Laboratories. Salt Lake City, UT: Myriad Genetics Laboratories; 2006. BRCA risk calculator and mutation prevalence tables. Accessed at www.myriadtests.com/provider/brca-mutation-prevalence.htm on 26 July 2007.
  68. Euhus DM. Understanding mathematical models for breast cancer risk assessment and counseling. Breast J 2001;7:224-232. [PubMed: 11678799]PMID: 11678799

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Hassan Roudgari

Familial Cancer, 2007

Background Accurate risk assessment is essential to genetic counselling for a family history of cancer. Several empiric and computer-based risk assessment models have been developed to estimate a counselee's probability of being a carrier of mutation in BRCA1 and/or 2 genes, and to predict the risk of developing breast cancer. The COS model was developed from the better-known BRCAPro model to estimate risk of carriage of BRCA1 or 2 mutation. The COS model remains to be validated in a population discrete from that used for its development. Methods Four probability estimation models including COS, Manchester scoring system (MSS), BOADICEA and Tyrer-Cuzick (T-C) were applied to 275 Scottish families tested for BRCA1/2 mutations ascertained through regional genetics centres to ascertain models' sensitivity, specificity and accuracy. A subset of 130 families from Grampian (North and Northeast Scotland) was used to assess the models' ability to estimate the prevalence of BRCA1/2 mutation carriers. Sensitivity, specificity and ROC plots were used to ascertain models' individual performance, in terms of number of cancer cases, type of cancer and age of diagnosis of breast cancer. Results The COS and MSS models demonstrated the greatest sensitivities and area under ROC curves for the majority of family structures. They also showed the highest sensitivities (91-92%) and AUCs (76-78%) for the entire dataset overall. However, BOADICEA and T-C had the highest specificities for the majority of the family structures. BOADICEA and T-C generated the best estimates for the prevalence of mutations in the population; BOADICEA was more accurate for BRCA1 and T-C for BRCA2. Conclusion The COS and MSS models are the most effective models for use in clinical practice to select families for mutation analysis, but BO-ADICEA and T-C are more accurate for estimating mutation prevalence within a population. Keywords BRCA1 Á BRCA2 Á Breast and ovarian analysis of disease incidence and carrier (BOADICEA) Á Breast cancer Á Carrier probability Á COS software Á Genetic counselling Á Manchester scoring system (MSS) Á Ovarian cancer Á Probability estimation models Á Tyrer-Cuzick (T-C) Abbreviations AUC Area under ROC curve BC Breast cancer BBC Bilateral breast cancer BOADICEA Breast and ovarian analysis of disease incidence and carrier COS Case only study software MSS

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Re: Pretest Prediction of BRCA1 or BRCA2 Mutation by Risk Counselors and the Computer Model BRCAPRO
Kristin Smith

CancerSpectrum Knowledge Environment, 2002

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Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of PENN II model to previous study
Katherine Hunt

Familial Cancer, 2010

A number of models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is variable. To compare the performance characteristics of a web-based BRCA1/BRCA2 gene mutation prediction model: the PENNII model (www.afcri.upenn.edu/itacc/penn2), with studies done previously at our institution using four

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Related topics

  • Medicine
  • Genetic counseling
  • Likelihood Functions
  • Medical and Health Sciences

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    The BRCAPRO 5.0 model is a useful tool in genetic counseling and clinical management of male breast cancer cases
    Paolo Aretini

    European Journal of Human Genetics, 2010

    No study has evaluated the performance of BRCA1/2 mutations prediction models in male breast cancer (MBC) series. Although rare, MBC deserves attention because male and female breast cancers share many characteristics, including the involvement of genetic predisposition factors such as BRCA1/BRCA2 mutations. Indeed, the occurrence of MBC is a commonly used criterion to select families for BRCA mutation testing. We evaluated the performance and clinical effectiveness of four different predictive models in a population-based series of 102 Italian MBC patients characterized for BRCA1/2 mutations. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated for each risk model at the 10% threshold. The area under the ROC (AUC) curves and its corresponding asymptotic 95% CIs were calculated as a measure of the accuracy. In our study, the BRCAPRO version 5.0 had the highest combination of sensitivity, specificity, NPV and PPV for the combined probability and for the discrimination of BRCA2 mutations. In individuals with negative breast-ovarian cancer family history, BRCAPRO 5.0 reached a high discriminatory capacity (AUC¼0.92) in predicting BRCA2 mutations and showed values of sensitivity, specificity, NPV and PPV of 0.5, 0.98, 0.97 and 0.67, respectively, for the combined probability. BRCAPRO version 5.0 can be particularly useful in dealing with non-familial MBC, a circumstance that often represents a challenging situation in genetic counseling.

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    The contribution of breast cancer pathology to statistical models to predict mutation risk in BRCA carriers
    ana vargas

    Familial Cancer, 2010

    BRCA1 and BRCA2 associated breast cancer comprises a small but important group of hereditary breast cancer. Testing for BRCA1 and BRCA2 has significant clinical and personal implications for the patients in terms of therapy and follow-up of individual family members. The sequencing of the genes is expensive and since the information derived may have a profound effect on the individual and family members, it is important that testing is done only when the risk of carrying a mutation is thought to be high. Over the last decade, researchers have developed a number of statistical models for predicting risk for harboring mutations in these genes and the risk of subsequently developing breast and ovarian cancer. These models usually take into account the type of tumor and age at occurrence as well as family history. Data from pathological analysis show that although breast tumours are heterogeneous, there are histological characteristics that are seen more frequently in carriers of BRCA1 germ line mutations compared to BRCA2 and sporadic breast cancers.

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    Validation of the Pedigree Assessment Tool (PAT) in Families with BRCA1 and BRCA2 Mutations
    Kent Hoskins

    Annals of Surgical Oncology, 2010

    Background. The lifetime risk of breast cancer (BC) in patients with hereditary breast cancer syndromes is as high as 80%. The Pedigree Assessment Tool (PAT) is a scoring system to aid in identifying these patients. This validation study compares the PAT to BRCA gene mutation probability models in predicting suitability for genetic referral. Methods. Retrospective review identified subjects undergoing genetic counseling and BRCA testing from 2001 to 2008 at two institutions. PAT score and BRCA mutation probabilities were calculated using Myriad II and Penn II models. Comparisons were made between models in ability to discriminate patients appropriate for genetic evaluation based on accuracy in predicting a positive test result. Results. Records evaluated represent 520 families. BRCA testing revealed 146 mutation-positive and 374 mutationnegative families. c-Statistic analysis was used to compare the discriminating ability of the models to correctly assign families as mutation (?) and (-). Both the PAT and Penn II model outperformed the Myriad II model. Using a threshold PAT score C8 and mutation probability C10% to assign families as mutation (?) versus (-), sensitivity, specificity, and positive and negative predictive values were calculated for each model. The PAT was more sensitive than the Myriad II model and more specific than the Penn II model. Conclusions. In overall performance, the PAT is at least comparable to the Myriad II and Penn II models in screening women appropriate for genetic referral.

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    Apparently “BRCA-Related” Breast and Ovarian Cancer Patient With Germline TP53 Mutation
    Ugnius Mickys

    The Breast Journal, 2011

    Germline TP53 gene mutations are associated with complex cancer predisposition syndrome, the Li--Fraumeni syndrome, and are not as rare as were previously thought. Currently, the identification of Li--Fraumeni syndrome is mostly based on a conformance to descriptive criteria, which recently were amended to include wider spectrum of malignancies. The presence of very young age-onset breast cancers in TP53 mutations families is a feature that overlaps with hereditary breast/ovarian cancer families with BRCA1/2 genes mutations. Peri-diagnostic germline TP53 testing results in breast cancer patients can significantly affect surgical and adjuvant radiotherapy choices. The aim of this case report is to emphasize the importance of peri-diagnostic germline TP53 molecular testing in patients with early-onset breast cancer and its effect on the management and outcome of the disease. We present the apparent BRCA1-related, although mutation negative, breast and ovarian cancer patient who subsequently was confirmed to be TP53 c.817C>T (p.R273C) mutation carrier and discuss the importance of peri-diagnostic oncogenetic TP53 testing in early breast cancer cases. Histopathology and genetic modifiers (MDM2 SNP309G; TP53 R72P, PIN3) data are also addressed.

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    Development and validation of a simple questionnaire for the identification of hereditary breast cancer in primary care
    Juliana Giacomazzi

    BMC Cancer, 2009

    Background: Breast cancer is a significant public health problem worldwide and the development of tools to identify individuals at-risk for hereditary breast cancer syndromes, where specific interventions can be proposed to reduce risk, has become increasingly relevant. A previous study in Southern Brazil has shown that a family history suggestive of these syndromes may be prevalent at the primary care level. Development of a simple and sensitive instrument, easily applicable in primary care units, would be particularly helpful in underserved communities in which identification and referral of high-risk individuals is difficult.

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