Clinical Pathology - Research Publications
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ItemEvaluation of models to predict BRCA germline mutations(NATURE PUBLISHING GROUP, 2006-10-09)The selection of candidates for BRCA germline mutation testing is an important clinical issue yet it remains a significant challenge. A number of risk prediction models have been developed to assist in pretest counselling. We have evaluated the performance and the inter-rater reliability of four of these models (BRCAPRO, Manchester, Penn and the Myriad-Frank). The four risk assessment models were applied to 380 pedigrees of families who had undergone BRCA1/2 mutation analysis. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operator characteristic (ROC) curve were calculated for each model. Using a greater than 10% probability threshold, the likelihood that a BRCA test result was positive in a mutation carrier compared to the likelihood that the same result would be expected in an individual without a BRCA mutation was 2.10 (95% confidence interval (CI) 1.66-2.67) for Penn, 1.74 (95% CI 1.48-2.04) for Myriad, 1.35 (95% CI 1.19-1.53) for Manchester and 1.68 (95% CI 1.39-2.03) for BRCAPRO. Application of these models, therefore, did not rule in BRCA mutation carrier status. Similar trends were observed for separate BRCA1/2 performance measures except BRCA2 assessment in the Penn model where the positive likelihood ratio was 5.93. The area under the ROC curve for each model was close to 0.75. In conclusion, the four models had very little impact on the pre-test probability of disease; there were significant clinical barriers to using some models and risk estimates varied between experts. Use of models for predicting BRCA mutation status is not currently justified for populations such as that evaluated in the current study.
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ItemCYP17 genetic polymorphism, breast cancer, and breast cancer risk factors:: Australian Breast Cancer Family Study(BMC, 2005)INTRODUCTION: Because CYP17 can influence the degree of exposure of breast tissues to oestrogen, the interaction between polymorphisms in this gene and hormonal risk factors is of particular interest. We attempted to replicate the findings of studies assessing such interactions with the -34T-->C polymorphism. METHODS: Risk factor and CYP17 genotyping data were derived from a large Australian population-based case-control-family study of 1,284 breast cancer cases and 679 controls. Crude and adjusted odds ratio (OR) estimates and 95% confidence intervals (CIs) were calculated by unconditional logistic regression analyses. RESULTS: We found no associations between the CYP17 genotype and breast cancer overall. Premenopausal controls with A2/A2 genotype had a later age at menarche (P < 0.01). The only associations near statistical significance were that postmenopausal women with A1/A1 (wild-type) genotype had an increased risk of breast cancer if they had ever used hormone replacement therapy (OR 2.40, 95% CI 1.0 to 5.7; P = 0.05) and if they had menopause after age 47 years (OR 2.59, 95% CI 1.0 to 7.0; P = 0.06). We found no associations in common with any other studies, and no evidence for interactions. CONCLUSION: We observed no evidence of effect modification of reproductive risk factors by CYP17 genotype, although the experiment did not have sufficient statistical power to detect small main effects and modest effects in subgroups. Associations found only in subgroup analyses based on relatively small numbers require cautious interpretation without confirmation by other studies. This emphasizes the need for replication in multiple and large population-based studies to provide convincing evidence for gene-environment interactions.
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ItemAnalysis of cancer risk and BRCA1 and BRCA2 mutation prevalence in the kConFab familial breast cancer resource(BMC, 2006)INTRODUCTION: The Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab) is a multidisciplinary, collaborative framework for the investigation of familial breast cancer. Based in Australia, the primary aim of kConFab is to facilitate high-quality research by amassing a large and comprehensive resource of epidemiological and clinical data with biospecimens from individuals at high risk of breast and/or ovarian cancer, and from their close relatives. METHODS: Epidemiological, family history and lifestyle data, as well as biospecimens, are collected from multiple-case breast cancer families ascertained through family cancer clinics in Australia and New Zealand. We used the Tyrer-Cuzick algorithms to assess the prospective risk of breast cancer in women in the kConFab cohort who were unaffected with breast cancer at the time of enrolment in the study. RESULTS: Of kConFab's first 822 families, 518 families had multiple cases of female breast cancer alone, 239 had cases of female breast and ovarian cancer, 37 had cases of female and male breast cancer, and 14 had both ovarian cancer as well as male and female breast cancer. Data are currently held for 11,422 people and germline DNAs for 7,389. Among the 812 families with at least one germline sample collected, the mean number of germline DNA samples collected per family is nine. Of the 747 families that have undergone some form of mutation screening, 229 (31%) carry a pathogenic or splice-site mutation in BRCA1 or BRCA2. Germline DNAs and data are stored from 773 proven carriers of BRCA1 or BRCA1 mutations. kConFab's fresh tissue bank includes 253 specimens of breast or ovarian tissue--both normal and malignant--including 126 from carriers of BRCA1 or BRCA2 mutations. CONCLUSION: These kConFab resources are available to researchers anywhere in the world, who may apply to kConFab for biospecimens and data for use in ethically approved, peer-reviewed projects. A high calculated risk from the Tyrer-Cuzick algorithms correlated closely with the subsequent occurrence of breast cancer in BRCA1 and BRCA2 mutation positive families, but this was less evident in families in which no pathogenic BRCA1 or BRCA2 mutation has been detected.