Marker segregation information in breast/ovarian cancer genetic counseling: is it still useful? Groupe Génétique et Cancer de la Fédération Nationale des Centres de Lutte Contre le Cancer

In silico functional profiling of human disease-associated and polymorphic amino acid substitutions

An important challenge in translational bioinformatics is to understand how genetic variation gives rise to molecular changes at the protein level that can precipitate both monogenic and complex disease. To this end, we compiled datasets of human disease-associated amino acid substitutions (AAS) in the contexts of inherited monogenic disease, complex disease, functional polymorphisms with no known disease association, and somatic mutations in cancer, and compared them with respect to predicted functional sites in proteins. Using the sequence homology-based tool SIFT to estimate the proportion of deleterious AAS in each dataset, only complex disease AAS were found to be indistinguishable from neutral polymorphic AAS. Investigation of monogenic disease AAS predicted to be nondeleterious by SIFT were characterized by a significant enrichment for inherited AAS within solvent accessible residues, regions of intrinsic protein disorder, and an association with the loss or gain of various posttranslational modifications. Sites of structural and/or functional interest were therefore surmised to constitute useful additional features with which to identify the molecular disruptions caused by deleterious AAS. A range of bioinformatic tools, designed to predict structural and functional sites in protein sequences, were then employed to demonstrate that intrinsic biases exist in terms of the distribution of different types of human AAS with respect to specific structural, functional and pathological features. Our Web tool, designed to potentiate the functional profiling of novel AAS, has been made available at http://profile.mutdb.org/.

Complex segregation analysis of pedigrees from the Gilda Radner Familial Ovarian Cancer Registry reveals evidence for mendelian dominant inheritance

BACKGROUND

Familial component is estimated to account for about 10% of ovarian cancer. However, the mode of inheritance of ovarian cancer remains poorly understood. The goal of this study was to investigate the inheritance model that best fits the observed transmission pattern of ovarian cancer among 7669 members of 1919 pedigrees ascertained through probands from the Gilda Radner Familial Ovarian Cancer Registry at Roswell Park Cancer Institute, Buffalo, New York.

METHODOLOGY/PRINCIPAL FINDINGS

Using the Statistical Analysis for Genetic Epidemiology program, we carried out complex segregation analyses of ovarian cancer affection status by fitting different genetic hypothesis-based regressive multivariate logistic models. We evaluated the likelihood of sporadic, major gene, environmental, general, and six types of Mendelian models. Under each hypothesized model, we also estimated the susceptibility allele frequency, transmission probabilities for the susceptibility allele, baseline susceptibility and estimates of familial association. Comparisons between models were carried out using either maximum likelihood ratio test in the case of hierarchical models, or Akaike information criterion for non-nested models. When assessed against sporadic model without familial association, the model with both parent-offspring and sib-sib residual association could not be rejected. Likewise, the Mendelian dominant model that included familial residual association provided the best-fitting for the inheritance of ovarian cancer. The estimated disease allele frequency in the dominant model was 0.21.

CONCLUSIONS/SIGNIFICANCE

This report provides support for a genetic role in susceptibility to ovarian cancer with a major autosomal dominant component. This model does not preclude the possibility of polygenic inheritance of combined effects of multiple low penetrance susceptibility alleles segregating dominantly.

BRCA1/2 testing: complex themes in result interpretation

Since the cloning of BRCA1 and BRCA2, genetic testing for breast and ovarian cancer susceptibility has become more widespread. However, interpretation of test results is not always straightforward. To illustrate this point, five vignettes adapted from actual cases are presented. As these cases demonstrate, in many high-risk families, a deleterious mutation in BRCA1 or BRCA2 is not identified in an affected proband. There are several potential explanations for such a finding, namely that an undetected mutation in BRCA1 or BRCA2 may exist, or there could be a mutation in a rare or undiscovered gene. In addition, the possibility that women with breast cancer represent sporadic cases within hereditary cancer families must also be considered. Finally, the occurrence of BRCA1/2 variants of uncertain significance, often missense mutations, further complicates the risk assessment. In some of these instances, extending testing to relatives can be helpful to clarify results. When hereditary breast cancer cannot be ruled out, individuals may still be at increased risk for cancer and therefore need to obtain appropriate surveillance. The process of genetic counseling is critical both before and after testing to ensure that patients understand the potential medical and psychosocial implications of testing and are aware of available options and resources. A multidisciplinary approach to service delivery, which includes clinicians in genetics and oncology, can facilitate patients' decision making and provide continued access to information and support.