Germline mutations in CDH1 are infrequent in women with early-onset or familial lobular breast cancers

BACKGROUND: Germline mutations in CDH1 are associated with hereditary diffuse gastric cancer; lobular breast cancer also occurs excessively in families with such condition. METHOD: To determine if CDH1 is a susceptibility gene for lobular breast cancer in women without a family history of diffuse gastric cancer, germline DNA was analysed for the presence of CDH1 mutations in 318 women with lobular breast cancer who were diagnosed before the age of 45 years or had a family history of breast cancer and were not known, or known not, to be carriers of germline mutations in BRCA1 or BRCA2. Cases were ascertained through breast cancer registries and high-risk cancer genetic clinics (Breast Cancer Family Registry, the kConFab and a consortium of breast cancer genetics clinics in the United States and Spain). Additionally, Multiplex Ligation-dependent Probe Amplification was performed for 134 cases to detect large deletions. RESULTS: No truncating mutations and no large deletions were detected. Six non-synonymous variants were found in seven families. Four (4/318 or 1.3%) are considered to be potentially pathogenic through in vitro and in silico analysis. CONCLUSION: Potentially pathogenic germline CDH1 mutations in women with early-onset or familial lobular breast cancer are at most infrequent

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure