Novel somatic mutations in the BRCA1 gene in sporadic breast tumors.

Hum Mutat

Department of Biochemistry and Experimental Oncology, Charles University in Prague, Prague, Czech Republic.

Published: March 2005

Germline mutations in two major susceptibility genes BRCA1 and BRCA2 contribute to the majority of inherited breast and ovarian cancers. Besides the germline mutation, tumor progression depends on the loss of a wild-type allele. Allelic losses in the BRCA1 and BRCA2 loci have also been detected in a high proportion of sporadic breast tumors, suggesting the role of these genes in the development of non-inherited breast cancer. Forty unselected breast tumors were analyzed for the loss of heterozygosity (LOH) at BRCA1 and BRCA2 regions and tumors with allelic deletions were screened for the presence of acquired genetic alterations in respective genes. 21.1% of 38 informative tumor samples carried LOH at the BRCA1 locus whereas 33.3% of 39 informative samples showed LOH at the BRCA2 locus. Pathogenic truncating mutations in the BRCA1 gene were found in two tumor samples with allelic losses, whereas no mutations were identified in the BRCA2 gene. Mutations were not detected in non-tumor samples from the same individuals, which indicated that the BRCA1 allele was inactivated by somatic mutations in tumor tissue. The c.1116G>A (1235G>A) nonsense mutation (p.W372X) belongs to the genetic abnormalities detected infrequently in hereditary tumors; the c.3862delG (3981delG) frameshift mutation (p.E1288fsX1306) is a novel gene alteration. The occurrence of inactivating somatic mutations in sporadic breast tumors suggested the role of the BRCA1 gene in tumorigenesis in at least a minor group of patients with non-familial breast cancer.

Download full-text PDF

Source
http://dx.doi.org/10.1002/humu.9308DOI Listing

Publication Analysis

Top Keywords

breast tumors
16
somatic mutations
12
brca1 gene
12
sporadic breast
12
brca1 brca2
12
brca1
8
mutations brca1
8
allelic losses
8
breast cancer
8
loh brca1
8

Similar Publications

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.

View Article and Find Full Text PDF

Metaplastic breast cancer (MpBC) is a highly chemoresistant subtype of breast cancer with no standardized therapy options. A clinical study in anthracycline-refractory MpBC patients suggested that nitric oxide synthase (NOS) inhibitor NG-monomethyl-l-arginine (L-NMMA) may augment anti-tumor efficacy of taxane. We report that NOS blockade potentiated response of human MpBC cell lines and tumors to phosphoinositide 3-kinase (PI3K) inhibitor alpelisib and taxane.

View Article and Find Full Text PDF

the evolution of axillary management in breast cancer has witnessed significant changes in recent decades, leading to an overall reduction in surgical interventions. There have been notable shifts in practice, aiming to minimize morbidity while maintaining oncologic outcomes and accurate staging for newly diagnosed breast cancer patients. These advancements have been facilitated by the improved efficacy of adjuvant therapies.

View Article and Find Full Text PDF

the axillary reverse mapping (ARM) procedure aims to preserve the lymphatic drainage structures of the upper extremity during axillary surgery for breast cancer, thereby reducing the risk of lymphedema in the upper limb. Material and this prospective study included 57 patients with breast cancer who underwent SLNB and ARM. The sentinel lymph node (SLN) was identified using a radioactive tracer.

View Article and Find Full Text PDF

Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection.

Brief Bioinform

November 2024

School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.

The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!