The Identification by Exome Sequencing of Candidate Genes in BRCA-Negative Tunisian Patients at a High Risk of Hereditary Breast/Ovarian Cancer

Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population

Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

Germline variants associated with breast cancer in Khakass women of North Asia

INTRODUCTION

Variants in the BRCA1/2 genes are responsible for familial breast cancer. Numerous studies showed a different spectrum of BRCA variants among breast cancer patients of different Ethnicity origin. In the available literature, no previous research has focused on breast cancer-associated variants among the Khakass people (the indigenous people of the Russian Federation).

METHODS

Twenty-six Khakass breast cancer patients were enrolled in the study. Genomic DNA was isolated from blood samples and used to prepare libraries using a Hereditary Cancer Solution kit. Next-generation sequencing (NGS) was performed using the MiSeq System (Illumina, USA).

RESULTS

In our study, 12% of patients (3/26) carried a single pathogenic variant; 54% of patients (14/26) carried variants of uncertain significance (VUS) or conflicting variants; and 35% of patients (9/26) did not carry any clinically significant variants. Germline pathogenic variant in the ATM gene (rs780619951, NC_000011.10:g.108259022C > T) was identified in two unrelated patients with a family history of cancer (7.6%, 2/26). The pathogenic truncating variant in the ATM gene (p. R805* or c.2413C > T) leads to the nonfunctional version of the protein. This variant has been earlier reported in individuals with a family history of breast cancer.

CONCLUSIONS

Our pilot study describes the germline variant in the ATM gene associated with breast cancer in Khakass women of North Asia.

Whole-exome sequencing of BRCA-negative breast cancer patients and case-control analyses identify variants associated with breast cancer susceptibility

BACKGROUND

For the majority of individuals with early-onset or familial breast cancer referred for genetic testing, the genetic basis of their familial breast cancer remains unexplained. To identify novel germline variants associated with breast cancer predisposition, whole-exome sequencing (WES) was performed.

METHODS

WES on 290 BRCA1/BRCA2-negative Singaporeans with early-onset breast cancer and/or a family history of breast cancer was done. Case-control analysis against the East-Asian subpopulation (EAS) from the Genome Aggregation Database (gnomAD) identified variants enriched in cases, which were further selected by occurrence in cancer gene databases. Variants were further evaluated in repeated case-control analyses using a second case cohort from the database of Genotypes and Phenotypes (dbGaP) comprising 466 early-onset breast cancer patients from the United States, and a Singapore SG10K_Health control cohort.

RESULTS

Forty-nine breast cancer-associated germline pathogenic variants in 37 genes were identified in Singapore cases versus gnomAD (EAS). Compared against SG10K_Health controls, 13 of 49 variants remain significantly enriched (False Discovery Rate (FDR)-adjusted p < 0.05). Comparing these 49 variants in dbGaP cases against gnomAD (EAS) and SG10K_Health controls revealed 23 concordant variants that were significantly enriched (FDR-adjusted p < 0.05). Fourteen variants were consistently enriched in breast cancer cases across all comparisons (FDR-adjusted p < 0.05). Seven variants in GPRIN2, NRG1, MYO5A, CLIP1, CUX1, GNAS and MGA were confirmed by Sanger sequencing.

CONCLUSIONS

In conclusion, we have identified pathogenic variants in genes associated with breast cancer predisposition. Importantly, many of these variants were significant in a second case cohort from dbGaP, suggesting that the strategy of using case-control analysis to select variants could potentially be utilized for identifying variants associated with cancer susceptibility.