Gene Panel Testing for Breast Cancer Reveals Differential Effect of Prior BRCA1/2 Probability

Germline RAD51C and RAD51D Mutations in High-Risk Chinese Breast and/or Ovarian Cancer Patients and Families

BACKGROUND

RAD51C and RAD51D are crucial in homologous recombination (HR) DNA repair. The prevalence of the RAD51C and RAD51D mutations in breast cancer varies across ethnic groups. Associations of RAD51C and RAD51D germline pathogenic variants (GPVs) with breast and ovarian cancer predisposition have been recently reported and are of interest.

METHODS

We performed multi-gene panel sequencing to study the prevalence of RAD51C and RAD51D germline mutations among 3728 patients with hereditary breast and/or ovarian cancer (HBOC).

RESULTS

We identified 18 pathogenic RAD51C and RAD51D mutation carriers, with a mutation frequency of 0.13% (5/3728) and 0.35% (13/3728), respectively. The most common recurrent mutation was RAD51D c.270_271dupTA; p.(Lys91Ilefs*13), with a mutation frequency of 0.30% (11/3728), which was also commonly identified in Asians. Only four out of six cases (66.7%) of this common mutation tested positive for homologous recombination deficiency (HRD).

CONCLUSIONS

Taking the family studies in our registry and tumor molecular pathology together, we concluded that this relatively common RAD51D variant showed incomplete penetrance in our local Chinese community. Personalized genetic counseling emphasizing family history for families with this variant, as suggested at the UK Cancer Genetics Group (UKCGG) Consensus meeting, would also be appropriate in Chinese families.

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 mutational variants of Turkish ovarian cancer patients suspected of Hereditary Breast and Ovarian Cancer (HBOC) by next-generation sequencing

Hereditary Breast and Ovarian Cancer (HBOC) syndrome is characterized by an increased risk of developing breast cancer (BC) and ovarian cancer (OC) due to inherited genetic mutations. Understanding the genetic variants associated with HBOC is crucial for identifying individuals at high risk and implementing appropriate preventive measures. The study included 630 Turkish OC patients with confirmed diagnostic criteria of The National Comprehensive Cancer Network (NCCN) concerning HBOC. Genomic DNA was extracted from peripheral blood samples, and targeted Next-generation sequencing (NGS) was performed. Bioinformatics analysis and variant interpretation were conducted to identify pathogenic variants (PVs). Our analysis revealed a spectrum of germline pathogenic variants associated with HBOC in Turkish OC patients. Notably, several pathogenic variants in BRCA1, BRCA2, and other DNA repair genes were identified. Specifically, we observed germline PVs in 130 individuals, accounting for 20.63% of the total cohort. 76 distinct PVs in genes, BRCA1 (40 PVs), BRCA2 (29 PVs), ATM (1 PV), CHEK2 (2 PVs), ERCC2 (1 PV), MUTYH (1 PV), RAD51C (1 PV), and TP53 (1PV) and also, two different PVs (i.e., c.135-2 A>G p.? in BRCA1 and c.6466_6469delTCTC in BRCA2) were detected in a 34-year-old OC patient. In conclusion, our study contributes to a better understanding of the genetic variants underlying HBOC in Turkish OC patients. These findings provide valuable insights into the genetic architecture of HBOC in the Turkish population and shed light on the potential contribution of specific germline PVs to the increased risk of OC.

Detection of pathogenic variants in breast cancer susceptibility genes in bilateral breast cancer

PURPOSE

To investigate the frequency of germline pathogenic variants (PVs) in women with bilateral breast cancer.

METHODS

We undertook BRCA1/2 and CHEK2 c.1100delC molecular analysis in 764 samples and a multigene panel in 156. Detection rates were assessed by age at first primary, Manchester Score, and breast pathology. Oestrogen receptor (ER) status of the contralateral versus first breast cancer was compared on 1081 patients with breast cancer with BRCA1/BRCA2 PVs.

RESULTS

764 women with bilateral breast cancer have undergone testing of BRCA1/2 and CHEK2; 407 were also tested for PALB2 and 177 for ATM. Detection rates were BRCA1 11.6%, BRCA2 14.0%, CHEK2 2.4%, PALB2 1.0%, ATM 1.1% and, for a subset of mainly very early onset tumours, TP53 4.6% (9 of 195). The highest PV detection rates were for triple negative cancers for BRCA1 (26.4%), grade 3 ER+HER2 for BRCA2 (27.9%) and HER2+ for CHEK2 (8.9%). ER status of the first primary in BRCA1 and BRCA2 PV heterozygotes was strongly predictive of the ER status of the second contralateral tumour since ~90% of second tumours were ER- in BRCA1 heterozygotes, and 50% were ER- in BRCA2 heterozygotes if the first was ER-.

CONCLUSION

We have shown a high rate of detection of BRCA1 and BRCA2 PVs in triple negative and grade 3 ER+HER2- first primary diagnoses, respectively. High rates of HER2+ were associated with CHEK2 PVs, and women ≤30 years were associated with TP53 PVs. First primary ER status in BRCA1/2 strongly predicts the second tumour will be the same ER status even if unusual for PVs in that gene.

Differential involvement of germline pathogenic variants in breast cancer genes between DCIS and low-grade invasive cancers

PURPOSE

To investigate frequency of germline pathogenic variants (PVs) in women with ductal carcinoma in situ (DCIS) and grade 1 invasive breast cancer (G1BC).

METHODS

We undertook BRCA1/2 analysis in 311 women with DCIS and 392 with G1BC and extended panel testing (non-BRCA1/2) in 176/311 with DCIS and 156/392 with G1BC. We investigated PV detection by age at diagnosis, Manchester Score (MS), DCIS grade and receptor status.

RESULTS

30/311 (9.6%) with DCIS and 16/392 with G1BC (4.1%) had a BRCA1/2 PV (p=0.003), and 24/176-(13.6%) and 7/156-(4.5%), respectively, a non-BRCA1/2 PV (p=0.004). Increasing MS was associated with increased likelihood of BRCA1/2 PV in both DCIS and G1BC, although the 10% threshold was not predictive for G1GB. 13/32 (40.6%) DCIS and 0/17 with G1BC <40 years had a non-BRCA1/2 PV (p<0.001). 0/16 DCIS G1 had a PV. For G2 and G3 DCIS, PV rates were 10/98 (BRCA1/2) and 9/90 (non-BRCA1/2), and 8/47 (BRCA1/2) and 8/45 (non-BRCA1/2), respectively. 6/9 BRCA1 and 3/26 BRCA2-associated DCIS were oestrogen receptor negative-(p=0.003). G1BC population testing showed no increased PV rate (OR=1.16, 95% CI 0.28 to 4.80).

CONCLUSION

DCIS is more likely to be associated with both BRCA1/2 and non-BRCA1/2 PVs than G1BC. Extended panel testing ought to be offered in young-onset DCIS where PV detection rates are highest.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.