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

Lee, Ning Yuan; Hum, Melissa; Amali, Aseervatham Anusha; Lim, Wei Kiat; Wong, Margaret; Myint, Matthew Khine; Tay, Ru Jin; Ong, Pei Yi; Samol, Jens; Lim, Chia Wei; Ang, Peter; Tan, Min‐Han; Lee, Soo‐Chin; Lee, Ann S. G.

doi:10.1186/s40246-022-00435-7

Cited by 5 publications

(6 citation statements)

References 50 publications

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“…This is comparable to the overall VUS rate of 49.9% reported in another cohort of Singaporean patients who underwent genetic testing [ 39 ]. These findings align with our previous studies on Asian populations, which have consistently shown higher VUS rates compared to populations of European descent, possibly due to the underrepresentation of Asians in reference databases [ 40 – 42 ]. However, as more data becomes available, VUS can be reclassified [ 43 , 44 ].…”

Section: Discussionsupporting

confidence: 92%

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Landscape of germline pathogenic variants in patients with dual primary breast and lung cancer

Lee,

Hum,

Zihara

et al. 2023

Hum Genomics

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Background Cancer predisposition is most often studied in the context of single cancers. However, inherited cancer predispositions can also give rise to multiple primary cancers. Yet, there is a paucity of studies on genetic predisposition in multiple primary cancers, especially those outside of well-defined cancer predisposition syndromes. This study aimed to identify germline variants associated with dual primary cancers of the breast and lung. Methods Exome sequencing was performed on germline DNA from 55 Singapore patients (52 [95%] never-smokers) with dual primaries in the breast and lung, confirmed by histopathology. Using two large control cohorts: the local SG10K_Health (n = 9770) and gnomAD non-cancer East Asians (n = 9626); and two additional local case cohorts of early-onset or familial breast cancer (n = 290), and lung cancer (n = 209), variants were assessed for pathogenicity in accordance with ACMG/AMP guidelines. In particular, comparisons were made with known pathogenic or likely pathogenic variants in the ClinVar database, pathogenicity predictions were obtained from in silico prediction software, and case–control association analyses were performed. Results Altogether, we identified 19 pathogenic or likely pathogenic variants from 16 genes, detected in 17 of 55 (31%) patients. Six of the 19 variants were identified using ClinVar, while 13 variants were classified pathogenic or likely pathogenic using ACMG/AMP guidelines. The 16 genes include well-known cancer predisposition genes such as BRCA2, TP53, and RAD51D; but also lesser known cancer genes EXT2, WWOX, GATA2, and GPC3. Most of these genes are involved in DNA damage repair, reaffirming the role of impaired DNA repair mechanisms in the development of multiple malignancies. These variants warrant further investigations in additional populations. Conclusions We have identified both known and novel variants significantly enriched in patients with primary breast and lung malignancies, expanding the body of known cancer predisposition variants for both breast and lung cancer. These variants are mostly from genes involved in DNA repair, affirming the role of impaired DNA repair in the predisposition and development of multiple cancers.

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Section: Discussionsupporting

confidence: 92%

“…The three case cohorts used are: (1) the 55 patients with dual primary breast and lung cancers, subjected to WES in this study; (2) a cohort of 290 patients with early-onset (≤ 40 years old) or familial breast cancer from Singapore [ 40 ]; and (3) a lung cancer case cohort of 209 lung adenocarcinoma patients from Singapore [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Landscape of germline pathogenic variants in patients with dual primary breast and lung cancer

Lee,

Hum,

Zihara

et al. 2023

Hum Genomics

Self Cite

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“…This finding suggests that FAN1 may be a promising candidate associated with hereditary breast and ovarian cancer susceptibility ( Felicio et al, 2021 ). In a recent study by Lee et al (2022) , the researchers used WES and case-control analyses to investigate genetic variants that might be linked to breast cancer susceptibility in individuals without BRCA mutations. Their findings uncovered new and potentially important genetic variants that could contribute to the risk of breast cancer in this particular population.…”

Section: Introductionmentioning

confidence: 99%

“…Their findings uncovered new and potentially important genetic variants that could contribute to the risk of breast cancer in this particular population. Among the candidate genes identified, one of note was MUC16, which has previously been associated with increased susceptibility to ovarian cancer ( Lee et al, 2022 ). Another study by BenAyed-Guerfali et al (2022) focused on BRCA-negative Tunisian patients at high risk of hereditary breast/ovarian cancer.…”

Section: Introductionmentioning

confidence: 99%

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

Alganmi,

Bashanfar,

Alotaibi

et al. 2024

PeerJ Computer Science

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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.

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“…Following publication of the original article [ 1 ], the authors reported Fig. 1 is not the updated version.…”

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confidence: 99%

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

et al. 2022

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

Cited by 5 publications

References 50 publications

Landscape of germline pathogenic variants in patients with dual primary breast and lung cancer

Landscape of germline pathogenic variants in patients with dual primary breast and lung cancer

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

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

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