Clinicopathological Features of BRCA1/2 Mutation-Positive Breast Cancer

Paik, Hyun-June; Jung, Young-Dae; Kim, Dong Il; Lee, Seungju; Jung, Chang Shin; Kang, Seok Kyung; Kim, Jae‐Joon; Oh, So Yeon; Joo, Ji Hyeon; Kim, Hyun Yul

doi:10.1159/000515790

Cited by 5 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clinical factors such as onset age and the family history of breast or ovarian cancer were significantly associated with mutations in BRCA1/2 in our study, which was consistent with an Asian study [30]. Luminal B and TNBC subtypes were considered high-risk factors of BRCA1/2 P/LP mutation [37]. Likewise, we identified that luminal B or TNBC subtype patients did have a higher frequency of BRCA1/2 P/LP mutation.…”

Section: Discussionsupporting

confidence: 90%

Germline Mutations in 32 Cancer Susceptibility Genes by Next-Generation Sequencing among Breast Cancer Patients

Ye¹,

Liu²,

Zhuo³

et al. 2023

Oncology

View full text Add to dashboard Cite

Introduction: BRCA1/2 germline mutations are the most well-known genetic determinants for breast cancer. However, the distribution of germline mutations in non-BRCA1/2 cancer susceptibility genes in Chinese breast cancer patients is unclear. The association between clinical characteristics and germline mutations remains to be explored. Methods: Consecutive breast cancer patients from Peking University People's Hospital were enrolled. Clinical characteristics were collected and next-generation sequencing (NGS) was performed using blood samples of participants to identify pathogenic/likely pathogenic (P/LP) germline mutations in 32 cancer susceptibility genes including homologous recombination repair (HRR) genes. Results: A total of 885 breast cancer patients underwent the detection of germline mutations. 107 P/LP germline mutations of 17 genes were identified in 116 breast cancer patients including 79 (8.9%) in BRCA1/2 and 40 (4.5%) in 15 non-BRCA1/2 genes. PALB2 was the most frequently mutated gene other than BRCA1/2 but still relatively rare (1.1%). There were 38 novel P/LP germline variants detected. P/LP germline mutations in BRCA1/2 were significantly associated with onset age(P<0.001), the family history of breast/ovarian cancer(P=0.010), and molecular subtype(P<0.001), while correlated with onset age (P<0.001), site of breast tumor (P=0.028) and molecular subtype (P< 0.001) in HRR genes. Conclusions: The multiple-gene panel prominently increased the detection rate of P/LP germline mutations in 32 cancer susceptibility genes compared to BRCA1/2 alone. Onset younger than or equal to 45 years of age, bilateral and triple-negative breast cancer (TNBC) patients may be more likely to be recommended for detecting P/LP germline mutations in HRR genes.

show abstract

Section: Discussionsupporting

confidence: 90%

Germline Mutations in 32 Cancer Susceptibility Genes by Next-Generation Sequencing among Breast Cancer Patients

Ye¹,

Liu²,

Zhuo³

et al. 2023

Oncology

View full text Add to dashboard Cite

show abstract

“… 33 BRCA1 and BRCA2 are the tumor suppressor genes, and highly penetrating mutations in these genes result in a loss of tumor suppressor function and thus an increased risk of ovarian cancer. 34 Understanding whether the unique clinical and pathological features of ovarian cancer are associated with the BRCA1/2 gene mutation is essential to mitigate prognosis differences in the female population.…”

Section: Discussionmentioning

confidence: 99%

Association Between Germline BRCA1/2 Gene Variants and Clinicopathological Features of Ovarian Cancer

Luo,

Pan,

Rao

et al. 2024

IJGM

View full text Add to dashboard Cite

Objective To investigate the relationship between BRCA1/2 gene mutation and clinicopathological features in ovarian cancer patients, so as to develop precise individualized treatment plan for patients. Methods Patients diagnosed with ovarian cancer between January 2018 and July 2023 who underwent BRCA1/2 genetic testing were retrospectively analyzed. The clinicopathological characteristics (age, body mass index (BMI), family history of ovarian cancer, pregnancy history, menopause status, tumor size, histopathology, Federation of Gynecology and Obstetrics (FIGO) staging, and ascites) of non-carriers and BRCA1/2 variant carriers were compared. Logistic regression analysis was used to explore the relationship between BRCA1/2 variants and clinicopathological characteristics of ovarian cancer. Results A total of 284 ovarian cancer patients were collected, and the subjects were divided into two groups, 197 non-carriers and 87 BRCA1/2 variants carriers. The proportion of serous ovarian carcinoma in BRCA1/2 variant carriers is higher than that in non- BRCA variant carriers (78.2% vs 60.9%, p =0.015). There were 51 patients with BRCA pathogenic or likely pathogenic variant, 22 patients with BRCA likely benign variant, and 14 patients with BRCA variants of uncertain significance (VUS). The proportion of serous ovarian carcinoma in patients with BRCA pathogenic/likely pathogenic variant is higher than that in patients with BRCA likely benign variant and BRCA VUS (94.1% vs 50.0% and 64.3%. p <0.001). There were no statistically significant differences in BMI, family history of ovarian cancer, pregnancy history, menopause status, maximum diameter of the tumor lesion, FIGO stage, and ascites among patients with different grades of variants. Multivariate logistic regression analysis showed that serous ovarian carcinoma was related to BRCA mutation (Serous carcinoma vs non-serous carcinoma: OR 2.145, 95% CI: 1.044–4.407) ( p =0.038). Conclusion Patients with BRCA1 variant develop ovarian cancer at a younger age than those with the BRCA2 variant. The proportion of FIGO stage III–IV in patients with BRCA pathogenic + likely pathogenic variant was significantly higher than those in patients with other variants. Germline BRCA1/2 variants were most frequently identified in serous ovarian carcinoma patients.

show abstract

“…Early studies on dual primary BC were mainly focused on the summary of clinicopathological practices and genome detection [5][8]. Referring to other machine learning-based BC prediction cases [33] [35], this study applied machine learning to the dual primary prediction of BC for the first time. Our method can aid doctors in the diagnosis and treatment of first-episode BC with theoretical and medical significance.…”

Section: P R E P R I N Tmentioning

confidence: 99%

Machine learning-based prediction of dual primary breast cancer

Wang

Fan

2022

Arch Med Sci

View full text Add to dashboard Cite

IntroductionPredicting dual primary tumors in patients diagnosed with first-episode breast cancer (BC) is crucial. This can assist physicians’ evaluation of treatment decisions. We applied eight machine learning algorithms to the BC data from the Surveillance, Epidemiology, and End Results program (SEER) database and evaluated the best model for predicting dual primary BC to help physicians assess patient prognoses.Material and methodsMachine learning models were established based on the retrospective study of 253,991 patients diagnosed with first-episode BC in the SEER database from 2010 to 2015. External validation was conducted on 6012 cases obtained through undersampling from the SEER database from 2004 to 2009. The decision tree (DT) and random forest (RF) models were employed using ten-fold cross-validation and grid search.ResultsSurgical information, lymph-node status, distant metastasis, tumor size, survival time, and histological type had significant influence as inputs. Compared with those of the other seven models (multinomial naïve bayes, logistic regression, k-nearest neighbor, one-dimensional convolutional neural network, recurrent neural network, long short-term memory, and DT), the accuracy of the RF model increased from 63.25 to 97.19%, whereas its precision, recall, F1 score, and area under the curve (AUC) increased from 62.92 to 95.01%, 64.36 to 99.48%, 63.63 to 97.19%, and 63.25 to 97.10%, respectively. RF was the only model where the AUC increased (0.24%) under external verification, which shows its excellent portability and generalization in the validation cohort.ConclusionsThe RF model can be used to predict dual primary BC and assist physicians with the diagnosis and treatment of first-episode BC patients.

show abstract

Clinicopathological Features of BRCA1/2 Mutation-Positive Breast Cancer

Cited by 5 publications

References 30 publications

Germline Mutations in 32 Cancer Susceptibility Genes by Next-Generation Sequencing among Breast Cancer Patients

Germline Mutations in 32 Cancer Susceptibility Genes by Next-Generation Sequencing among Breast Cancer Patients

Association Between Germline BRCA1/2 Gene Variants and Clinicopathological Features of Ovarian Cancer

Machine learning-based prediction of dual primary breast cancer

Contact Info

Product

Resources

About