Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

Escala-Garcia, Maria; Morra, Anna; Canisius, Sander; Chang‐Claude, Jenny; Kar, Siddhartha; Wang, Zheng; Bojesen, Stig E.; Easton, Doug; Pharoah, Paul D.P.; Schmidt, Marjanka K.

doi:10.1186/s12916-020-01797-2

Cited by 60 publications

(35 citation statements)

References 67 publications

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“…While little is known about this proline form, studies for the close family member PYCR1 have found that higher levels of mRNA were associated with reduced survival from breast cancer patients 26 . To support further our hypothesis that the two genome-wide significant SNPs (rs138569520 and rs146023652) were specific for survival in patients with metastatic disease, we confirmed that there were no associations (HR = 1.04, P = 0.58, MAF = 0.02 and HR = 1.03, P = 0.60, MAF = 0.02 respectively) with breast cancer-specific survival in the most recent BCAC dataset for all invasive early (stages I–III) breast cancers (OncoArray and iCOGS, n = 86,627) 27 .…”

Section: Discussionsupporting

confidence: 68%

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Escala-Garcia¹,

Canisius²,

Keeman³

et al. 2021

Sci Rep

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Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10−8 and 4.42 × 10−8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

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Section: Discussionsupporting

confidence: 68%

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Escala-Garcia¹,

Canisius²,

Keeman³

et al. 2021

Sci Rep

Self Cite

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“…The rapid increase in the number of features from different data sources and the use of heterogeneous features have led to great challenges in survival prediction. With the deepening of research on breast cancer, many new variables that are significantly related to breast cancer prognosis have been gradually discovered [14,76,77], such as the level of anxiety and depression. Thus, the above factors should be taken into account in the prediction.…”

Section: Plos Onementioning

confidence: 99%

Predicting breast cancer 5-year survival using machine learning: A systematic review

Zhou

Dong

et al. 2021

PLoS ONE

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Background Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Results Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Conclusions Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.

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“…An increase in the number of reported cases and deaths has inspired numerous studies to understand the disease characteristics, causal associations and suitable solutions to mitigate this public health problem. In addition to established risks e.g., higher chronological age [ 5 ], genetic influence [ 6 ], and being non-Hispanic white [ 5 ], several environmental factors [ 7 ] play a crucial role beneath in the underlying mechanism. Weight and diet are important environmental factors that influence plasma lipids and lipoproteins, and their role in breast cancer incidence has been a relatively new concept in recent years [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Association of hyperlipidemia with breast cancer in Bangladeshi women

et al. 2021

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Background The association of circulating lipids with breast cancer is being debated. The objective of this study was to examine the relationship between abnormal plasma lipids and breast cancer risk in Bangladeshi women. Methods This was a case-control study designed using a population of 150 women (50 women in each group). The lipid levels of women with breast cancer were compared to the lipid levels of women with benign breast disease (control group 1) and healthy women (control group 2). Study samples were collected from the Department of Surgery, Bangabandhu Sheikh Mujib Medical University, for a period of 1 year. Ethical measures were in compliance with the current Declaration of Helsinki. Statistical analysis was performed with SPSS version 26. Results All of the comparison groups shared similar sociodemographic, anthropometric and obstetric characteristics. The incidence of dyslipidemia was significantly higher in breast cancer patients (96%) than in healthy women (84%) and patients with benign breast disease (82%) (P < 0.05 for both). The levels of total cholesterol, triglycerides, and low-density lipoprotein (LDL) cholesterol among the breast cancer patient group were significantly higher than those among both benign breast disease patients and healthy women (P < 0.05), except for high-density lipoprotein (HDL) cholesterol. Adjusting for other factors, body mass index (BMI) (kg/m2) (> 23) [OR 53.65; 95% CI: 5.70–504.73; P < 0.001] and total cholesterol (mg/dl) (≥ 200) [OR 16.05; 95% CI: 3.13–82.29; P < 0.001] were independently associated with breast cancer. Conclusions Total cholesterol and BMI are independent predictors of breast cancer risk among Bangladeshi women.

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Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

Cited by 60 publications

References 67 publications

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Predicting breast cancer 5-year survival using machine learning: A systematic review

Association of hyperlipidemia with breast cancer in Bangladeshi women

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