Biology subjects

Czene, K.

Publications and source records attributed to Czene, K..

4 recordsLinked to original sources

bioRxiv2021

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

BackgroundRisk prediction is crucial for early detection and prognosis of breast cancer. Circulating plasma proteins could provide a valuable source to increase the validity of risk prediction models, however, no such markers have yet been identified for clinical use. MethodsEDTA plasma samples from 183 breast cancer cases and 366 age-matched controls were collected prior to diagnosis from the Swedish breast cancer cohort KARMA. The samples were profiled on 700 circulating proteins using an exploratory affinity proteomics approach. Linear association analyses were performed on case-control status and a data-driven analysis strategy was applied to cluster the women on their plasma proteome profiles in an unsupervised manner. The resulting clusters were subsequently annotated for the differences in phenotypic characteristics, clinical parameters, and genetic risk. ResultsUsing the data-driven approach we identified five clusters with distinct proteomic plasma profiles. Women in a particular sub-group (cluster 1) were significantly more likely to have used menopausal hormonal therapy (MHT), more likely to get a breast cancer diagnosis, and were older compared to the remaining clusters. The levels of circulating proteins in cluster 1 were decreased for proteins related to DNA repair and cell replication and increased for proteins related to mammographic density and female tissues. In contrast, classical dichotomous case-control analyses did not reveal any proteins significantly associated with future breast cancer. ConclusionUsing a data-driven approach, we identified a subset of women with circulating proteins associated with previous use of MHT and risk of breast cancer. Our findings point to the potential long-lasting effects of MHT on the circulating proteome even after ending the treatment, and hence provide valuable insights concerning risk predication of breast cancer. HighlightsO_LICurrent risk prediction models use a variety of factors to identify women at risk of developing breast cancer. C_LIO_LIProteins circulating in blood represent an attractive but currently still underrepresented source of candidates serving as molecular risk factors. C_LIO_LIPlasma proteomes from women participating in a prospective breast cancer cohort study were studied for proteomic risk factors related to a future breast cancer diagnosis. C_LIO_LIUsing data-driven approaches, women with future breast cancers and previous use of menopausal hormone therapy were identified based on their circulating proteins. C_LIO_LIMenopausal hormone therapy was found to altered the levels of the circulating proteins even years after the treatment ended. C_LI

systems biology↗

bioRxiv2021

Rare copy number variants (CNVs) and breast cancer risk

BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

genetics↗

bioRxiv2020

Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance

Somatically acquired whole chromosome loss in nucleated blood cells is an indicator of immune aging and genomic instability. However, little is known about aging, lifestyle and genetic factors influencing mitochondrial (MT) DNA abundance. In this study, MT DNA abundance was estimated from the weighted intensities of probes mapping to the MT genome in 295,150 participants from the UK Biobank. We found that abundance of MT DNA was significantly elevated in women compared to men, was negatively correlated with advanced age, more packyears, greater body-mass index, higher frailty index as well as elevated red and white blood cell count and, importantly, lower mortality. In addition, several biochemistry markers in blood related to cholesterol metabolism, ion homeostasis and kidney function were found to be significantly associated with MT DNA abundance. By performing a genome-wide association study, we identified 50 independent regions genome-wide significantly associated with MT DNA abundance which harbour multiple genes involved in the immune system, cancer as well as mitochondrial function. Using mixed effects models, we estimated the SNP-heritability of MT DNA abundance to be around 8%. To investigate the consequence of altered MT DNA abundance, we performed a phenome-wide association study and found that MT DNA abundance is involved in risk for leukaemia, hematologic diseases as well as hypertension. Thus, estimating MT DNA abundance from genotyping arrays has the potential to provide novel insights into age- and disease relevant processes, particularly those related to immunity and established mitochondrial functions.

genomics↗

bioRxiv2019

Common breast cancer risk loci predispose to distinct tumor subtypes

BackgroundGenome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER), but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. MethodsAmong 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. ResultsEighty-five of 173 variants were associated with at least one tumor feature (false discovery rate <5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at P<0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. ConclusionThis report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

genetics↗