Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

5 mC modification of steroid hormone biosynthesis-related genes orchestrates feminization of channel catfish induced by high-temperature

To elucidate the mechanism behind channel catfish feminization induced by high temperature, gonad samples were collected from XY pseudo-females and wild-type females and subjected to high-throughput sequencing for Whole-Genome-Bisulfite-Seq (WGBS) and transcriptome sequencing (RNA-Seq). The analysis revealed 50 differentially methylated genes between wild-type females and XY pseudo-females, identified through the analysis of KEGG pathways and GO enrichment in the promoter of the genome and differentially methylated regions (DMRs). Among these genes, multiple differential methylation sites observed within the srd5a2 gene. Repeatability tests confirmed 7 differential methylation sites in the srd5a2 gene in XY pseudo-females compared to normal males, with 1 specific differential methylation site (16608174) distinguishing XY pseudo-females from normal females. Interestingly, the expression of these genes in the transcriptome showed no difference between wild-type females and XY pseudo-females. Our study concluded that methylation of the srd5a2 gene sequence leads to decreased expression, which inhibits testosterone synthesis while promoting the synthesis of 17β-estradiol from testosterone. This underscores the significance of the srd5a2 gene in the sexual differentiation of channel catfish, as indicated by the ipu00140 KEGG pathway analysis.

Mosaic BRCA1 promoter methylation contribution in hereditary breast/ovarian cancer pedigrees

PURPOSE

Mosaic BRCA1 promoter methylation (BRCA1meth) increases the risk of early-onset breast cancer, triple-negative breast cancer and ovarian cancer. As mosaic BRCA1meth are believed to occur de novo, their role in family breast/ovarian cancer has not been assessed.

PATIENTS

Blood-derived DNA from 20 unrelated affected cases from families with aggregation of breast/ovarian cancer, but with no germline pathogenic variants in BRCA1/2, PALB2 or RAD51C/D, were screened by methylation-sensitive high-resolution melting. CpG analysis was performed by pyrosequencing on blood and buccal swab. Two probands carried a pathogenic variant in a moderate-penetrance gene (ATM and BARD1), and 8 of 18 others (44%) carried BRCA1meth (vs none of the 20 age-matched controls). Involvement of BRCA1 in tumourigenesis in methylated probands was demonstrated in most tested cases by detection of a loss of heterozygosity and a homologous recombination deficiency signature. Among the eight methylated probands, two had relatives with breast cancer with detectable BRCA1meth in blood, including one with high methylation levels in two non-tumour tissues.

CONCLUSIONS

The high prevalence of mosaic BRCA1meth in patients with breast/ovarian cancer with affected relatives, as well as this first description of a family aggregation of mosaic BRCA1meth, shows how this de novo event can contribute to hereditary breast/ovarian cancer pedigrees.

Male breast cancer: No evidence for mosaic BRCA1 promoter methylation involvement

Breast cancers (BC) are rare in men and are often caused by constitutional predisposing factors. In women, mosaic BRCA1 promoter methylations (MBPM) are frequent events, detected in 4-8% of healthy subjects. This constitutional epimutation increases risk of early-onset and triple-negative BC. However, the role of MBPM in male BC predisposition has never been assessed. We screened 40 blood samples from men affected by BC, and performed extensive tumour analysis on MBPM-positive patients. We detected two patients carrying MBPM. Surprisingly, tumour analysis revealed that neither of these two male BCs were caused by the constitutional BRCA1 epimutations carried by the patients.

A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival

Cancer has been a significant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. The high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. There is a lot of emphasis in present survival studies on determining whether or not a patient will survive five years. The personal issue of how long a lung cancer patient will survive remains unanswered. The proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determining whether a patient will survive for more than five years is a simple binary question. The second step is to develop a five-year survival model using regression analysis. When asked to forecast how long a lung cancer patient would survive within five years, the mean absolute error (MAE) of this technique's predictions is accurate within a month. Several biomarker genes have been associated with lung cancers. The accuracy, recall, and precision achieved from this algorithm are 98.78%, 98.4%, and 98.6%, respectively.

Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer

Simple Summary

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous breast cancer (BC) type which is difficult to treat and accompanied by disease recurrence. A better understanding of TNBC and the identification of novel biomarkers is needed to facilitate clinical decisions. Immune-related biomarkers are of particular interest, since immune responses play an important role in BC outcome. Transcriptome studies of peripheral blood cells can help us to understand the systemic immune responses to cancer cells and the mechanisms underlying cancer initiation and progression. They enable the identification of novel immune biomarkers for early cancer detection and personalized BC management and may bring forward new immunotherapy options. Recent transcriptome analyses of peripheral blood cells have delineated novel BC-patient immune subgroups. This categorization has implications for cancer prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Additionally, transcriptome studies have identified TNBC-enriched blood transcriptional signatures that can differentiate TNBC from other classical BC subtypes.

Abstract

Transcriptome studies of peripheral blood cells can advance our understanding of the systemic immune response to the presence of cancer and the mechanisms underlying cancer onset and progression. This enables the identification of novel minimally invasive immune biomarkers for early cancer detection and personalized cancer management and may bring forward new immunotherapy options. Recent blood gene expression analyses in breast cancer (BC) identified distinct patient subtypes that differed in the immune reaction to cancer and were distinct from the clinical BC subtypes, which are categorized based on expression of specific receptors on tumor cells. Introducing new BC subtypes based on peripheral blood gene expression profiles may be appropriate, since it may assist in BC prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous, and difficult-to-treat disease, and identification of novel biomarkers for this BC is crucial for clinical decision-making. A few studies have reported TNBC-enriched blood transcriptional signatures, mostly related to strong inflammation and augmentation of altered immune signaling, that can differentiate TNBC from other classical BC subtypes and facilitate diagnosis. Future research is geared toward transitioning from expression signatures in unfractionated blood cells to those in immune cell subpopulations.

The effects of endocrine-disrupting chemicals (EDCs) on the epigenome-A short overview

To understand the effects of endocrine-disrupting chemicals (EDCs), the mechanism(s) by which EDCs exert their harmful effects on humans and their offspring needs careful examination and clarification. Epigenetic modification, including DNA methylation, expression of aberrant microRNA (miRNA), and histone modification, is one mechanism assumed to be a primary pathway leading to the untoward effects of endocrine disruptors. However, it remains unclear whether such epigenetic changes caused by EDCs are truly predicting adverse outcomes. Therefore, it is important to understand the relationship between epigenetic changes and various endocrine endpoints or markers. This paper highlights the possibility that certain chemicals (Cd, As, Pb, bisphenol A, phthalate, polychlorinated biphenyls) reported having ED properties may adversely affect the epigenome. Electronic database sources PubMed, SCOPUS, JSTOR, and the Google Scholar web browser were used to search the literature. The search was based on keywords from existing theories and basic knowledge of endocrine disorders and epigenetic effects, well-known EDCs, and previous search results. Unclear and often conflicting results regarding the effects of EDCs indicate the need for further research to support better risk assessments and management of these chemicals.

Repeatability of methylation measures using a QIAseq targeted methyl panel and comparison with the Illumina HumanMethylation450 assay

OBJECTIVE

In previous studies using Illumina Infinium methylation arrays, we have identified DNA methylation marks associated with cancer predisposition and progression. In the present study, we have sought to find appropriate technology to both technically validate our data and expand our understanding of DNA methylation in these genomic regions. Here, we aimed to assess the repeatability of methylation measures made using QIAseq targeted methyl panel and to compare them with those obtained from the Illumina HumanMethylation450 (HM450K) assay. We included in the analysis high molecular weight DNA extracted from whole blood (WB) and DNA extracted from formalin-fixed paraffin-embedded tissues (FFPE).

RESULTS

The repeatability of QIAseq-methylation measures was assessed at 40 CpGs, using the Intraclass Correlation Coefficient (ICC). The mean ICCs and 95% confidence intervals (CI) were 0.72 (0.62-0.81), 0.59 (0.47-0.71) and 0.80 (0.73-0.88) for WB, FFPE and both sample types combined, respectively. For technical replicates measured using QIAseq and HM450K, the mean ICCs (95% CI) were 0.53 (0.39-0.68), 0.43 (0.31-0.56) and 0.70 (0.59-0.80), respectively. Bland-Altman plots indicated good agreement between QIAseq and HM450K measurements. These results demonstrate that the QIAseq targeted methyl panel produces reliable and reproducible methylation measurements across the 40 CpGs that were examined.

Blood DNA methylation profiles improve breast cancer prediction

Although blood DNA methylation (DNAm) profiles are reported to be associated with breast cancer incidence, they have not been widely used in breast cancer risk assessment. Among a breast cancer case–cohort of 2774 women (1551 cases) in the Sister Study, we used candidate CpGs and DNAm estimators of physiologic characteristics to derive a methylation‐based breast cancer risk score, mBCRS. Overall, 19 CpGs and five DNAm estimators were selected using elastic net regularization to comprise mBCRS. In a test set, higher mBCRS was positively associated with breast cancer incidence, showing similar strength to the polygenic risk score (PRS) based on 313 single nucleotide polymorphisms (313 SNPs). Area under the curve for breast cancer prediction was 0.60 for self‐reported risk factors (RFs), 0.63 for PRS, and 0.63 for mBCRS. Adding mBCRS to PRS and RFs improved breast cancer prediction from 0.66 to 0.71. mBCRS findings were replicated in a nested case–control study within the EPIC‐Italy cohort. These results suggest that mBCRS, a risk score derived using blood DNAm, can be used to enhance breast cancer prediction.

Recent Advances in Enhancing the Therapeutic Index of PARP Inhibitors in Breast Cancer

Simple Summary

Two to three percent of breast cancer patients harbor germline mutation of either BRCA1 or BRCA2 genes. Their tumor cells are deficient in homologous recombination, a BRCA-dependent DNA repair machinery. These deficient cells survive thanks to the PARP-mediated alternative pathway. Therefore, PARP inhibitors have already shown some level of efficiency in the treatment of metastatic breast cancer patients. Unfortunately, some tumor cells inevitably resist PARP inhibitors by different mechanisms. In this review, we (i) present the notion of homologous recombination deficiency and its evaluation methods, (ii) detail the PARP inhibitor clinical trials in breast cancer, (iii) briefly describe the mechanisms to PARP inhibitors resistance, and (iv) discuss some strategies currently under evaluation to enhance the therapeutic index of PARP inhibitors in breast cancer.

Abstract

As poly-(ADP)-ribose polymerase (PARP) inhibition is synthetic lethal with the deficiency of DNA double-strand (DSB) break repair by homologous recombination (HR), PARP inhibitors (PARPi) are currently used to treat breast cancers with mutated BRCA1/2 HR factors. Unfortunately, the increasingly high rate of PARPi resistance in clinical practice has dented initial hopes. Multiple resistance mechanisms and acquired vulnerabilities revealed in vitro might explain this setback. We describe the mechanisms and vulnerabilities involved, including newly identified modes of regulation of DSB repair that are now being tested in large cohorts of patients and discuss how they could lead to novel treatment strategies to improve the therapeutic index of PARPi.

ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

MOTIVATION

With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyze such events.

RESULTS

We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public datasets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages.

AVAILABILITY AND IMPLEMENTATION

The R package is freely available at https://github.com/BBCG/ramr and https://bioconductor.org/packages/ramr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediagnostic White Blood Cell DNA Methylation and Risk of Breast Cancer in the Prostate Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort

BACKGROUND

White blood cell (WBC) DNA may contain methylation patterns that are associated with subsequent breast cancer risk. Using a high-throughput array and samples collected, on average, 1.3 years prior to diagnosis, a case-cohort analysis nested in the prospective Sister Study identified 250 individual CpG sites that were differentially methylated between breast cancer cases and noncases. We examined five of the top 40 CpG sites in a case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort.

METHODS

We investigated the associations between prediagnostic WBC DNA methylation in 297 breast cancer cases and 297 frequency-matched controls. Two WBC DNA specimens from each participant were used: a proximate sample collected 1 to 2.9 years and a distant sample collected 4.2-7.3 years prior to diagnosis in cases or the comparable timepoints in controls. WBC DNA methylation level was measured using targeted bisulfite amplification sequencing. We used logistic regression to obtain ORs and 95% confidence intervals (CI).

RESULTS

A one-unit increase in percent methylation in ERCC1 in proximate WBC DNA was associated with increased breast cancer risk (adjusted OR = 1.29; 95% CI, 1.06-1.57). However, a one-unit increase in percent methylation in ERCC1 in distant WBC DNA was inversely associated with breast cancer risk (adjusted OR = 0.83; 95% CI, 0.69-0.98). None of the other ORs met the threshold for statistical significance.

CONCLUSIONS

There was no convincing pattern between percent methylation in the five CpG sites and breast cancer risk.

IMPACT

The link between prediagnostic WBC DNA methylation marks and breast cancer, if any, is poorly understood.

Understanding breast cancer heterogeneity through non-genetic heterogeneity

Intricacy in treatment and diagnosis of breast cancer has been an obstacle due to genotype and phenotype heterogeneity. Understanding of non-genetic heterogeneity mechanisms along with considering role of genetic heterogeneity may fill the gaps in landscape painting of heterogeneity. The main factors contribute to non-genetic heterogeneity including: transcriptional pulsing/bursting or discontinuous transcriptions, stochastic partitioning of components at cell division and various signal transduction from tumor ecosystem. Throughout this review, we desired to provide a conceptual framework focused on non-genetic heterogeneity, which has been intended to offer insight into prediction, diagnosis and treatment of breast cancer.