Gene Expression Profiling in Hereditary, BRCA1-linked Breast Cancer: Preliminary Report

Metabolism of Selenium, Selenocysteine, and Selenoproteins in Ferroptosis in Solid Tumor Cancers

A potential target of precision nutrition in cancer therapeutics is the micronutrient selenium (Se). Se is metabolized and incorporated as the amino acid selenocysteine (Sec) into 25 human selenoproteins, including glutathione peroxidases (GPXs) and thioredoxin reductases (TXNRDs), among others. Both the processes of Se and Sec metabolism for the production of selenoproteins and the action of selenoproteins are utilized by cancer cells from solid tumors as a protective mechanism against oxidative damage and to resist ferroptosis, an iron-dependent cell death mechanism. Protection against ferroptosis in cancer cells requires sustained production of the selenoprotein GPX4, which involves increasing the uptake of Se, potentially activating Se metabolic pathways such as the trans-selenation pathway and the TXNRD1-dependent decomposition of inorganic selenocompounds to sustain GPX4 synthesis. Additionally, endoplasmic reticulum-resident selenoproteins also affect apoptotic responses in the presence of selenocompounds. Selenoproteins may also help cancer cells adapting against increased oxidative damage and the challenges of a modified nutrient metabolism that result from the Warburg switch. Finally, cancer cells may also rewire the selenoprotein hierarchy and use Se-related machinery to prioritize selenoproteins that are essential to the adaptations against ferroptosis and oxidative damage. In this review, we discuss both the evidence and the gaps in knowledge on how cancer cells from solid tumors use Se, Sec, selenoproteins, and the Se-related machinery to promote their survival particularly via resistance to ferroptosis.

Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers

Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype "portrayal" with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.

Genome-Wide Gene Expression Analyses of BRCA1- and BRCA2-Associated Breast and Ovarian Tumours

Germline pathogenic variants in BRCA1 and BRCA2 increase cumulative lifetime risk up to 75% for breast cancer and 76% for ovarian cancer. Genetic testing for BRCA1 and BRCA2 pathogenic variants has become an important part of clinical practice for cancer risk assessment and for reducing individual risk of developing cancer. Genetic testing can produce three outcomes: positive (a pathogenic variant), uninformative (no pathogenic variant) and uncertain significance (a variant of unknown clinical significance). More than one third of BRCA1 and BRCA2 variants identified have been classified as variants of uncertain significance, presenting a challenge for clinicians. To address this important clinical challenge, a number of studies have been undertaken to establish a gene expression phenotype for pathogenic BRCA1 and BRCA2 variant carriers in several diseased and normal tissues. However, the consistency of gene expression phenotypes described in studies has been poor. To determine if gene expression analysis has been a successful approach for variant classification, we describe the design and comparability of 23 published gene expression studies that have profiled cells from BRCA1 and BRCA2 pathogenic variant carriers. We show the impact of advancements in expression-based technologies, the importance of developing larger study cohorts and the necessity to better understand variables affecting gene expression profiles across different tissue types.

Structural analysis of human SEPHS2 protein, a selenocysteine machinery component, over-expressed in triple negative breast cancer

Selenophosphate synthetase 2 (SEPHS2) synthesizes selenide and ATP into selenophosphate, the selenium donor for selenocysteine (Sec), which is cotranslationally incorporated into selenoproteins. The action and regulatory mechanisms of SEPHS2 as well as its role in carcinogenesis (especially breast cancer) remain ambiguous and need further clarification. Therefore, lacking an experimentally determined structure for SEPHS2, we first analyzed the physicochemical properties of its sequence, modeled its three-dimensional structure and studied its conformational behavior to identify the key residues (named HUB nodes) responsible for protein stability and to clarify the molecular mechanisms by which it induced its function. Bioinformatics analysis evidenced higher amplification frequencies of SEPHS2 in breast cancer than in other cancer types. Therefore, because triple negative breast cancer (TNBC) is biologically the most aggressive breast cancer subtype and its treatment represents a challenge due to the absence of well-defined molecular targets, we evaluated SEPHS2 expression in two TNBC cell lines and patient samples. We demonstrated mRNA and protein overexpression to be correlated with aggressiveness and malignant tumor grade, suggesting that this protein could potentially be considered a prognostic marker and/or therapeutic target for TNBC.

Liquid biopsy in breast cancer: A comprehensive review

Breast cancer is the most common cancer among women worldwide. Due to its complexity in nature, effective breast cancer treatment can encounter many challenges. Traditional methods of cancer detection such as tissue biopsy are not comprehensive enough to capture the entire genomic landscape of breast tumors. However, with the introduction of novel techniques, the application of liquid biopsy has been enhanced, enabling the improvement of various aspects of breast cancer management including early diagnosis and screening, prediction of prognosis, early detection of relapse, serial sampling and efficient longitudinal monitoring of disease progress and response to treatment. Various components of tumor cells released into the blood circulation can be analyzed in liquid biopsy sampling, some of which include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free RNA, tumor-educated platelets and exosomes. These components can be utilized for different purposes. As an example, ctDNA can be sequenced for genetic profiling of the tumors to enhance individualized treatment and longitudinal screening. CTC plasma count analysis or ctDNA detection after curative tumor resection surgery could facilitate early detection of minimal residual disease, aiding in the initiation of adjuvant therapy to prevent recurrence. Furthermore, CTC plasma count can be assessed to determine the stage and prognosis of breast cancer. In this review, we discuss the advantages and limitations of the various components of liquid biopsy used in breast cancer diagnosis and will expand on aspects that require further focus in future research.

Selenoproteins in colon cancer

Selenocysteine-containing proteins (selenoproteins) have been implicated in the regulation of various cell signaling pathways, many of which are linked to colorectal malignancies. In this in-depth excurse into the selenoprotein literature, we review possible roles for human selenoproteins in colorectal cancer, focusing on the typical hallmarks of cancer cells and their tumor-enabling characteristics. Human genome studies of single nucleotide polymorphisms in various genes coding for selenoproteins have revealed potential involvement of glutathione peroxidases, thioredoxin reductases, and other proteins. Cell culture studies with targeted down-regulation of selenoproteins and studies utilizing knockout/transgenic animal models have helped elucidate the potential roles of individual selenoproteins in this malignancy. Those selenoproteins, for which strong links to development or progression of colorectal cancer have been described, may be potential future targets for clinical interventions.

Differences in the transcriptome of medullary thyroid cancer regarding the status and type of RET gene mutations

Medullary thyroid cancer (MTC) can be caused by germline mutations of the RET proto-oncogene or occurs as a sporadic form. It is well known that RET mutations affecting the cysteine-rich region of the protein (MEN2A-like mutations) are correlated with different phenotypes than those in the kinase domain (MEN2B-like mutations). Our aim was to analyse the whole-gene expression profile of MTC with regard to the type of RET gene mutation and the cancer genetic background (hereditary vs sporadic). We studied 86 MTC samples. We demonstrated that there were no distinct differences in the gene expression profiles of hereditary and sporadic MTCs. This suggests a homogeneous nature of MTC. We also noticed that the site of the RET gene mutation slightly influenced the gene expression profile of MTC. We found a significant association between the localization of RET mutations and the expression of three genes: NNAT (suggested to be a tumour suppressor gene), CDC14B (involved in cell cycle control) and NTRK3 (tyrosine receptor kinase that undergoes rearrangement in papillary thyroid cancer). This study suggests that these genes are significantly deregulated in tumours with MEN2A-like and MEN2B-like mutations; however, further investigations are necessary to demonstrate any clinical impact of these findings.

Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival

Purpose

Ovarian cancer is typically diagnosed at late stages, and thus, patients’ prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.

Methods

An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.

Results

We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them—DSPG3 and LOX—were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.

Conclusions

We distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-016-2147-y) contains supplementary material, which is available to authorized users.

The emerging role of the transcriptional coregulator RIP140 in solid tumors

RIP140 is a transcriptional coregulator (also known as NRIP1) which plays very important physiological roles by finely tuning the activity of a large number of transcription factors. Noticeably, the RIP140 gene has been shown to be involved in the regulation of energy expenditure, in mammary gland development and intestinal homeostasis as well as in behavior and cognition. RIP140 is also involved in the regulation of various oncogenic signaling pathways and participates in the development and progression of solid tumors. This short review aims to summarize the role of this transcription factor on nuclear estrogen receptors, E2F and Wnt signaling pathways based on recent observations focusing on breast, ovary, liver and colon tumors.

Hereditary breast cancer: clinical, pathological and molecular characteristics

Pathogenic mutations in BRCA1 or BRCA2 are only detected in 25% of families with a strong history of breast cancer, though hereditary factors are expected to be involved in the remaining families with no recognized mutation. Molecular characterization is expected to provide new insight into the tumor biology to guide the search of new high-risk alleles and provide better classification of the growing number of BRCA1/2 variants of unknown significance (VUS). In this review, we provide an overview of hereditary breast cancer, its genetic background, and clinical implications, before focusing on the pathologically and molecular features associated with the disease. Recent transcriptome and genome profiling studies of tumor series from BRCA1/2 mutation carriers as well as familial non-BRCA1/2 will be discussed. Special attention is paid to its association with molecular breast cancer subtypes as well as the latest advances in predicting BRCA1/2 involvement (BRCAness) using molecular signatures, for improved diagnostics and selection of patients sensitive to targeted therapeutics.

Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure

DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.