Novel insights into breast cancer genetic variance through RNA sequencing

Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples

OBJECTIVE

RNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

RESULTS

Here, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

Down-regulation of ESRP2 inhibits breast cancer cell proliferation via inhibiting cyclinD1

Epithelial splicing regulatory protein 2 (ESRP2),an important alternative splicing protein of mRNA, is reported to have a dual role in tumors, which can promote or inhibit the occurrence and development of tumors. However, the function and mechanism of ESRP2 in breast cancer (BC) remain unclear. The distribution of ESRP2 expression in breast cancer and the correlation between ESRP2 expression and the overall survival rate were detected by The Cancer Genome Atlas (TCGA) database. Gene Ontology(GO)analysis, containing biological process, cellular components, and molecular function, was utilized to evaluate the potential mechanism of ESRP2 in breast cancer. The ESRP2 expression in breast cancer cell lines was detected by real-time quantitative PCR analysis (RT-qPCR) and western blotting. Cell clone was performed to examine the proliferation of ESRP2 knockdown in MCF-7 cells. The cell cycle was measured by flow cytometry assays. The role of ESRP2 knockdown in synergistic effect with chemotherapeutic agents was also determined by MTT assay. Bioinformatics analysis demonstrated that the ESRP2 gene was elevated in breast cancer cells and its overexpression was strongly correlated with shorter overall survival. GO analysis revealed that ESRP2 expression was related to cell proliferation. ESRP2 mRNA and protein expression were elevated in breast cancer cell lines, compared to the normal human breast cell line MCF-10 A. Dwon-regulation of ESRP2 inhibited cell proliferation and promoted the sensitivity of chemotherapy drug, Cisplatin(DDP) and Paclitaxel (TAXOL), in MCF-7 cells.Additionally, ESRP2 knockdown obstructed the cell cycle at the G1 phase and caused a decrease in cyclinD1 protein expression. These findings reveal that ESRP2 is highly expressed in breast cancer and is correlated with poor prognosis in breast cancer patients. ESRP2 knockdown can inhibit MCF-7 cell proliferation by arresting the cell cycle at the G1 phase and promoting the sensitivity of chemotherapy drugs (DDP and TAXOL)in MCF-7 cells. ESRP2 may be required for the regulation of breast cancer progression, as well as a critical target for the clinical treatment of breast cancer.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)-rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG-was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10-2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15-2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13-3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be "likely cancer driver" SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women.

Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia

In our work, the associations of GWAS (genome-wide associative studies) impact for sex-hormone-binding globulin (SHBG)-level SNPs with the risk of breast cancer (BC) in the cohort of Caucasian women of Russia were assessed. The work was performed on a sample of 1498 women (358 BC patients and 1140 control (non BC) subjects). SHBG correlated in previously GWAS nine polymorphisms such as rs780093 GCKR, rs17496332 PRMT6, rs3779195 BAIAP2L1, rs10454142 PPP1R21, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs440837 ZBTB10, rs12150660 SHBG, and rs8023580 NR2F2 have been genotyped. BC risk effects of allelic and non-allelic SHBG-linked gene SNPs interactions were detected by regression analysis. The risk genetic factor for BC developing is an SHBG-lowering allele variant C rs10454142 PPP1R21 ([additive genetic model] OR = 1.31; 95%CI = 1.08-1.65; pperm = 0.024; power = 85.26%), which determines 0.32% of the cancer variance. Eight of the nine studied SHBG-related SNPs have been involved in cancer susceptibility as part of nine different non-allelic gene interaction models, the greatest contribution to which is made by rs10454142 PPP1R21 (included in all nine models, 100%) and four more SNPs-rs7910927 JMJD1C (five models, 55.56%), rs17496332 PRMT6 (four models, 44.44%), rs780093 GCKR (four models, 44.44%), and rs440837 ZBTB10 (four models, 44.44%). For SHBG-related loci, pronounced functionality in the organism (including breast, liver, fibroblasts, etc.) was predicted in silico, having a direct relationship through many pathways with cancer pathophysiology. In conclusion, our results demonstrated the involvement of SHBG-correlated genes polymorphisms in BC risk in Caucasian women in Russia.

VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants

Summary

Next-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization.

Availability and implementation

The source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.

The discovery, function, and regulation of epithelial splicing regulatory proteins (ESRP) 1 and 2

Alternative splicing is a broad and evolutionarily conserved mechanism to diversify gene expression and functionality. The process relies on RNA binding proteins (RBPs) to recognize and bind target sequences in pre-mRNAs, which allows for the inclusion or skipping of various alternative exons. One recently discovered family of RBPs is the epithelial splicing regulatory proteins (ESRP) 1 and 2. Here, we discuss the structure and physiological function of the ESRPs in a variety of contexts. We emphasize the current understanding of their splicing activities, using the classic example of fibroblast growth factor receptor 2 mutually exclusive splicing. We also describe the mechanistic roles of ESRPs in coordinating the splicing and functional output of key signaling pathways that support the maintenance of, or shift between, epithelial and mesenchymal cell states. In particular, we highlight their functions in the development of mammalian limbs, the inner ear, and craniofacial structure while discussing the genetic and biochemical evidence that showcases their conserved roles in tissue regeneration, disease, and cancer pathogenesis.

Nanopore Sequencing Techniques: A Comparison of the MinKNOW and the Alignator Sequencers

Whole genome and transcriptome analyses represent powerful tools. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Here, we compare the sequence data analysis software MinKNOW and our tool Alignator. Furthermore, we provide a walk-through from RNA isolation and preparation for MinION sequencing as well as insides in the processing of sequencing data using both tools.

Underlying mechanisms of epithelial splicing regulatory proteins in cancer progression

Cancer is the second-leading disease-related cause of global mortality after cardiovascular disease. Despite significant advances in cancer therapeutic strategies, cancer remains one of the major obstacles to human life extension. Cancer pathogenesis is extremely complicated and not fully understood. Epithelial splicing regulatory proteins (ESRPs), including ESRP1 and ESRP2, belong to the heterogeneous nuclear ribonucleoprotein family of RNA-binding proteins and are crucial regulators of the alternative splicing of messenger RNAs (mRNAs). The expression and activity of ESRPs are modulated by various mechanisms, including post-translational modifications and non-coding RNAs. Although a growing body of evidence suggests that ESRP dysregulation is closely associated with cancer progression, the detailed mechanisms remain inconclusive. In this review, we summarize recent findings on the structures, functions, and regulatory mechanisms of ESRPs and focus on their underlying mechanisms in cancer progression. We also highlight the clinical implications of ESRPs as prognostic biomarkers and therapeutic targets in cancer treatment. The information reviewed herein could be extremely beneficial to the development of individualized therapeutic strategies for cancer patients.

AKT1 Transcriptomic Landscape in Breast Cancer Cells

Overexpression and hyperactivation of the serine/threonine protein kinase B (AKT) pathway is one of the most common cellular events in breast cancer progression. However, the nature of AKT1-specific genome-wide transcriptomic alterations in breast cancer cells and breast cancer remains unknown to this point. Here, we delineate the impact of selective AKT1 knock down using gene-specific siRNAs or inhibiting the AKT activity with a pan-AKT inhibitor VIII on the nature of transcriptomic changes in breast cancer cells using the genome-wide RNA-sequencing analysis. We found that changes in the cellular levels of AKT1 lead to changes in the levels of a set of differentially expressed genes and, in turn, imply resulting AKT1 cellular functions. In addition to an expected positive relationship between the status of AKT1 and co-expressed cellular genes, our study unexpectedly discovered an inherent role of AKT1 in inhibiting the expression of a subset of genes in both unstimulated and growth factor stimulated breast cancer cells. We found that depletion of AKT1 leads to upregulation of a subset of genes—many of which are also found to be downregulated in breast tumors with elevated high AKT1 as well as upregulated in breast tumors with no detectable AKT expression. Representative experimental validation studies in two breast cancer cell lines showed a reasonable concurrence between the expression data from the RNA-sequencing and qRT-PCR or data from ex vivo inhibition of AKT1 activity in cancer patient-derived cells. In brief, findings presented here provide a resource for further understanding of AKT1-dependent modulation of gene expression in breast cancer cells and broaden the scope and significance of AKT1 targets and their functions.

Investigation of the effects of overexpression of jumping translocation breakpoint (JTB) protein in MCF7 cells for potential use as a biomarker in breast cancer

Jumping translocation breakpoint (JTB) gene acts as a tumor suppressor or an oncogene in different malignancies, including breast cancer (BC), where it was reported as overexpressed. However, the molecular functions, biological processes and underlying mechanisms through which JTB protein causes increased cell growth, proliferation and invasion is still not fully deciphered. Our goal is to identify the functions of JTB protein by cellular proteomics approaches. MCF7 breast cancer cells were transfected with sense orientation of hJTB cDNA in HA, His and FLAG tagged CMV expression vector to overexpress hJTB and the expression levels were confirmed by Western blotting (WB). Proteins extracted from transfected cells were separated by SDS-PAGE and the in-gel digested peptides were analyzed by nano-liquid chromatography tandem mass spectrometry (nanoLC-MS/MS). By comparing the proteome of cells with upregulated conditions of JTB vs control and identifying the protein dysregulation patterns, we aim to understand the function of this protein and its contribution to tumorigenesis. Gene Set Enrichment Analysis (GSEA) algorithm was performed to investigate the biological processes and pathways that are associated with the JTB protein upregulation. The results demonstrated four significantly enriched gene sets from the following significantly upregulated pathways: mitotic spindle assembly, estrogen response late, epithelial-to-mesenchymal transition (EMT) and estrogen response early. JTB protein itself is involved in mitotic spindle pathway by its role in cell division/cytokinesis, and within estrogen response early and late pathways, contributing to discrimination between luminal and mesenchymal breast cancer. Thus, the overexpressed JTB condition was significantly associated with an increased expression of ACTNs, FLNA, FLNB, EZR, MYOF, COL3A1, COL11A1, HSPA1A, HSP90A, WDR, EPPK1, FASN and FOXA1 proteins related to deregulation of cytoskeletal organization and biogenesis, mitotic spindle organization, ECM remodeling, cellular response to estrogen, proliferation, migration, metastasis, increased lipid biogenesis, endocrine therapy resistance, antiapoptosis and discrimination between different breast cancer subtypes. Other upregulated proteins for overexpressed JTB condition are involved in multiple cellular functions and pathways that become dysregulated, such as tumor microenvironment (TME) acidification, the transmembrane transport pathways, glycolytic flux, iron metabolism and oxidative stress, metabolic reprogramming, nucleocytosolic mRNA transport, transcriptional activation, chromatin remodeling, modulation of cell death pathways, stress responsive pathways, and cancer drug resistance. The downregulated proteins for overexpressed JTB condition are involved in adaptive communication between external and internal environment of cells and maintenance between pro-apoptotic and anti-apoptotic signaling pathways, vesicle trafficking and secretion, DNA lesions repair and suppression of genes involved in tumor progression, proteostasis, redox state regulation, biosynthesis of macromolecules, lipolytic pathway, carbohydrate metabolism, dysregulation of ubiquitin-mediated degradation system, cancer cell immune escape, cell-to-cell and cell-to-ECM interactions, and cytoskeletal behaviour. There were no significantly enriched downregulated pathways.

TranSNPs: A class of functional SNPs affecting mRNA translation potential revealed by fraction-based allelic imbalance

Few studies have explored the association between SNPs and alterations in mRNA translation potential. We developed an approach to identify SNPs that can mark allele-specific protein expression levels and could represent sources of inter-individual variation in disease risk. Using MCF7 cells under different treatments, we performed polysomal profiling followed by RNA sequencing of total or polysome-associated mRNA fractions and designed a computational approach to identify SNPs showing a significant change in the allelic balance between total and polysomal mRNA fractions. We identified 147 SNPs, 39 of which located in UTRs. Allele-specific differences at the translation level were confirmed in transfected MCF7 cells by reporter assays. Exploiting breast cancer data from TCGA we identified UTR SNPs demonstrating distinct prognosis features and altering binding sites of RNA-binding proteins. Our approach produced a catalog of tranSNPs, a class of functional SNPs associated with allele-specific translation and potentially endowed with prognostic value for disease risk.

Mining sponge phenomena in RNA expression data

In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway "microRNAs in cancer" when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.

Canady Helios Cold Plasma Induces Breast Cancer Cell Death by Oxidation of Histone mRNA

Breast cancer is the most common cancer among women worldwide. Its molecular receptor marker status and mutational subtypes complicate clinical therapies. Cold atmospheric plasma is a promising adjuvant therapy to selectively combat many cancers, including breast cancer, but not normal tissue; however, the underlying mechanisms remain unexplored. Here, four breast cancer cell lines with different marker status were treated with Canady Helios Cold Plasma™ (CHCP) at various dosages and their differential progress of apoptosis was monitored. Inhibition of cell proliferation, induction of apoptosis, and disruption of the cell cycle were observed. At least 16 histone mRNA types were oxidized and degraded immediately after CHCP treatment by 8-oxoguanine (8-oxoG) modification. The expression of DNA damage response genes was up-regulated 12 h post-treatment, indicating that 8-oxoG modification and degradation of histone mRNA during the early S phase of the cell cycle, rather than DNA damage, is the primary cause of cancer cell death induced by CHCP. Our report demonstrates for the first time that CHCP effectively induces cell death in breast cancer regardless of subtyping, through histone mRNA oxidation and degradation during the early S phase of the cell cycle.

Defects in Emerin-Nucleoskeleton Binding Disrupt Nuclear Structure and Promote Breast Cancer Cell Motility and Metastasis

Nuclear envelope proteins play an important role in regulating nuclear size and structure in cancer. Altered expression of nuclear lamins are found in many cancers and its expression is correlated with better clinical outcomes. The nucleus is the largest organelle in the cell with a diameter between 10 and 20 μm. Nuclear size significantly impacts cell migration. Nuclear structural changes are predicted to impact cancer metastasis by regulating cancer cell migration. Here we show emerin regulates nuclear structure in invasive breast cancer cells to impact cancer metastasis. Invasive breast cancer cells had 40% to 50% less emerin than control cells, which resulted in decreased nuclear size. Overexpression of GFP-emerin in invasive breast cancer cells rescued nuclear size and inhibited migration through 3.0 and 8.0 μm pores. Mutational analysis showed emerin binding to nucleoskeletal proteins was important for its regulation of nuclear structure, migration, and invasion. Importantly, emerin expression inhibited lung metastasis by 91% in orthotopic mouse models of breast cancer. Emerin nucleoskeleton-binding mutants failed to inhibit metastasis. These results support a model whereby emerin binding to the nucleoskeleton regulates nuclear structure to impact metastasis. In this model, emerin plays a central role in metastatic transformation, because decreased emerin expression during transformation causes the nuclear structural defects required for increased cell migration, intravasation, and extravasation. IMPLICATIONS: Modulating emerin expression and function represents new targets for therapeutic interventions of metastasis, because increased emerin expression rescued cancer metastasis.

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

BACKGROUND

Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell.

RESULTS

Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci.

CONCLUSION

ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset.

AVAILABILITY

https://github.com/HorvathLab/NGS/tree/master/scReQTL.

A Review of Natural Therapies Potentially Relevant in Triple Negative Breast Cancer Aimed at Targeting Cancer Cell Vulnerabilities

We reviewed the research into the mechanisms of growth of triple negative breast cancer (TNBC) based on laboratory pre-clinical studies that have shaped understanding of the disease over the past decade. In response to these findings, we propose an approach to potentially prevent cancer metabolic adaptation and recurrence. This paper collates pre-clinical results, first to determine the tumor’s mechanisms of growth and then to source natural substances that could potentially suppress those mechanisms. The results from in vivo and in vitro studies of TNBC were combined first to select 10 primary mechanisms (Hypoxia-inducible factor 1α, Hedgehog, MAPK, MTAP, NF-κ B, Notch, P13K, STAT3, and Wnt signaling pathways plus p53 and POL2A gene expression) that promote TNBC growth, and second to propose a treatment array of 21 natural compounds that suppress laboratory models of TNBC via these mechanisms. We included BRCA mutations in the review process, but only pathways with the most preclinical studies utilizing natural products were included. Then we outlined potential biomarkers to assess the changes in the micro-environment and monitor biochemical pathway suppression. This suppression-centric aim targets these mechanisms of growth with the goal of potentially halting tumor growth and preventing cancer cell metabolic adaptation. We chose TNBC to demonstrate this 5-step strategy of supplementary therapy, which may be replicated for other tumor types.

Comprehensive analysis of the expression of SLC30A family genes and prognosis in human gastric cancer

The solute carrier 30 (SLC30) family genes play a fundamental role in various cancers. However, the diverse expression patterns, prognostic value, and potential mechanism of SLC30A family genes in gastric cancer (GC) remain unknown. Herein, we analyzed the expression and survival data of SLC30A family genes in GC patients using multiple bioinformatic approaches. Expression data of SLC30A family genes for GC patients were extracted from the Cancer Genome Atlas (TCGA) and genetic alteration frequency assessed by using cBioportal database. And validated the expression of SLC30A family genes in GC tissues and corresponding normal tissues. The prognostic value of SLC30A family genes in gastric cancer patients were explored using Kaplan–Meier plotter database. Functional enrichment analysis performed using DAVID database and clusterProfiler package. And ssGSEA algorithm was performed to explore the relationship between the SLC30A family genes and the infiltration of immune cells. We found that the median expression levels of SLC30A1-3, 5–7, and 9 were significantly upregulated in gastric cancer tissues compared to non-cancerous tissues, while SLC30A4 was downregulated. Meanwhile, SLC30A1-7, and 9 were significantly correlated with advanced tumor stage and nodal metastasis status, SLC30A5-7, and 9–10 were significantly related to the Helicobacter pylori infection status of GC patients. High expression of five genes (SLC30A1, 5–7, and 9) was significantly correlated with better overall survival (OS), first progression survival (FPS), and post progression survival (PPS). Conversely, upregulated SLC30A2-4, 8, and 10 expression was markedly associated with poor OS, FP and PPS. And SLC30A family genes were closely associated with the infiltration of immune cells. The present study implied that SLC30A5 and 7 may be potential biomarkers for predicting prognosis in GC patients, SLC30A2 and 3 play an oncogenic role in GC patients and could provide a new strategy for GC patients treatment.

The mutational landscape of the SCAN-B real-world primary breast cancer transcriptome

Breast cancer is a disease of genomic alterations, of which the panorama of somatic mutations and how these relate to subtypes and therapy response is incompletely understood. Within SCAN-B (ClinicalTrials.gov: NCT02306096), a prospective study elucidating the transcriptomic profiles for thousands of breast cancers, we developed a RNA-seq pipeline for detection of SNVs/indels and profiled a real-world cohort of 3,217 breast tumors. We describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population-based cohort and relate it to patient survival. We demonstrate that RNA-seq can be used to call mutations in genes such as PIK3CA, TP53, and ERBB2, as well as the status of molecular pathways and mutational burden, and identify potentially druggable mutations in 86.8% of tumors. To make this rich dataset available for the research community, we developed an open source web application, the SCAN-B MutationExplorer (http://oncogenomics.bmc.lu.se/MutationExplorer). These results add another dimension to the use of RNA-seq as a clinical tool, where both gene expression- and mutation-based biomarkers can be interrogated in real-time within 1 week of tumor sampling.

Streamlining Quantitative Analysis of Long RNA Sequencing Reads

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.

ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

MOTIVATION

By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets.

RESULTS

We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci.

AVAILABILITY AND IMPLEMENTATION

A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The potential of circulating exosomal RNA biomarkers in cancer

INTRODUCTION

There are great potentials of using exosomal RNAs (exoRNA) as biomarkers in cancers. The isolation of exoRNA requires the use of ultracentrifugation to isolate cell-free RNA followed by detection using real-time PCR, microarray, next-generation sequencing, or Nanostring nCounter system. The use of exoRNA enrichment panels has largely increased the detection sensitivity and specificity when compared to traditional diagnostic tests. Moreover, using exoRNA as biomarkers can assist the early detection of chemo and radioresistance cancer, and in turn opens up the possibility of personalized treatment to patients. Finally, exoRNA can be detected at an early stage of cancer recurrence to improve the survival rate.

AREAS COVERED

In this review, the authors summarized the detection methods of exoRNA as well as its potential as a biomarker in cancer diagnosis and chemo and radioresistance.

EXPERT OPINION

The application of exoRNAs in clinical diagnosis is still in its infancy. Further researches on extracellular vesicles isolation, detection protocols, exoRNA classes and subclasses, and the regulatory biological pathways have to be performed before exoRNA can be applied translationally.

New Insights Into the Complex Mutational Landscape of Sézary Syndrome

Sézary syndrome (SS) is a genetically and clinically distinct entity among cutaneous T-cell lymphomas (CTCL). SS is characterized by more aggressive disease compared to the most common indolent type of CTCL, mycosis fungoides. However, there are limited available genomic data regarding SS. To characterize and expand current mappings of the genomic landscape of CTCL, whole exome sequencing (WES) was performed on peripheral blood samples from seven patients with SS. We detected 21,784 variants, of which 21,140 were novel and 644 were previously described. Filtering revealed 551 nonsynonymous variants among 525 mutated genes−25 recurrent mutations and 1 recurrent variant. Several recurrently mutated genes crucial to pathogenesis pathways, including Janus kinase (JAK)/signal transducers and activators of transcription (STAT), peroxisome proliferator-activated receptors (PPAR), PI3K-serine/threonine protein kinases (AKT), and fibroblast growth factor receptors (FGFR), were identified. Furthermore, genetic mutations spanned both known and novel genes, supporting the idea of a long-tail distribution of mutations in lymphoma. Acknowledging these genetic variants and their affected pathways may inspire future targeted therapies. WES of a limited number of SS patients revealed both novel findings and corroborated complexities of the “long-tail” distribution of previously reported mutations.

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAF_RNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAF_RNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of TNBC-specific expressed genes. Further multivariate Cox proportional hazard regression was performed to evaluate the effect of these genes on TNBC prognosis. In this report, we identified a total of 148 TNBC-specific expressed genes that were primarily enriched in mammary gland morphogenesis and hormone levels related pathways, suggesting that mammary gland morphogenesis might play a unique role in TNBC patients differing from other breast cancer types. Further survival analysis revealed that nine genes ( FSIP1, ADCY5, FSD1, HMSD, CMTM5, AFF3, CYP2A7, ATP1A2, and C11orf86) were significantly associated with the prognosis of TNBC patients, while three of them ( ADCY5, CYP2A7, and ATP1A2) were involved in the hormone-related pathways. These findings indicated the vital role of the hormone-related genes in TNBC tumorigenesis and may provide some independent prognostic markers as well as novel therapeutic targets for TNBC.

Emerging Role of Genomics and Cell-Free DNA in Breast Cancer

OPINION STATEMENT

Precision Medicine is gaining momentum as the future gold standard healthcare strategy as it enables treatment optimization and consequently a potential improvement for quality of life and survival. This paradigm shift was possible thanks to new high-throughput genomics technologies, which provide prognostic and predictive information on tumor biology and potential treatment options, as standard pathological procedures are unable to capture both spatial and temporal tumor heterogeneity. As a result of decreasing costs, both solid and liquid-based genomics have an increasingly important role in clinical trials' screening procedures and are gradually being incorporated into clinical practice. Notwithstanding the great potential, its clinical utility is still a matter of debate and clinicians need to be aware of caveats in interpreting resulting data.

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p <0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p <0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p <0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

Hexosamine Biosynthetic Pathway and Glycosylation Regulate Cell Migration in Melanoma Cells

The Hexosamine Biosynthetic Pathway (HBP) is a branch of glycolysis responsible for the production of a key substrate for protein glycosylation, UDP-GlcNAc. Cancer cells present altered glucose metabolism and aberrant glycosylation, pointing to alterations on HBP. Recently it was demonstrated that HBP influences many aspects of tumor biology, including the development of metastasis. In this work we characterize HBP in melanoma cells and analyze its importance to cellular processes related to the metastatic phenotype. We demonstrate that an increase in HBP flux, as well as increased O-GlcNAcylation, leads to decreased cell motility and migration in melanoma cells. In addition, inhibition of N- and O-glycosylation glycosylation reduces cell migration. High HBP flux and inhibition of N-glycosylation decrease the activity of metalloproteases 2 and 9. Our data demonstrates that modulation of HBP and different types of glycosylation impact cell migration.

A novel FADS2 isoform identified in human milk fat globule suppresses FADS2 mediated Δ6-desaturation of omega-3 fatty acids

INTRODUCTION

The only known non-pharmacological means to alter long chain polyunsaturated fatty acid (LCPUFA) abundance in mammalian tissue is by altering substrate fatty acid ratios. Alternative mRNA splicing is increasingly recognized as a modulator of protein structure and function. Here we report identification of a novel alternative transcript (AT) of fatty acid desaturase 2 (FADS2) that inhibits production of omega-3 but not omega-6 LCPUFA, discovered during study of ATs in human milk fat globules (MFG).

METHODS

Human breastmilk collected from a single donor was used to isolate MFG. An mRNA-sequencing library was constructed from the total RNA isolated from the MFG. The constructed library was sequenced using an Illumina HiSeq instrument operating in high output mode. Expression levels of evolutionary conserved FADSAT were measured using cDNA from MFG by semi-quantitative RT-PCR assay.

RESULTS

RNA sequencing revealed >15,000 transcripts, including moderate expression of the FADS2 classical transcript (CS). A novel FADS2 alternative transcript (FADS2AT2) with 386 amino acids was discovered. When FADS2AT2 was transiently transfected into MCF7 cells stably expressing FADS2, delta-6 desaturation (D6D) of alpha-linolenic acid 18:3n-3 → 18:4n-3 was suppressed as were downstream products 20:4n-3 and 20:5n-3. In contrast, no significant effect on D6D of linoleic acid 18:2n-6 → 18:3n-6 or downstream products was observed. FADS2, FADS2AT1 and 5 out of 8 known FADS3AT were expressed in MFG. FADS1, FADS3AT3, and FADS3AT5 are undetectable.

CONCLUSION

The novel, noncatalytic FADS2AT2 regulates FADS2CS-mediated Δ6-desaturation of omega-3 but not omega-6 PUFA biosynthesis. This spliced isoform mediated interaction is the first molecular mechanism by which desaturation of one PUFA family but not the other is modulated.

T lymphocytes facilitate brain metastasis of breast cancer by inducing Guanylate-Binding Protein 1 expression

The discovery of genes and molecular pathways involved in the formation of brain metastasis would direct the development of therapeutic strategies to prevent this deadly complication of cancer. By comparing gene expression profiles of Estrogen Receptor negative (ER-) primary breast tumors between patients who developed metastasis to brain and to organs other than brain, we found that T lymphocytes promote the formation of brain metastases. To functionally test the ability of T cells to promote brain metastasis, we used an in vitro blood–brain barrier (BBB) model. By co-culturing T lymphocytes with breast cancer cells, we confirmed that T cells increase the ability of breast cancer cells to cross the BBB. Proteomics analysis of the tumor cells revealed Guanylate-Binding Protein 1 (GBP1) as a key T lymphocyte-induced protein that enables breast cancer cells to cross the BBB. The GBP1 gene appeared to be up-regulated in breast cancer of patients who developed brain metastasis. Silencing of GBP1 reduced the ability of breast cancer cells to cross the in vitro BBB model. In addition, the findings were confirmed in vivo in an immunocompetent syngeneic mouse model. Co-culturing of ErbB2 tumor cells with activated T cells induced a significant increase in Gbp1 expression by the cancer cells. Intracardial inoculation of the co-cultured tumor cells resulted in preferential seeding to brain. Moreover, intracerebral outgrowth of the tumor cells was demonstrated. The findings point to a role of T cells in the formation of brain metastases in ER- breast cancers, and provide potential targets for intervention to prevent the development of cerebral metastases.

Soluble syntaxin 3 functions as a transcriptional regulator

Syntaxins are a conserved family of SNARE proteins and contain C-terminal transmembrane anchors required for their membrane fusion activity. Here we show that Stx3 (syntaxin 3) unexpectedly also functions as a nuclear regulator of gene expression. We found that alternative splicing creates a soluble isoform that we termed Stx3S, lacking the transmembrane anchor. Soluble Stx3S binds to the nuclear import factor RanBP5 (RAN-binding protein 5), targets to the nucleus, and interacts physically and functionally with several transcription factors, including ETV4 (ETS variant 4) and ATF2 (activating transcription factor 2). Stx3S is differentially expressed in normal human tissues, during epithelial cell polarization, and in breast cancer versus normal breast tissue. Inhibition of endogenous Stx3S expression alters the expression of cancer-associated genes and promotes cell proliferation. Similar nuclear-targeted, soluble forms of other syntaxins were identified, suggesting that nuclear signaling is a conserved, novel function common among these membrane-trafficking proteins.

Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis

Circular RNAs are highly stable molecules present in all eukaryotes generated by distinct transcript processing. We have exploited poly(A-) RNA-Seq data generated in our lab in MCF-7 breast cancer cells to define a compilation of exonic circRNAs more comprehensive than previously existing lists. Development of a novel computational tool, named CircHunter, allowed us to more accurately characterize circRNAs and to quantitatively evaluate their expression in publicly available RNA-Seq data from breast cancer cell lines and tumor tissues. We observed and confirmed, by ChIP analysis, that exons involved in circularization events display significantly higher levels of the histone post-transcriptional modification H3K36me3 than non-circularizing exons. This result has potential impact on circRNA biogenesis since H3K36me3 has been involved in alternative splicing mechanisms. By analyzing an Ago-HITS-CLIP dataset we also found that circularizing exons overlapped with an unexpectedly higher number of Ago binding sites than non-circularizing exons. Finally, we observed that a subset of MCF-7 circRNAs are specific to tumor versus normal tissue, while others can distinguish Luminal from other tumor subtypes, thus suggesting that circRNAs can be exploited as novel biomarkers and drug targets for breast cancer.

Overexpressed somatic alleles are enriched in functional elements in Breast Cancer

Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome.

Society of Surgical Oncology Breast Disease Working Group Statement on Prophylactic (Risk-Reducing) Mastectomy

Over the past several years, there has been an increasing rate of bilateral prophylactic mastectomy (BPM) and contralateral prophylactic mastectomy (CPM) surgeries. Since publication of the 2007 SSO position statement on the use of risk-reducing mastectomy, there have been significant advances in the understanding of breast cancer biology and treatment. The purpose of this manuscript is to review the current literature as a resource to facilitate a shared and informed decision-making process regarding the use of risk-reducing mastectomy.

Somatic Variations in Cervical Cancers in Indian Patients

There are very few reports that describe the mutational landscape of cervical cancer, one of the leading cancers in Indian women. The aim of the present study was to investigate the somatic mutations that occur in cervical cancer. Whole exome sequencing of 10 treatment naïve tumour biopsies with matched blood samples, from a cohort of Indian patients with locally advanced disease, was performed. The data revealed missense mutations across 1282 genes, out of 1831 genes harbouring somatic mutations. These missense mutations (nonsynonymous + stop-gained) when compared with pre-existing mutations in the COSMIC database showed that 272 mutations in 250 genes were already reported although from cancers other than cervical cancer. More than 1000 novel somatic variations were obtained in matched tumour samples. Pathways / genes that are frequently mutated in various other cancers were found to be mutated in cervical cancers. A significant enrichment of somatic mutations in the MAPK pathway was observed, some of which could be potentially targetable. This is the first report of whole exome sequencing of well annotated cervical cancer samples from Indian women and helps identify trends in mutation profiles that are found in an Indian cohort of cervical cancer.

A Genome-Wide Association Study to Identify Potential Germline Copy Number Variants for Sporadic Breast Cancer Susceptibility

Breast cancer (BC) predisposition in populations arises from both genetic and nongenetic risk factors. Structural variations such as copy number variations (CNVs) are heritable determinants for disease susceptibility. The primary objectives of this study are (1) to identify CNVs associated with sporadic BC using a genome-wide association study (GWAS) design; (2) to utilize 2 distinct CNV calling algorithms to identify concordant CNVs as a strategy to reduce false positive associations in the hypothesis-generating GWAS discovery phase, and (3) to identify potential candidate CNVs for follow-up replication studies. We used Affymetrix SNP Array 6.0 data profiled on Caucasian subjects (422 cases/348 controls) to call CNVs using algorithms implemented in Nexus Copy Number and Partek Genomics Suite software. Nexus algorithm identified CNVs associated with BC (731 autosomal CNVs with >5% frequency in the total sample and Q < 0.05). Thirteen CNVs were identified when Partek algorithm-called CNVs were overlapped with Nexus-identified CNVs; these CNVs showed concordances for frequency, effect size, and direction. Coding genes present within BC-associated CNVs were known to play a role in disease etiology and prognosis. Long noncoding RNAs identified within CNVs showed tissue-specific expression, indicating potential functional relevance of the findings. The identified candidate CNVs warrant independent replication.

A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer

BACKGROUND

Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions.

METHODS

We captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.

RESULTS

15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels).

CONCLUSIONS

We have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.

Using high-throughput sequencing transcriptome data for INDEL detection: challenges for cancer drug discovery

INTRODUCTION

A cancer cell is a mosaic of genomic and epigenomic alterations. Distinct cancer molecular signatures can be observed depending on tumor type or patient genetic background. One type of genomic alteration is the insertion and/or deletion (INDEL) of nucleotides in the DNA sequence, which may vary in length, and may change the encoded protein or modify protein domains. INDELs are associated to a large number of diseases and their detection is done based on low-throughput techniques. However, high-throughput sequencing has also started to be used for detection of novel disease-causing INDELs. This search may identify novel drug targets.

AREAS COVERED

This review presents examples of using high-throughput sequencing (DNA-Seq and RNA-Seq) to investigate the incidence of INDELs in coding regions of human genes. Some of these examples successfully utilized RNA-Seq to identify INDELs associated to diseases. In addition, other studies have described small INDELs related to chemo-resistance or poor outcome of patients, while structural variants were associated with a better clinical outcome.

EXPERT OPINION

On average, there is twice as much RNA-Seq data available at the most used repositories for such data compared to DNA-Seq. Therefore, using RNA-Seq data is a promising strategy for studying cancer samples with unknown mechanisms of drug resistance, aiming at the discovery of proteins with potential as novel drug targets.

The Arkadia-ESRP2 axis suppresses tumor progression: analyses in clear-cell renal cell carcinoma

Tumor-specific alternative splicing is implicated in the progression of cancer, including clear-cell renal cell carcinoma (ccRCC). Using ccRCC RNA sequencing data from The Cancer Genome Atlas, we found that epithelial splicing regulatory protein 2 (ESRP2), one of the key regulators of alternative splicing in epithelial cells, is expressed in ccRCC. ESRP2 mRNA expression did not correlate with the overall survival rate of ccRCC patients, but the expression of some ESRP-target exons correlated with the good prognosis and with the expression of Arkadia (also known as RNF111) in ccRCC. Arkadia physically interacted with ESRP2, induced polyubiquitination and modulated its splicing function. Arkadia and ESRP2 suppressed ccRCC tumor growth in a coordinated manner. Lower expression of Arkadia correlated with advanced tumor stages and poor outcomes in ccRCC patients. This study thus reveals a novel tumor-suppressive role of the Arkadia-ESRP2 axis in ccRCC.

Molecular biomarkers screened by next-generation RNA sequencing for non-sentinel lymph node status prediction in breast cancer patients with metastatic sentinel lymph nodes

BACKGROUND

Non-sentinel lymph node (NSLN) status prediction with molecular biomarkers may make some sentinel lymph node (SLN) positive breast cancer patients avoid the axillary lymph node dissection, but the available markers remain limited.

METHODS

SLN positive patients with and without NSLN invasion were selected, and genes differentially expressed or fused in SLN metastasis were screened by next-generation RNA sequencing.

RESULTS

Six candidates (all ER/PR+, HER2-, Ki-67 <20%) with metastatic SLNs selected from 305 patients were equally categorized as NSLN negative and positive. We identified 103 specifically expressed genes in the NSLN negative group and 47 in the NSLN positive group. Among them, FABP1 (negative group) and CYP2A13 (positive group) were the only 2 protein-encoding genes with expression levels in the 8th to 10th deciles. Using a false discovery rate threshold of <0.05, 62 up-regulated genes and 98 down-regulated genes were discovered in the NSLN positive group. Furthermore, 10 gene fusions were identified in this group with the most frequently fused gene being IGLL5.

CONCLUSIONS

The biomarkers screened in present study may broaden our understanding of the mechanisms of breast cancer metastasis to the lymph nodes and contribute to the axillary surgery selection for SLN positive patients.

Computational Inferences of the Functions of Alternative/Noncanonical Splice Isoforms Specific to HER2+/ER-/PR- Breast Cancers, a Chromosome 17 C-HPP Study

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The main objective is to identify and evaluate functionality of a set of specific noncanonical isoforms expressed in HER2-neu positive, estrogen receptor negative (ER-), and progesterone receptor negative (PR-) breast cancers (HER2+/ER-/PR- BC), an aggressive subtype of breast cancers that cause significant morbidity and mortality. We identified 11 alternative splice isoforms that were differentially expressed in HER2+/ER-/PR- BC compared to normal mammary, triple negative breast cancer and triple positive breast cancer tissues (HER2+/ER+/PR+). We used a stringent criterion that differentially expressed noncanonical isoforms (adjusted p value < 0.05) and have to be expressed in all replicates of HER2+/ER-/PR- BC samples, and the trend in differential expression (up or down) is the same in all comparisons. Of the 11 protein isoforms, six were overexpressed in HER2+/ER-/PR- BC. We explored possible functional roles of these six proteins using several complementary computational tools. Biological processes including cell cycle events and glycolysis were linked to four of these proteins. For example, glycolysis was the top ranking functional process for DMXL2 isoform 3, with a fold change of 27 compared to just two for the canonical protein. No previous reports link DMXL2 with any metabolic processes; the canonical protein is known to participate in signaling pathways. Our results clearly indicate distinct functions for the six overexpressed alternative splice isoforms, and these functions could be specific to HER2+/ER-/PR- tumor progression. Further detailed analysis is warranted as these proteins could be explored as potential biomarkers and therapeutic targets for HER2+/ER-/PR- BC patients.

OncoRep: an n-of-1 reporting tool to support genome-guided treatment for breast cancer patients using RNA-sequencing

Background

Breast cancer comprises multiple tumor entities associated with different biological features and clinical behaviors, making individualized medicine a powerful tool to bring the right drug to the right patient. Next generation sequencing of RNA (RNA-Seq) is a suitable method to detect targets for individualized treatment. Challenges that arise are i) preprocessing and analyzing RNA-Seq data in the n-of-1 setting, ii) extracting clinically relevant and actionable targets from complex data, iii) integrating drug databases, and iv) reporting results to clinicians in a timely and understandable manner.

Results

To address these challenges, we present OncoRep, an RNA-Seq based n-of-1 reporting tool for breast cancer patients. It reports molecular classification, altered genes and pathways, gene fusions, clinically actionable mutations and drug recommendations. It visualizes the data in an approachable html-based interactive report and a PDF clinical report, providing the clinician and tumor board with a tool to guide the treatment decision making process.

Conclusions

OncoRep is free and open-source (https://bitbucket.org/sulab/oncorep/), thereby offering a platform for future development and innovation by the community.

Identification of novel exonic mobile element insertions in epithelial ovarian cancers

Mobile elements comprise about half of the human genome. Three active mobile element families (L1, Alu, and SVA) possibly cause diseases such as cancer. We conducted mobile element insertion (MEI) profiling of 44 epithelial ovarian cancers using exome-sequencing data. We identified a total of 106 MEIs using the Mobster program, 8 of which were novel exonic MEIs.

Global transcriptome and sequenome analysis of formalin-fixed salivary epithelial-myoepithelial carcinoma specimens

Diverse microarray and sequencing technologies have been widely used to characterize molecular changes in malignant epithelial cells in salivary neoplasms. Such gene expression studies to identify markers and targets in tumor cells are, however, compromised by the cellular heterogeneity of these tumors and by the difficulties to accrue matching controls representing normal salivary glands. Seventeen samples of primary salivary epithelial-myoepithelial carcinoma along with tissue from six normal major salivary glands were microdissected from paraffin-embedded tissue. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using a whole-transcriptome shotgun sequencing experiment. In parallel, extracted genomic DNA was used for the 50 gene hotspot panel sequenome. KRAS mutations in three patients (18%), NRAS mutations in one patient (6%), but no HRAS, MET, PIK3CA, or BRAF mutations. Using strict and conservative criteria, 220 differentially expressed transcripts were found, with 36% up- and 64% downregulated. The transcripts were annotated using NCBI Entrez Gene, and computationally analyzed with the Ingenuity Pathway Analysis program. From these significantly changed expressions, the analysis identified 26 cancer-related transcripts and 16 transcripts related to mitochondrial dysfunction overlapping with three cancer-related genes. These 220 differentially expressed genes including microRNAs provide here a sufficiently large set to specifically define epithelial-myoepithelial carcinoma and to identify novel and potentially important targets for diagnosis, prognosis, and therapy of this cancer.

The association of the expression of miR-122-5p and its target ADAM10 with human breast cancer

MicroRNAs can regulate many biological functions. miR-122-5p has a tumor suppressor function through different molecular pathways. Also, our second hit, ADAM10, targeted by miR-122-5p, is a major determinant of HER2 shedding causing that trastuzumab cannot bind to HER2 receptors. Therefore, our analysis upon ADAM10 expression and miR-122-5p was a good point to understand molecular mechanism of breast cancer. In our study, we investigated the expression profiles of miR-122-5p and its target ADAM10 in 71 breast cancer patients. Immunohistochemical analysis of ER, PR and HER2 gene products was used to categorize tumors in patients. Expression data and immunohistochemical findings were evaluated to comment on the relationship between miR-122-5p and ADAM10. ADAM10 expression was higher in tumor than that of normal tissue but miR-122-5p expression was lower in tumor than that of normal tissue. The expression pattern in HER2+ patients was reverse of the overall result. It can be explained like that miR-122-5p expression increases especially in HER2+ cancer cell to suppress ADAM10 shedding activity on HER2 receptor. However, increase in expression of tumor suppressor miR-122-5p is not enough to inhibit ADAM10. All in all, we can think miR-122-5p as potential regulator of ADAM10 and trastuzumab resistance. Since if we increase miR-122-5p activity together with trastuzumab administration, then HER2+ breast cancer cells may overcome trastuzumab resistance by inhibiting ADAM10 shedding activity on HER2 receptors and increase the efficiency of trastuzumab.

Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies

The transformation of normal cells into cancer cells and maintenance of the malignant state and phenotypes are associated with genetic and epigenetic deregulations, altered cellular signaling responses and aberrant interactions with the microenvironment. These alterations are constantly evolving as tumor cells face changing selective pressures induced by the cells themselves, the microenvironment and drug treatments. Tumors are also complex ecosystems where different, sometime heterogeneous, subclonal tumor populations and a variety of nontumor cells coexist in a constantly evolving manner. The interactions between molecules and between cells that arise as a result of these alterations and ecosystems are even more complex. The cancer research community is increasingly embracing this complexity and adopting a combination of systems biology methods and integrated analyses to understand and predictively model the activity of cancer cells. Systems biology approaches are helping to understand the mechanisms of tumor progression and design more effective cancer therapies. These approaches work in tandem with rapid technological advancements that enable data acquisition on a broader scale, with finer accuracy, higher dimensionality and higher throughput than ever. Using such data, computational and mathematical models help identify key deregulated functions and processes, establish predictive biomarkers and optimize therapeutic strategies. Moving forward, implementing patient-specific computational and mathematical models of cancer will significantly improve the specificity and efficacy of targeted therapy, and will accelerate the adoption of personalized and precision cancer medicine.