Genomic insights into triple-negative and HER2-positive breast cancers using isogenic model systems

Introduction In general, genomic signatures of breast cancer subtypes have little or no overlap owing to the heterogeneous genetic backgrounds of study samples. Thus, obtaining a reliable signature in the context of isogenic nature of the cells has been challenging and the precise contribution of isogenic triple negative breast cancer (TNBC) versus non-TNBC remains poorly defined. Methods We established isogenic stable cell lines representing TNBC and Human Epidermal Growth Factor Receptor 2 positive (HER2+) breast cancers by introducing HER2 in TNBC cell lines MDA-MB-231 and MDA-MB-468. We examined protein level expression and functionality of the transfected receptor by treatment with an antagonist of HER2. Using microarray profiling, we obtained a comprehensive gene list of differentially expressed between TNBC and HER2+ clones. We identified and validated underlying isogenic components using qPCR and also compared results with expression data from patients with similar breast cancer subtypes. Results We identified 544 and 1087 statistically significant differentially expressed genes between isogenic TNBC and HER2+ samples in MDA-MB-231 and MDA-MB-468 backgrounds respectively and a shared signature of 49 genes. By comparing results from MDA-MB-231 and MDA-MB-468 backgrounds with two patient microarray datasets, we identified 17 and 22 common genes with same expression trend respectively. Additionally, we identified 56 and 78 genes from MDA-MB-231 and MDA-MB-468 comparisons respectively present in our published RNA-seq data. Conclusions Using our unique model system, we have identified an isogenic gene expression signature between TNBC and HER2+ breast cancer. A portion of our results was also verified in patient data samples, indicating an existence of isogenic element associated with HER2 status between genetically heterogeneous breast cancer samples. These findings may potentially contribute to the development of molecular platform that would be valuable for diagnostic and therapeutic decision for TNBC and in distinguishing it from HER2+ subtype

Analysis for co-occurring sequence features identifies link between common synonymous variant and an early-terminated NPC1 isoform

Direct assessment of allelic phase for DNA and RNA features of diploid genomes has been challenging for Sanger sequencing, due to its allele-conflating base-calling signal. Massively parallel sequencing technologies are based on the generation of a continuous copy of a single strand sequence segments, thus preserving the allelic relation between the features of the original molecules. We have performed a transcriptome-wide search for co-occurrence of variant nucleotides and exon-intron boundaries positioned within the length of a single sequencing read. Analysis of 75 human transcriptomes from retinal pigment epithelia (RPE), glioblastoma, low-grade brain tumor, breast cancer and colon cancer, have identified an association between the synonymous variant rs1140458 and an early-terminated NPC1 isoform lacking exons 19–25. Higher proportion of molecules bearing the variant nucleotide (versus the reference) incorporates the intron (P \u3c0.0001), which turns the last codon of exon 18 into a stop codon. The significance is highest in RPE cells (P = 3.88 × 10−12). NPC1 protein is involved in the control of the cholesterol trafficking. NPC1 mutations lead, in an autosomal recessive manner, to the neurological disorder Niemann-Pick syndrome type C (NP-C), and, ablation of NPC1 causes age-progressive retinal degeneration in mice and drosophila. The vast majority of the NP-C causative variants consist of missense/nonsense substitutions, small indels, and, intronic splice variants. Rs1140458 is a common exonic synonymous substitution that has never been linked to alternative splicing or pathogenicity. Our analysis suggests that rs1140458 may affect the levels of the functional NPC1 protein, and to contribute to some of the cholesterol-implicated cellular phenotype

RNA sequencing of cancer reveals novel splicing alterations

Breast cancer transcriptome acquires a myriad of regulation changes, and splicing is critical for the cell to “tailor-make” specific functional transcripts. We systematically revealed splicing signatures of the three most common types of breast tumors using RNA sequencing: TNBC, non-TNBC and HER2-positive breast cancer. We discovered subtype specific differentially spliced genes and splice isoforms not previously recognized in human transcriptome. Further, we showed that exon skip and intron retention are predominant splice events in breast cancer. In addition, we found that differential expression of primary transcripts and promoter switching are significantly deregulated in breast cancer compared to normal breast. We validated the presence of novel hybrid isoforms of critical molecules like CDK4, LARP1, ADD3, and PHLPP2. Our study provides the first comprehensive portrait of transcriptional and splicing signatures specific to breast cancer sub-types, as well as previously unknown transcripts that prompt the need for complete annotation of tissue and disease specific transcriptome

RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.

We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained binary package for 64-bit Linux systems

Novel insights into breast cancer genetic variance through RNA sequencing

Using RNA sequencing of triple-negative breast cancer (TNBC), non-TBNC and HER2-positive breast cancer sub-types, here we report novel expressed variants, allelic prevalence and abundance, and coexpression with other variation, and splicing signatures. To reveal the most prevalent variant alleles, we overlaid our findings with cancer- and population-based datasets and validated a subset of novel variants of cancer-related genes: ESRP2, GBP1, TPP1, MAD2L1BP, GLUD2 and SLC30A8. As a proof-of-principle, we demonstrated that a rare substitution in the splicing coordinator ESRP2(R353Q) impairs its ability to bind to its substrate FGFR2 pre-mRNA. In addition, we describe novel SNPs and INDELs in cancer relevant genes with no prior reported association of point mutations with cancer, such as MTAP and MAGED1. For the first time, this study illustrates the power of RNA-sequencing in revealing the variation landscape of breast transcriptome and exemplifies analytical strategies to search regulatory interactions among cancer relevant molecules

Extraction of molecular features through exome to transcriptome alignment

Integrative Next Generation Sequencing (NGS) DNA and RNA analyses have very recently become feasible, and the published to date studies have discovered critical disease implicated pathways, and diagnostic and therapeutic targets. A growing number of exomes, genomes and transcriptomes from the same individual are quickly accumulating, providing unique venues for mechanistic and regulatory features analysis, and, at the same time, requiring new exploration strategies. In this study, we have integrated variation and expression information of four NGS datasets from the same individual: normal and tumor breast exomes and transcriptomes. Focusing on SNP-centered variant allelic prevalence, we illustrate analytical algorithms that can be applied to extract or validate potential regulatory elements, such as expression or growth advantage, imprinting, loss of heterozygosity (LOH), somatic changes, and RNA editing. In addition, we point to some critical elements that might bias the output and recommend alternative measures to maximize the confidence of findings. The need for such strategies is especially recognized within the growing appreciation of the concept of systems biology: integrative exploration of genome and transcriptome features reveal mechanistic and regulatory insights that reach far beyond linear addition of the individual datasets

Validation of microarray results.

<p>Selected genes showing significant deregulation (p-value ≤0.05 and fold change ≥ 1.5) of mRNA expression between TNBC and HER2 clones from microarray data were validated using qPCR. Shown here are the expression levels of four candidates A) <i>LUM</i> B) <i>LIPG</i> C) <i>LOXL2</i> and D) <i>CTSB</i>. The expression levels measured by qPCR are shown in the left, while those from microarray are shown in the right. The expression values for qPCR were calculated using ΔΔ Ct method using 18S for normalization. Microarray values represent normalized and preprocessed data that have been log transformed. The plotted data represent mean ± S.E. Two-tailed student’s t-test was used for statistical analysis of qPCR data. Statistically significant differences in expression are indicated with *. Similar trend of regulation was observed for data from both techniques for these four genes. *, <i>p ≤0.05</i>; **, <i>p≤0</i>.<i>01</i>.</p

Genomic Insights into Triple-Negative and HER2-Positive Breast Cancers Using Isogenic Model Systems

<div><p>Introduction</p><p>In general, genomic signatures of breast cancer subtypes have little or no overlap owing to the heterogeneous genetic backgrounds of study samples. Thus, obtaining a reliable signature in the context of isogenic nature of the cells has been challenging and the precise contribution of isogenic triple negative breast cancer (TNBC) versus non-TNBC remains poorly defined.</p> <p>Methods</p><p>We established isogenic stable cell lines representing TNBC and Human Epidermal Growth Factor Receptor 2 positive (HER2+) breast cancers by introducing HER2 in TNBC cell lines MDA-MB-231 and MDA-MB-468. We examined protein level expression and functionality of the transfected receptor by treatment with an antagonist of HER2. Using microarray profiling, we obtained a comprehensive gene list of differentially expressed between TNBC and HER2+ clones. We identified and validated underlying isogenic components using qPCR and also compared results with expression data from patients with similar breast cancer subtypes.</p> <p>Results</p><p>We identified 544 and 1087 statistically significant differentially expressed genes between isogenic TNBC and HER2+ samples in MDA-MB-231 and MDA-MB-468 backgrounds respectively and a shared signature of 49 genes. By comparing results from MDA-MB-231 and MDA-MB-468 backgrounds with two patient microarray datasets, we identified 17 and 22 common genes with same expression trend respectively. Additionally, we identified 56 and 78 genes from MDA-MB-231 and MDA-MB-468 comparisons respectively present in our published RNA-seq data.</p> <p>Conclusions</p><p>Using our unique model system, we have identified an isogenic gene expression signature between TNBC and HER2+ breast cancer. A portion of our results was also verified in patient data samples, indicating an existence of isogenic element associated with HER2 status between genetically heterogeneous breast cancer samples. These findings may potentially contribute to the development of molecular platform that would be valuable for diagnostic and therapeutic decision for TNBC and in distinguishing it from HER2+ subtype.</p> </div

Comparison of microarray data from isogenic clones and RNA-seq data from patient samples with similar breast cancer subtypes.

<p>A) Comparison of differentially expressed genes obtained from RNA-seq data from patient samples and microarray data in A) MDA-MB-231 background B) MDA-MB-468 background. The overlap in the Venn diagram show common genes between the comparisons and the number circled in red indicates the candidates that show same trend of regulation in both microarray and RNA-seq dataset (shown as a heatmap).</p