3 research outputs found
Contribution of Retrotransposons to Breast Cancer Malignancy
The components contributing to cancer progression, especially the transition from early to invasive are unknown. Consequently, the biological reasons are unclear as to why some patients diagnosed with atypia and ductal carcinoma in situ (DCIS) never progress into invasive breast cancer. The “one gene at a time” approach does not sufficiently predict progression. To elucidate the early stage progression to invasive ductal cancer, expression signature of transcripts and transposable elements in micropunched samples of formalin-fixed, paraffin embedded (FFPE) tissue was conducted. A bioinformatics pipeline to analyze poor quality, short reads (\u3e36 nts) from RNA-Seq data was created to compare the most common tools for alignment and differential expression. Most samples from patients prepared for RNA-seq analysis are acquired through archived FFPE tissue collections, which have low RNA quality. The pipeline analytics revealed that STAR alignment software outperformed others. Furthermore, our comparison revealed both DESeq2 and edgeR, with the estimateDisp function applied, both perform well when analyzing greater than 12 replicates. Transcriptome analysis revealed progressive diversification into known oncogenic pathways, a few novel biochemical pathways, in addition to antiviral and interferon activation. Furthermore, the transposable element (TE) signature during breast cancer progression at early stages indicated long terminal repeat (LTRs) as the most abundantly differentially expressed TEs. LTRs belong to endogenous retroviruses (ERV), a subclass of TEs. The retroviral and innate immune response activity in DCIS, which indirectly corroborates the increase in ERV expression in this pre-malignant stage. Finally, to demonstrate the potential role of TEs in the transition from pre-malignant to malignant breast cancer we used pharmacological approaches to alter global TE expression and inhibit retrotransposition activity in control and breast cancer cell lines. It was expected that dysregulation of TEs be associated with increased invasiveness and growth. However, our results indicated that DNA methyltransferase inhibitor 5-Azacytidine (AZA) consistently retarded cell migration and growth. While unexpected, these findings corroborate recent studies that AZA may induce an interferon response in cancer via increased ERV expression. This body of work illustrates the importance of understanding bioinformatics methods used in RNA-seq analysis of common clinical samples. These studies suggest the potential for TEs as biomarkers for disease progression and novel therapeutic approach to investigate in additional model systems
Aligning the Aligners: Comparison of RNA Sequencing Data Alignment and Gene Expression Quantification Tools for Clinical Breast Cancer Research
The rapid expansion of transcriptomics and affordability of next-generation sequencing (NGS) technologies generate rocketing amounts of gene expression data across biology and medicine, including cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between two predominant programs for read alignment, HISAT2, and STAR, and two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from breast cancer progression series, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners’ performance: HISAT2 was prone to misalign reads to retrogene genomic loci, STAR generated more precise alignments, especially for early neoplasia samples. edgeR and DESeq2 produced similar lists of differentially expressed genes, with edgeR producing more conservative, though shorter, lists of genes. Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO terms identified among differentially expressed genes by edgeR versus DESeq2. As transcriptomics of FFPE samples becomes a vanguard of precision medicine, choice of bioinformatics tools becomes critical for clinical research. Our results indicate that STAR and edgeR are well-suited tools for differential gene expression analysis from FFPE samples
The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis
As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data