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Human epidermal growth factor receptor 2-positive (HER2+) breast cancer accounts for ∼20% of invasive breast cancers and is associated with poor prognostics. The recent outcome of HER2+ breast cancer treatment has been vastly improved owing to the application of antibody-targeted therapies. Trastuzumab (Herceptin) is a monoclonal antibody designed to target HER2+ breast cancer cells. In addition to improved survival in the adjuvant treatment of HER2+ breast cancer, trastuzumab treatment has also been associated with cardiotoxicity side effect. However, the molecular mechanisms of trastuzumab action and trastuzumab-mediated cardiotoxicity are still not fully understood. Previous research utilized bulk transcriptomics analysis to study the underlining mechanisms, which relied on averaging molecular signals from bulk tumor samples and might have overlooked key expression features within breast cancer tumor. In contrast to previous research, we compared the single cancer cell level transcriptome profile between trastuzumab-treated and nontreated patients to reveal a more in-depth transcriptome profile. A total of 461 significantly differential expressed genes were identified, including previously defined and novel gene expression signatures. In addition, we found that trastuzumab-enhanced MGP gene expression could be used as prognostics marker for longer patient survival in breast invasive carcinoma patients, and validated our finding using TCGA (The Cancer Genome Atlas) breast cancer dataset. Moreover, our study revealed a 48-gene expression signature that is associated with cell death of cardiomyocytes, which could be used as early biomarkers for trastuzumab-mediated cardiotoxicity. This work is the first study to look at single cell level transcriptome profile of trastuzumab-treated patients, providing a new understanding of the molecular mechanism(s) of trastuzumab action and trastuzumab-induced cardiotoxicity side effects.
Pubmed ID: 31261495
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A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
View all literature mentionsSoftware package for quantifying gene and isoform abundances from single end or paired end RNA Seq data. Accurate transcript quantification from RNA Seq data with or without reference genome. Used for accurate quantification of gene and isoform expression from RNA-Seq data.
View all literature mentionsSoftware as R package designed for QC, analysis, and exploration of single cell RNA-seq data. Enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.
View all literature mentionsSoftware package to arrange multiple heatmaps and support various annotation graphics. Used to visualize associations between different sources of data sets and to reveal potential patterns.
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