Kevin J. Thompson

Circular RNAs and their associations with breast cancer subtypes

Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse biological functions. They are implicated in modulation of gene expression thus affecting various cellular and disease processes. Based on existing bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq to identify and report expressed circRNAs. Circ-Seq also provides informative genomic annotation along circRNA fused junctions thus allowing prioritization of circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast cancer cell lines and validated one of the large circRNAs identified. Circ-Seq was then applied to a larger cohort of breast cancer samples (n = 885) provided by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue samples. Notably, circRNA results reveal that normal-adjacent tissues in estrogen receptor positive (ER+) subtype have relatively higher numbers of circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers were observed in normal breast-mammary tissues from the Genotype-Tissue Expression (GTEx) project. Finally, we observed that number of circRNAs in normal-adjacent samples of ER+ subtype is inversely correlated to the risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting that circRNA frequency may be a marker for cell proliferation in breast cancer. The Circ-Seq workflow will function for both single and multi-threaded compute environments. We believe that Circ-Seq will be a valuable tool to identify circRNAs useful in the diagnosis and treatment of other cancers and complex diseases.

Gene expression, single nucleotide variant and fusion transcript discovery in archival material from breast tumors.

Advantages of RNA-Seq over array based platforms are quantitative gene expression and discovery of expressed single nucleotide variants (eSNVs) and fusion transcripts from a single platform, but the sensitivity for each of these characteristics is unknown. We measured gene expression in a set of manually degraded RNAs, nine pairs of matched fresh-frozen, and FFPE RNA isolated from breast tumor with the hybridization based, NanoString nCounter (226 gene panel) and with whole transcriptome RNA-Seq using RiboZeroGold ScriptSeq V2 library preparation kits. We performed correlation analyses of gene expression between samples and across platforms. We then specifically assessed whole transcriptome expression of lincRNA and discovery of eSNVs and fusion transcripts in the FFPE RNA-Seq data. For gene expression in the manually degraded samples, we observed Pearson correlations of >0.94 and >0.80 with NanoString and ScriptSeq protocols, respectively. Gene expression data for matched fresh-frozen and FFPE samples yielded mean Pearson correlations of 0.874 and 0.783 for NanoString (226 genes) and ScriptSeq whole transcriptome protocols respectively, p<2x10-16. Specifically for lincRNAs, we observed superb Pearson correlation (0.988) between matched fresh-frozen and FFPE pairs. FFPE samples across NanoString and RNA-Seq platforms gave a mean Pearson correlation of 0.838. In FFPE libraries, we detected 53.4% of high confidence SNVs and 24% of high confidence fusion transcripts. Sensitivity of fusion transcript detection was not overcome by an increase in depth of sequencing up to 3-fold (increase from ~56 to ~159 million reads). Both NanoString and ScriptSeq RNA-Seq technologies yield reliable gene expression data for degraded and FFPE material. The high degree of correlation between NanoString and RNA-Seq platforms suggests discovery based whole transcriptome studies from FFPE material will produce reliable expression data. The RiboZeroGold ScriptSeq protocol performed particularly well for lincRNA expression from FFPE libraries, but detection of eSNV and fusion transcripts was less sensitive.

AT-101 downregulates BCL2 and MCL1 and potentiates the cytotoxic effects of lenalidomide and dexamethasone in preclinical models of multiple myeloma and Waldenström macroglobulinaemia.

Multiple myeloma, the second most common haematological malignancy in the U.S., is currently incurable. Disruption of the intrinsic apoptotic pathway by BCL2 and MCL1 upregulation is observed in >80% of myeloma cases and is associated with an aggressive clinical course. Remarkably, there is no approved drug with the ability to target BCL2 or MCL1. Thus, we investigated the anti‐tumour effects of a pan‐BCL2 inhibitor, AT‐101, which has high binding specificity for BCL2 and MCL1 in preclinical models of plasma cell cancers (Multiple myeloma and Waldenström macroglobulinaemia). Gene expression and immunoblot analysis of six plasma cell cancer models showed upregulation of BCL2 family members. AT‐101 was able to downregulate BCL2 and MCL1 in all plasma cell cancer models and induced apoptotic cell death in a caspase‐dependent manner by altering mitochondrial membrane permeability. This cytotoxic effect and BCL2 downregulation were further potentiated when AT‐101 was combined with lenalidomide/dexamethasone (LDA). NanoString nCounter mRNA quantification and Ingenuity Pathways Analysis revealed differential changes in the CCNA2, FRZB, FYN, IRF1, PTPN11 genes in LDA‐treated cells. In summary, we describe for the first time the cellular and molecular events associated with the use of AT‐101 in combination with lenalidomide/dexamethasone in preclinical models of plasma cell malignancy.

The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data

Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6–96.8% precision and 91.6–95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.

Tumor Sequencing and Patient-Derived Xenografts in the Neoadjuvant Treatment of Breast Cancer

Background: Breast cancer patients with residual disease after neoadjuvant chemotherapy (NAC) have increased recurrence risk. Molecular characterization, knowledge of NAC response, and simultaneous generation of patient-derived xenografts (PDXs) may accelerate drug development. However, the feasibility of this approach is unknown. Methods: We conducted a prospective study of 140 breast cancer patients treated with NAC and performed tumor and germline sequencing and generated patient-derived xenografts (PDXs) using core needle biopsies. Chemotherapy response was assessed at surgery. Results: Recurrent “targetable” alterations were not enriched in patients without pathologic complete response (pCR); however, upregulation of steroid receptor signaling and lower pCR rates (16.7%, 1/6) were observed in triple-negative breast cancer (TNBC) patients with luminal androgen receptor (LAR) vs basal subtypes (60.0%, 21/35). Within TNBC, TP53 mutation frequency (75.6%, 31/41) did not differ comparing basal (74.3%, 26/35) and LAR (83.3%, 5/6); however, TP53 stop-gain mutations were more common in basal (22.9%, 8/35) vs LAR (0.0%, 0/6), which was confirmed in The Cancer Genome Atlas and British Columbia data sets. In luminal B tumors, Ki-67 responses were observed in tumors that harbored mutations conferring endocrine resistance (p53, AKT, and IKBKE). PDX take rate (27.4%, 31/113) varied according to tumor subtype, and in a patient with progression on NAC, sequencing data informed drug selection (olaparib) with in vivo antitumor activity observed in the primary and resistant (postchemotherapy) PDXs. Conclusions: In this study, we demonstrate the feasibility of tumor sequencing and PDX generation in the NAC setting. “Targetable” alterations were not enriched in chemotherapy-resistant tumors; however, prioritization of drug testing based on sequence data may accelerate drug development.

An integrated model of the transcriptome of HER2-positive breast cancer.

Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

A comprehensive analysis of breast cancer microbiota and host gene expression

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Establishing and characterizing patient-derived xenografts using pre-chemotherapy percutaneous biopsy and post-chemotherapy surgical samples from a prospective neoadjuvant breast cancer study

BackgroundPatient-derived xenografts (PDXs) are increasingly used in cancer research as a tool to inform cancer biology and drug response. Most available breast cancer PDXs have been generated in the metastatic setting. However, in the setting of operable breast cancer, PDX models both sensitive and resistant to chemotherapy are needed for drug development and prospective data are lacking regarding the clinical and molecular characteristics associated with PDX take rate in this setting.MethodsThe Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective neoadjuvant chemotherapy (NAC) trial of stage I-III breast cancer patients treated with neoadjuvant weekly taxane+/-trastuzumab followed by anthracycline-based chemotherapy. Using percutaneous tumor biopsies (PTB), we established and characterized PDXs from both primary (untreated) and residual (treated) tumors. Tumor take rate was defined as percent of patients with the development of at least one stably transplantable (passed at least for four generations) xenograft that was pathologically confirmed as breast cancer.ResultsBaseline PTB samples from 113 women were implanted with an overall take rate of 27.4% (31/113). By clinical subtype, the take rate was 51.3% (20/39) in triple negative (TN) breast cancer, 26.5% (9/34) in HER2+, 5.0% (2/40) in luminal B and 0% (0/3) in luminal A. The take rate for those with pCR did not differ from those with residual disease in TN (p = 0.999) and HER2+ (p = 0.2401) tumors. The xenografts from 28 of these 31 patients were such that at least one of the xenografts generated had the same molecular subtype as the patient. Among the 35 patients with residual tumor after NAC adequate for implantation, the take rate was 17.1%. PDX response to paclitaxel mirrored the patients’ clinical response in all eight PDX tested.ConclusionsThe generation of PDX models both sensitive and resistant to standard NAC is feasible and these models exhibit similar biological and drug response characteristics as the patients’ primary tumors. Taken together, these models may be useful for biomarker discovery and future drug development.

BBBomics-Human Blood Brain Barrier Transcriptomics Hub

Blood-brain barrier (BBB) is a monolayer of endothelial cells that line brain capillaries. The BBB protects brain by blocking the entry of harmful substances from blood and shielding the brain from peripheral fluctuations in hormones, fatty acids, and electrolytes. In addition, the BBB effectively clears brain metabolites and serves as a major conduit for the delivery of crucial nutrients and growth factors needed for proper brain function. Owing to these critical responsibilities, any functional and structural impairment of the BBB may result in severe pathophysiological consequences in the brain. BBB dysfunction is implicated in several neurodegenerative disorders including Alzheimers disease (Carmeliet and De Strooper, 2012), Parkinsons disease (Kortekaas et al., 2005), and cerebrovascular diseases (Yang and Rosenberg, 2011) such as cerebral amyloid angiopathy, stroke, and vascular dementia. Hence, the research community has been actively investigating the cerebrovascular contributions to neurological diseases with major emphasis on the BBB. The success of these efforts is heavily dependent upon the availability of reliable in vitro as well as in vivo BBB models. Polarized monolayers of human cerebrovascular endothelial cells (hCMEC/D3) described in the current work serves as one such in vitro model that can be easily cultured and manipulated in the lab (Poller et al., 2008; Vu et al., 2009; Weksler et al., 2013). The barrier properties and the expression of several classes of receptors, transporters, and enzymes in hCMEC/D3 cells have been previously investigated (Urich et al., 2012; Lopez-Ramirez et al., 2013, 2014; Bamji-Mirza et al., 2014; Ilina et al., 2015; Naik et al., 2015; Sajja and Cucullo, 2015). Thus, far, the genomic data for hCMEC/D3 cell lines have been generated using array-based approaches (Lopez-Ramirez et al., 2013). However, a comprehensive transcriptomic landscape of hCMEC/D3 cells, which is required for investigating molecular mechanisms using sophisticated computational biology approaches, is not currently available. Next-generation sequencing technology unveils the full potential of systems biology approaches to resolve cellular and molecular interaction networks that regulate the functional integrity of the BBB. Such a panoramic view of the interaction networks could enable us to isolate key players regulating a physiological process and investigate how they are affected in various diseases. To our knowledge, we are the first group to generate deep RNA sequencing and microRNA sequencing of a human BBB cell line. This data report describes BBBomics hub as a comprehensive portal for BBB transcriptomics data, obtained by sequencing mRNA (mRNA-seq) and microRNA (miRNA-seq) of polarized hCMEC/D3 cell monolayers. This data encompasses coding (gene expression, alternate splice forms, expressed single nucleotide variants -eSNVs) and non-coding (microRNA, LincRNA, circular RNA) counts that are easily accessible through BBBomics hub database. We also superimposed the RNA-seq coding data on 285 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which include canonical, non-canonical, and/or atypical pathways retrievable using BBBomics hub. The data is easily accessible and freely available at http://bioinformaticstools.mayo.edu/bbbomics/.

Differential Effects of Alzheimer’s Disease Aβ40 and 42 on Endocytosis and Intraneuronal Trafficking

Anomalous neuronal accumulation of Aβ peptides was shown to affect synaptic transmission and contribute to neurodegeneration in Alzheimers disease (AD) brain. Neuronal cells internalize amyloid beta (Aβ) peptides from the brain extracellular space even under normal physiological conditions, and these endocytotic pathways go awry during AD progression. We hypothesized that exposure to toxic Aβ species accumulating in AD brain contributes to perturbations in neuronal endocytosis. We have shown substantial down-regulation of KEGG endocytotic pathway genes in AD patient brain regions that accumulate Aβ compared to those in non-demented individuals. While both Aβ40 and Aβ42 perturbed endocytosis and intracellular trafficking in neuronal cells, Aβ40 had a greater effect than Aβ42. Moreover, Aβ40 decreased the neuronal uptake and lysosomal accumulation of Aβ42, which tends to oligomerize at low lysosomal pH. Hence, Aβ40 may reduce the prevalence of stable Aβ42 oligomers that are closely associated with neurodegeneration and are intercellularly propagated across the vulnerable brain regions to eventually nucleate as amyloid plaques. In conclusion, elevated brain Aβ levels and Aβ42:40 ratio apparent in the early stages of AD could perturb intraneuronal trafficking, augment the anomalous accumulation of amyloid peptides in AD brain, and drive AD pathogenesis.