Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression. RESULTS: We used the de Almeida laboratory's sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways. CONCLUSIONS: Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD's mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species

HER-2 overexpression differentially alters transforming growth factor-β responses in luminal versus mesenchymal human breast cancer cells

INTRODUCTION: Amplification of the HER-2 receptor tyrosine kinase has been implicated in the pathogenesis and aggressive behavior of approximately 25% of invasive human breast cancers. Clinical and experimental evidence suggest that aberrant HER-2 signaling contributes to tumor initiation and disease progression. Transforming growth factor beta (TGF-β) is the dominant factor opposing growth stimulatory factors and early oncogene activation in many tissues, including the mammary gland. Thus, to better understand the mechanisms by which HER-2 overexpression promotes the early stages of breast cancer, we directly assayed the cellular and molecular effects of TGF-β1 on breast cancer cells in the presence or absence of overexpressed HER-2. METHODS: Cell proliferation assays were used to determine the effect of TGF-β on the growth of breast cancer cells with normal or high level expression of HER-2. Affymetrix microarrays combined with Northern and western blot analysis were used to monitor the transcriptional responses to exogenous TGF-β1 in luminal and mesenchymal-like breast cancer cells. The activity of the core TGF-β signaling pathway was assessed using TGF-β1 binding assays, phospho-specific Smad antibodies, immunofluorescent staining of Smad and Smad DNA binding assays. RESULTS: We demonstrate that cells engineered to over-express HER-2 are resistant to the anti-proliferative effect of TGF-β1. HER-2 overexpression profoundly diminishes the transcriptional responses induced by TGF-β in the luminal MCF-7 breast cancer cell line and prevents target gene induction by a novel mechanism that does not involve the abrogation of Smad nuclear accumulation, DNA binding or changes in c-myc repression. Conversely, HER-2 overexpression in the context of the mesenchymal MDA-MB-231 breast cell line potentiated the TGF-β induced pro-invasive and pro-metastatic gene signature. CONCLUSION: HER-2 overexpression promotes the growth and malignancy of mammary epithelial cells, in part, by conferring resistance to the growth inhibitory effects of TGF-β. In contrast, HER-2 and TGF-β signaling pathways can cooperate to promote especially aggressive disease behavior in the context of a highly invasive breast tumor model

Gene Expression Profiles Can Predict Panitumumab Monotherapy Responsiveness in Human Tumor Xenograft Models

Background Epidermal growth factor receptor (EGFR)-targeted agents have demonstrated clinical benefit in patients with cancer. Identifying tissue-of-origin-independent predictive biomarkers is important to optimally treat patients. We sought to identify a gene array profile that could predict responsiveness to panitumumab, a fully human EGFR-binding antibody, using preclinical models of human cancer. Methods Mice bearing 25 different xenograft models were treated twice weekly with panitumumab or immunoglobulin G2 control to determine their responsiveness to panitumumab. Samples from these xenografts and untreated xenografts were arrayed on the Affymetrix human U133A gene chip to identify gene sets predicting responsiveness to panitumumab using univariate and multivariate analyses. The predictive models were validated using the leave-one-group-out (LOO) method. Results Of the 25 xenograft models tested, 12 were responsive and 13 were resistant to panitumumab. Unsupervised analysis demonstrated that the xenograft models clustered by tissue type rather than responsiveness to panitumumab. After normalizing for tissue effects, samples clustered by responsiveness using an unsupervised multidimensional scaling. A multivariate selection algorithm was used to select 13 genes that could stratify xenograft models based on responsiveness after adjustment for tissue effects. The method was validated using the LOO method on a training set of 22 models and confirmed independently on three new models. In contrast, a univariate gene selection method resulted in higher misclassification rates. Conclusion A model was constructed from microarray data that prospectively predict responsiveness to panitumumab in xenograft models. This approach may help identify patients, independent of disease origin, likely to benefit from panitumumab

Multi-omics: Differential expression of IFN-γ results in distinctive mechanistic features linking chronic inflammation, gut dysbiosis, and autoimmune diseases

Low grade, chronic inflammation is a critical risk factor for immunologic dysfunction including autoimmune diseases. However, the multiplicity of complex mechanisms and lack of relevant murine models limit our understanding of the precise role of chronic inflammation. To address these hurdles, we took advantage of multi-omics data and a unique murine model with a low but chronic expression of IFN-γ, generated by replacement of the AU-rich element (ARE) in the 3’ UTR region of IFN-γ mRNA with random nucleotides. Herein, we demonstrate that low but differential expression of IFN-γ in mice by homozygous or heterozygous ARE replacement triggers distinctive gut microbial alterations, of which alteration is female-biased with autoimmune-associated microbiota. Metabolomics data indicates that gut microbiota-dependent metabolites have more robust sex-differences than microbiome profiling, particularly those involved in fatty acid oxidation and nuclear receptor signaling. More importantly, homozygous ARE-Del mice have dramatic changes in tryptophan metabolism, bile acid and long-chain lipid metabolism, which interact with gut microbiota and nuclear receptor signaling similarly with sex-dependent metabolites. Consistent with these findings, nuclear receptor signaling, encompassing molecules such as PPARs, FXR, and LXRs, was detectable as a top canonical pathway in comparison of blood and tissue-specific gene expression between female homozygous vs heterozygous ARE-Del mice. Further analysis implies that dysregulated autophagy in macrophages is critical for breaking self-tolerance and gut homeostasis, while pathways interact with nuclear receptor signaling to regulate inflammatory responses. Overall, pathway-based integration of multi-omics data provides systemic and cellular insights about how chronic inflammation driven by IFN-γ results in the development of autoimmune diseases with specific etiopathological features

Rilotumumab Resistance Acquired by Intracrine Hepatocyte Growth Factor Signaling

Drug resistance is a long-standing impediment to effective systemic cancer therapy and acquired drug resistance is a growing problem for molecularly-targeted therapeutics that otherwise have shown unprecedented successes in disease control. The hepatocyte growth factor (HGF)/Met receptor pathway signaling is frequently involved in cancer and has been a subject of targeted drug development for nearly 30 years. To anticipate and study specific resistance mechanisms associated with targeting this pathway, we engineered resistance to the HGF-neutralizing antibody rilotumumab in glioblastoma cells harboring autocrine HGF/Met signaling, a frequent abnormality of this brain cancer in humans. We found that rilotumumab resistance was acquired through an unusual mechanism comprising dramatic HGF overproduction and misfolding, endoplasmic reticulum (ER) stress-response signaling and redirected vesicular trafficking that effectively sequestered rilotumumab and misfolded HGF from native HGF and activated Met. Amplification of MET and HGF genes, with evidence of rapidly acquired intron-less, reverse-transcribed copies in DNA, was also observed. These changes enabled persistent Met pathway activation and improved cell survival under stress conditions. Point mutations in the HGF pathway or other complementary or downstream growth regulatory cascades that are frequently associated with targeted drug resistance in other prevalent cancer types were not observed. Although resistant cells were significantly more malignant, they retained sensitivity to Met kinase inhibition and acquired sensitivity to inhibition of ER stress signaling and cholesterol biosynthesis. Defining this mechanism reveals details of a rapidly acquired yet highly-orchestrated multisystem route of resistance to a selective molecularly-targeted agent and suggests strategies for early detection and effective intervention