Transcriptomic analysis of inter- and intra-patient variation in human iPSC cardiomyocytes: Platform for precision medicine to predict drug toxicity
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE83668 | GEO | 2016/08/24
SECONDARY ACCESSION(S): PRJNA326668
REPOSITORIES: GEO
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Project description:In the present study we created and analyzed cardiomyocytes from two separate iPSC clones from the fibroblasts of five different female individuals and two male individuals, using footprint-free Sendai virus RNA-seq of iPSC cardiomyocytes, Ampli-seq of heart left ventricle and iPSC cardiomyocytes (with and without drug treatment) and Exome-seq of patient fibroblasts.
2016-08-24 | E-GEOD-83668 | biostudies-arrayexpress
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Project description:Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms (Agilent, Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems.
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Project description:Prostate cancers exhibit a spectrum of molecular aberrations of which a substantial subset are amenable to targeted therapeutics. To determine the diversity of somatic alterations present in metastasis within and between individuals we characterized the genomic landscapes of 176 tumors acquired from 63 men. In contrast to the considerable variation across individuals, the molecular diversity of tumors within an individual was substantially less: alterations in putative drivers of cancer growth and cell cycle progression status were highly concordant. While androgen receptor activity was inversely related to proliferation, the expression of Fanconi Anemia complex genes was strongly associated with increased cell cycle progression. Inhibition of FANCA, FANCC, FANCD2 and BRCA2 expression reduced prostate cancer growth. The limited molecular diversity across metastases may result from bottlenecks imposed by the dissemination process, limited evolutionary time between metastatic seeding and tumor sampling, intermixing of tumor clones, and selection resulting from treatment pressures. Though exceptions exist, evaluating a single metastasis provides a reasonable assessment of the key molecular processes that occur throughout the spectrum of disseminated tumors within an individual, and may be used for selecting treatments based on predicted molecular vulnerabilities. Custom Agilent 44K whole human genome expression oligonucleotide microarrays were used to profile 171 CRPC tumors from 63 patients. RNA was amplified prior to hybridization against a common reference pool of prostate tumor cell lines.
2015-12-18 | E-GEOD-74685 | biostudies-arrayexpress
Project description:PURPOSE:Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community. METHODS:A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS:PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION:PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
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Project description:UnlabelledWe present rCGH, a comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH supports the major microarray platforms, ensures a full traceability and facilitates profiles interpretation and decision-making through sharable interactive visualizations.Availability and implementationThe rCGH R package is available on bioconductor (under Artistic-2.0). The aCGH-viewer is available at https://fredcommo.shinyapps.io/aCGH_viewer, and the application implementation is freely available for installation at https://github.com/fredcommo/aCGH_viewerContactfrederic.commo@gustaveroussy.frSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Metastatic cancer is a medical challenge that has been historically resistant to treatments. One area of leverage in cancer care is the development of molecularly-driven combination therapies, offering the possibility to overcome resistance. The selection of optimized treatments based on the complex molecular features of a patient's tumor may be rendered easier by using a computer-assisted program. We used the PreciGENE® platform that uses multi-pathway molecular analysis to identify personalized therapeutic options. These options are ranked using a predictive score reflecting the degree to which a therapy or combination of therapies matches the patient's biomarker profile. We searched PubMed from February 2010 to June 2017 for all patients described as exceptional responders who also had molecular data available. Altogether, 70 patients with cancer who had received 202 different treatment lines and who had responded (stable disease ≥12 months/partial or complete remission) to ≥1 regimen were curated. We demonstrate that an algorithm reflecting the degree to which patients were matched to the drugs administered correctly ranked the response to the regimens with a sensitivity of 84% and a specificity of 77%. The difference in matching score between successful and unsuccessful treatment lines was significant (median, 65% versus 0%, p-value <0.0001).
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Project description:Background: Gene expression variation is a phenotypic trait of particular interest as it represents the initial link between genotype and other phenotypes. Analyzing how such variation apportions among and within groups allows for the evaluation of how genetic and environmental factors influence such traits. It also provides opportunities to identify genes and pathways that may have been influenced by non-neutral processes. Here we use a population genetics framework and next generation sequencing to evaluate how gene expression variation is apportioned among four human groups in a natural biological tissue, the placenta. Results: We estimate that on average, 33.2%, 58.9% and 7.8% of the placental transcriptome is explained by variation within individuals, among individuals and among human groups, respectively. Additionally, when technical and biological traits are included in models of gene expression they account for roughly 2% of total gene expression variation. Notably, the variation that is significantly different among groups is enriched in biological pathways associated with immune response, cell signaling and metabolism. Many biological traits demonstrated correlated changes in expression in numerous pathways of potential interest to clinicians and evolutionary biologists. Finally, we estimate that the majority of the human placental transcriptome (65% of expressed genes) exhibits expression profiles consistent with neutrality; the remainder are consistent with stabilizing selection (26%), directional selection (4.9%), or diversifying selection (4.8%). Conclusion: We apportion placental gene expression variation into individual, population and biological trait factors and identify how each influence the transcriptome. Additionally, we advance methods to associate expression profiles with different forms of selection. Placental mRNA was sequenced on an Illumina GAIIx. Samples were derived from 4 human groups, 10 individuals per group, 2 samples per individual
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