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The Molecular Microscope Diagnostic System: Assessment of Rejection and Injury in Heart Transplant Biopsies. Transplantation 2023; 107:27-44. [PMID: 36508644 DOI: 10.1097/tp.0000000000004323] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This review describes the development of the Molecular Microscope Diagnostic System (MMDx) for heart transplant endomyocardial biopsies (EMBs). MMDx-Heart uses microarrays to measure biopsy-based gene expression and ensembles of machine learning algorithms to interpret the results and compare each new biopsy to a large reference set of earlier biopsies. MMDx assesses T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), recent parenchymal injury, and atrophy-fibrosis, continually "learning" from new biopsies. Rejection-associated transcripts mapped in kidney transplants and experimental systems were used to identify TCMR, AMR, and recent injury-induced inflammation. Rejection and injury emerged as gradients of intensity, rather than binary classes. AMR was one-third donor-specific antibody (DSA)-negative, and many EMBs first considered to have no rejection displayed minor AMR-like changes, with increased probability of DSA positivity and subtle inflammation. Rejection-associated transcript-based algorithms now classify EMBs as "Normal," "Minor AMR changes," "AMR," "possible AMR," "TCMR," "possible TCMR," and "recent injury." Additionally, MMDx uses injury-associated transcript sets to assess the degree of parenchymal injury and atrophy-fibrosis in every biopsy and study the effect of rejection on the parenchyma. TCMR directly injures the parenchyma whereas AMR usually induces microcirculation stress but relatively little initial parenchymal damage, although slowly inducing parenchymal atrophy-fibrosis. Function (left ventricular ejection fraction) and short-term risk of failure are strongly determined by parenchymal injury. These discoveries can guide molecular diagnostic applications, either as a central MMDx system or adapted to other platforms. MMDx can also help calibrate noninvasive blood-based biomarkers to avoid unnecessary biopsies and monitor response to therapy.
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Chou E, Zhang H, Guan Y. Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction. STAR Protoc 2022; 3:101583. [PMID: 35880126 PMCID: PMC9307566 DOI: 10.1016/j.xpro.2022.101583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Designing robust, generalizable models based on cross-platform data to predict clinical outcomes remains challenging. Building explainable models is important because models may perform differently depending on the conditions of the samples. Here, we describe the use of Ciclops (cross-platform training in clinical outcome predictions), freely available software that can build explainable models to deliver across cross-platform datasets for predicting clinical outcomes. This protocol also utilizes SHAP, a post-training analysis allowing for assessing potential biomarkers of the clinical outcome under study. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022). Build robust clinical outcome prediction models using cross-platform transcriptome data Applicable to datasets from different studies measuring different clinical outcomes Perform key preprocessing steps of imputation and cross-platform quantile normalization Analyze feature importance in LightGBM, XGBoost, and Random Forest models with SHAP
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Elysia Chou
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
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Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol 2019; 94:1-58. [DOI: 10.1007/s00204-019-02613-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022]
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4
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Bushel PR, Paules RS, Auerbach SS. A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples. Front Genet 2018; 9:485. [PMID: 30420870 PMCID: PMC6217592 DOI: 10.3389/fgene.2018.00485] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 09/28/2018] [Indexed: 11/13/2022] Open
Abstract
The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.
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Affiliation(s)
- Pierre R Bushel
- Biostatistics and Computational Biology Branch, NIEHS, Research Triangle Park, Durham, NC, United States
| | - Richard S Paules
- Biomolecular Screening Branch, National Toxicology Program, NIEHS, Research Triangle Park, Durham, NC, United States
| | - Scott S Auerbach
- Biomolecular Screening Branch, National Toxicology Program, NIEHS, Research Triangle Park, Durham, NC, United States
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5
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Mecozzi L, Gennari O, Coppola S, Olivieri F, Rega R, Mandracchia B, Vespini V, Bramanti A, Ferraro P, Grilli S. Easy Printing of High Viscous Microdots by Spontaneous Breakup of Thin Fibers. ACS APPLIED MATERIALS & INTERFACES 2018; 10:2122-2129. [PMID: 29278322 DOI: 10.1021/acsami.7b17358] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Electrohydrodynamic jetting is emerging as a successful technique for printing inks with resolutions well beyond those offered by conventional inkjet printers. However, the variety of printable inks is still limited to those with relatively low viscosities (typically <20 mPa s) due to nozzle clogging problems. Here, we show the possibility of printing ordered microdots of high viscous inks such as poly(lactic-co-glycolic acid) (PLGA) by exploiting the spontaneous breakup of a thin fiber generated through nozzle-free pyro-electrospinning. The PLGA fiber is deposited onto a partially wetting surface, and the breakup is achieved simply by applying an appropriate thermal stimulation, which is able to induce polymer melting and hence a mechanism of surface area minimization due to the Plateau-Rayleigh instability. The results show that this technique is a good candidate for extending the printability at the microscale to high viscous inks, thus extending their applicability to additional applications, such as cell behavior under controlled morphological constraints.
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Affiliation(s)
- L Mecozzi
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - O Gennari
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - S Coppola
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - F Olivieri
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
- Department of Chemical Materials and Production Engineering of the University "Federico II" , P.le Tecchio 80, 80125 Naples, Italy
| | - R Rega
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - B Mandracchia
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - V Vespini
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - A Bramanti
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - P Ferraro
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - S Grilli
- Institute of Applied Sciences & Intelligent Systems of the National Research Council (CNR-ISASI) , Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
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6
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The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook. Regul Toxicol Pharmacol 2017; 91 Suppl 1:S14-S26. [DOI: 10.1016/j.yrtph.2017.09.020] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/12/2017] [Accepted: 09/13/2017] [Indexed: 11/21/2022]
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7
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Beger R, Yu LR, Daniels J, Mattes W. Exploratory biomarkers: Analytical approaches and their implications. CURRENT OPINION IN TOXICOLOGY 2017. [DOI: 10.1016/j.cotox.2017.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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8
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Sutherland JJ, Webster YW, Willy JA, Searfoss GH, Goldstein KM, Irizarry AR, Hall DG, Stevens JL. Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity. THE PHARMACOGENOMICS JOURNAL 2017; 18:377-390. [DOI: 10.1038/tpj.2017.17] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 02/19/2017] [Accepted: 02/28/2017] [Indexed: 12/11/2022]
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Blayney JK, Davison T, McCabe N, Walker S, Keating K, Delaney T, Greenan C, Williams AR, McCluggage WG, Capes-Davis A, Harkin DP, Gourley C, Kennedy RD. Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line. Nucleic Acids Res 2016; 44:e137. [PMID: 27353327 PMCID: PMC5041471 DOI: 10.1093/nar/gkw578] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/17/2016] [Accepted: 06/16/2016] [Indexed: 01/03/2023] Open
Abstract
Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.
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Affiliation(s)
- Jaine K Blayney
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK
| | - Timothy Davison
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
| | - Nuala McCabe
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
| | - Steven Walker
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
| | | | | | - Caroline Greenan
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
| | - Alistair R Williams
- Department of Pathology, The University of Edinburgh, Royal Infirmary of Edinburgh, EH16 4SA, UK
| | - W Glenn McCluggage
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Department of Pathology, Belfast Health and Social Care Trust, Belfast, BT12 6BA, UK
| | - Amanda Capes-Davis
- CellBank Australia, Children's Medical Research Institute, University of Sydney, Westmead, NSW, Australia
| | - D Paul Harkin
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
| | - Charlie Gourley
- Edinburgh Cancer Research Centre, The University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XR, UK
| | - Richard D Kennedy
- Centre for Cancer Research and Cell Biology, Queen's University, Belfast, BT9 7BL, UK Almac Diagnostics, Craigavon, BT63 5QD, UK
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Borup R, Thuesen LL, Andersen CY, Nyboe-Andersen A, Ziebe S, Winther O, Grøndahl ML. Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age. PLoS One 2016; 11:e0153562. [PMID: 27128483 PMCID: PMC4851390 DOI: 10.1371/journal.pone.0153562] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 03/31/2016] [Indexed: 12/23/2022] Open
Abstract
Objective By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study. Methods MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set. Results We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level. Conclusion We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.
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Affiliation(s)
- Rehannah Borup
- Center for Genomic Medicine, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
- * E-mail:
| | - Lea Langhoff Thuesen
- Fertility Clinic, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Claus Yding Andersen
- Laboratory of Reproductive Biology, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Anders Nyboe-Andersen
- Fertility Clinic, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Søren Ziebe
- Fertility Clinic, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Ole Winther
- Bioinformatics Center, Department of Biology and Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - Marie Louise Grøndahl
- Fertility Clinic, University Hospital of Copenhagen, Herlev Hospital, Copenhagen, Denmark
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11
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Xu J, Thakkar S, Gong B, Tong W. The FDA's Experience with Emerging Genomics Technologies-Past, Present, and Future. AAPS JOURNAL 2016; 18:814-8. [PMID: 27116022 PMCID: PMC4973466 DOI: 10.1208/s12248-016-9917-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 04/12/2016] [Indexed: 01/30/2023]
Abstract
The rapid advancement of emerging genomics technologies and their application for assessing safety and efficacy of FDA-regulated products require a high standard of reliability and robustness supporting regulatory decision-making in the FDA. To facilitate the regulatory application, the FDA implemented a novel data submission program, Voluntary Genomics Data Submission (VGDS), and also to engage the stakeholders. As part of the endeavor, for the past 10 years, the FDA has led an international consortium of regulatory agencies, academia, pharmaceutical companies, and genomics platform providers, which was named MicroArray Quality Control Consortium (MAQC), to address issues such as reproducibility, precision, specificity/sensitivity, and data interpretation. Three projects have been completed so far assessing these genomics technologies: gene expression microarrays, whole genome genotyping arrays, and whole transcriptome sequencing (i.e., RNA-seq). The resultant studies provide the basic parameters for fit-for-purpose application of these new data streams in regulatory environments, and the solutions have been made available to the public through peer-reviewed publications. The latest MAQC project is also called the SEquencing Quality Control (SEQC) project focused on next-generation sequencing. Using reference samples with built-in controls, SEQC studies have demonstrated that relative gene expression can be measured accurately and reliably across laboratories and RNA-seq platforms. Besides prediction performance comparable to microarrays in clinical settings and safety assessments, RNA-seq is shown to have better sensitivity for low expression and reveal novel transcriptomic features. Future effort of MAQC will be focused on quality control of whole genome sequencing and targeted sequencing.
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Affiliation(s)
- Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Shraddha Thakkar
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA.
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12
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Liu Y, Ai N, Liao J, Fan X. Transcriptomics: a sword to cut the Gordian knot of traditional Chinese medicine. Biomark Med 2015; 9:1201-13. [DOI: 10.2217/bmm.15.91] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The systemic effects of traditional Chinese medicine (TCM) seem to be a Gordian knot, impossible to untie for decades. With the advent of transcriptomics, a useful sword is provided to cut the knot and shed some light on complex bioprocesses and intrinsic connections among them. Here, we revisit studies on TCM ZHENGs using this approach, highlight its applications on elucidating the potential scientific basis of ZHENG and investigating mechanisms of action for the TCM formula, and demonstrating its unique role in novel TCM drug design and discovery through active ingredient detection from TCM and compatibility theory study of TCM. The limitations and future perspectives of transcriptomics approaches to TCM study are also discussed.
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Affiliation(s)
- Yufeng Liu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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13
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Zheng Y, Qing T, Song Y, Zhu J, Yu Y, Shi W, Pusztai L, Shi L. Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine. Biomark Med 2015; 9:1265-72. [PMID: 26502353 DOI: 10.2217/bmm.15.99] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Microarrays and next-generation sequencing technologies have been increasingly employed in biomedical research. However, before they can be reliably used as clinical biomarker tests, standardization and quality control measures need to be developed to ensure their analytical validity. This review summarizes community-wide efforts such as the MicroArray and Sequencing Quality Control (MAQC/SEQC) project which have identified factors influencing the performance of these technologies. Consequently, consensus-based standards and well-documented best practices have been developed to improve the quality of scientific research, and reference materials and reference datasets have been made available for evaluating the technical proficiency in future studies. These efforts have built the foundation on which the translational application of genomics based technologies can help realize precision medicine.
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Affiliation(s)
- Yuanting Zheng
- Center for Pharmacogenomics & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, China
| | - Tao Qing
- Center for Pharmacogenomics & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, China
| | - Yunjie Song
- Center for Pharmacogenomics & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, China
| | - Jinhang Zhu
- Center for Pharmacogenomics & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, China
| | - Ying Yu
- Collaborative Innovation Center for Genetics & Development, State Key Laboratory of Genetic Engineering & MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Weiwei Shi
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Lajos Pusztai
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Leming Shi
- Center for Pharmacogenomics & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, China.,Collaborative Innovation Center for Genetics & Development, State Key Laboratory of Genetic Engineering & MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
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14
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Sandhu KS, Veeramachaneni V, Yao X, Nie A, Lord P, Amaratunga D, McMillian MK, Verheyen GR. Release of (and lessons learned from mining) a pioneering large toxicogenomics database. Pharmacogenomics 2015; 16:779-801. [PMID: 26067483 DOI: 10.2217/pgs.15.38] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. MATERIALS & METHODS The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. RESULTS The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. CONCLUSION Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.
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Affiliation(s)
| | | | - Xiang Yao
- Data Sciences, R&D IT, Janssen Pharmaceutical Research & Development, LLC, 3120 Merryfield Row, San Diego, CA 92121, USA
| | - Alex Nie
- Special Counsel, Patent Atterney, Sheppard, Mullin, Richter & Hampton LLP, 379 Lytton Ave, Palo Alto, CA 94301, USA
| | - Peter Lord
- Discotox Ltd, Hebden Bridge, West Yorkshire, UK
| | | | | | - Geert R Verheyen
- Radius Group, Thomas More University College, Kleinhoefstraat 4, 2440 Geel, Belgium
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15
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Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova M, Yang X, Ning B, Gong B, Meehan J, Xu J, Ge W, Perkins R, Fischer M, Tong W. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol 2014; 15:523. [PMID: 25633159 PMCID: PMC4290828 DOI: 10.1186/s13059-014-0523-y] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 10/31/2014] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? RESULTS We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. CONCLUSIONS Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.
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Affiliation(s)
- Zhenqiang Su
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
- />Thomson Reuters, IP & Science, 22 Thomson Place, Boston, MA 02210 USA
| | - Hong Fang
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Huixiao Hong
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Leming Shi
- />State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University, Shanghai, 201203 China
- />Fudan-Zhangjiang Center for Clinical Genomics, Shanghai, 201203 China
- />Zhanjiang Center for Translational Medicine, Shanghai, 201203 China
| | - Wenqian Zhang
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Wenwei Zhang
- />BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong 518083 China
| | - Yanyan Zhang
- />BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong 518083 China
| | - Zirui Dong
- />BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong 518083 China
- />BGI-Guangzhou, Guangzhou, China
| | - Lee J Lancashire
- />Thomson Reuters, IP & Science, 22 Thomson Place, Boston, MA 02210 USA
| | | | - Xi Yang
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Baitang Ning
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Binsheng Gong
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Joe Meehan
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Joshua Xu
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Weigong Ge
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Roger Perkins
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Matthias Fischer
- />Department of Pediatric Oncology and Hematology and Center for Molecular Medicine (CMMC), University Children’s Hospital of Cologne, Kerpener Strasse 62, D-50924 Cologne, Germany
| | - Weida Tong
- />National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
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16
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Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Łabaj PP, Kreil DP, Megherbi D, Florian C, Gaj S, van Delft J, Kleinjans J, Scherer A, Viswanath D, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol 2014; 32:926-32. [PMID: 25150839 PMCID: PMC4243706 DOI: 10.1038/nbt.3001] [Citation(s) in RCA: 339] [Impact Index Per Article: 33.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 07/29/2014] [Indexed: 12/22/2022]
Abstract
The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.
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Affiliation(s)
- Charles Wang
- Center for Genomics and Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, California, USA
| | - Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Pierre R. Bushel
- Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
- Biostatistics Branch, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Hong Fang
- The Office of Scientific Coordination, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Jie Shen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Zhenqiang Su
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Joe Meehan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Xiaojin Li
- Functional Genomics Core, Department of Molecular Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Lu Yang
- Functional Genomics Core, Department of Molecular Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Haiqing Li
- Functional Genomics Core, Department of Molecular Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | | | - David P. Kreil
- Boku University Vienna, Austria
- Life Sciences, University of Warwick, Coventry, U.K
| | - Dalila Megherbi
- CMINDS Research Center, Department of Electrical and Computer Engineering, Francis College of Engineering, University of Massachusetts, Lowell, Massachusetts, USA
| | - Caiment Florian
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | - Stan Gaj
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | - Joost van Delft
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | - Jos Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands
| | - Andreas Scherer
- Australian Genome Research Facility Ltd, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | | | - Jian Wang
- Research Informatics and Statistics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | - Yong Yang
- Research Informatics and Statistics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | - Hui-Rong Qian
- Research Informatics and Statistics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | | | | | - Yuri Nikolsky
- Thomson Reuters, IP & Science, Carlsbad, California, USA
| | | | | | | | | | | | | | | | - Ke K. Zhang
- Bioinformatics core, Department of Pathology, University of North Dakota, Grand Forks, North Dakota, USA
| | - Jianying Li
- Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
- Kelly Government Solutions, Inc., Durham, North Carolina, USA
| | - Jui-Hua Hsieh
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
| | | | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Youping Deng
- Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, Illinois, USA
| | - Leming Shi
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
- Center for Pharmacogenomics, Schools of Life Sciences and Pharmacy, Fudan University, Shanghai, China
| | - Richard S. Paules
- Laboratory of Toxicology and Pharmacology, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
| | - Scott S. Auerbach
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, RTP, North Carolina, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
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17
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Larsen MJ, Thomassen M, Tan Q, Sørensen KP, Kruse TA. Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency. BIOMED RESEARCH INTERNATIONAL 2014; 2014:651751. [PMID: 25101291 PMCID: PMC4101981 DOI: 10.1155/2014/651751] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Revised: 04/17/2014] [Accepted: 06/09/2014] [Indexed: 12/13/2022]
Abstract
Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.
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Affiliation(s)
- Martin J. Larsen
- Department of Clinical Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, Winsløwvej 19, 5000 Odense C, Denmark
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, Winsløwvej 19, 5000 Odense C, Denmark
| | - Qihua Tan
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, Winsløwvej 19, 5000 Odense C, Denmark
- Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J.B. Winsløws Vej 9B, 5000 Odense C, Denmark
| | - Kristina P. Sørensen
- Department of Clinical Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, Winsløwvej 19, 5000 Odense C, Denmark
| | - Torben A. Kruse
- Department of Clinical Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, Winsløwvej 19, 5000 Odense C, Denmark
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18
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Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling. BIOMED RESEARCH INTERNATIONAL 2013; 2013:202497. [PMID: 23762827 PMCID: PMC3673329 DOI: 10.1155/2013/202497] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 04/23/2013] [Accepted: 04/25/2013] [Indexed: 11/17/2022]
Abstract
Addressing safety concerns such as drug-induced kidney injury (DIKI) early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril) was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC). The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound's well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity), not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.
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19
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A network pharmacology approach to evaluating the efficacy of chinese medicine using genome-wide transcriptional expression data. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2013; 2013:915343. [PMID: 23737854 PMCID: PMC3666440 DOI: 10.1155/2013/915343] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 02/13/2013] [Accepted: 02/17/2013] [Indexed: 11/17/2022]
Abstract
The research of multicomponent drugs, such as in Chinese Medicine, on both mechanism dissection and drug discovery is challenging, especially the approaches to systematically evaluating the efficacy at a molecular level. Here, we presented a network pharmacology-based approach to evaluating the efficacy of multicomponent drugs by genome-wide transcriptional expression data and applied it to Shenmai injection (SHENMAI), a widely used Chinese Medicine composed of red ginseng (RG) and Radix Ophiopogonis (RO) in clinically treating myocardial ischemia (MI) diseases. The disease network, MI network in this case, was constructed by combining the protein-protein interactions (PPI) involved in the MI enriched pathways. The therapeutic efficacy of SHENMAI, RG, and RO was therefore evaluated by a network parameter, namely, network recovery index (NRI), which quantitatively evaluates the overall recovery rate in MI network. The NRI of SHENMAI, RG, and RO were 0.865, 0.425, and 0.271 respectively [corrected], which indicated SHENMAI exerts protective effects and the synergistic effect of RG and RO on treating myocardial ischemia disease. The successful application of SHENMAI implied that the proposed network pharmacology-based approach could help researchers to better evaluate a multicomponent drug on a systematic and molecular level.
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20
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Abstract
Gene expression patterns change dramatically in aging and age-related events. The DNA microarray is now recognized as a useful device in molecular biology and widely used to identify the molecular mechanisms of aging and the biological effects of drugs for therapeutic purpose in age-related diseases. Recently, numerous technological advantages have led to the evolution of DNA microarrays and microarray-based techniques, revealing the genomic modification and all transcriptional activity. Here, we show the step-by-step methods currently used in our lab to handling the oligonucleotide microarray and miRNA microarray. Moreover, we introduce the protocols of ribonucleoprotein [RNP] immunoprecipitation followed by microarray analysis (RIP-chip) which reveal the target mRNA of age-related RNA-binding proteins.
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21
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Quo CF, Kaddi C, Phan JH, Zollanvari A, Xu M, Wang MD, Alterovitz G. Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities. Brief Bioinform 2012; 13:430-45. [PMID: 22833495 DOI: 10.1093/bib/bbs026] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.
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Affiliation(s)
- Chang F Quo
- Georgia Institute of Technology, Atlanta, GA 30332, USA
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22
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Inagaki Y, Matsumoto Y, Kataoka K, Matsuhashi N, Sekimizu K. Evaluation of drug-induced tissue injury by measuring alanine aminotransferase (ALT) activity in silkworm hemolymph. BMC Pharmacol Toxicol 2012; 13:13. [PMID: 23137391 PMCID: PMC3545979 DOI: 10.1186/2050-6511-13-13] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Accepted: 09/13/2012] [Indexed: 01/23/2023] Open
Abstract
Background Our previous studies suggest silkworms can be used as model animals instead of mammals in pharmacologic studies to develop novel therapeutic medicines. We examined the usefulness of the silkworm larvae Bombyx mori as an animal model for evaluating tissue injury induced by various cytotoxic drugs. Drugs that induce hepatotoxic effects in mammals were injected into the silkworm hemocoel, and alanine aminotransferase (ALT) activity was measured in the hemolymph 1 day later. Results Injection of CCl4 into the hemocoel led to an increase in ALT activity. The increase in ALT activity was attenuated by pretreatment with N-acetyl-L-cysteine. Injection of benzoic acid derivatives, ferric sulfate, sodium valproate, tetracycline, amiodarone hydrochloride, methyldopa, ketoconazole, pemoline (Betanamin), N-nitroso-fenfluramine, and D-galactosamine also increased ALT activity. Conclusions These findings indicate that silkworms are useful for evaluating the effects of chemicals that induce tissue injury in mammals.
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Affiliation(s)
- Yoshinori Inagaki
- Laboratory of Microbiology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
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23
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Turnbull AK, Kitchen RR, Larionov AA, Renshaw L, Dixon JM, Sims AH. Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis. BMC Med Genomics 2012; 5:35. [PMID: 22909195 PMCID: PMC3443058 DOI: 10.1186/1755-8794-5-35] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2012] [Accepted: 08/15/2012] [Indexed: 12/18/2022] Open
Abstract
Background Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis. Results Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets. Conclusion Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.
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Affiliation(s)
- Arran K Turnbull
- Breakthrough Research Unit, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
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24
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Tang W, Hu Z, Muallem H, Gulley ML. Quality assurance of RNA expression profiling in clinical laboratories. J Mol Diagn 2012; 14:1-11. [PMID: 22020152 PMCID: PMC3338342 DOI: 10.1016/j.jmoldx.2011.09.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2011] [Revised: 09/09/2011] [Accepted: 09/14/2011] [Indexed: 12/12/2022] Open
Abstract
RNA expression profiles are increasingly used to diagnose and classify disease, based on expression patterns of as many as several thousand RNAs. To ensure quality of expression profiling services in clinical settings, a standard operating procedure incorporates multiple quality indicators and controls, beginning with preanalytic specimen preparation and proceeding thorough analysis, interpretation, and reporting. Before testing, histopathological examination of each cellular specimen, along with optional cell enrichment procedures, ensures adequacy of the input tissue. Other tactics include endogenous controls to evaluate adequacy of RNA and exogenous or spiked controls to evaluate run- and patient-specific performance of the test system, respectively. Unique aspects of quality assurance for array-based tests include controls for the pertinent outcome signatures that often supersede controls for each individual analyte, built-in redundancy for critical analytes or biochemical pathways, and software-supported scrutiny of abundant data by a laboratory physician who interprets the findings in a manner facilitating appropriate medical intervention. Access to high-quality reagents, instruments, and software from commercial sources promotes standardization and adoption in clinical settings, once an assay is vetted in validation studies as being analytically sound and clinically useful. Careful attention to the well-honed principles of laboratory medicine, along with guidance from government and professional groups on strategies to preserve RNA and manage large data sets, promotes clinical-grade assay performance.
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Affiliation(s)
- Weihua Tang
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Zhiyuan Hu
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Hind Muallem
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Margaret L. Gulley
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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25
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Liu J, Jolly RA, Smith AT, Searfoss GH, Goldstein KM, Uversky VN, Dunker K, Li S, Thomas CE, Wei T. Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery. PLoS One 2011; 6:e24233. [PMID: 21935387 PMCID: PMC3174148 DOI: 10.1371/journal.pone.0024233] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2011] [Accepted: 08/03/2011] [Indexed: 01/24/2023] Open
Abstract
Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.
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Affiliation(s)
- Jiangang Liu
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Robert A. Jolly
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Aaron T. Smith
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - George H. Searfoss
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Keith M. Goldstein
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Vladimir N. Uversky
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
- Department of Molecular Medicine, University of South Florida, Tampa, Florida, United States of America
- Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russia
| | - Keith Dunker
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Shuyu Li
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Craig E. Thomas
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
| | - Tao Wei
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
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26
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Tang W, Hu Z, Muallem H, Gulley ML. Clinical implementation of RNA signatures for pharmacogenomic decision-making. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2011; 4:95-107. [PMID: 23226056 PMCID: PMC3513222 DOI: 10.2147/pgpm.s14888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2011] [Indexed: 12/14/2022]
Abstract
RNA profiling is increasingly used to predict drug response, dose, or toxicity based on analysis of drug pharmacokinetic or pharmacodynamic pathways. Before implementing multiplexed RNA arrays in clinical practice, validation studies are carried out to demonstrate sufficient evidence of analytic and clinical performance, and to establish an assay protocol with quality assurance measures. Pathologists assure quality by selecting input tissue and by interpreting results in the context of the input tissue as well as the technologies that were used and the clinical setting in which the test was ordered. A strength of RNA profiling is the array-based measurement of tens to thousands of RNAs at once, including redundant tests for critical analytes or pathways to promote confidence in test results. Instrument and reagent manufacturers are crucial for supplying reliable components of the test system. Strategies for quality assurance include careful attention to RNA preservation and quality checks at pertinent steps in the assay protocol, beginning with specimen collection and proceeding through the various phases of transport, processing, storage, analysis, interpretation, and reporting. Specimen quality is checked by probing housekeeping transcripts, while spiked and exogenous controls serve as a check on analytic performance of the test system. Software is required to manipulate abundant array data and present it for interpretation by a laboratory physician who reports results in a manner facilitating therapeutic decision-making. Maintenance of the assay requires periodic documentation of personnel competency and laboratory proficiency. These strategies are shepherding genomic arrays into clinical settings to provide added value to patients and to the larger health care system.
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Affiliation(s)
- Weihua Tang
- Department of Pathology and Laboratory Medicine
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Mendrick DL. Transcriptional profiling to identify biomarkers of disease and drug response. Pharmacogenomics 2011; 12:235-49. [PMID: 21332316 DOI: 10.2217/pgs.10.184] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The discovery, biological qualification and analytical validation of genomic biomarkers requires extensive collaborations between individuals with expertise in biology, statistics, bioinformatics, chemistry, clinical medicine, regulatory science and so on. For clinical utility, blood-borne biomarkers (e.g., mRNA and miRNA) of organ damage, drug toxicity and/or response would be preferred to those that are tissue based. Currently used biomarkers such as serum creatinine (indicating renal dysfunction) denote organ damage whether caused by disease, physical injury or drugs. Therefore, it is anticipated that studies of disease will discover biomarkers that can also be used to identify drug-induced injury and vice versa. This article describes transcriptomic blood-borne biomarkers that have been reported to be connected with disease and drug toxicity. Much more qualification and validation needs to be carried out before many of these biomarkers can prove useful. Discussed here are some of the lessons learned and roadblocks to success.
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Affiliation(s)
- Donna L Mendrick
- Division of Systems Biology, HFT-230, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson, AR 72079-4502, USA.
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Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 2011; 9:34. [PMID: 21627854 PMCID: PMC3104486 DOI: 10.1186/1741-7007-9-34] [Citation(s) in RCA: 342] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2010] [Accepted: 05/31/2011] [Indexed: 12/11/2022] Open
Abstract
Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing - RNA-Seq - which has considerable advantages for examining transcriptome fine structure - for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.
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Chen JJ. Research Highlights. Pharmacogenomics 2011; 12:461-3. [DOI: 10.2217/pgs.11.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- James J Chen
- Division of Personalized Nutrition & Medicine, National Center for Toxicological Research, US FDA, Jefferson, AR 72079, USA
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30
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Bureeva S, Nikolsky Y. Quantitative knowledge-based analysis in compound safety assessment. Expert Opin Drug Metab Toxicol 2011; 7:287-98. [DOI: 10.1517/17425255.2011.553191] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Cross-platform comparison of microarray-based multiple-class prediction. PLoS One 2011; 6:e16067. [PMID: 21264309 PMCID: PMC3019174 DOI: 10.1371/journal.pone.0016067] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2010] [Accepted: 12/06/2010] [Indexed: 02/03/2023] Open
Abstract
High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.
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Janikova A, Tichy B, Supikova J, Stano-Kozubik K, Pospisilova S, Kren L, Vasova I, Salek D, Mayer J. Gene expression profiling in follicular lymphoma and its implication for clinical practice. Leuk Lymphoma 2010; 52:59-68. [PMID: 21133732 DOI: 10.3109/10428194.2010.531412] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Follicular lymphoma (FL) is characterized by an indolent and relapsing course. Recently, the clinical outcome of FL has been distinguished by immune microenvironment-associated gene signatures. In our study, gene expression profiling (GEP) was performed in 31 non-selected patients with follicular lymphoma (FL), 12 of whom were in relapse and the remaining 19 newly diagnosed. A custom oligonucleotide microarray (Agilent 8 × 15K) was used which contained probes for about 3500 genes, including those that had been previously published as demonstrating significant prognostic value. An unsupervised approach was not able to recognize clinically different FLs. As the previously published prognostically relevant gene signatures could not be properly verified, probably due to microarray platform differences, template matching was therefore used in order to define two gene sets with differential gene expression among our samples. These gene sets shared an overrepresentation of genes with similar biological functions and were termed 'T-CELL' and 'PROLIFERATION' profiles. The 'poor profile' was then defined by a high PROLIFERATION score (upper tertile) and/or low T-CELL score (lower tertile). The 'poor profile' cohort contained a significantly higher proportion of relapsed cases (p < 0.05, Fisher's exact test). Additionally, a comparison of samples from initial diagnosis and from relapse showed significant differences mainly in the T-CELL profile (p = 0.036; χ(2)). This supports the hypothesis that the number of T-cells and their expression pattern play a major role in FL development.
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Affiliation(s)
- Andrea Janikova
- Department of Haematooncology, University Hospital Brno, Czech Republic.
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Luo J, Schumacher M, Scherer A, Sanoudou D, Megherbi D, Davison T, Shi T, Tong W, Shi L, Hong H, Zhao C, Elloumi F, Shi W, Thomas R, Lin S, Tillinghast G, Liu G, Zhou Y, Herman D, Li Y, Deng Y, Fang H, Bushel P, Woods M, Zhang J. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. THE PHARMACOGENOMICS JOURNAL 2010; 10:278-91. [PMID: 20676067 PMCID: PMC2920074 DOI: 10.1038/tpj.2010.57] [Citation(s) in RCA: 192] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.
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Affiliation(s)
- J Luo
- Systems Analytics Inc., Waltham, MA, USA
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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 2010; 28:827-38. [PMID: 20676074 DOI: 10.1038/nbt.1665] [Citation(s) in RCA: 581] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2010] [Accepted: 06/30/2010] [Indexed: 11/09/2022]
Abstract
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
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