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Vishwakarma GK, Bhattacharjee A, Tank F, Pashchenko AF. Subgroup identification of targeted therapy effects on biomarker for time to event data. Cancer Biomark 2023; 38:413-424. [PMID: 37980650 DOI: 10.3233/cbm-230181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. OBJECTIVES This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. METHODS A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. RESULTS The MTA is observed with range 12-16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. CONCLUSION This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting patients.
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Affiliation(s)
| | | | | | - Alexander F Pashchenko
- Laboratory of Intellectual Control Systems and Simulation, V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia
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Ba-Alawi W, Kadambat Nair S, Li B, Mammoliti A, Smirnov P, Mer AS, Penn LZ, Haibe-Kains B. Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity. Cancer Res 2022; 82:2378-2387. [PMID: 35536872 DOI: 10.1158/0008-5472.can-21-2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 02/24/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.
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Affiliation(s)
| | | | - Bo Li
- University of Toronto, Toronto, Canada
| | | | | | | | - Linda Z Penn
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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3
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Yang X, Luo S, Zhang Z, Wang Z, Zhou T, Zhang J. Silent transcription intervals and translational bursting lead to diverse phenotypic switching. Phys Chem Chem Phys 2022; 24:26600-26608. [DOI: 10.1039/d2cp03703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, P. R. China
| | - Songhao Luo
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zhenquan Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zihao Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
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4
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Justino JR, Reis CFD, Fonseca AL, Souza SJD, Stransky B. An integrated approach to identify bimodal genes associated with prognosis in câncer. Genet Mol Biol 2021; 44:e20210109. [PMID: 34617951 PMCID: PMC8495773 DOI: 10.1590/1678-4685-gmb-2021-0109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/08/2021] [Indexed: 02/08/2023] Open
Abstract
Bimodal gene expression (where a gene expression distribution has two maxima) is
associated with phenotypic diversity in different biological systems. A critical
issue, thus, is the integration of expression and phenotype data to identify
genuine associations. Here, we developed tools that allow both: i) the
identification of genes with bimodal gene expression and ii) their association
with prognosis in cancer patients from The Cancer Genome Atlas (TCGA).
Bimodality was observed for 554 genes in expression data from 25 tumor types.
Furthermore, 96 of these genes presented different prognosis when patients
belonging to the two expression peaks were compared. The software to execute the
method and the corresponding documentation are available at the Data access
section.
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Affiliation(s)
- Josivan Ribeiro Justino
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal de Rondônia, Departamento de Matemática e Estatística, Ji-Parana, RO, Brazil
| | - Clovis Ferreira Dos Reis
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil
| | - Andre Luis Fonseca
- Universidade de São Paulo, Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Sandro Jose de Souza
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Instituto do Cérebro, Natal, RN, Brazil.,Sichuan University, West China Hospital, Institutes for Systems Genetics, Chengdu, China
| | - Beatriz Stransky
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Centro de Tecnologia, Departamento de Engenharia Biomédica, Natal, RN, Brazil
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5
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Finn RS, Liu Y, Zhu Z, Martin M, Rugo HS, Diéras V, Im SA, Gelmon KA, Harbeck N, Lu DR, Gauthier E, Huang Bartlett C, Slamon DJ. Biomarker Analyses of Response to Cyclin-Dependent Kinase 4/6 Inhibition and Endocrine Therapy in Women with Treatment-Naïve Metastatic Breast Cancer. Clin Cancer Res 2019; 26:110-121. [DOI: 10.1158/1078-0432.ccr-19-0751] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 07/01/2019] [Accepted: 09/11/2019] [Indexed: 11/16/2022]
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6
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Del Giudice M, Bosia C, Grigolon S, Bo S. Stochastic sequestration dynamics: a minimal model with extrinsic noise for bimodal distributions and competitors correlation. Sci Rep 2018; 8:10387. [PMID: 29991682 PMCID: PMC6039506 DOI: 10.1038/s41598-018-28647-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/21/2018] [Indexed: 12/26/2022] Open
Abstract
Many biological processes are known to be based on molecular sequestration. This kind of dynamics involves two types of molecular species, namely targets and sequestrants, that bind to form a complex. In the simple framework of mass-action law, key features of these systems appear to be threshold-like profiles of the amounts of free molecules as a function of the parameters determining their possible maximum abundance. However, biochemical processes are probabilistic and take place in stochastically fluctuating environments. How these different sources of noise affect the final outcome of the network is not completely characterised yet. In this paper we specifically investigate the effects induced by a source of extrinsic noise onto a minimal stochastic model of molecular sequestration. We analytically show how bimodal distributions of the targets can appear and characterise them as a result of noise filtering mediated by the threshold response. We then address the correlations between target species induced by the sequestrant and discuss how extrinsic noise can turn the negative correlation caused by competition into a positive one. Finally, we consider the more complex scenario of competitive inhibition for enzymatic kinetics and discuss the relevance of our findings with respect to applications.
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Affiliation(s)
- Marco Del Giudice
- Department of Applied Science and Technology, Politecnico di Torino corso Duca degli Abruzzi 24, Turin, IT-10129, Italy
- Italian Institute for Genomic Medicine, via Nizza 52, I-10126, Torino, Italy
| | - Carla Bosia
- Department of Applied Science and Technology, Politecnico di Torino corso Duca degli Abruzzi 24, Turin, IT-10129, Italy
- Italian Institute for Genomic Medicine, via Nizza 52, I-10126, Torino, Italy
| | - Silvia Grigolon
- The Francis Crick Institute, 1, Midland Road, London, NW1 1AT, United Kingdom
| | - Stefano Bo
- Nordita, Royal Institute of Technology and Stockholm University, Roslagstullsbacken 23, SE-106 91, Stockholm, Sweden.
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7
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Del Giudice M, Bo S, Grigolon S, Bosia C. On the role of extrinsic noise in microRNA-mediated bimodal gene expression. PLoS Comput Biol 2018; 14:e1006063. [PMID: 29664903 PMCID: PMC5922620 DOI: 10.1371/journal.pcbi.1006063] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 04/27/2018] [Accepted: 02/28/2018] [Indexed: 01/01/2023] Open
Abstract
Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.
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Affiliation(s)
- Marco Del Giudice
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy
- Italian Institute for Genomic Medicine, Torino, Italy
| | - Stefano Bo
- Nordita, Royal Institute of Technology and Stockholm University, Stockholm, Sweden
| | | | - Carla Bosia
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy
- Italian Institute for Genomic Medicine, Torino, Italy
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8
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Yousef EM, Furrer D, Laperriere DL, Tahir MR, Mader S, Diorio C, Gaboury LA. MCM2: An alternative to Ki-67 for measuring breast cancer cell proliferation. Mod Pathol 2017; 30:682-697. [PMID: 28084344 DOI: 10.1038/modpathol.2016.231] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 11/28/2016] [Accepted: 11/29/2016] [Indexed: 11/10/2022]
Abstract
Breast cancer is a heterogeneous disease comprising a diversity of tumor subtypes that manifest themselves in a wide variety of clinical, pathological, and molecular features. One important subset, luminal breast cancers, comprises two clinically distinct subtypes luminal A and B each of them endowed with its own genetic program of differentiation and proliferation. Luminal breast cancers were operationally defined as follows: Luminal A: ER+, PR+, HER2-, Ki-67<14% and Luminal B: ER+ and/or PR+, HER2-,Ki-67≥14% or, alternatively ER+ and/or PR+, HER2+, any Ki-67. There is currently a need for a clinically robust and validated immunohistochemical assay that can help distinguish between luminal A and B breast cancer. MCM2 is a family member of the minichromosome maintenance protein complex whose role in DNA replication and cell proliferation is firmly established. As MCM2 appears to be an attractive alternative to Ki-67, we sought to study the expression of MCM2 and Ki-67 in different histological grades and molecular subtypes of breast cancer focusing primarily on ER-positive tumors. MCM2 and Ki-67 mRNA expression were studied using in silico analysis of available DNA microarray and RNA-sequencing data of human breast cancer. We next used immunohistochemistry to evaluate protein expression of MCM2 and Ki-67 on tissue microarrays of invasive breast carcinoma. We found that MCM2 and Ki-67 are highly expressed in breast tumors of high histological grades, comprising clinically aggressive tumors such as triple-negative, HER2-positive and luminal B subtypes. MCM2 expression was detected at higher levels than that of Ki-67 in normal breast tissues and in breast cancers. The bimodal distribution of MCM2 scores in ER+/HER2- breast tumors led to the identification of two distinct subgroups with different relapse-free survival rates. In conclusion, MCM2 expression can help sorting out two clinically important subsets of luminal breast cancer whose treatment and clinical outcomes are likely to diverge.
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Affiliation(s)
- Einas M Yousef
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada.,Department of Histology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Daniela Furrer
- Cancer Research Centre at Laval University, Quebec City, QC, Canada.,Oncology Axis, CHU of Quebec Research Center, Hôpital du Saint-Sacrement, Quebec City, QC, Canada.,Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Quebec City, QC, Canada
| | - David L Laperriere
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada
| | - Muhammad R Tahir
- The University of Montreal Hospital Research Centre, Montreal, QC, Canada
| | - Sylvie Mader
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada.,Department of Biochemistry, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Caroline Diorio
- Cancer Research Centre at Laval University, Quebec City, QC, Canada.,Oncology Axis, CHU of Quebec Research Center, Hôpital du Saint-Sacrement, Quebec City, QC, Canada.,Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Quebec City, QC, Canada.,Deschênes-Fabia Center for Breast Diseases, Hôpital du St-Sacrement, Quebec City, QC, Canada
| | - Louis A Gaboury
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada.,Department of Pathology and Cell Biology, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
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9
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RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells. Genome Biol 2017; 18:37. [PMID: 28219439 PMCID: PMC5319025 DOI: 10.1186/s13059-017-1162-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 01/27/2017] [Indexed: 12/14/2022] Open
Abstract
Background Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other’s expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. Results We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other’s fluctuations in a miRNA-dependent manner in single cells. We show that miRNA–target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10–1000 copies of targets per cell. Conclusions Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1162-x) contains supplementary material, which is available to authorized users.
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10
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Hiller J, Vallejo C, Betthauser L, Keesling J. Characteristic patterns of cancer incidence: Epidemiological data, biological theories, and multistage models. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2016; 124:41-48. [PMID: 27836510 DOI: 10.1016/j.pbiomolbio.2016.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 11/05/2016] [Indexed: 02/07/2023]
Abstract
We investigate and classify several patterns in cancer incidence and relative risk data which persist across different countries and multiple published studies. We then explore biological hypotheses as well as many mathematical models in the literature that attempt to explain these patterns. A general modeling framework is presented which is general enough to model most of observed behaviors. It is our belief that this model has sufficient flexibility to be adapted to new information as it is discovered. As one application of this framework, we give a model for the effect of aging on the process of carcinogenesis.
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Affiliation(s)
- Josh Hiller
- Department of Mathematics, University of Florida, USA.
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11
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de Araujo LS, Vaas LAI, Ribeiro-Alves M, Geffers R, Mello FCQ, de Almeida AS, Moreira ADSR, Kritski AL, Lapa E Silva JR, Moraes MO, Pessler F, Saad MHF. Transcriptomic Biomarkers for Tuberculosis: Evaluation of DOCK9. EPHA4, and NPC2 mRNA Expression in Peripheral Blood. Front Microbiol 2016; 7:1586. [PMID: 27826286 PMCID: PMC5078140 DOI: 10.3389/fmicb.2016.01586] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 09/21/2016] [Indexed: 01/27/2023] Open
Abstract
Lately, much effort has been made to find mRNA biomarkers for tuberculosis (TB) disease/infection with microarray-based approaches. In a pilot investigation, through RNA sequencing technology, we observed a prominent modulation of DOCK9, EPHA4, and NPC2 mRNA abundance in the blood of TB patients. To corroborate these findings, independent validations were performed in cohorts from different areas. Gene expression levels in blood were evaluated by quantitative real-time PCR (Brazil, n = 129) or reanalysis of public microarray data (UK: n = 96; South Africa: n = 51; Germany: n = 26; and UK/France: n = 63). In the Brazilian cohort, significant modulation of all target-genes was observed comparing TB vs. healthy recent close TB contacts (rCt). With a 92% specificity, NPC2 mRNA high expression (NPC2high) showed the highest sensitivity (85%, 95% CI 65%–96%; area under the ROC curve [AUROC] = 0.88), followed by EPHA4 (53%, 95% CI 33%–73%, AUROC = 0.73) and DOCK9 (19%, 95% CI 7%–40%; AUROC = 0.66). All the other reanalyzed cohorts corroborated the potential of NPC2high as a biomarker for TB (sensitivity: 82–100%; specificity: 94–97%). An NPC2high profile was also observed in 60% (29/48) of the tuberculin skin test positive rCt, and additional follow-up evaluation revealed changes in the expression levels of NPC2 during the different stages of Mycobacterium tuberculosis infection, suggesting that further studies are needed to evaluate modulation of this gene during latent TB and/or progression to active disease. Considering its high specificity, our data indicate, for the first time, that NPC2high might serve as an accurate single-gene biomarker for TB.
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Affiliation(s)
- Leonardo S de Araujo
- Laboratório de Microbiologia Celular, Fundação Oswaldo Cruz, Instituto Oswaldo Cruz Rio de Janeiro, Brazil
| | - Lea A I Vaas
- TWINCORE, Center for Experimental and Clinical Infection Research Hannover, Germany
| | - Marcelo Ribeiro-Alves
- Laboratório de Pesquisa Clínica em DST-AIDS, Fundação Oswaldo Cruz, Instituto de Pesquisa Clínica Evandro Chagas Rio de Janeiro, Brazil
| | - Robert Geffers
- Helmholtz Centre for Infection Research Braunschweig, Germany
| | - Fernanda C Q Mello
- Thoracic Diseases Institute, Federal University of Rio de Janeiro Rio de Janeiro, Brazil
| | - Alexandre S de Almeida
- Laboratório de Hanseníase, Fundação Oswaldo Cruz, Instituto Oswaldo Cruz Rio de Janeiro, Brazil
| | - Adriana da S R Moreira
- Thoracic Diseases Institute, Federal University of Rio de Janeiro Rio de Janeiro, Brazil
| | - Afrânio L Kritski
- Thoracic Diseases Institute, Federal University of Rio de Janeiro Rio de Janeiro, Brazil
| | - José R Lapa E Silva
- Thoracic Diseases Institute, Federal University of Rio de Janeiro Rio de Janeiro, Brazil
| | - Milton O Moraes
- Laboratório de Hanseníase, Fundação Oswaldo Cruz, Instituto Oswaldo Cruz Rio de Janeiro, Brazil
| | - Frank Pessler
- TWINCORE, Center for Experimental and Clinical Infection ResearchHannover, Germany; Helmholtz Centre for Infection ResearchBraunschweig, Germany
| | - Maria H F Saad
- Laboratório de Microbiologia Celular, Fundação Oswaldo Cruz, Instituto Oswaldo Cruz Rio de Janeiro, Brazil
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12
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Zhu Z, Ihle NT, Rejto PA, Zarrinkar PP. Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine. BMC Genomics 2016; 17:455. [PMID: 27296290 PMCID: PMC4907009 DOI: 10.1186/s12864-016-2807-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 05/27/2016] [Indexed: 01/22/2023] Open
Abstract
Background Genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency. Results Genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles. Conclusions The novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2807-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhou Zhu
- Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, San Diego, CA, 92121, USA.
| | - Nathan T Ihle
- Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, San Diego, CA, 92121, USA
| | - Paul A Rejto
- Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, San Diego, CA, 92121, USA
| | - Patrick P Zarrinkar
- Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, San Diego, CA, 92121, USA.
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13
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Wappett M, Dulak A, Yang ZR, Al-Watban A, Bradford JR, Dry JR. Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. BMC Genomics 2016; 17:65. [PMID: 26781748 PMCID: PMC4717622 DOI: 10.1186/s12864-016-2375-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022] Open
Abstract
Background Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp. Results Applying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens. Conclusions This work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2375-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mark Wappett
- Oncology Innovative Medicines, AstraZeneca, Macclesfield, UK.
| | - Austin Dulak
- Oncology Innovative Medicines, AstraZeneca, Waltham, USA.
| | | | | | - James R Bradford
- Oncology Innovative Medicines, AstraZeneca, Macclesfield, UK. .,Present address: Department of Oncology, University of Sheffield, Sheffield, UK.
| | - Jonathan R Dry
- Oncology Innovative Medicines, AstraZeneca, Waltham, USA.
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Graziani R, Guindani M, Thall PF. Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome. Biometrics 2014; 71:188-197. [PMID: 25319212 DOI: 10.1111/biom.12250] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 08/01/2014] [Accepted: 09/01/2014] [Indexed: 11/30/2022]
Abstract
The effect of a targeted agent on a cancer patient's clinical outcome putatively is mediated through the agent's effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarker's distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post-treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time.
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Affiliation(s)
| | - Michele Guindani
- University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A
| | - Peter F Thall
- University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A
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15
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Wolf DM, Lenburg ME, Yau C, Boudreau A, van ‘t Veer LJ. Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity. PLoS One 2014; 9:e88309. [PMID: 24516633 PMCID: PMC3917875 DOI: 10.1371/journal.pone.0088309] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Accepted: 01/06/2014] [Indexed: 12/25/2022] Open
Abstract
Co-expression modules are groups of genes with highly correlated expression patterns. In cancer, differences in module activity potentially represent the heterogeneity of phenotypes important in carcinogenesis, progression, or treatment response. To find gene expression modules active in breast cancer subpopulations, we assembled 72 breast cancer-related gene expression datasets containing ∼5,700 samples altogether. Per dataset, we identified genes with bimodal expression and used mixture-model clustering to ultimately define 11 modules of genes that are consistently co-regulated across multiple datasets. Functionally, these modules reflected estrogen signaling, development/differentiation, immune signaling, histone modification, ERBB2 signaling, the extracellular matrix (ECM) and stroma, and cell proliferation. The Tcell/Bcell immune modules appeared tumor-extrinsic, with coherent expression in tumors but not cell lines; whereas most other modules, interferon and ECM included, appeared intrinsic. Only four of the eleven modules were represented in the PAM50 intrinsic subtype classifier and other well-established prognostic signatures; although the immune modules were highly correlated to previously published immune signatures. As expected, the proliferation module was highly associated with decreased recurrence-free survival (RFS). Interestingly, the immune modules appeared associated with RFS even after adjustment for receptor subtype and proliferation; and in a multivariate analysis, the combination of Tcell/Bcell immune module down-regulation and proliferation module upregulation strongly associated with decreased RFS. Immune modules are unusual in that their upregulation is associated with a good prognosis without chemotherapy and a good response to chemotherapy, suggesting the paradox of high immune patients who respond to chemotherapy but would do well without it. Other findings concern the ECM/stromal modules, which despite common themes were associated with different sites of metastasis, possibly relating to the “seed and soil” hypothesis of cancer dissemination. Overall, co-expression modules provide a high-level functional view of breast cancer that complements the “cancer hallmarks” and may form the basis for improved predictors and treatments.
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Affiliation(s)
- Denise M. Wolf
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
| | - Marc E. Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Christina Yau
- Department of Surgery, University of California San Francisco, San Francisco, California, United States of America
- Buck Institute for Research on Aging, Novato, California, United States of America
| | - Aaron Boudreau
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Laura J. van ‘t Veer
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California, United States of America
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Renner M, Wolf T, Meyer H, Hartmann W, Penzel R, Ulrich A, Lehner B, Hovestadt V, Czwan E, Egerer G, Schmitt T, Alldinger I, Renker EK, Ehemann V, Eils R, Wardelmann E, Büttner R, Lichter P, Brors B, Schirmacher P, Mechtersheimer G. Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas. Genome Biol 2013; 14:r137. [PMID: 24345474 PMCID: PMC4054884 DOI: 10.1186/gb-2013-14-12-r137] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 12/17/2013] [Indexed: 12/13/2022] Open
Abstract
Background High-grade soft tissue sarcomas are a heterogeneous, complex group of aggressive malignant tumors showing mesenchymal differentiation. Recently, soft tissue sarcomas have increasingly been classified on the basis of underlying genetic alterations; however, the role of aberrant DNA methylation in these tumors is not well understood and, consequently, the usefulness of methylation-based classification is unclear. Results We used the Infinium HumanMethylation27 platform to profile DNA methylation in 80 primary, untreated high-grade soft tissue sarcomas, representing eight relevant subtypes, two non-neoplastic fat samples and 14 representative sarcoma cell lines. The primary samples were partitioned into seven stable clusters. A classification algorithm identified 216 CpG sites, mapping to 246 genes, showing different degrees of DNA methylation between these seven groups. The differences between the clusters were best represented by a set of eight CpG sites located in the genes SPEG, NNAT, FBLN2, PYROXD2, ZNF217, COL14A1, DMRT2 and CDKN2A. By integrating DNA methylation and mRNA expression data, we identified 27 genes showing negative and three genes showing positive correlation. Compared with non-neoplastic fat, NNAT showed DNA hypomethylation and inverse gene expression in myxoid liposarcomas, and DNA hypermethylation and inverse gene expression in dedifferentiated and pleomorphic liposarcomas. Recovery of NNAT in a hypermethylated myxoid liposarcoma cell line decreased cell migration and viability. Conclusions Our analysis represents the first comprehensive integration of DNA methylation and transcriptional data in primary high-grade soft tissue sarcomas. We propose novel biomarkers and genes relevant for pathogenesis, including NNAT as a potential tumor suppressor in myxoid liposarcomas.
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Ma Z, Teschendorff AE. A variational Bayes beta mixture model for feature selection in DNA methylation studies. J Bioinform Comput Biol 2013; 11:1350005. [PMID: 23859269 DOI: 10.1142/s0219720013500054] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.
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Affiliation(s)
- Zhanyu Ma
- KTH-Royal Institute of Technology, School of Electrical Engineering, SE-100 44, Stockholm, Sweden.
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18
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Li L, Petsch K, Shimizu R, Liu S, Xu WW, Ying K, Yu J, Scanlon MJ, Schnable PS, Timmermans MCP, Springer NM, Muehlbauer GJ. Mendelian and non-Mendelian regulation of gene expression in maize. PLoS Genet 2013; 9:e1003202. [PMID: 23341782 PMCID: PMC3547793 DOI: 10.1371/journal.pgen.1003202] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 11/14/2012] [Indexed: 11/25/2022] Open
Abstract
Transcriptome variation plays an important role in affecting the phenotype of an organism. However, an understanding of the underlying mechanisms regulating transcriptome variation in segregating populations is still largely unknown. We sought to assess and map variation in transcript abundance in maize shoot apices in the intermated B73×Mo17 recombinant inbred line population. RNA–based sequencing (RNA–seq) allowed for the detection and quantification of the transcript abundance derived from 28,603 genes. For a majority of these genes, the population mean, coefficient of variation, and segregation patterns could be predicted by the parental expression levels. Expression quantitative trait loci (eQTL) mapping identified 30,774 eQTL including 96 trans-eQTL “hotspots,” each of which regulates the expression of a large number of genes. Interestingly, genes regulated by a trans-eQTL hotspot tend to be enriched for a specific function or act in the same genetic pathway. Also, genomic structural variation appeared to contribute to cis-regulation of gene expression. Besides genes showing Mendelian inheritance in the RIL population, we also found genes whose expression level and variation in the progeny could not be predicted based on parental difference, indicating that non-Mendelian factors also contribute to expression variation. Specifically, we found 145 genes that show patterns of expression reminiscent of paramutation such that all the progeny had expression levels similar to one of the two parents. Furthermore, we identified another 210 genes that exhibited unexpected patterns of transcript presence/absence. Many of these genes are likely to be gene fragments resulting from transposition, and the presence/absence of their transcripts could influence expression levels of their ancestral syntenic genes. Overall, our results contribute to the identification of novel expression patterns and broaden the understanding of transcriptional variation in plants. Phenotypes are determined by the expression of genes, the environment, and the interaction of gene expression and the environment. However, a complete understanding of the inheritance of and genome-wide regulation of gene expression is lacking. One approach, called expression quantitative trait locus (eQTL) mapping provides the opportunity to examine the genome-wide inheritance and regulation of gene expression. In this paper, we conducted high-throughput sequencing of gene transcripts to examine gene expression in the shoot apex of a maize biparental mapping population. We quantified expression levels from 28,603 genes in the population and showed that the vast majority of genes exhibited the expected pattern of Mendelian inheritance. We genetically mapped the expression patterns and identified genomic regions associated with gene expression. Notably, we detected gene expression patterns that exhibited non-Mendelian inheritance. These included 145 genes that exhibited expression patterns in the progeny that were similar to only one of the parents and 210 genes with unexpected presence/absence expression patterns. The findings of non-Mendelian inheritance underscore the complexity of gene expression and provide a framework for understanding these complexities.
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Affiliation(s)
- Lin Li
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Katherine Petsch
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Rena Shimizu
- Department of Plant Biology, Cornell University, Ithaca, New York, United States of America
| | - Sanzhen Liu
- Department of Genetics, Development, and Cell Biology, and Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | - Wayne Wenzhong Xu
- Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Kai Ying
- Department of Genetics, Development, and Cell Biology, and Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | - Jianming Yu
- Department of Agronomy, Kansas State University, Manhattan, Kansas, United States of America
| | - Michael J. Scanlon
- Department of Plant Biology, Cornell University, Ithaca, New York, United States of America
| | - Patrick S. Schnable
- Department of Genetics, Development, and Cell Biology, and Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | | | - Nathan M. Springer
- Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Gary J. Muehlbauer
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, United States of America
- Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota, United States of America
- * E-mail:
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Linderman GC, Patel VN, Chance MR, Bebek G. BiC: a web server for calculating bimodality of coexpression between gene and protein networks. Bioinformatics 2011; 27:1174-5. [PMID: 21345871 PMCID: PMC3072551 DOI: 10.1093/bioinformatics/btr086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2010] [Revised: 02/01/2011] [Accepted: 02/09/2011] [Indexed: 11/13/2022] Open
Abstract
UNLABELLED Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data. AVAILABILITY The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software. CONTACT gurkan@case.edu.
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Affiliation(s)
- George C Linderman
- Department of Biomedical Engineering, Case Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
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20
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Mason CC, Hanson RL, Ossowski V, Bian L, Baier LJ, Krakoff J, Bogardus C. Bimodal distribution of RNA expression levels in human skeletal muscle tissue. BMC Genomics 2011; 12:98. [PMID: 21299892 PMCID: PMC3044673 DOI: 10.1186/1471-2164-12-98] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2010] [Accepted: 02/07/2011] [Indexed: 01/24/2023] Open
Abstract
Background Many human diseases and phenotypes are related to RNA expression, levels of which are influenced by a wide spectrum of genetic and exposure-related factors. In a large genome-wide study of muscle tissue expression, we found that some genes exhibited a bimodal distribution of RNA expression, in contrast to what is usually assumed in studies of a single healthy tissue. As bimodality has classically been considered a hallmark of genetic control, we assessed the genome-wide prevalence, cause, and association of this phenomenon with diabetes-related phenotypes in skeletal muscle tissue from 225 healthy Pima Indians using exon array expression chips. Results Two independent batches of microarrays were used for bimodal assessment and comparison. Of the 17,881 genes analyzed, eight (GSTM1, HLA-DRB1, ERAP2, HLA-DRB5, MAOA, ACTN3, NR4A2, and THNSL2) were found to have bimodal expression replicated in the separate batch groups, while 24 other genes had evidence of bimodality in only one group. Some bimodally expressed genes had modest associations with pre-diabetic phenotypes, of note ACTN3 with insulin resistance. Most of the other bimodal genes have been reported to be involved with various other diseases and characteristics. Association of expression with cis genetic variation in a subset of 149 individuals found all but one of the confirmed bimodal genes and nearly half of all potential ones to be highly significant expression quantitative trait loci (eQTL). The rare prevalence of these bimodally expressed genes found after controlling for batch effects was much lower than the prevalence reported in other studies. Additional validation in data from separate muscle expression studies confirmed the low prevalence of bimodality we observed. Conclusions We conclude that the prevalence of bimodal gene expression is quite rare in healthy muscle tissue (<0.2%), and is much lower than limited reports from other studies. The major cause of these clearly bimodal expression patterns in homogeneous tissue appears to be cis-polymorphisms, indicating that such bimodal genes are, for the most part, eQTL. The high frequency of disease associations reported with these genes gives hope that this unique feature may identify or actually be an underlying factor responsible for disease development.
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Affiliation(s)
- Clinton C Mason
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, 1550 E, Indian School Rd, Phoenix, AZ 85014, USA.
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Li Q, Eklund AC, Juul N, Haibe-Kains B, Workman CT, Richardson AL, Szallasi Z, Swanton C. Minimising immunohistochemical false negative ER classification using a complementary 23 gene expression signature of ER status. PLoS One 2010; 5:e15031. [PMID: 21152022 PMCID: PMC2995741 DOI: 10.1371/journal.pone.0015031] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2010] [Accepted: 10/12/2010] [Indexed: 12/21/2022] Open
Abstract
Background Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold[1], [2]. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome. Methodology/Principal Findings Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer. Conclusion/Significance Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies.
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Affiliation(s)
- Qiyuan Li
- Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
| | - Aron C. Eklund
- Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
| | - Nicolai Juul
- Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
| | - Benjamin Haibe-Kains
- Computational Biology and Functional Genomics Laboratory, Harvard School of Public Health, Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Christopher T. Workman
- Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
| | - Andrea L. Richardson
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Zoltan Szallasi
- Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
- Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (CS); (ZS)
| | - Charles Swanton
- Translational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
- Breast and Drug Development Units, Royal Marsden Hospital, Sutton, United Kingdom
- * E-mail: (CS); (ZS)
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Wang J, Yu H, Xie W, Xing Y, Yu S, Xu C, Li X, Xiao J, Zhang Q. A global analysis of QTLs for expression variations in rice shoots at the early seedling stage. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2010; 63:1063-74. [PMID: 20626655 DOI: 10.1111/j.1365-313x.2010.04303.x] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Analyses of quantitative trait loci (QTLs) for expression levels (eQTLs) of genes reveal a genetic relationship between expression variation and the regulator, thus unlocking information for identifying the regulatory network. Oligo-nucleotide expression microarrays hybridized with RNA can simultaneously provide data for molecular markers and transcript abundance. In this study, we used an Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice shoots at 72 h after germination from 110 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. In total, 1632 single-feature polymorphisms (SFPs) plus 23 PCR markers were identified and placed into 601 recombinant bins, spanning 1459 cM in length, which were used as markers to genotype the RILs. We obtained 16,372 expression traits (e-traits) each with at least one eQTL, resulting in 26,051 eQTLs in total, including both cis- and trans-eQTLs. We also identified 171 eQTL hot spots in the rice genome, each of which controls transcript variations of many e-traits. Gene ontology analysis revealed an enrichment of certain functional categories of genes in some of the eQTL hot spots. In particular, eQTLs for e-traits involving the DNA metabolic process was significantly enriched in several eQTL hot spots on chromosomes 3, 5 and 10. Several e-traits co-localizing with cis-eQTLs showed significant correlations with hundreds of e-traits, indicating possible co-regulation. We also detected correlations between QTLs for shoot dry weight and eQTLs, revealing possible candidate genes for the trait. These results provided clues for the identification and characterization of the regulatory network in the whole genome at the transcriptional level.
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Affiliation(s)
- Jia Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, China
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Abstract
Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.
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Affiliation(s)
- Yue Zhang
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, 99 Brookline Avenue, Boston, MA 02215, USA
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