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Forouzandeh A, Rutar A, Kalmady SV, Greiner R. Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets. PLoS One 2022; 17:e0252697. [PMID: 35901020 PMCID: PMC9333302 DOI: 10.1371/journal.pone.0252697] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/29/2022] [Indexed: 11/19/2022] Open
Abstract
Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).
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Affiliation(s)
- Amir Forouzandeh
- Department of Computing Science, University of Alberta, Edmonton, Canada
- * E-mail:
| | - Alex Rutar
- Department of Pure Math, University of Waterloo, Waterloo, ON, Canada
| | - Sunil V. Kalmady
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute, Edmonton, Canada
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2
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Li Y, Wang F, Li R, Sun Y. Semiparametric integrative interaction analysis for non-small-cell lung cancer. Stat Methods Med Res 2020; 29:2865-2880. [PMID: 32281490 DOI: 10.1177/0962280220909969] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In genomic analysis, it is significant though challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets, we propose a novel integrative interaction approach under a semiparametric model, in which genetic and environmental factors are included as the parametric and nonparametric components, respectively. The goal of this approach is to identify the genetic factors and gene-gene interactions associated with cancer outcomes, while estimating the nonlinear effects of environmental factors. The proposed approach is based on the threshold gradient-directed regularisation technique. Simulation studies indicate that the proposed approach outperforms alternative methods at identifying the main effects and interactions, and has favourable estimation and prediction accuracy. We analysed non-small-cell lung carcinoma datasets from the Cancer Genome Atlas, and the results demonstrate that the proposed approach can identify markers with important implications and that it performs favourably in terms of prediction accuracy, identification stability, and computation cost.
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Affiliation(s)
- Yang Li
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China.,Statistical Consulting Center, Renmin University of China, Beijing, China
| | - Fan Wang
- School of Statistics, Renmin University of China, Beijing, China.,Statistical Consulting Center, Renmin University of China, Beijing, China
| | - Rong Li
- School of Statistics, Renmin University of China, Beijing, China.,Statistical Consulting Center, Renmin University of China, Beijing, China
| | - Yifan Sun
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
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3
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Georgakopoulou EA, Achtari MD, Evangelou K, Kittas C. Oral non-Hodgkin's lymphoma in a patient with rheumatoid arthritis treated with etanercept and methotrexate. J Clin Exp Dent 2015; 7:e180-2. [PMID: 25810835 PMCID: PMC4368011 DOI: 10.4317/jced.51922] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 10/09/2014] [Indexed: 02/06/2023] Open
Abstract
Oral non-Hodgkin’s lymphomas (O-NHLs) are a rare group of diverse lymphoid tissue malignancies and represent less than 5% of the oral cavity malignancies and 2% of all extra-nodal NHLs. Oral-NHLs affect the Waldeyer’s-ring, the salivary glands, the bone of the jaws and the oral mucosa, their clinical appearance is very heterogeneous. Among the risk factors for NHLs are immunosuppression (primary or secondary), autoimmunity and inflammation. O-NHLs share the same risk factors. This case report describes a patient with O-NHL which was possibly linked to the combination of methotrexate and etanercept for the treatment of her rheumatoid arthritis. To our knowledge this is probably among the first cases of O-NHL with possible relation to the use of a Tumor Necrosis Factor (TNF) antagonist biological agent (etanercept). This case could contribute to the sensitization of the dentists for the signs and symptoms of this rare malignancy. It also underlines the need for thorough medical history and medication recording for all the dental patients.
Key words:Lymphoma (oral) methotrexate, etanercept.
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Affiliation(s)
| | - Marina D Achtari
- DDS, MS. Dental Clinic, General Hospital Paidon Pentelis, Athens, Greece
| | - Kostas Evangelou
- MD, PhD, Pathologist. Department of Histology and Embryology, School of Medicine, University of Athens, Greece
| | - Christos Kittas
- MD, PhD, Pathologist. Department of Histology and Embryology, School of Medicine, University of Athens, Greece
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Zhang L, Morris JS, Zhang J, Orlowski RZ, Baladandayuthapani V. Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics. Cancer Inform 2014; 13:113-23. [PMID: 25520554 PMCID: PMC4260770 DOI: 10.4137/cin.s13787] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Revised: 07/16/2014] [Accepted: 07/17/2014] [Indexed: 12/30/2022] Open
Abstract
It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct simultaneous variable selection at the pathway (group level) and the gene (within-group) level. To adapt to the overlapping group structure present in the pathway–gene hierarchy of the data, we developed an overlap-HSVS method that introduces latent partial effect variables that partition the marginal effect of the covariates and corresponding weights for a proportional shrinkage of the partial effects. Combining gene expression data with prior pathway information from the KEGG databases, we identified several gene–pathway combinations that are significantly associated with clinical outcomes of multiple myeloma. Biological discoveries support this relationship for the pathways and the corresponding genes we identified.
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Affiliation(s)
- Lin Zhang
- Postdoctoral fellow, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jeffrey S Morris
- Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jiexin Zhang
- Principal Statistical Analyst, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Robert Z Orlowski
- Professor, Department of Lymphoma & Myeloma, and of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S. Integrative analysis of prognosis data on multiple cancer subtypes. Biometrics 2014; 70:480-8. [PMID: 24766212 DOI: 10.1111/biom.12177] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2013] [Revised: 03/01/2014] [Accepted: 03/01/2014] [Indexed: 11/29/2022]
Abstract
In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance.
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Affiliation(s)
- Jin Liu
- Division of Epidemiology and Biostatistics, UIC School of Public Health, Chicago, Illinois, U.S.A
| | - Jian Huang
- Department of Statistics & Actuarial Science, University of Iowa, Iowa City, Iowa, U.S.A
| | - Yawei Zhang
- School of Public Health, Yale University, New Haven, Connecticut, U.S.A
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland, U.S.A
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland, U.S.A
| | - Tongzhang Zheng
- School of Public Health, Yale University, New Haven, Connecticut, U.S.A
| | - Shuangge Ma
- School of Public Health, Yale University, New Haven, Connecticut, U.S.A
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Zhang L, Baladandayuthapani V, Mallick BK, Manyam GC, Thompson PA, Bondy ML, Do KA. Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer. J R Stat Soc Ser C Appl Stat 2014; 63:595-620. [PMID: 25705056 DOI: 10.1111/rssc.12053] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The analysis of alterations that may occur in nature when segments of chromosomes are copied (known as copy number alterations) has been a focus of research to identify genetic markers of cancer. One high-throughput technique recently adopted is the use of molecular inversion probes (MIPs) to measure probe copy number changes. The resulting data consist of high-dimensional copy number profiles that can be used to ascertain probe-specific copy number alterations in correlative studies with patient outcomes to guide risk stratification and future treatment. We propose a novel Bayesian variable selection method, the hierarchical structured variable selection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Through simulations we establish that our method results in lower model errors than other methods when a natural grouping structure exists. We apply our method to an MIP study of breast cancer and show that it identifies genes and probes that are significantly associated with clinically relevant subtypes of breast cancer.
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Affiliation(s)
- Lin Zhang
- Department of Statistics, Texas A&M University, College Station, Texas, U.S.A
| | | | - Bani K Mallick
- Department of Statistics, Texas A&M University, College Station, Texas, U.S.A
| | - Ganiraju C Manyam
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A
| | | | | | - Kim-Anh Do
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A
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Lu C, Latourelle J, O'Connor GT, Dupuis J, Kolaczyk ED. Network-guided sparse regression modeling for detection of gene-by-gene interactions. ACTA ACUST UNITED AC 2013; 29:1241-9. [PMID: 23599501 DOI: 10.1093/bioinformatics/btt139] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
MOTIVATION Genetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability. We propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty. RESULTS We tested our new method on simulated and real data. Simulation showed that with reasonable outside biological knowledge, our method performs noticeably better than stage-wise strategies (i.e. selecting main effects first, and interactions second, from those main effects selected) in finding true interactions, especially when the marginal strength of main effects is weak. We applied our method to Framingham Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found a number of interactions among different classes of human leukocyte antigen genes that may interact to influence the risk of developing IgE dysregulation and allergy. AVAILABILITY The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html. CONTACT chenlu@bu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chen Lu
- Department of Biostatistics, Boston University School of Public Health, Pulmonary Center, Department of Medicine and Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
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Ma S, Zhang Y, Huang J, Huang Y, Lan Q, Rothman N, Zheng T. Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent. Genet Epidemiol 2012; 36:829-38. [PMID: 22851516 PMCID: PMC3729731 DOI: 10.1002/gepi.21669] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2012] [Revised: 06/07/2012] [Accepted: 06/26/2012] [Indexed: 11/10/2022]
Abstract
In cancer research, high-throughput profiling studies have been extensively conducted, searching for genes/single nucleotide polymorphisms (SNPs) associated with prognosis. Despite seemingly significant differences, different subtypes of the same cancer (or different types of cancers) may share common susceptibility genes. In this study, we analyze prognosis data on multiple subtypes of the same cancer but note that the proposed approach is directly applicable to the analysis of data on multiple types of cancers. We describe the genetic basis of multiple subtypes using the heterogeneity model that allows overlapping but different sets of susceptibility genes/SNPs for different subtypes. An accelerated failure time (AFT) model is adopted to describe prognosis. We develop a regularized gradient descent approach that conducts gene-level analysis and identifies genes that contain important SNPs associated with prognosis. The proposed approach belongs to the family of gradient descent approaches, is intuitively reasonable, and has affordable computational cost. Simulation study shows that when prognosis-associated SNPs are clustered in a small number of genes, the proposed approach outperforms alternatives with significantly more true positives and fewer false positives. We analyze an NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements and identify genes associated with the three major subtypes of NHL, namely, DLBCL, FL, and CLL/SLL. The proposed approach identifies genes different from using alternative approaches and has the best prediction performance.
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Affiliation(s)
| | | | - Jian Huang
- Departments of Statistics & Actuarial Science, and Biostatistics, University of Iowa
| | - Yuan Huang
- Department of Statistics, Penn State University
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH
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Li S, Cui Y. Gene-centric gene–gene interaction: A model-based kernel machine method. Ann Appl Stat 2012. [DOI: 10.1214/12-aoas545] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Abstract
INTRODUCTION: Non-Hodgkin Lymphoma (NHL) is a heterogeneous group of malignancies with over thirty different subtypes. Follicular lymphoma (FL) is the most common form of indolent NHL and the second most common form of NHL overall. It has morphologic, immunophenotypic and clinical features significantly different from other subtypes. Considerable effort has been devoted to the identification of risk factors for etiology and prognosis of FL. These risk factors may advance our understanding of the biology of FL and have an impact on clinical practice. AREAS COVERED: The epidemiology of NHL and FL is briefly reviewed. For FL etiology and prognosis separately, we review clinical, environmental and molecular (including genetic, genomic, epigenetic and others) risk factors suggested in the literature. EXPERT OPINION: A large number of potential risk factors have been suggested in recent studies. However, there is a lack of consensus, and many of the suggested risk factors have not been rigorously validated in independent studies. There is a need for large-scale, prospective studies to consolidate existing findings and discover new risk factors. Some of the identified risk factors are successful at the population level. More effective individual-level risk factors and models remain to be identified.
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Abstract
INTRODUCTION: Despite decades of intensive research, Non-Hodgkin Lymphoma (NHL) remains poorly understood and is largely incurable. NHL is a heterogeneous group of malignancies with multiple subtypes, each of which has distinct morphologic, immunophenotypic, and clinical features. Identifying the risk factors for NHL may improve our understanding of the underlying biological mechanisms and have an impact on clinical practice. AREAS COVERED: This article provides a review of several aspects of NHL, including epidemiology and subtype classification, clinical, environmental, genetic, and genomic risk factors identified for etiology and prognosis, and available statistical and bioinformatics tools for identification of genetic and genomic risk factors from the analysis of high-throughput studies. EXPERT OPINION: Multiple clinical and environmental risk factors have been identified. However, they have failed to provide practically effective prediction. Genetic and genomic risk factors identified from high-throughput studies have suffered a lack of reproducibility. The identification of genetic/genomic risk factors demands innovative statistical and bioinformatics tools. Although multiple analysis methods have been developed, there is still room for improvement. There is a critical need for well-designed, prospective, large-scale pangenomic studies.
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Affiliation(s)
- Yawei Zhang
- School of Public Health, Yale University, 60 College ST, New Haven, CT 06520, USA
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Xu JZ, Wong CW. Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and recent methodological developments. Cancer Lett 2010; 296:9-16. [PMID: 20579805 DOI: 10.1016/j.canlet.2010.05.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2010] [Revised: 05/05/2010] [Accepted: 05/18/2010] [Indexed: 12/24/2022]
Abstract
Gene microarray is a powerful platform to investigate the expression patterns of thousands of genes simultaneously. One central objective of such analysis is to select sets of genes (i.e., gene signatures) which correlate with clinical characteristics, such as disease subtype diagnosis, response to drug treatment and prognosis. However, previous studies have found that mRNA signatures are highly unstable and strongly depend on the selection of patient samples. Based on five large microRNA profiling datasets, we empirically found that microRNA signatures are also generally unstable. Therefore, concerns arise regarding the reproducibility and clinical applicability of these derived gene signatures. Here, we first provide a brief review on the sources of variability and different interpretations of multiple distinct gene signatures. We then focus on those recent methodological progresses aimed at developing more stable gene signatures.
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Affiliation(s)
- Jian-Zhen Xu
- College of Bioengineering, Henan University of Technology, Zhengzhou 450001, China.
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