1
|
Bailey R, Sarkar A, Singh A, Dobra A, Kahveci T. Optimal Supervised Reduction of High Dimensional Transcription Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3093-3105. [PMID: 37276117 DOI: 10.1109/tcbb.2023.3280557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The plight of navigating high-dimensional transcription datasets remains a persistent problem. This problem is further amplified for complex disorders, such as cancer as these disorders are often multigenic traits with multiple subsets of genes collectively affecting the type, stage, and severity of the trait. We are often faced with a trade off between reducing the dimensionality of our datasets and maintaining the integrity of our data. To accomplish both tasks simultaneously for very high dimensional transcriptome for complex multigenic traits, we propose a new supervised technique, Class Separation Transformation (CST). CST accomplishes both tasks simultaneously by significantly reducing the dimensionality of the input space into a one-dimensional transformed space that provides optimal separation between the differing classes. Furthermore, CST offers an means of explainable ML, as it computes the relative importance of each feature for its contribution to class distinction, which can thus lead to deeper insights and discovery. We compare our method with existing state-of-the-art methods using both real and synthetic datasets, demonstrating that CST is the more accurate, robust, scalable, and computationally advantageous technique relative to existing methods. Code used in this paper is available on https://github.com/richiebailey74/CST.
Collapse
|
2
|
Smith BAH, Deutzmann A, Correa KM, Delaveris CS, Dhanasekaran R, Dove CG, Sullivan DK, Wisnovsky S, Stark JC, Pluvinage JV, Swaminathan S, Riley NM, Rajan A, Majeti R, Felsher DW, Bertozzi CR. MYC-driven synthesis of Siglec ligands is a glycoimmune checkpoint. Proc Natl Acad Sci U S A 2023; 120:e2215376120. [PMID: 36897988 PMCID: PMC10089186 DOI: 10.1073/pnas.2215376120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/06/2022] [Indexed: 03/12/2023] Open
Abstract
The Siglecs (sialic acid-binding immunoglobulin-like lectins) are glycoimmune checkpoint receptors that suppress immune cell activation upon engagement of cognate sialoglycan ligands. The cellular drivers underlying Siglec ligand production on cancer cells are poorly understood. We find the MYC oncogene causally regulates Siglec ligand production to enable tumor immune evasion. A combination of glycomics and RNA-sequencing of mouse tumors revealed the MYC oncogene controls expression of the sialyltransferase St6galnac4 and induces a glycan known as disialyl-T. Using in vivo models and primary human leukemias, we find that disialyl-T functions as a "don't eat me" signal by engaging macrophage Siglec-E in mice or the human ortholog Siglec-7, thereby preventing cancer cell clearance. Combined high expression of MYC and ST6GALNAC4 identifies patients with high-risk cancers and reduced tumor myeloid infiltration. MYC therefore regulates glycosylation to enable tumor immune evasion. We conclude that disialyl-T is a glycoimmune checkpoint ligand. Thus, disialyl-T is a candidate for antibody-based checkpoint blockade, and the disialyl-T synthase ST6GALNAC4 is a potential enzyme target for small molecule-mediated immune therapy.
Collapse
Affiliation(s)
- Benjamin A. H. Smith
- Sarafan ChEM-H, Stanford University, Stanford, CA94305
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA94305
| | - Anja Deutzmann
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | | | - Corleone S. Delaveris
- Sarafan ChEM-H, Stanford University, Stanford, CA94305
- Department of Chemistry, Stanford University, Stanford, CA94305
| | - Renumathy Dhanasekaran
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Christopher G. Dove
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA94305
| | - Delaney K. Sullivan
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
| | - Simon Wisnovsky
- Faculty of Pharmaceutical Sciences, University of British Columbia, British Columbia, BC V6T 1Z3, Canada
| | - Jessica C. Stark
- Sarafan ChEM-H, Stanford University, Stanford, CA94305
- Department of Chemistry, Stanford University, Stanford, CA94305
| | - John V. Pluvinage
- Department of Neurology, University of California, San Francisco, CA94143
| | - Srividya Swaminathan
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
- Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA91016
- Department of Pediatrics, Beckman Research Institute of City of Hope, Duarte, CA91010
| | | | - Anand Rajan
- Department of Pathology, University of Iowa, Iowa City, IA52242
| | - Ravindra Majeti
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA94305
| | - Dean W. Felsher
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA94305
- Department of Pathology, Stanford University School of Medicine, Stanford, CA94305
| | - Carolyn R. Bertozzi
- Sarafan ChEM-H, Stanford University, Stanford, CA94305
- Department of Chemistry, Stanford University, Stanford, CA94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA94305
| |
Collapse
|
3
|
Perrone M, Chiodoni C, Lecchi M, Botti L, Bassani B, Piva A, Jachetti E, Milani M, Lecis D, Tagliabue E, Verderio P, Sangaletti S, Colombo MP. ATF3 Reprograms the Bone Marrow Niche in Response to Early Breast Cancer Transformation. Cancer Res 2023; 83:117-129. [PMID: 36318106 PMCID: PMC9811157 DOI: 10.1158/0008-5472.can-22-0651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/13/2022] [Accepted: 10/28/2022] [Indexed: 11/05/2022]
Abstract
Cancer is a systemic disease able to reprogram the bone marrow (BM) niche towards a protumorigenic state. The impact of cancer on specific BM subpopulations can qualitatively differ according to the signals released by the tumor, which can vary on the basis of the tissue of origin. Using a spontaneous model of mammary carcinoma, we identified BM mesenchymal stem cells (MSC) as the first sensors of distal cancer cells and key mediators of BM reprogramming. Through the release of IL1B, BM MSCs induced transcriptional upregulation and nuclear translocation of the activating transcription factor 3 (ATF3) in hematopoietic stem cells. ATF3 in turn promoted the formation of myeloid progenitor clusters and sustained myeloid cell differentiation. Deletion of Atf3 specifically in the myeloid compartment reduced circulating monocytes and blocked their differentiation into tumor-associated macrophages. In the peripheral blood, the association of ATF3 expression in CD14+ mononuclear cells with the expansion CD11b+ population was able to discriminate between women with malignant or benign conditions at early diagnosis. Overall, this study identifies the IL1B/ATF3 signaling pathway in the BM as a functional step toward the establishment of a tumor-promoting emergency myelopoiesis, suggesting that ATF3 could be tested in a clinical setting as a circulating marker of early transformation and offering the rationale for testing the therapeutic benefits of IL1B inhibition in patients with breast cancer. Significance: Bone marrow mesenchymal stem cells respond to early breast tumorigenesis by upregulating IL1B to promote ATF3 expression in hematopoietic stem cells and to induce myeloid cell differentiation that supports tumor development.
Collapse
Affiliation(s)
- Milena Perrone
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudia Chiodoni
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Mara Lecchi
- Bioinformatics and Biostatistics Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Laura Botti
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Barbara Bassani
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Annamaria Piva
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Elena Jachetti
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Matteo Milani
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Daniele Lecis
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Elda Tagliabue
- Molecular Targeting Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Paolo Verderio
- Bioinformatics and Biostatistics Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Sabina Sangaletti
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Corresponding Authors: Mario P. Colombo, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Amadeo 42, 20133, Milan, Italy. Phone: 223-902-252; Fax: 223-902-630; E-mail: ; and Sabina Sangaletti,
| | - Mario P. Colombo
- Molecular Immunology Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Corresponding Authors: Mario P. Colombo, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Amadeo 42, 20133, Milan, Italy. Phone: 223-902-252; Fax: 223-902-630; E-mail: ; and Sabina Sangaletti,
| |
Collapse
|
4
|
Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms. Genes (Basel) 2021; 12:genes12111814. [PMID: 34828418 PMCID: PMC8621246 DOI: 10.3390/genes12111814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023] Open
Abstract
Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.
Collapse
|
5
|
Yang Y, Zhang T, Xiao R, Hao X, Zhang H, Qu H, Xie B, Wang T, Fang X. Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells. Brief Bioinform 2021; 21:1006-1015. [PMID: 30895303 DOI: 10.1093/bib/bbz027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 02/04/2019] [Accepted: 02/18/2019] [Indexed: 01/08/2023] Open
Abstract
Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.
Collapse
Affiliation(s)
- Yadong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Tao Zhang
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Rudan Xiao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Hao
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Huiqiang Zhang
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Hongzhu Qu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Bingbing Xie
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Tao Wang
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
6
|
Han Y, Huang L, Zhou F. A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers. Bioinformatics 2021; 37:2183-2189. [PMID: 33515240 DOI: 10.1093/bioinformatics/btab055] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/23/2020] [Accepted: 01/25/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets. RESULTS The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yuanyuan Han
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Lan Huang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| |
Collapse
|
7
|
Quantitative STAU2 measurement in lymphocytes for breast cancer risk assessment. Sci Rep 2021; 11:915. [PMID: 33441653 PMCID: PMC7806934 DOI: 10.1038/s41598-020-79622-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/08/2020] [Indexed: 01/29/2023] Open
Abstract
Although mammograms play a key role in early breast cancer detection, the test is not applicable to all women, for example, women under the age of 40. The development of a noninvasive blood test with high sensitivity and accessibility will improve the effectiveness of breast cancer screening programmes. Secretory factors released from cancer cells can induce the expression of certain genes in a large number of white blood cells (WBCs). Therefore, cancer-dependent proteins in WBCs can be used as tumour markers with high sensitivity. Five proteins (LMAN1, AZI2, STAU2, MMP9 and PLOD1) from a systemic analysis of a variety of array data of breast cancer patients were subjected to immunofluorescence staining to evaluate the presence of fixed WBCs on 96-well plates from 363 healthy females and 358 female breast cancer patients. The results revealed that the average fluorescence intensity of anti-STAU2 and the percentage of STAU2-positive T and B lymphocytes in breast cancer patients (110.50 ± 23.38 and 61.87 ± 12.44, respectively) were significantly increased compared with those in healthy females (56.47 ± 32.03 and 33.02 ± 18.10, respectively) (p = 3.56 × 10-71, odds ratio = 24.59, 95% CI = 16.64-36.34). The effect of secreted molecules from breast cancer cells was proven by the increase in STAU2 intensity in PBMCs cocultured with MCF-7 and T47D cells at 48 h (p = 0.0289). The test demonstrated 98.32%, 82.96%, and 48.32% sensitivity and 56.47%, 83.47%, and 98.62% specificity in correlation with the percentage of STAU2-positive cells at 40, 53.34 and 63.38, respectively. We also demonstrated how to use the STAU2 test for the assessment of risk in women under the age of 40. STAU2 is a novel breast cancer marker that can be assessed by quantitative immunofluorescence staining of fixed WBCs that are transportable at room temperature via mail, representing a useful risk assessment tool for women without access to mammograms.
Collapse
|
8
|
Shen H, Liu T, Cui J, Borole P, Benjamin A, Kording K, Issadore D. A web-based automated machine learning platform to analyze liquid biopsy data. LAB ON A CHIP 2020; 20:2166-2174. [PMID: 32420563 DOI: 10.1039/d0lc00096e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Liquid biopsy (LB) technologies continue to improve in sensitivity, specificity, and multiplexing and can measure an ever growing library of disease biomarkers. However, clinical interpretation of the increasingly large sets of data these technologies generate remains a challenge. Machine learning is a popular approach to discover and detect signatures of disease. However, limited machine learning expertise in the LB field has kept the discipline from fully leveraging these tools and risks improper analyses and irreproducible results. In this paper, we develop a web-based automated machine learning tool tailored specifically for LB, where machine learning models can be built without the user's input. We also incorporate a differential privacy algorithm, designed to limit the effects of overfitting that can arise from users iteratively developing a panel with feedback from our platform. We validate our approach by performing a meta-analysis on 11 published LB datasets, and found that we had similar or better performance compared to those reported in the literature. Moreover, we show that our platform's performance improved when incorporating information from prior LB datasets, suggesting that this approach can continue to improve with increased access to LB data. Finally, we show that by using our platform the results achieved in the literature can be matched using 40% of the number of subjects in the training set, potentially reducing study cost and time. This self-improving and overfitting-resistant automatic machine learning platform provides a new standard that can be used to validate machine learning works in the LB field.
Collapse
Affiliation(s)
- Hanfei Shen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | | | | | | | | | | | | |
Collapse
|
9
|
Farahbod M, Pavlidis P. Untangling the effects of cellular composition on coexpression analysis. Genome Res 2020; 30:849-859. [PMID: 32580998 PMCID: PMC7370889 DOI: 10.1101/gr.256735.119] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
Coexpression analysis is widely used for inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies, based on methods such as clustering. The majority of such studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis has not been studied in detail. Here, we examine this issue for the case of human RNA analysis. Focusing on brain tissue, we found that, for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels (median R 2 = 0.68). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression and the formation of coexpression clusters can be attributed to this effect for both brain and blood transcriptomes. For brain, we further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. An attempt to correct for composition yielded mixed results. Our conclusion is that the dominant coexpression signal in brain, blood, and, likely, other complex tissues can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships. These results have implications for the relevance and interpretation of coexpression analysis.
Collapse
Affiliation(s)
- Marjan Farahbod
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 2A1, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V5T 4S6, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 2A1, Canada
| |
Collapse
|
10
|
Verda D, Parodi S, Ferrari E, Muselli M. Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC Bioinformatics 2019; 20:390. [PMID: 31757200 PMCID: PMC6873393 DOI: 10.1186/s12859-019-2953-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 06/14/2019] [Indexed: 12/27/2022] Open
Abstract
Background Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. Results LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98–1.0) and outperformed any other method except SVM. Conclusions LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.
Collapse
Affiliation(s)
| | - Stefano Parodi
- Epidemiology and Biostatistics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Marco Muselli
- Rulex Inc., Newton, MA, USA. .,Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy, Via De Marini, 6, 16149, Genoa, Italy.
| |
Collapse
|
11
|
Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat Commun 2019; 10:2209. [PMID: 31101809 PMCID: PMC6525259 DOI: 10.1038/s41467-019-09990-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 04/11/2019] [Indexed: 11/08/2022] Open
Abstract
Changes in bulk transcriptional profiles of heterogeneous samples often reflect changes in proportions of individual cell types. Several robust techniques have been developed to dissect the composition of such mixed samples given transcriptional signatures of the pure components or their proportions. These approaches are insufficient, however, in situations when no information about individual mixture components is available. This problem is known as the complete deconvolution problem, where the composition is revealed without any a priori knowledge about cell types and their proportions. Here, we identify a previously unrecognized property of tissue-specific genes - their mutual linearity - and use it to reveal the structure of the topological space of mixed transcriptional profiles and provide a noise-robust approach to the complete deconvolution problem. Furthermore, our analysis reveals systematic bias of all deconvolution techniques due to differences in cell size or RNA-content, and we demonstrate how to address this bias at the experimental design level.
Collapse
|
12
|
Fleischer LM, Somaiya RD, Miller GM. Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers. Front Pharmacol 2018; 9:683. [PMID: 29997511 PMCID: PMC6029583 DOI: 10.3389/fphar.2018.00683] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 06/06/2018] [Indexed: 12/29/2022] Open
Abstract
Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.
Collapse
Affiliation(s)
- Lisa M Fleischer
- Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, United States
| | - Rachana D Somaiya
- Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, United States
| | - Gregory M Miller
- Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, United States.,Department of Chemical Engineering, Northeastern University, Boston, MA, United States.,Center for Drug Discovery, Northeastern University, Boston, MA, United States
| |
Collapse
|
13
|
Li WX, He K, Tang L, Dai SX, Li GH, Lv WW, Guo YC, An SQ, Wu GY, Liu D, Huang JF. Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets. Oncotarget 2018; 8:6775-6786. [PMID: 28036274 PMCID: PMC5351668 DOI: 10.18632/oncotarget.14286] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 12/07/2016] [Indexed: 01/04/2023] Open
Abstract
Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.
Collapse
Affiliation(s)
- Wen-Xing Li
- Institute of Health Sciences, Anhui University, Hefei 230601, Anhui, China.,State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
| | - Kan He
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China.,Department of Biostatistics, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
| | - Ling Tang
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
| | - Shao-Xing Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, Yunnan, China
| | - Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, Yunnan, China
| | - Wen-Wen Lv
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital/Faculty of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yi-Cheng Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
| | - San-Qi An
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, Yunnan, China
| | - Guo-Ying Wu
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
| | - Dahai Liu
- Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
| | - Jing-Fei Huang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, Yunnan, China.,KIZ-SU Joint Laboratory of Animal Models and Drug Development, College of Pharmaceutical Sciences, Soochow University, Kunming 650223, Yunnan, China.,Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan, Kunming 650223, Yunnan, China.,Chinese University of Hong Kong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming 650223, Yunnan, China
| |
Collapse
|
14
|
Li H, Hong G, Lin M, Shi Y, Wang L, Jiang F, Zhang F, Wang Y, Guo Z. Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of Alzheimer's disease. Sci Rep 2017; 7:14027. [PMID: 29070791 PMCID: PMC5656592 DOI: 10.1038/s41598-017-13700-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/27/2017] [Indexed: 11/08/2022] Open
Abstract
Blood-based test has been considered as a promising way to diagnose and study Alzheimer's disease (AD). However, the changed proportions of the leukocytes under disease states could confound the aberrant expression signals observed in mixed-cell blood samples. We have previously proposed a method, Ref-REO, to detect the leukocyte specific expression alterations from mixed-cell blood samples. In this study, by applying Ref-REO, we detect 42 and 45 differentially expressed genes (DEGs) between AD and normal peripheral whole blood (PWB) samples in two datasets, respectively. These DEGs are mainly associated with AD-associated functions such as Wnt signaling pathways and mitochondrion dysfunctions. They are also reproducible in AD brain tissue, and tend to interact with the reported AD-associated biomarkers and overlap with targets of AD-associated PWB miRNAs. Moreover, they are closely associated with aging and have severer expression alterations in the younger adults with AD. Finally, diagnostic signatures are constructed from these leukocyte specific alterations, whose area under the curve (AUC) for predicting AD is higher than 0.73 in the two AD PWB datasets. In conclusion, gene expression alterations in leukocytes could be extracted from AD PWB samples, which are closely associated with AD progression, and used as a diagnostic signature of AD.
Collapse
Affiliation(s)
- Hongdong Li
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Guini Hong
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.
| | - Mengna Lin
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Yidan Shi
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Lili Wang
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Fengle Jiang
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Fan Zhang
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Yuhang Wang
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| |
Collapse
|
15
|
Luz MCDB, Perez MM, Azzalis LA, Sousa LVDA, Adami F, Fonseca FLA, Alves BDCA. Evaluation of MCT1, MCT4 and CD147 Genes in Peripheral Blood Cells of Breast Cancer Patients and Their Potential Use as Diagnostic and Prognostic Markers. Int J Mol Sci 2017; 18:ijms18040170. [PMID: 28333070 PMCID: PMC5412261 DOI: 10.3390/ijms18040170] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/26/2016] [Accepted: 01/08/2017] [Indexed: 12/18/2022] Open
Abstract
Background: Patients with breast cancer—the deadliest cancer among women—are at constant risk of developing metastasis. Oxidative stress and hypoxia are common feature of tumor cells that can proliferate even in a resultant metabolic acidosis. Despite the low extracellular pH, intracellular pH of tumor cells remains relatively normal, or even more alkaline due to the action of a membrane protein family known as monocarboxylate transporters (MCTs). The objective of this study was to verify the diagnostic and prognostic value of MCT1, MCT4 and CD147 in tumor and peripheral blood samples of patients with breast cancer undergoing chemotherapic treatment. Methods: Differential expression of MCT1, MCT4 and CD147 obtained by qPCR was determined by 2−ΔΔCq method between biological samples (tumor and serial samples of peripheral) of patients (n = 125) and healthy women (n = 25). Results: tumor samples with higher histological grades have shown higher expression of these markers; this higher expression was also observed in blood samples obtained at diagnosis of patients when compared to healthy women and in patients with positive progression of the disease (metastasis development). Conclusion: markers studied here could be a promising strategy in routine laboratory evaluations as breast cancer diagnosis and prognosis.
Collapse
Affiliation(s)
- Maria Cláudia de B Luz
- Clinical Laboratory, Faculdade de Medicina do ABC (FMABC), Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
| | - Matheus M Perez
- Clinical Laboratory, Faculdade de Medicina do ABC (FMABC), Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
| | - Ligia A Azzalis
- Biological Science Department, UNIFESP, Rua Prof. Artur Riedel, 275, CEP 09972-270 Diadema, SP, Brazil.
| | - Luiz Vinícius de A Sousa
- Epidemiology Laboratory and Data Analysis, FMABC, Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
| | - Fernando Adami
- Epidemiology Laboratory and Data Analysis, FMABC, Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
| | - Fernando L A Fonseca
- Clinical Laboratory, Faculdade de Medicina do ABC (FMABC), Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
- Biological Science Department, UNIFESP, Rua Prof. Artur Riedel, 275, CEP 09972-270 Diadema, SP, Brazil.
| | - Beatriz da C A Alves
- Clinical Laboratory, Faculdade de Medicina do ABC (FMABC), Av. Príncipe de Gales, 821, CEP 09060-650 Santo André, SP, Brazil.
| |
Collapse
|
16
|
Hong G, Li H, Li M, Zheng W, Li J, Chi M, Cheng J, Guo Z. A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples. Brief Bioinform 2017; 19:613-621. [DOI: 10.1093/bib/bbx009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Indexed: 12/15/2022] Open
Affiliation(s)
- Guini Hong
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Hongdong Li
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Mengyao Li
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Weicheng Zheng
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Jing Li
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Meirong Chi
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Jun Cheng
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| |
Collapse
|
17
|
He L, Wang D, Wei N, Guo Z. Integrated Bioinformatics Approach Reveals Crosstalk Between Tumor Stroma and Peripheral Blood Mononuclear Cells in Breast Cancer. Asian Pac J Cancer Prev 2016; 17:1003-8. [PMID: 27039717 DOI: 10.7314/apjcp.2016.17.3.1003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.
Collapse
Affiliation(s)
- Lang He
- Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China E-mail :
| | | | | | | |
Collapse
|
18
|
Seo M, Yoon J, Park T. GRACOMICS: software for graphical comparison of multiple results with omics data. BMC Genomics 2015; 16:256. [PMID: 25887412 PMCID: PMC4387734 DOI: 10.1186/s12864-015-1461-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 03/09/2015] [Indexed: 12/18/2022] Open
Abstract
Background Analysis of large-scale omics data has become more and more challenging due to high dimensionality. More complex analysis methods and tools are required to handle such data. While many methods already exist, those methods often produce different results. To help users obtain more appropriate results (i.e. candidate genes), we propose a tool, GRACOMICS that compares numerous analysis results visually in a more systematic way; this enables the users to easily interpret the results more comfortably. Results GRACOMICS has the ability to visualize multiple analysis results interactively. We developed GRACOMICS to provide instantaneous results (plots and tables), corresponding to user-defined threshold values, since there are yet no other up-to-date omics data visualization tools that provide such features. In our analysis, we successfully employed two types of omics data: transcriptomic data (microarray and RNA-seq data) and genomic data (SNP chip and NGS data). Conclusions GRACOMICS is a graphical user interface (GUI)-based program written in Java for cross-platform computing environments, and can be applied to compare analysis results for any type of large-scale omics data. This tool can be useful for biologists to identify genes commonly found by intersected statistical methods, for further experimental validation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1461-0) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Minseok Seo
- Interdiciplinary of Bioinformatics Program, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Korea.
| | - Joon Yoon
- Interdiciplinary of Bioinformatics Program, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Korea.
| | - Taesung Park
- Interdiciplinary of Bioinformatics Program, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Korea. .,Department of Statistics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Korea.
| |
Collapse
|
19
|
Sfakianakis S, Bei ES, Zervakis M, Vassou D, Kafetzopoulos D. On the identification of circulating tumor cells in breast cancer. IEEE J Biomed Health Inform 2015; 18:773-82. [PMID: 24808221 DOI: 10.1109/jbhi.2013.2295262] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.
Collapse
|
20
|
Phelix CF, Villareal G, LeBaron RG, Roberson DJ. Biomarkers from biosimulations: Transcriptome-To-Reactome™ Technology for individualized medicine. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:3452-5. [PMID: 25570733 DOI: 10.1109/embc.2014.6944365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
We validated a model of the TGF-β signaling pathway using reactions from Reactome. Using a patentpending technique, gene expression profiles from individual patients are used to determine model parameters. Gene expression profiles from 45 women, normal, or benign tumor and malignant breast cancer were used as training and validating sets for assessing clinical sensitivity and specificity. Biomarkers were identified from the biosimulation results using sensitivity analyses and derivative properties from the model. A membrane signaling marker had sensitivity of 80% and specificity of 60%; while a nuclear transcription factor marker had sensitivity of 80% and specificity of 90% to predict malignancy. Use of Fagan's nomogram increased probability from 7.5% for positive mammogram to 39% with positive results of the biosimulation for the nuclear marker. Our technology will allow researchers to identify and develop biomarkers and assist clinicians in diagnostic and treatment decision making.
Collapse
|
21
|
Understanding disease mechanisms with models of signaling pathway activities. BMC SYSTEMS BIOLOGY 2014; 8:121. [PMID: 25344409 PMCID: PMC4213475 DOI: 10.1186/s12918-014-0121-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Accepted: 10/13/2014] [Indexed: 02/02/2023]
Abstract
BACKGROUND Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. RESULTS Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. CONCLUSIONS The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.
Collapse
|
22
|
Ay A, Gong D, Kahveci T. Network-based Prediction of Cancer under Genetic Storm. Cancer Inform 2014; 13:15-31. [PMID: 25368507 PMCID: PMC4214593 DOI: 10.4137/cin.s14025] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Revised: 06/17/2014] [Accepted: 06/24/2014] [Indexed: 01/01/2023] Open
Abstract
Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50–300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types.
Collapse
Affiliation(s)
- Ahmet Ay
- Department of Mathematics, Colgate University, Hamilton, NY, USA. ; Department of Biology, Colgate University, Hamilton, NY, USA
| | - Dihong Gong
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
| | - Tamer Kahveci
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
| |
Collapse
|
23
|
Dumeaux V, Ursini-Siegel J, Flatberg A, Fjosne HE, Frantzen JO, Holmen MM, Rodegerdts E, Schlichting E, Lund E. Peripheral blood cells inform on the presence of breast cancer: a population-based case-control study. Int J Cancer 2014; 136:656-67. [PMID: 24931809 PMCID: PMC4278533 DOI: 10.1002/ijc.29030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 04/04/2014] [Accepted: 06/02/2014] [Indexed: 01/21/2023]
Abstract
Tumor–host interactions extend beyond the local microenvironment and cancer development largely depends on the ability of malignant cells to hijack and exploit the normal physiological processes of the host. Here, we established that many genes within peripheral blood cells show differential expression when an untreated breast cancer (BC) is present, and harnessed this fact to construct a 50-gene signature that distinguish BC patients from population-based controls. Our results were derived from a series of large datasets within our unique population-based Norwegian Women and Cancer cohort that allowed us to investigate the influence of medications and tumor characteristics on our blood-based test, and were further tested in two external datasets. Our 50-gene signature contained cytostatic signals including the specific suppression of the immune response and medications influencing transcription involved in those processes were identified as confounders. Through analysis of the biological processes differentially expressed in blood, we were able to provide a rationale as to why the systemic response of the host may be a reliable marker of BC, characterized by the underexpression of both immune-specific pathways and “universal” cell programs driven by MYC (i.e., metabolism, growth and cell cycle). In conclusion, gene expression of peripheral blood cells is markedly perturbed by the specific presence of carcinoma in the breast and these changes simultaneously engage a number of systemic cytostatic signals emerging connections with immune escape of BC.
Collapse
Affiliation(s)
- Vanessa Dumeaux
- Institute of Community Medicine, University of Tromsø, Tromsø, Norway; Department of Oncology, Faculty of Medicine, McGill University, Montreal, Canada
| | | | | | | | | | | | | | | | | |
Collapse
|
24
|
Xie B, Wang D, Duan Y, Yu J, Lei H. Functional networking of human divergently paired genes (DPGs). PLoS One 2013; 8:e78896. [PMID: 24205343 PMCID: PMC3815023 DOI: 10.1371/journal.pone.0078896] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2013] [Accepted: 09/17/2013] [Indexed: 11/18/2022] Open
Abstract
Divergently paired genes (DPGs), also known as bidirectional (head-to-head positioned) genes, are conserved across species and lineages, and thus deemed to be exceptional in genomic organization and functional regulation. Despite previous investigations on the features of their conservation and gene organization, the functional relationship among DPGs in a given species and lineage has not been thoroughly clarified. Here we report a network-based comprehensive analysis on human DPGs and our results indicate that the two members of the DPGs tend to participate in different biological processes while enforcing related functions as modules. Comparing to randomly paired genes as a control, the DPG pairs have a tendency to be clustered in similar “cellular components” and involved in similar “molecular functions”. The functional network bridged by DPGs consists of three major modules. The largest module includes many house-keeping genes involved in core cellular activities. This module also shows low variation in expression in both CNS (central nervous system) and non-CNS tissues. Based on analyses of disease transcriptome data, we further suggest that this particular module may play crucial roles in HIV infection and its disease mechanism.
Collapse
Affiliation(s)
- Bin Xie
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dapeng Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Yong Duan
- UC Davis Genome Center and Department of Biomedical Engineering, Davis, California, United States of America
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- * E-mail: (JY); (HL)
| | - Hongxing Lei
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- UC Davis Genome Center and Department of Biomedical Engineering, Davis, California, United States of America
- * E-mail: (JY); (HL)
| |
Collapse
|
25
|
Pang Y, Gara SK, Achyut BR, Li Z, Yan HH, Day CP, Weiss JM, Trinchieri G, Morris JC, Yang L. TGF-β signaling in myeloid cells is required for tumor metastasis. Cancer Discov 2013; 3:936-51. [PMID: 23661553 DOI: 10.1158/2159-8290.cd-12-0527] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
TGF-β is overexpressed in advanced human cancers. It correlates with metastasis and poor prognosis. However, TGF-β functions as both a tumor suppressor and a tumor promoter. Here, we report for the first time that genetic deletion of Tgfbr2 specifically in myeloid cells (Tgfbr2(MyeKO)) significantly inhibited tumor metastasis. Reconstitution of tumor-bearing mice with Tgfbr2(MyeKO) bone marrow recapitulated the inhibited metastasis phenotype. This effect is mediated through decreased production of type II cytokines, TGF-β1, arginase 1, and inducible nitric oxide synthase, which promoted IFN-γ production and improved systemic immunity. Depletion of CD8 T cells diminished the metastasis defect in the Tgfbr2(MyeKO) mice. Consistent with animal studies, myeloid cells from patients with advanced-stage cancer showed increased TGF-β receptor II expression. Our studies show that myeloid-specific TGF-β signaling is an essential component of the metastasis-promoting puzzle of TGF-β. This is in contrast to the previously reported tumor-suppressing phenotypes in fibroblasts, epithelial cells, and T cells.
Collapse
Affiliation(s)
- Yanli Pang
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Sharma-Kuinkel BK, Zhang Y, Yan Q, Ahn SH, Fowler VG. Host gene expression profiling and in vivo cytokine studies to characterize the role of linezolid and vancomycin in methicillin-resistant Staphylococcus aureus (MRSA) murine sepsis model. PLoS One 2013; 8:e60463. [PMID: 23565251 PMCID: PMC3614971 DOI: 10.1371/journal.pone.0060463] [Citation(s) in RCA: 19] [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: 12/06/2012] [Accepted: 02/26/2013] [Indexed: 01/11/2023] Open
Abstract
Linezolid (L), a potent antibiotic for Methicillin Resistant Staphylococcus aureus (MRSA), inhibits bacterial protein synthesis. By contrast, vancomycin (V) is a cell wall active agent. Here, we used a murine sepsis model to test the hypothesis that L treatment is associated with differences in bacterial and host characteristics as compared to V. Mice were injected with S. aureus USA300, and then intravenously treated with 25 mg/kg of either L or V at 2 hours post infection (hpi). In vivo alpha-hemolysin production was reduced in both L and V-treated mice compared to untreated mice but the reduction did not reach the statistical significance [P = 0.12 for L; P = 0.70 for V). PVL was significantly reduced in L-treated mice compared to untreated mice (P = 0.02). However the reduction of in vivo PVL did not reach the statistical significance in V- treated mice compared to untreated mice (P = 0.27). Both antibiotics significantly reduced IL-1β production [P = 0.001 for L; P = 0.006 for V]. IL-6 was significantly reduced with L but not V antibiotic treatment [P<0.001 for L; P = 0.11 for V]. Neither treatment significantly reduced production of TNF-α. Whole-blood gene expression profiling showed no significant effect of L and V on uninfected mice. In S. aureus-infected mice, L altered the expression of a greater number of genes than V (95 vs. 42; P = 0.001). Pathway analysis for the differentially expressed genes identified toll-like receptor signaling pathway to be common to each S. aureus-infected comparison. Expression of immunomodulatory genes like Cxcl9, Cxcl10, Il1r2, Cd14 and Nfkbia was different among the treatment groups. Glycerolipid metabolism pathway was uniquely associated with L treatment in S. aureus infection. This study demonstrates that, as compared to V, treatment with L is associated with reduced levels of toxin production, differences in host inflammatory response, and distinct host gene expression characteristics in MRSA sepsis.
Collapse
Affiliation(s)
- Batu K. Sharma-Kuinkel
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
- * E-mail: (BKSK); (SHA)
| | - Yurong Zhang
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Qin Yan
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Sun Hee Ahn
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
- * E-mail: (BKSK); (SHA)
| | - Vance G. Fowler
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
- Duke Clinical Research Institute, Durham, North Carolina, United States of America
| |
Collapse
|
27
|
Ahn SH, Tsalik EL, Cyr DD, Zhang Y, van Velkinburgh JC, Langley RJ, Glickman SW, Cairns CB, Zaas AK, Rivers EP, Otero RM, Veldman T, Kingsmore SF, Lucas J, Woods CW, Ginsburg GS, Fowler VG. Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans. PLoS One 2013; 8:e48979. [PMID: 23326304 PMCID: PMC3541361 DOI: 10.1371/journal.pone.0048979] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2012] [Accepted: 09/27/2012] [Indexed: 12/31/2022] Open
Abstract
Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.
Collapse
Affiliation(s)
- Sun Hee Ahn
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
| | - Ephraim L. Tsalik
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
- Section on Infectious Diseases, Durham Veteran’s Affairs Medical Center, Durham, North Carolina, United States of America
| | - Derek D. Cyr
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
| | - Yurong Zhang
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
| | - Jennifer C. van Velkinburgh
- van Velkinburgh Initiative for Collaborative BioMedical Research, Santa Fe, New Mexico, United States of America
| | - Raymond J. Langley
- Immunology Division, Lovelace Respiratory Research Institute, Albuquerque, New Mexico, United States of America
| | - Seth W. Glickman
- Department of Emergency Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Charles B. Cairns
- Department of Emergency Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Aimee K. Zaas
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
| | - Emanuel P. Rivers
- Department of Emergency Medicine, Henry Ford Hospital, Wayne State University, Detroit, Michigan, United States of America
| | - Ronny M. Otero
- Department of Emergency Medicine, Henry Ford Hospital, Wayne State University, Detroit, Michigan, United States of America
| | - Tim Veldman
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
| | - Stephen F. Kingsmore
- Center for Pediatric Genomic Medicine, Children’s Mercy Hospitals and Clinics, Kansas City, Missouri, United States of America
| | - Joseph Lucas
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
| | - Christopher W. Woods
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
- Section on Infectious Diseases, Durham Veteran’s Affairs Medical Center, Durham, North Carolina, United States of America
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
| | - Geoffrey S. Ginsburg
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
- * E-mail: (GSG); (VGF)
| | - Vance G. Fowler
- Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, North Carolina, United States of America
- Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America
- Duke Clinical Research Institute, Durham, North Carolina, United States of America
- * E-mail: (GSG); (VGF)
| |
Collapse
|
28
|
Brennan K, Garcia-Closas M, Orr N, Fletcher O, Jones M, Ashworth A, Swerdlow A, Thorne H, Riboli E, Vineis P, Dorronsoro M, Clavel-Chapelon F, Panico S, Onland-Moret NC, Trichopoulos D, Kaaks R, Khaw KT, Brown R, Flanagan JM. Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res 2012; 72:2304-13. [PMID: 22374981 DOI: 10.1158/0008-5472.can-11-3157] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Few studies have evaluated the association between DNA methylation in white blood cells (WBC) and the risk of breast cancer. The evaluation of WBC DNA methylation as a biomarker of cancer risk is of particular importance as peripheral blood is often available in prospective cohorts and easier to obtain than tumor or normal tissues. Here, we used prediagnostic blood samples from three studies to analyze WBC DNA methylation of two ATM intragenic loci (ATMmvp2a and ATMmvp2b) and genome-wide DNA methylation in long interspersed nuclear element-1 (LINE1) repetitive elements. Samples were from a case-control study derived from a cohort of high-risk breast cancer families (KConFab) and nested case-control studies in two prospective cohorts: Breakthrough Generations Study (BGS) and European Prospective Investigation into Cancer and Nutrition (EPIC). Bisulfite pyrosequencing was used to quantify methylation from 640 incident cases of invasive breast cancer and 741 controls. Quintile analyses for ATMmvp2a showed an increased risk of breast cancer limited to women in the highest quintile [OR, 1.89; 95% confidence interval (CI), 1.36-2.64; P = 1.64 × 10(-4)]. We found no significant differences in estimates across studies or in analyses stratified by family history or menopausal status. However, a more consistent association was observed in younger than in older women and individually significant in KConFab and BGS, but not EPIC. We observed no differences in LINE1 or ATMmvp2b methylation between cases and controls. Together, our findings indicate that WBC DNA methylation levels at ATM could be a marker of breast cancer risk and further support the pursuit of epigenome-wide association studies of peripheral blood DNA methylation.
Collapse
Affiliation(s)
- Kevin Brennan
- Epigenetics Unit, Department of Surgery and Cancer, Imperial College, London, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
29
|
Zhao Y, Sheng Z, Huang J. A systematic analysis of heart transcriptome highlights divergent cardiovascular disease pathways between animal models and humans. ACTA ACUST UNITED AC 2012; 8:504-10. [DOI: 10.1039/c1mb05415e] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|