1
|
Gupta R, Kala N, Pai A, Malviya R. Bioinformatics Approach for Data Capturing: The Case of Breast Cancer. CURRENT CANCER THERAPY REVIEWS 2021. [DOI: 10.2174/1573394717666210203112941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background:
With the rapid evolution in advanced computer systems and various statistical
algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an
interface between computational and biological assemblies. It is applied in various fields of biological
as well as medical sciences.
Aim:
The manuscript aims to summarize the developments in the field of breast cancer research
through the applications of bioinformatics.
Methods:
Various search engines like google, science direct, Scopus, PubMed, etc., were used for
the literature survey.
Results:
It describes the bioinformatics analysis tools and models, which include mainly artificial
neural network models.
Conclusion:
Bioinformatics is the evolutionary approach that is used for the capturing of data from
the various case studies related to breast cancer.
Collapse
Affiliation(s)
- Ramji Gupta
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, U.P.,India
| | - Nidhi Kala
- Saraswathi College of Pharmacy, Pilkhuwa, Hapur, U.P.,India
| | - Aravinda Pai
- Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka,India
| | - Rishabha Malviya
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, U.P.,India
| |
Collapse
|
2
|
Yamada K, Nishimura T, Wakiya M, Satoh E, Fukuda T, Amaya K, Bando Y, Hirano H, Ishikawa T. Protein co-expression networks identified from HOT lesions of ER+HER2-Ki-67high luminal breast carcinomas. Sci Rep 2021; 11:1705. [PMID: 33462336 PMCID: PMC7814020 DOI: 10.1038/s41598-021-81509-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/05/2021] [Indexed: 01/20/2023] Open
Abstract
Patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative/Ki-67-high (ER+HER2-Ki-67high) luminal breast cancer have a worse prognosis and do not respond to hormonal treatment and chemotherapy. This study sought to identify disease-related protein networks significantly associated with this subtype, by assessing in-depth proteomes of 10 lesions of high and low Ki-67 values (HOT, five; COLD, five) microdissected from the five tumors. Weighted correlation network analysis screened by over-representative analysis identified the five modules significantly associated with the HOT lesions. Pathway enrichment analysis, together with causal network analysis, revealed pathways of ribosome-associated quality controls, heat shock response by oxidative stress and hypoxia, angiogenesis, and oxidative phosphorylation. A semi-quantitative correlation of key-protein expressions, protein co-regulation analysis, and multivariate correlation analysis suggested co-regulations via network-network interaction among the four HOT-characteristic modules. Predicted highly activated master and upstream regulators were most characteristic to ER-positive breast cancer and associated with oncogenic transformation, as well as resistance to chemotherapy and endocrine therapy. Interestingly, inhibited intervention causal networks of numerous chemical inhibitors were predicted within the top 10 lists for the WM2 and WM5 modules, suggesting involvement of potential therapeutic targets in those data-driven networks. Our findings may help develop therapeutic strategies to benefit patients.
Collapse
Affiliation(s)
- Kimito Yamada
- Department of Breast Surgery, Tokyo Medical University Hachioji Medical Centre, Tokyo, 193-0998, Japan
- Department of Breast Surgery, Tokyo Medical University Hospital, Tokyo, 160-0023, Japan
| | - Toshihide Nishimura
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine, Kanagawa, 216-8511, Japan.
| | - Midori Wakiya
- Department of Diagnostic Pathology, Tokyo Medical University Hachioji Medical Centre, Tokyo, 193-0998, Japan
| | - Eiichi Satoh
- Department of Pathology, Institute of Medical Science, Tokyo Medical University, Tokyo, 160-0023, Japan
| | - Tetsuya Fukuda
- Research and Development, Biosys Technologies Inc, Tokyo, 152-0031, Japan
| | - Keigo Amaya
- Department of Breast Surgery, Tokyo Medical University Hachioji Medical Centre, Tokyo, 193-0998, Japan
| | - Yasuhiko Bando
- Research and Development, Biosys Technologies Inc, Tokyo, 152-0031, Japan
| | - Hiroshi Hirano
- Department of Diagnostic Pathology, Tokyo Medical University Hachioji Medical Centre, Tokyo, 193-0998, Japan
| | - Takashi Ishikawa
- Department of Breast Surgery, Tokyo Medical University Hospital, Tokyo, 160-0023, Japan
| |
Collapse
|
3
|
Caveolin-1 Modulates Mechanotransduction Responses to Substrate Stiffness through Actin-Dependent Control of YAP. Cell Rep 2019; 25:1622-1635.e6. [PMID: 30404014 PMCID: PMC6231326 DOI: 10.1016/j.celrep.2018.10.024] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 08/16/2018] [Accepted: 10/03/2018] [Indexed: 02/04/2023] Open
Abstract
The transcriptional regulator YAP orchestrates many cellular functions, including tissue homeostasis, organ growth control, and tumorigenesis. Mechanical stimuli are a key input to YAP activity, but the mechanisms controlling this regulation remain largely uncharacterized. We show that CAV1 positively modulates the YAP mechanoresponse to substrate stiffness through actin-cytoskeleton-dependent and Hippo-kinase-independent mechanisms. RHO activity is necessary, but not sufficient, for CAV1-dependent mechanoregulation of YAP activity. Systematic quantitative interactomic studies and image-based small interfering RNA (siRNA) screens provide evidence that this actin-dependent regulation is determined by YAP interaction with the 14-3-3 protein YWHAH. Constitutive YAP activation rescued phenotypes associated with CAV1 loss, including defective extracellular matrix (ECM) remodeling. CAV1-mediated control of YAP activity was validated in vivo in a model of pancreatitis-driven acinar-to-ductal metaplasia. We propose that this CAV1-YAP mechanotransduction system controls a significant share of cell programs linked to these two pivotal regulators, with potentially broad physiological and pathological implications.
Collapse
|
4
|
Mueller C, Haymond A, Davis JB, Williams A, Espina V. Protein biomarkers for subtyping breast cancer and implications for future research. Expert Rev Proteomics 2018; 15:131-152. [PMID: 29271260 PMCID: PMC6104835 DOI: 10.1080/14789450.2018.1421071] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.
Collapse
Affiliation(s)
- Claudius Mueller
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| | - Amanda Haymond
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| | - Justin B Davis
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| | - Alexa Williams
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| | - Virginia Espina
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| |
Collapse
|
5
|
Ortea I, Ruiz-Sánchez I, Cañete R, Caballero-Villarraso J, Cañete MD. Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection. J Proteomics 2018; 175:105-113. [PMID: 29317355 DOI: 10.1016/j.jprot.2018.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 11/30/2017] [Accepted: 01/04/2018] [Indexed: 12/13/2022]
Abstract
A typical clinical manifestation of growth hormone deficiency (GHD) is a short stature resulting from delayed growth, but GHD affects bone health, cardiovascular function and metabolic profile and therefore quality of life. Although early GH treatment during childhood has been shown to improve outcomes, no single biochemical parameter is currently available for the accurate diagnosis of GHD in children. There is hence a need for non-invasive biomarkers. In this study, the relative abundance of serum proteins from GHD children and healthy controls was measured by next-generation proteomics SWATH-MS technology. The data generated was analysed by machine-learning feature-selection algorithms in order to discover the minimum number of protein biomarkers that best discriminate between both groups. The analysis of serum proteins by a SWATH-MS approach yielded a useful method for discovering potential biomarkers of GHD in children. A total of 263 proteins were confidently detected and quantified in each sample. Pathway analysis indicated an effect on tissue/organ structure and morphogenesis. The top ten serum protein biomarker candidates were identified after applying feature-selection data analysis. The combination of three proteins - apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein - demonstrated the best classification performance for our data. In addition, the apolipoprotein group resulted in strong over-representation, thus highlighting these proteins as an additional promising biomarker panel. SIGNIFICANCE Currently there is no single biochemical parameter available for the accurate diagnosis of growth hormone (GH) deficiency (GHD) in children. Simple GH measurements are not an option: because GH is released in a pulsatile action, its blood levels fluctuate throughout the day and remain nearly undetectable for most of that time. This makes measurements of GH in a single blood sample useless for assessing GH deficiency. Actually, the diagnosis of GHD includes a combination of direct and indirect non-accurate measurements, such as taking several body measurements, testing GH levels in multiple blood samples after provocative tests (GH peak <7.3ng/mL, using radioimmunoassay), and conducting magnetic resonance imaging (MRI), among others. Therefore, there is a need for simple, non-invasive, accurate and cost-effective biomarkers. Here we report a case-control study, where relative abundance of serum proteins were measured by next-generation proteomics SWATH-MS technology in 15 GHD children and 15healthy controls matched by age, sex, and not receiving any treatment. Data generated was analysed by machine learning feature selection algorithms. 263 proteins could be confidently detected and quantified on each sample. The top 10 serum protein biomarker candidates could be identified after applying a feature selection data analysis. The combination of three proteins, apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein, showed the best classification performance for our data. In addition, the fact that the pathway and GO analysis we performed pointed to the apolipoproteins as over-represented highlights this protein group as an additional promising biomarker panel for the diagnosis of GHD and for treatment evaluation.
Collapse
Affiliation(s)
- Ignacio Ortea
- Proteomics Unit, IMIBIC, Hospital Universitario Reina Sofía, Universidad de Córdoba, Cordoba, Spain.
| | | | - Ramón Cañete
- Universidad de Córdoba, Córdoba, Spain; GA-05, IMIBIC, Córdoba, Spain
| | | | | |
Collapse
|
6
|
Borrebaeck CAK. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat Rev Cancer 2017; 17:199-204. [PMID: 28154374 DOI: 10.1038/nrc.2016.153] [Citation(s) in RCA: 252] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.
Collapse
Affiliation(s)
- Carl A K Borrebaeck
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Medicon Village (Bldg 406), Lund University, 223 81 Lund, Sweden
| |
Collapse
|
7
|
Oh S, Kim HS. Emerging power of proteomics for delineation of intrinsic tumor subtypes and resistance mechanisms to anti-cancer therapies. Expert Rev Proteomics 2016; 13:929-939. [PMID: 27599289 DOI: 10.1080/14789450.2016.1233063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Despite extreme genetic heterogeneity, tumors often show similar alterations in the expression, stability, and activation of proteins important in oncogenic signaling pathways. Thus, classifying tumor samples according to shared proteomic features may help facilitate the identification of cancer subtypes predictive of therapeutic responses and prognostic for patient outcomes. Meanwhile, understanding mechanisms of intrinsic and acquired resistance to anti-cancer therapies at the protein level may prove crucial to devising reversal strategies. Areas covered: Herein, we review recent advances in quantitative proteomic technology and their applications in studies to identify intrinsic tumor subtypes of various tumors, to illuminate mechanistic aspects of pharmacological and oncogenic adaptations, and to highlight interaction targets for anti-cancer compounds and cancer-addicted proteins. Expert commentary: Quantitative proteomic technologies are being successfully employed to classify tumor samples into distinct intrinsic subtypes, to improve existing DNA/RNA based classification methods, and to evaluate the activation status of key signaling pathways.
Collapse
Affiliation(s)
- Sejin Oh
- a Brain Korea 21 Project for Medical Science, Severance Biomedical Science Institute , Yonsei University College of Medicine , Seoul , Korea
| | - Hyun Seok Kim
- a Brain Korea 21 Project for Medical Science, Severance Biomedical Science Institute , Yonsei University College of Medicine , Seoul , Korea
| |
Collapse
|
8
|
von der Heyde S, Sonntag J, Kramer F, Bender C, Korf U, Beißbarth T. Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data. Methods Mol Biol 2016; 1362:227-246. [PMID: 26519181 DOI: 10.1007/978-1-4939-3106-4_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this chapter, we describe an approach to reconstruct cellular signaling networks based on measurements of protein activation after different stimulation experiments. As experimental platform reverse-phase protein arrays (RPPA) are used. RPPA allow the measurement of proteins and phosphoproteins across many samples in parallel with minimal sample consumption using a panel of highly target protein-specific antibodies. Functional interactions of proteins are modeled using a Boolean network. We describe the Boolean network reconstruction approach ddepn (dynamic deterministic effects propagation networks), which uses time course data to derive protein interactions based on perturbation experiments. We explain how the method works, give a practical application example, and describe how the results can be interpreted. Furthermore prior knowledge on signaling pathways is essential for network reconstruction. Here we describe the use of our software rBiopaxParser to integrate prior knowledge on protein signaling available in public databases. All applied methods are freely available as open-source R software packages. We describe the preparation of RPPA data as well as all relevant programming steps to format the RPPA data, to infer the prior knowledge, and to reconstruct and analyze the protein signaling networks.
Collapse
Affiliation(s)
- Silvia von der Heyde
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
- IndivuTest GmbH, Falkenried 88, 20251, Hamburg, Germany.
| | - Johanna Sonntag
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frank Kramer
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Bender
- TRON-Translational Oncology at the University Medical Center Mainz, Mainz, Germany
| | - Ulrike Korf
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim Beißbarth
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| |
Collapse
|
9
|
Wachter A, Bernhardt S, Beissbarth T, Korf U. Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery. ACTA ACUST UNITED AC 2015; 4:520-39. [PMID: 27600238 PMCID: PMC4996411 DOI: 10.3390/microarrays4040520] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Revised: 10/12/2015] [Accepted: 10/20/2015] [Indexed: 12/21/2022]
Abstract
Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.
Collapse
Affiliation(s)
- Astrid Wachter
- Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Goettingen, Humboldtallee 32, D-37073 Goettingen, Germany.
| | | | - Tim Beissbarth
- Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Goettingen, Humboldtallee 32, D-37073 Goettingen, Germany.
| | | |
Collapse
|
10
|
Kaddi CD, Wang MD. Models for Predicting Stage in Head and Neck Squamous Cell Carcinoma Using Proteomic and Transcriptomic Data. IEEE J Biomed Health Inform 2015; 21:246-253. [PMID: 26462248 DOI: 10.1109/jbhi.2015.2489158] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Late diagnosis is one of the reasons that head and neck squamous cell carcinoma (HNSCC) patients experience relative five-year survival rates ranging from 40%-66%. The molecular-level differences between early and advanced stage HNSCC may provide insight into therapeutic targets and strategies. Previous bioinformatics studies have shown mixed or limited results in identifying gene and protein markers and in developing models for discriminating between early and advanced stage HNSCC. Thus, we have investigated models for HNSCC stage prediction using RNAseq and reverse phase protein array data from The Cancer Genome Atlas and The Cancer Proteome Atlas. We systematically assessed individual and ensemble binary classifiers, using filter and wrapper feature selection methods, to develop several well-performing models. In particular, integrated models harnessing both data types consistently resulted in better performance. This study identifies informative protein and gene feature sets which may increase understanding of HNSCC progression.
Collapse
|
11
|
Sonntag J, Schlüter K, Bernhardt S, Korf U. Subtyping of breast cancer using reverse phase protein arrays. Expert Rev Proteomics 2015; 11:757-70. [PMID: 25400094 DOI: 10.1586/14789450.2014.971113] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Reverse phase protein arrays (RPPAs) present a robust and sensitive high capacity platform for targeted proteomics that relies on highly specific antibodies to obtain a quantitative readout regarding phosphorylation state and abundance of proteins of interest. This review summarizes the current state of RPPA-based proteomic profiling of breast cancer in the context of existing preanalytical strategies and sample preparation protocols. RPPA-based subtypes identified so far are compared to those obtained by other approaches such as immunohistochemistry, genomics and transcriptomics. Special attention is given to discussing the potential of RPPA for biomarker discovery and biomarker validation.
Collapse
Affiliation(s)
- Johanna Sonntag
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ) Im Neuenheimer Feld 580 69120 Heidelberg, Germany
| | | | | | | |
Collapse
|
12
|
Boellner S, Becker KF. Recent progress in protein profiling of clinical tissues for next-generation molecular diagnostics. Expert Rev Mol Diagn 2015. [DOI: 10.1586/14737159.2015.1070098] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
13
|
Zhang PW, Chen L, Huang T, Zhang N, Kong XY, Cai YD. Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One 2015; 10:e0123147. [PMID: 25822500 PMCID: PMC4378934 DOI: 10.1371/journal.pone.0123147] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 02/24/2015] [Indexed: 12/20/2022] Open
Abstract
Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.
Collapse
Affiliation(s)
- Pei-Wei Zhang
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, P.R. China
| | - Tao Huang
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
- * E-mail: (TH); (NZ); (XYK); (YDC)
| | - Ning Zhang
- Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, P.R. China
- * E-mail: (TH); (NZ); (XYK); (YDC)
| | - Xiang-Yin Kong
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
- * E-mail: (TH); (NZ); (XYK); (YDC)
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, P.R. China
- * E-mail: (TH); (NZ); (XYK); (YDC)
| |
Collapse
|
14
|
Boellner S, Becker KF. Reverse Phase Protein Arrays-Quantitative Assessment of Multiple Biomarkers in Biopsies for Clinical Use. MICROARRAYS 2015; 4:98-114. [PMID: 27600215 PMCID: PMC4996393 DOI: 10.3390/microarrays4020098] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 03/09/2015] [Accepted: 03/18/2015] [Indexed: 12/19/2022]
Abstract
Reverse Phase Protein Arrays (RPPA) represent a very promising sensitive and precise high-throughput technology for the quantitative measurement of hundreds of signaling proteins in biological and clinical samples. This array format allows quantification of one protein or phosphoprotein in multiple samples under the same experimental conditions at the same time. Moreover, it is suited for signal transduction profiling of small numbers of cultured cells or cells isolated from human biopsies, including formalin fixed and paraffin embedded (FFPE) tissues. Owing to the much easier sample preparation, as compared to mass spectrometry based technologies, and the extraordinary sensitivity for the detection of low-abundance signaling proteins over a large linear range, RPPA have the potential for characterization of deregulated interconnecting protein pathways and networks in limited amounts of sample material in clinical routine settings. Current aspects of RPPA technology, including dilution curves, spotting, controls, signal detection, antibody validation, and calculation of protein levels are addressed.
Collapse
Affiliation(s)
- Stefanie Boellner
- Institut für Pathologie, Technische Universität München, Trogerstrasse 18, 81675 München, Germany.
| | - Karl-Friedrich Becker
- Institut für Pathologie, Technische Universität München, Trogerstrasse 18, 81675 München, Germany.
| |
Collapse
|
15
|
List M, Block I, Pedersen ML, Christiansen H, Schmidt S, Thomassen M, Tan Q, Baumbach J, Mollenhauer J. Microarray R-based analysis of complex lysate experiments with MIRACLE. ACTA ACUST UNITED AC 2015; 30:i631-8. [PMID: 25161257 PMCID: PMC4147925 DOI: 10.1093/bioinformatics/btu473] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected. Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis. Availability: Project URL: http://www.nanocan.org/miracle/ Contact: mlist@health.sdu.dk Supplementary information:Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Markus List
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Ines Block
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Marlene Lemvig Pedersen
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Helle Christiansen
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Steffen Schmidt
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Mads Thomassen
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Qihua Tan
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Jan Baumbach
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| | - Jan Mollenhauer
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, Molecular Oncology, Institute of Molecular Medicine, Human Genetics, Institute of Clinical Research, Epidemiology, Biostatistics and Biodemography, Institute of Public Health and Department of Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark
| |
Collapse
|
16
|
RPPanalyzer toolbox: an improved R package for analysis of reverse phase protein array data. Biotechniques 2014; 57:125-35. [PMID: 25209047 DOI: 10.2144/000114205] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 07/20/2014] [Indexed: 11/23/2022] Open
Abstract
Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new time course plotting function. We demonstrate the robustness of our enhanced RPPanalyzer platform by analyzing longitudinal RPPA data of MET receptor signaling upon stimulation with different hepatocyte growth factor concentrations.
Collapse
|