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Shin IJ, Tangrea M, Emmert-Buck M, Johann DJ. A Microdissection Protocol for Proteogenomic Analysis of Histological Sections to Advance Drug Development. Methods Mol Biol 2024; 2823:55-75. [PMID: 39052214 DOI: 10.1007/978-1-0716-3922-1_5] [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: 07/27/2024]
Abstract
Combining proteogenomics with laser capture microdissection (LCM) in cancer research offers a targeted way to explore the intricate interactions between tumor cells and the different microenvironment components. This is especially important for immuno-oncology (IO) research where improvements in the predictability of IO-based drugs are sorely needed, and depends on a better understanding of the spatial relationships involving the tumor, blood supply, and immune cell interactions, in the context of their associated microenvironments. LCM is used to isolate and obtain distinct histological cell types, which may be routinely performed on complex and heterogeneous solid tumor specimens. Once cells have been captured, nucleic acids and proteins may be extracted for in-depth multimodality molecular profiling assays. Optimizing the minute tissue quantities from LCM captured cells is challenging. Following the isolation of nucleic acids, RNA-seq may be performed for gene expression and DNA sequencing performed for the discovery and analysis of actionable mutations, copy number variation, methylation profiles, etc. However, there remains a need for highly sensitive proteomic methods targeting small-sized samples. A significant part of this protocol is an enhanced liquid chromatography mass spectrometry (LC-MS) analysis of micro-scale and/or nano-scale tissue sections. This is achieved with a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) approach developed for LC-MS analysis of fresh-frozen tissue specimens obtained via LCM. Included is a detailed in-gel digestion method adjusted and specifically designed to maximize the proteome coverage from amount-limited LCM samples to better facilitate in-depth molecular profiling. Described is a proteogenomic approach leveraged from microdissected fresh frozen tissue. The protocols may also be applicable to other types of specimens having limited nucleic acids, protein quantity, and/or sample volume.
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Affiliation(s)
- Ik Jae Shin
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Michael Tangrea
- Department of Biology, Loyola University Maryland, Baltimore, MD, USA
| | | | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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2
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Punetha A, Kotiya D. Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice. Proteomes 2023; 11:2. [PMID: 36648960 PMCID: PMC9844371 DOI: 10.3390/proteomes11010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.
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Affiliation(s)
- Ankita Punetha
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, Rutgers University, 225 Warren St., Newark, NJ 07103, USA
| | - Deepak Kotiya
- Department of Pharmacology and Nutritional Sciences, University of Kentucky, 900 South Limestone St., Lexington, KY 40536, USA
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Ye X, Luke BT, Wei BR, Kaczmarczyk JA, Loncarek J, Dwyer JE, Johann DJ, Saul RG, Nissley DV, McCormick F, Whiteley GR, Blonder J. Direct molecular dissection of tumor parenchyma from tumor stroma in tumor xenograft using mass spectrometry-based glycoproteomics. Oncotarget 2018; 9:26431-26452. [PMID: 29899869 PMCID: PMC5995176 DOI: 10.18632/oncotarget.25449] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 05/02/2018] [Indexed: 12/18/2022] Open
Abstract
The most widely used cancer animal model is the human-murine tumor xenograft. Unbiased molecular dissection of tumor parenchyma versus stroma in human-murine xenografts is critical for elucidating dysregulated protein networks/pathways and developing therapeutics that may target these two functionally codependent compartments. Although antibody-reliant technologies (e.g., immunohistochemistry, imaging mass cytometry) are capable of distinguishing tumor-proper versus stromal proteins, the breadth or extent of targets is limited. Here, we report an antibody-free targeted cross-species glycoproteomic (TCSG) approach that enables direct dissection of human tumor parenchyma from murine tumor stroma at the molecular/protein level in tumor xenografts at a selectivity rate presently unattainable by other means. This approach was used to segment/dissect and obtain the protein complement phenotype of the tumor stroma and parenchyma of the metastatic human lung adenocarcinoma A549 xenograft, with no need for tissue microdissection prior to mass-spectrometry analysis. An extensive molecular map of the tumor proper and the associated microenvironment was generated along with the top functional N-glycosylated protein networks enriched in each compartment. Importantly, immunohistochemistry-based cross-validation of selected parenchymal and stromal targets applied on human tissue samples of lung adenocarcinoma and normal adjacent tissue is indicative of a noteworthy translational capacity for this unique approach that may facilitate identifications of novel targets for next generation antibody therapies and development of real time preclinical tumor models.
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Affiliation(s)
- Xiaoying Ye
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Brian T. Luke
- Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Bih-Rong Wei
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jan A. Kaczmarczyk
- Cancer Research Technology Program, Antibody Characterization Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Jadranka Loncarek
- Laboratory of Protein Dynamics and Signaling, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Jennifer E. Dwyer
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Donald J. Johann
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72209, USA
| | - Richard G. Saul
- Cancer Research Technology Program, Antibody Characterization Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Dwight V. Nissley
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Frank McCormick
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94158, USA
| | - Gordon R. Whiteley
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
| | - Josip Blonder
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702, USA
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Protocol for the Analysis of Laser Capture Microdissected Fresh-Frozen Tissue Homogenates by Silver-Stained 1D SDS-PAGE. Methods Mol Biol 2018; 1723:95-110. [PMID: 29344855 DOI: 10.1007/978-1-4939-7558-7_4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The heterogeneity present in solid tumors adds significant difficulty to scientific analysis and improved understanding. Fundamentally, solid tumor formation consists of cancer cells proper along with stromal elements. The burgeoning malignant process is dependent upon modified stromal elements. Collectively, the stroma forms an essential microenvironment, which is indispensable for the survival and growth of the malignant neoplasm. This cellular heterogeneity makes molecular profiling of solid tumors via mass spectrometry (MS)-based proteomics a daunting task. Laser capture microdissection (LCM) is commonly used to obtain distinct histological cell types (e.g., tumor parenchymal cells, stromal cells) from tumor tissue and attempt to address the tumor heterogeneity interference with downstream liquid chromatography (LC) MS analysis. To provide optimal LC-MS analysis of micro-scale and/or nano-scale tissue sections, we modified and optimized a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) protocol for the LC-MS analysis of LCM-procured fresh-frozen tissue specimens. Presented is a detailed in-gel digestion protocol adjusted specifically to maximize the proteome coverage of amount-limited LCM samples, and facilitate in-depth molecular profiling. Following LCM, targeted tissue sections are further fractionated using silver-stained 1D-SDS-PAGE to resolve and visualize tissue proteins prior to in-gel digestion and subsequent LC-MS analysis.
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Delcourt V, Franck J, Quanico J, Gimeno JP, Wisztorski M, Raffo-Romero A, Kobeissy F, Roucou X, Salzet M, Fournier I. Spatially-Resolved Top-down Proteomics Bridged to MALDI MS Imaging Reveals the Molecular Physiome of Brain Regions. Mol Cell Proteomics 2017; 17:357-372. [PMID: 29122912 PMCID: PMC5795397 DOI: 10.1074/mcp.m116.065755] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 10/11/2017] [Indexed: 12/14/2022] Open
Abstract
Tissue spatially-resolved proteomics was performed on 3 brain regions, leading to the characterization of 123 reference proteins. Moreover, 8 alternative proteins from alternative open reading frames (AltORF) were identified. Some proteins display specific post-translational modification profiles or truncation linked to the brain regions and their functions. Systems biology analysis performed on the proteome identified in each region allowed to associate sub-networks with the functional physiology of each brain region. Back correlation of the proteins identified by spatially-resolved proteomics at a given tissue localization with the MALDI MS imaging data, was then performed. As an example, mapping of the distribution of the matrix metallopeptidase 3-cleaved C-terminal fragment of α-synuclein (aa 95–140) identified its specific distribution along the hippocampal dentate gyrus. Taken together, we established the molecular physiome of 3 rat brain regions through reference and hidden proteome characterization.
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Affiliation(s)
- Vivian Delcourt
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France.,§Département de Biochimie Lab. Z8-2001, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Canada
| | - Julien Franck
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France
| | - Jusal Quanico
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France
| | - Jean-Pascal Gimeno
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France
| | - Maxence Wisztorski
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France
| | - Antonella Raffo-Romero
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France
| | - Firas Kobeissy
- ¶Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Xavier Roucou
- §Département de Biochimie Lab. Z8-2001, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Canada
| | - Michel Salzet
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France;
| | - Isabelle Fournier
- From the ‡Laboratoire Proteomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM) - INSERM U1192, Université Lille 1, Bât SN3, 1 étage, Cité Scientifique, F-59655 Villeneuve d'Ascq Cedex, France;
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Visan S, Balacescu O, Berindan-Neagoe I, Catoi C. In vitro comparative models for canine and human breast cancers. ACTA ACUST UNITED AC 2016; 89:38-49. [PMID: 27004024 PMCID: PMC4777467 DOI: 10.15386/cjmed-519] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2015] [Accepted: 09/15/2015] [Indexed: 12/12/2022]
Abstract
During the past four decades, an increased number of similarities between canine mammary tumors and human breast cancer have been reported: molecular, histological, morphological, clinical and epidemiological, which lead to comparative oncological studies. One of the most important goals in human and veterinary oncology is to discover potential molecular biomarkers that could detect breast cancer in an early stage and to develop new effective therapies. Recently, cancer cell lines have successfully been used as an in vitro model to study the biology of cancer, to investigate molecular pathways and to test the efficiency of anticancer drugs. Moreover, establishment of an experimental animal model for the study of human breast cancer will improve testing potential anti-cancer therapies and the discovery of effective therapeutic schemes suitable for human clinical trials. In this review, we collected data from previous studies that strengthen the value of canine mammary cancer cell lines as an in vitro model for the study of human breast cancer.
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Affiliation(s)
- Simona Visan
- Department of Pathological Anatomy, Necropsy and Veterinary Forensic Medicine, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania; Department of Functional Genomics, Proteomics and Experimental Pathology, Prof. Dr. Ion Chiricuta Oncology Institute, Cluj-Napoca, Romania
| | - Ovidiu Balacescu
- Department of Functional Genomics, Proteomics and Experimental Pathology, Prof. Dr. Ion Chiricuta Oncology Institute, Cluj-Napoca, Romania
| | - Ioana Berindan-Neagoe
- Department of Functional Genomics, Proteomics and Experimental Pathology, Prof. Dr. Ion Chiricuta Oncology Institute, Cluj-Napoca, Romania; Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; Department of Immunology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; Department of Experimental Therapeutics, MD Anderson Cancer Center, Houston, Texas, USA
| | - Cornel Catoi
- Department of Pathological Anatomy, Necropsy and Veterinary Forensic Medicine, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
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Frost AR, Eltoum I, Siegal GP, Emmert‐Buck MR, Tangrea MA. Laser Microdissection. ACTA ACUST UNITED AC 2015; 112:25A.1.1-25A.1.30. [DOI: 10.1002/0471142727.mb25a01s112] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Andra R. Frost
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | - Isam‐Eldin Eltoum
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | - Gene P. Siegal
- Department of Pathology, University of Alabama at Birmingham Birmingham Alabama
| | | | - Michael A. Tangrea
- Alvin & Lois Lapidus Cancer Institute, Sinai Hospital Baltimore Maryland
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8
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Unger C, Kramer N, Walzl A, Scherzer M, Hengstschläger M, Dolznig H. Modeling human carcinomas: physiologically relevant 3D models to improve anti-cancer drug development. Adv Drug Deliv Rev 2014; 79-80:50-67. [PMID: 25453261 DOI: 10.1016/j.addr.2014.10.015] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 09/02/2014] [Accepted: 10/15/2014] [Indexed: 12/18/2022]
Abstract
Anti-cancer drug development is inefficient, mostly due to lack of efficacy in human patients. The high fail rate is partly due to the lack of predictive models or the inadequate use of existing preclinical test systems. However, progress has been made and preclinical models were improved or newly developed, which all account for basic features of solid cancers, three-dimensionality and heterotypic cell interaction. Here we give an overview of available in vivo and in vitro models of cancer, which meet the criteria of being 3D and mirroring human tumor-stroma interactions. We only focus on drug response models without touching models for pharmacokinetic and dynamic, toxicity or delivery aspects.
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Mesri M. Advances in Proteomic Technologies and Its Contribution to the Field of Cancer. Adv Med 2014; 2014:238045. [PMID: 26556407 PMCID: PMC4590950 DOI: 10.1155/2014/238045] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2014] [Accepted: 06/30/2014] [Indexed: 12/12/2022] Open
Abstract
Systematic studies of the cancer genome have generated a wealth of knowledge in recent years. These studies have uncovered a number of new cancer genes not previously known to be causal targets in cancer. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies are not widely available for most cancers. Precision care plans still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics is continuing to make major strides in the discovery of fundamental biological processes as well as more recent transition into an assay platform capable of measuring hundreds of proteins in any biological system. As such, proteomics can translate basic science discoveries into the clinical practice of precision medicine. The proteomic field has progressed at a fast rate over the past five years in technology, breadth and depth of applications in all areas of the bioscience. Some of the previously experimental technical approaches are considered the gold standard today, and the community is now trying to come to terms with the volume and complexity of the data generated. Here I describe contribution of proteomics in general and biological mass spectrometry in particular to cancer research, as well as related major technical and conceptual developments in the field.
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Affiliation(s)
- Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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10
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Gorlov IP, Yang JY, Byun J, Logothetis C, Gorlova OY, Do KA, Amos C. How to get the most from microarray data: advice from reverse genomics. BMC Genomics 2014; 15:223. [PMID: 24656147 PMCID: PMC3997969 DOI: 10.1186/1471-2164-15-223] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 03/10/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes. RESULTS We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples. CONCLUSIONS The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes.
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Affiliation(s)
- Ivan P Gorlov
- Department of Genitourinary Medical Oncology, Unit 1374, The University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Houston, TX 77030-3721, USA.
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11
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Zamani-Ahmadmahmudi M, Nassiri SM, Rahbarghazi R. Serological proteome analysis of dogs with breast cancer unveils common serum biomarkers with human counterparts. Electrophoresis 2014; 35:901-10. [PMID: 24338489 DOI: 10.1002/elps.201300461] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2013] [Revised: 11/05/2013] [Accepted: 11/22/2013] [Indexed: 12/17/2022]
Abstract
Canine mammary tumor is being touted as a model for investigating the human breast cancer. Breast cancer of the both species has similar biological behavior, histopathologic characteristics, and metastatic pattern. In this study, we used the serological proteome analysis to detect autoantigens that elicit a humoral response in dogs with mammary tumor in order to identify serum biomarkers with potential usefulness as diagnostic markers and to better understand molecular mechanisms underlying canine breast cancer development. Protein extract from a cell line was subject to 2DE followed by Western blotting using sera from 15 dogs with mammary tumor and sera from 15 healthy control dogs. Immunoreactive autoantigens were subsequently identified by the MALDI-TOF MS. Four autoantigens, including manganese-superoxide dismutase, triose phosphate isomerase, alpha-enolase, and phosphoglycerate mutase1, with significantly higher immunoreactivity in the tumor samples than in the normal samples were identified as biomarker candidates. Immunohistochemistry and Western blotting revealed higher expression of these biomarkers in the malignant tumors than in the normal or benign tumors. The autoantigens found in this study have been reported to elicit autoantibody response in the human breast cancer, indicating the similarity of breast cancer proteome profile in dogs with that in human beings.
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Affiliation(s)
- Mohamad Zamani-Ahmadmahmudi
- Department of Clinical Pathology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran; Department of Clinical Sciences, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
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Isolation and characterization of a canine mammary cell line prepared for proteomics analysis. Tissue Cell 2013; 45:183-90. [DOI: 10.1016/j.tice.2012.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Revised: 10/28/2012] [Accepted: 11/29/2012] [Indexed: 11/18/2022]
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13
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Toponome imaging system: multiplex biomarkers in oncology. Trends Mol Med 2012; 18:723-31. [DOI: 10.1016/j.molmed.2012.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Revised: 10/03/2012] [Accepted: 10/09/2012] [Indexed: 12/30/2022]
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14
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Shapiro JP, Biswas S, Merchant AS, Satoskar A, Taslim C, Lin S, Rovin BH, Sen CK, Roy S, Freitas MA. A quantitative proteomic workflow for characterization of frozen clinical biopsies: laser capture microdissection coupled with label-free mass spectrometry. J Proteomics 2012; 77:433-40. [PMID: 23022584 DOI: 10.1016/j.jprot.2012.09.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Revised: 08/28/2012] [Accepted: 09/17/2012] [Indexed: 12/24/2022]
Abstract
This paper describes a simple, highly efficient and robust proteomic workflow for routine liquid-chromatography tandem mass spectrometry analysis of Laser Microdissection Pressure Catapulting (LMPC) isolates. Highly efficient protein recovery was achieved by optimization of a "one-pot" protein extraction and digestion allowing for reproducible proteomic analysis on as few as 500 LMPC isolated cells. The method was combined with label-free spectral count quantitation to characterize proteomic differences from 3000-10,000 LMPC isolated cells. Significance analysis of spectral count data was accomplished using the edgeR tag-count R package combined with hierarchical cluster analysis. To illustrate the capability of this robust workflow, two examples are presented: 1) analysis of keratinocytes from human punch biopsies of normal skin and a chronic diabetic wound and 2) comparison of glomeruli from needle biopsies of patients with kidney disease. Differentially expressed proteins were validated by use of immunohistochemistry. These examples illustrate that tissue proteomics carried out on limited clinical material can obtain informative proteomic signatures for disease pathogenesis and demonstrate the suitability of this approach for biomarker discovery.
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Affiliation(s)
- John P Shapiro
- Department of Molecular Virology, Immunology and Medical Genetics, College of Medicine, Columbus, OH 43210, USA
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15
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Electrostatic charge conversion processes in engineered tumor-identifying polypeptides for targeted chemotherapy. Biomaterials 2012; 33:1884-93. [DOI: 10.1016/j.biomaterials.2011.11.026] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Accepted: 11/13/2011] [Indexed: 11/19/2022]
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