Patterns of Proteomic Information in Serum Hypothesis

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Analysis of the Human Serum Proteome Dr. Timothy D. Veenstra Director, Laboratory of Proteomics and Analytical Technologies and NCI-Frederick Biomedical Proteomics Program

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TRANSLATIONAL RESEARCH Take research from the bench to bedside. Obligation to public health. Allow physicians to make better decisions in cancer management.

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Three Keys to Translational Cancer Research Early Detection Development of improved proteomics and bioinformatic tools for diagnostic medicine. Molecular Diagnostics New Target Discovery (Global Proteomics) Signal Transduction Pathway Profiling (Targeted Proteomics) Molecular Targeted Therapeutics Implementation of new technologies to ongoing NCI-based clinical trials.

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The Importance of Early Detection of Ovarian Cancer% 5 YR SURVIVAL% STAGE DISTRIBUTIONA SHIFT IN NUMBER OF PATIENTS DIAGNOSED AT EARLY STAGE WILL DRAMATICALLY EFFECT PATIENT SURVIVAL!

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Hence, the false negative rates using CA-125 will never be lower than 20%. CA 125: a high-molecular-weight glycoprotein.False Negative rates of 40-50% for stage I disease.CA-125 cannot be detected in tissue sections from 20% of ovarian cancers. Current Status of Ovarian Cancer ScreeningCA 125 is elevated in 83% of patients with ovarian cancer.

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Perfused TissueTissues are continuously perfused by serum -- their histopathology may be reflected in serum proteomic “patterns.”Patterns of Proteomic Information in SerumHypothesis: 1. Signature proteins are products of the tumor-host microenvironment, and thereby unique to the tissue site and pathophysiological state. 2. These biomarkers are likely to be modified or cleaved “reporter” proteins/peptides that are produced/amplified at the tumor/host interface, are released, and partition to circulating carrier proteins.

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“Proteomic” Mass SpectrumPatterns of Proteomic Information in SerumCAN PROTEIN PROFILING IDENTIFY PROTEIN EXPRESSION PATTERNS DIAGNOSTIC OF INVASIVE EPITHELIAL OVARIAN CANCER?

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Serum Proteomic Pattern Diagnostic WorkflowProtein ChipSerum

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WCX2 ProteinChip ArrayCiphergen SELDI-TOF MSABI QSTAR Pulsar QqTOF MS Application and Implementation of SELDI-QqTOF for Diagnostic ProteomicsWidely accessible Extensive m/z range (5-300,000) Low Resolution (~ 100-200) Low Mass Accuracy (~1000 ppm)More specialized knowledge required…? Limited m/z range? (5-12,000) Higher resolution (>9000 at m/z 1500) High mass accuracy (>50 ppm - external cal)

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a. Unaffected samplesb. Cancer samplesGenetic algorithm + self-organizing cluster analysisPhase I: Pattern DiscoveryPhase 2: Pattern MatchingLead diagnostic fingerprint (from training set)“Survival of the fittest” discriminatory Patterns that discriminate “a” from “b” in the training setTest/validation sample for diagnosis100020003000400050006000 m/z100020003000400050006000 m/zNormalCancerNewBioinformatic Analysis for the Discovery of Diagnostic Patterns

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A. 84 training samples (28 Unaffected and 56 Ovarian Cancer) B. 87 blind testing samples (30 Unaffected and 57 Cancer) C. 77 blind validation samples (37 Unaffected and 40 Cancer)Sample and Modeling BreakdownSamples obtained from National Ovarian Cancer Early Detection Program, Northwestern University (Director: Dr. David Fishman)Total: 153 Ovarian Cancer; 95 Unaffected

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Results: 100% sensitivity; 100% specificity Metrics of “High Fitness” Models from QqTOF DataConrads, T. P., Zhou, M., Petricoin, E, Liotta, L., and Veenstra, T. D., Expert Rev. Mol. Diagn., 3, 411-420.

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Pattern Recognition Method #3Pattern Recognition Method #2Pattern Recognition Method #1Key ion features selected (m/z)SensitivitySpecificity6851.505 2378.046 2371.398 6675.697 10070.302 2210.224 2620.747 4471.636 4914.232 5086.187 6649.053 6854.245 6854.456Benign vs. Malignant (Spiral CT +)98% 85%69%71%95% 89% 1028, 1035, 1050, 1289, 1980, 2080, 2210, 2212, 2365, 2366, 2485, 2589 2897, 3158, 3435, 3538, 3763, 4062, 4071, 4307, 4315, 4482, 4491, 4559 4643, 5138, 5139, 5800, 5861, 5879 6414, 6432, 6629, 6646, 6660, 6852 6978, 7834, 7835, 7908, 7922, 7923 7935, 7953, 8329, 8330, 8601, 8617 8619, 8634, 8913, 8931, 9120BLINDED TEST RESULTS: Collaborators: Denise Ching, Kim Lyerly, Sam Wells, David Harpole; Duke U.

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1000.02000.03000.04000.05000.06000.07000.08000.09000.01.0e41.1e41.2e4 1000.02000.03000.04000.05000.06000.07000.08000.09000.01.0e41.1e41.2e4.1000.02000.03000.04000.05000.06000.07000.08000.09000.01.0e41.1e41.2e4BenignAdenocarcinomaSquamousRelative Intensity (%)10050010050010050068502370237523652370237523702375236568206880685068206880m/z

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420% Relative Intensity6m/z68356875

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Do we detect clinical biomarkers such as CA125 or PSA in proteomic patterns using SELDI? Short Answer: No. Is this due to the sensitivity of the instrument? Short Answer: No, it is a dynamic range issue. A SELDI-TOF can detect below 10-12 mol/L. Will a straight MALDI approach and high resolution MS without specifically targeting PSA, for example, allow detection of these low abundant biomarkers? Short Answer: No (see above) Are we trying to detect PSA and CA125 Short Answer: No Do we need better ways of diagnosing early stage cancer beyond CA125 and PSA? Short answer: Absolutely. Are all of the steps necessary to make proteomic pattern diagnostics clinically useful being evaluated? Short answer: Absolutely.

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90%10%22 PROTEINS COMPRISE 99% OF THE PROTEIN MASS IN SERUM!Characterization of the Human Serum Proteome

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Human Serum Proteomic InvestigationThree tracks:Global serum proteome surveyLow molecular weight protein/peptide proteomeCan we account for the presence of histopathologically-related proteins/peptides in serum?Can we deplete the high molecular weight fraction for more effective interrogation of the source of the diagnostic information?Is there histopathological content bound to the highly abundant carrier proteins, such as albumin?Investigation of bound peptides to high abundant serum proteins

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Strong Cation Exchange (140 Fractions)Analyze by LC/MS/MSGlobal Serum Proteome Survey

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IEF473 Proteins 957 Unique PeptidesGLOBAL ANALYSIS OF THE SERUM PROTEOME bpp.nci.nih.govAnalysis of the Human Serum Proteome King C. Chan, David A. Lucas, Denise Hise, Carl F. Schaefer, Zhen Xiao, George M. Janini, Kenneth H. Buetow, Haleem J. Issaq, Timothy D. Veenstra and Thomas P. Conrads Clinical Proteomics (2004) In Press

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Analysis of Identified Human Serum ProteinsMolecular FunctionBiological Processes

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Cellular Component of Human Serum ProteinsExtracellular15%virion<1%cellular component unknown7%Nuclear 30%Membrane 39%Intracellular 8%Endoplasmic Reticulum 3%Cytoskeletal 3%Golgi 2%Mitochondrial 4%Lysosomal 1%Extracellular 8%Cytoplasmic 3%GO of Human ProteomeGO of Human Serum Proteome

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Human Serum Proteomic InvestigationThree tracks:Global serum proteome surveyLow molecular weight protein/peptide proteomeCan we account for the presence of histopathologically-related proteins in serum?Can we deplete the high molecular weight fraction for more effective interrogation of the source of the diagnostic information?Is there histopathological content bound to the highly abundant carrier proteins, such as albumin?Investigation of bound peptides to high abundant serum proteins

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Last Updated: 8th March 2018

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