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The Critical Role of Algorithms in the Interpretation of Multiplexed Protein Biomarker ... · 2008....
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The Critical Role of Algorithms in the Interpretation of Multiplexed
Protein Biomarker PanelsStephen TC Wong
Departments of Radiology and Pathology, The Methodist HospitalThe Center for Biotechnology and Informatics, TMH Research Institute,
Weill Cornell Medical College
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Common Proteomics Techniques
2D gel analysisKronberg, et al, Clin Chem. 1984
MS (Mass Spectrometry) analysis Blonder, J. et al. Nature Protocols 2007Protein array analysis
Pajak, B. et al. J Physiol Pharmacol. 2005
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Sample CollectionSerum/plasmaTissue/bio-fluid
Experimental design
MS Proteomics Operation FlowchartHigh throughput
Sample processingLab robot
Enrichment Fractionation
Albumin depletion
Albumin-bound
Magnetic beads
Mass Spec
BioinformaticsStatistics Algorithms
ImmunoassayValidation
Multiple biomarker
Diagnostic kit
Tissue array
ELISA tests
Protein array
SELDI
MALDI
High resolution
Data preprocessing
Feature selection
Biomarker discovery
Protein identification
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Comparison of Proteomics Platforms
Negm et al, TRENDS in Molecular Medicine Vol.8 No.6 June 2002
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Comparison of MALDI MS Types
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Challenges of Sample Prep
Is blood the best place to look?
Extraordinary dynamic range in blood proteins
Depletion of abundant or dominant plasma proteins is not without risk as these proteins may act as carriers for low-abundance molecules
Variations due to environmental factors: genetic polymorphism, gender, age, ethnicity, life style, dietary influences, diurnal factors, and co-morbidities
Variations arising from experimentation: blood collection, processing time, sample preparation, storage temperature, and variable release of platelet contents.
Current Opinion in Chemical Biology 2008 12:72-77
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Challenges for Peptidome
Does the blood ‘peptidome’ reveal the disease?A number of peptides may be missed or wrongly assigned due to experimental limitations related to blood collection, processing, and storage protocols
Current Opinion in Chemical Biology 2008, 12:72-77
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Algorithms: Baseline Removal
From: Methods in Molecular Biology, vol.328: new and Emerging proteomic Techniques
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Algorithms : Normalization
http://www.mathworks.com/products/demos/bioinfo/massspec_prepro/mspreprodemo_07.png
Normalizes a group of mass spectra
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Algorithms : DenoisingWavelet
Estimate noiseProteomics 2005, 5:4107-4117
DOI 10.1002/mass.20072
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Algorithms : Peak detection
Mean Max
Doi: 10.1093/bioinformatics/bt254
Proteomics 2006,6:5106-5116
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Reversible Jump MCMC Approach of Peak detection for SELDI-MS
Yuan Wang et al, Bioinformatics, 2008
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A Bayesian Peak Detection Algorithm for PrOTOF MALDI-MS
Jianqiu Zhang et al, Bioinformatics,2008
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Algorithms: Peak alignment
Msignt software
W.Yu et al. Computational Biology and Chemistry, 2006DOI 10.1002/mas.20072
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Algorithms: Feature Selection for Biomarker Discovery
T test; F test
Genetic algorithm
Natural inspired
Random forest/random decision tree
Weight vector of SVM
Neural network
Saeys et al, Bioinformatics. 2007
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Panel biomarkers using genetic algorithms with Recursive Local Floating Search for feature selection
Xiaobo Zhou et al, Proteomics, in revision, 2008
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Algorithms: Classification
KNN
PCA
Decision Tree (DT)− SVM (Support Vector Machine)
− Decision Tree (DT)
− Neural Network
− KNN (K-Nearest Neighbors)
− PCA (Principal Component Analysis)
− LDA, QDA, Logistic Regression
http://www.mirrorservice.org/sites/home.ubalt.edu/ntsbarsh/Business-stat/opre/RiskTree.gifhttp://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/279px-KnnClassification.svg.png
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Algorithms: Protein/Peptide Identification
http://www.scq.ubc.ca/wp-content/uploads/2006/08/Proteomics.gif
http://news.thomasnet.com/images/large/801/801252.jpg
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Problems in MS Proteomics
Inconsistent and often irreproducible results due to
different methods and algorithms.
Feature selection is a NP-hard (exponential) problem
Classification: Models selected, parameters chosen,
and overfitting
Difficult to scale up for high throughput protein
identification.
Diverse strategies, algorithms, parameters result in:
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Validation via Co-Immunoprecipitation
http://www.piercenet.com/Products/Browse.cfm?fldID=9C471132-0F72-4F39-8DF0-455FB515718F
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KIYONAKA et al, Semi-wet peptide/protein array using supramolecular hydrogel, nature materials, 2004
Validation via Protein Array
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Validation via Tissue Array
• Validates genes or proteins identified by genomic or proteomic analysis
• Validates new biomarkers of tumor progression
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Validation via Nanoparticles
http://www.nsti.org/news/item.html?id=140
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Current Opinion in Chemical Biology, Volume 12, Issue 1, February 2008, Pages 72-77
What’s the problem?
Confronting the Biomarker Verification Bottleneck
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New FDA-Approved (CLIA) Diagnostic Protein Tests in Serum/Plasma Declined for the Last Decade
Anderson, N. L. Anderson, N. G., The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics (2002) 1:845-67.
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Successful Clinical Biomarkers Found So Far by Proteomics
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L Anderson, Plasma Proteome Institute
1. Inefficient biomarker discovery processes
2. Lack of verification technology & pipelineSourcing new markers
3. No solid theory or uniform framework of biomarker discovery
4. Slow adoption of new technology platforms
5. Combining patented markersMultiplex panels
6. Lack of regulatory precedents (FDA)
7. Slow physician acceptance
8. Poor public understanding of IVDHealthcare system
9. Positioning of the IVD industry in healthcare
. Major Barriers to New Protein IVD Tests
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ChallengesTissues versus fluids
Validation:
In vitroIn vitro In vivoIn vivo
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Identify Lung-induced Endothelial Cell Surface Proteins
2D gels analysis
MS/MS identify target proteinsSPECT imaging after injection of APP antibody
Phil Oh, et al. NATURE 2004
Proteomics analysis + SPECT analysis (Animal)
Validation in vivo: Medical Imaging
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Payload probeLabelTracerTherapeutic
Targeting LigandAntibodyLow-weight organic
moleculepharma chemistryViruses (gene therapy)
Molecular Target
Targeted Probes Medical ImagingHigh-sensitivity, high-resolution in vivo imaging
In Vivo Molecular Imaging
Drug Development - Bring to market life saving drugs tailored to individual’s genetic make-up faster
Therapy Monitoring - See drugs hitting in-vivo targets in real time
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Functional genomics/molecular diagnosis
Molecular and functionalimaging for disease diagnosis and targeted therapeutic
Iterative Paradigm Between Top Down Imaging and Bottom Up Biology
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ConclusionsBioinformatics algorithms play a key role in protein biomarker discovery− Challenges in improving the reliability, consistency,
and robustness of algorithms
The gap between biomarker discovery and biomarker validation is a major barrier to application of biomarkers in clinics.− The validation of protein biomarker should be
changed from in vitro to in vivo
Improving diagnostic medicine involves major scientific, technical and policy challenges
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Thank You