Proteomic fingerprinting initiatives. Dr Dan Agranoff Imperial College, London Hammersmith Hospitals NHS Trust

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1 Proteomic fingerprinting initiatives Dr Dan Agranoff Imperial College, London Hammersmith Hospitals NHS Trust

2 Overview Biomarkers Proteomic fingerprints Serum proteome Mass spectrometry Pattern recognition Current projects Protein identification

3 Current areas of research Technical refinements Q/A: technical reproducibility plasma vs serum timing and storage Experimental design New diagnostic projects

4 The Omics explosion Genomics/Transcriptomics Metabonomics Proteomics -Phosphoproteomics -Glycoproteomics

5 Proteomics - a new technology

6 Diagnostic Proteomic Fingerprints Patterns of proteins/peptides (in blood or other tissues) that uniquely define a disease state.

7 Combinatorial biomarkers Combinations of biomarkers are usually much more powerful than single markers for accurate diagnosis

8 Some existing biomarkers NON-SPECIFIC ~ sensitivity CRP Clin Microbiol Infect. 5;11:11-8 Application SPECIFIC - Bacterial vs viral?pneumococcal low sensitivity Galactomannan Lancet Infect Dis. 4; 4: ESR Bacterial vs viral Tuberculosteric acid Int J Tuberc Lung Dis. 4; 8: Procalcitonin Eur J Haematol. 5 74: IL-6 Eur J Haematol. 5 74: Neopterin Clin Chim Acta. 5; 351:17-29 Adenosine deaminase J Clin Lab Anal. 5;19(2):4-6 Serum amyloid A Eur J Haematol. 5 74: Bacterial vs viral. Infants Bacterial vs viral Viral, TB TB Bacterial vs viral. TB Application Aspergillosis TB

9 Applicability to infectious diseases Fingerprints to define disease states rather than just pathogen detection Requires no prior assumptions about nature of proteins Protein identification not essential for diagnostic utility Accomodates biological heterogeneity in disease expression

10 Some applications of proteomic fingerprinting Cancer diagnostics Ovarian, prostate, breast, HCC, gliomas renal, bladder Alcoholism Schizophrenia Haemorrhagic vs thrombotic stroke Coronary artery disease

11 Proteomic fingerprinting for infectious diseases

12 Reference intervals for 7 protein analytes in plasma Anderson, N. L. (3) Mol. Cell. Proteomics 2: Copyright 3 American Society for Biochemistry and Molecular Biology

14 The Serum/Plasma Proteome Mass spectrometry Immensely rich source of biological information SELDI: 1. High throughput 2D SDS-PAGE 2. Quantitative output

15 Proteomic profiling SELDI-ToF Mass spectrometry mass/charge (m/z) 1 1

16 MALDI/SELDI-Tof MS (Matrix Assisted Laser Desorption Ionisation Time of Flight MS) Flight tube Laser Protein chip Cathode Detector

17 Surface Enhanced Laser Desorption Ionisation - Time of Flight Mass Spectrometry (SELDI-ToF MS) Binding Washing

18 Effect of albumin binding Albumin Albumin S S S S

19 Plasma vs Serum 3 mins vs 24 mins Early plasma ph4. 3 mins H PLASMA 4 Late plasma ph4. 24 mins H H 4 Early serum ph4. 3 mins SERUM H Late serum ph4. 24 mins

20 Pattern recognition Principal component analysis Pattern recognition algorithms: Support vector machines Neural Network Genetic algorithm Decision Tree Classifier Training and Testing sets k-fold cross-validation, random re-sampling

21 Training and testing sets Training set Testing set Training Signature Testing

22 Over-fitting

23 Over-fitting

24 Support vector machines Machine-learning based classifiers Kernel-based Project data into high dimensional vector space Generalise well (less over-fitting)

25 Linear classifiers +1 But which one.? -1

26 Linear classifiers

27 Maximum margin linear classifiers +1 Maximise margin -1 Support vectors

28 Work-flow DISCOVERY TRANSLATION High throughput plasma profiling by mass spectrometry Lab-on-a-chip technologies Pattern recognition (machine learning) Compare cases and controls Novel biomarker discovery Biomarker identification Translation to near-patient platforms Pattern-based diagnostics Dipsticks Classifier validation X 2 testing sets

29 Current applications Tuberculosis Invasive aspergillosis Sleeping sickness Chagas disease

30 Vertebral TB, pyogenic infection or neoplasia?

31 Sleeping sickness TRAINING TESTING DIAGNOSTIC ACCURACY Classified as: Classified as: Control HAT Control HAT Sensitivity Actual control: (95% C.I.) Actual % HAT: (89.1-1) (W.H.O. funded) Specificity (95% C.I.) 98.6% ( ) Lancet (4) 363:

32 Invasive fungal disease ( G. Wilson, North Manchester general Hospital)

33 Chagas disease planned study Caryn Bern, Bob Gilman and others Peru/Bolivia Proteomic profiles predictive of cure in infected patients <18 yrs treated with benznidazole (Peru, Bolivia) Indeterminate stage disease Correlates of different clinical manifestations Megasyndromes, Chagasic cardiomyopathy

34 Protein identification Biological plausibility Translational technologies Insights into pathophysiology Enhanced precision

35 High throughput protein identification cases POOL Fractionate controls POOL Fractionate PMF and ESI MS/MS Compare Pick and elute selected spots

36 ESI MS/MS

37 Further applications New diseases Chagas disease, invasive fungal infections, RSV, ITU sepsis/inflammation, nvcjd, etc. Integration of different data types for pattern recognition Spectra, immunological data, clinical data Translation - evaluation of different Ab-based detection technologies for multiple markers

38 Summary Novel approach to diagnostics at proof of principle stage Several projects in progress: TB, invasive aspergillosis and sleeping sickness most advanced Defined pathways for translation Intellectual property issues being addressed Longer term translational platform at early stages of exploration.

39 Acknowledgments Rob Edwards, Gurj Sandhu, Justin Green, Anwen Bullen - Imperial College Nathan Harris - Ciphergen Sanjeev Krishna, Ted Tarelli, Alison Loosemore, Emily Hsieh, Marios Papadopoulos, Tom Harrison, Gary Coulton, Jo Sheldon - St George s Hospital Medical School, London. Paulo Abel, Gustl Stich - AngoTrip, Angola Delmiro Fernandez-Reyes, Sergio Rojas - National Institute for Medical Research, London. Funding: WHO (TDR), Wellcome Trust, Neurosciences Research Foundation, HHNRT

Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data

Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data M. Cannataro, P. H. Guzzi, T. Mazza, and P. Veltri Università Magna Græcia di Catanzaro, Italy 1 Introduction Mass Spectrometry