Using CART to Mine SELDI ProteinChip Data for Biomarkers and Disease Stratification
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1 Using CART to Mine SELDI ProteinChip Data for Biomarkers and Disease Stratification Kenna Mawk, D.V.M. Informatics Product Manager Ciphergen Biosystems, Inc.
2 Outline Introduction to ProteinChip Technology Profiling Data Analysis Identifying Biomarkers Biomarker Patterns Software 5. Case studies Summary
3 ProteinChip Technology ProteinChip Arrays for use with SELDI-TOF MS detection Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry
4 ProteinChip Array Preparation. Apply Crude Sample Proteins within the sample bind to chemical or biological docking sites on the ProteinChip surface through an affinity interaction.. Wash ProteinChip Array Proteins that bind non-specifically or buffer (salts, additives..) contaminants are washed away, eliminating sample noise. 3. Add Energy Absorbing Molecules After sample processing the chip is dried and EAM is applied to each spot to facilitate desorption and ionization in the TOF-MS.
5 SELDI-TOF-MS Detection Retained proteins are eluted from the ProteinChip Array by Laser Desorption/Ionization Ionized proteins are detected and their mass accurately determined by Time-of-Flight Mass Spectrometry Laser Intensity Detector
6 ProteinChip Arrays: Available proprietary surfaces Chemical Surfaces HO HO OH OH Si Si Si Si M y+ Cu(II) Hydrophobic Hydrophilic Ionic IMAC Biochemical Surfaces Antibody DNA Enzyme Receptor Drug
7 Protein Profiling: Multidimensional Separation Surface Interaction Potential IMAC 5(+) 5(-) 5RP Selectivity Selectivity Selectivity Selectivity Stringency
8 Disease Biomarker Discovery: Prostate Cancer Serum H H Patient Ret Map H Control Ret Map -3 Ret Map -4() GelView TM Ret Map -3() comp Difference map
9 ProteinChip Applications Clinical Research Cancer and other disease biomarker discovery Clinical research Pharmaceutical Clinical Development Toxicology Clinical trials diagnostics Protein interaction & pathway research Genome to Proteome research Target discovery Secondary screening/target validation
10 Profiling Data Analysis Identifying Biomarkers
11 Flowchart of SELDI Data Analysis Preprocess Baseline subtract Normalize Calibrate Univariate analysis Biomarker Wizard or CiphergenExpress Cluster Analysis, Detect peaks, Calculate P-values, ROC/AUC Multivariate analysis CiphergenExpress Heirarchical Clustering, PCA; Biomarker Patterns Software
12 Challenge: To convert lots of raw data to a manageable sized dataset that can be analyzed
13 What is clustering? Cluster Cluster C,.75 The peak clustering feature in Biomarker Wizard software allows grouping of similar masses across multiple spectra. To visualize the potential differences (patterns), a scatter plot is generated by plotting the intensity vs. mass-to-charge D, E, F, G, H, E,() F,() 5 5 5
14 Plotting the intensity vs. M/Z (scatter plot) allows rapid visualization of potential pattern(s) Log Normalized Intensity Non-distinctive patterns (overlapping) Potentially distinctive patterns M/Z fm 3 fm
15 Multiple Versus Single Markers Single markers often have low predictive sensitivity and specificity Multiple markers are needed for good predictive accuracy Univariate analysis may yield a long list of potential markers but does not indicate relationships Multivariate analysis more likely to identify a small panel of markers for biomarker assays
16 Effect of Patient Variability on Univariate Analysis Candidate Marker for Cancer Diagnosis Candidate Biomarker for Drug Treatment - Animal Model 5. Intensity 4 3 Intensity CANCER Sample Group NORMAL. TREATED UNTREATED P = <.
17 Combinations of Markers Improve Group Segregation Pair-wise Analysis Group A Group B
18 Multi-Markers Have Higher Specificity & Sensitivity than Single Markers Markers Cancer Sensitivity Specificity PSA Prostate 65% 35% SELDI Multi-Marker Profile Prostate 83% 97% CA5.3 Breast 3% 69% SELDI Multi-Marker Profile Breast 93% 9% CA5 Ovarian 35% 98% SELDI Multi-Marker Profile Ovarian % 95%
19 Diagnostics: a continuum Single marker Mulitiple markers Pattern
20 Biomarker Patterns Software Output: Classification trees defining patterns of multiple biomarkers Predicted Treated Control Treated Actual Control 8 9 Terminal Node N = 9 Node M44 N = 7 Node M553 N = 4 Terminal Node N = 8 Terminal Node 3 N = 3 Sensitivity: 9% Specificity: 95%
21 Biomarker Patterns Software 5. Based on CART 5. Includes feature selection tool that uses TreeNet functionality
22 Dimensionality - Feature analysis Preferred: Input data Typical: Input data A B C D E F A B C D E F G H I 3 Patients 3 Patients
23 Automatic Best Predictor Discovery
24 Discover mode allows user review and modification of predictors
25 Resultant model is created using only selected predictors
26 Case Studies
27 ProteinChip Technology Coupled with Biomarker Patterns Software for the Diagnosis of Breast Cancer Benjamin Reed Steve Cleverley William Gallagher Nathan Harris
28 Overview Breast cancer is the most common cancer in women other than skin cancer The American Cancer Society estimates that in the United States 3,5 new cases are diagnosed and about 4, women die each year from cancer originating from the breast One in 8 American women who live to 85 will develop this illness at some stage during life Appearance of a lump in the breast is not conclusive
29 Materials and Methods Serum samples were submitted for evaluation (3 control patients versus 3 patients with breast cancer) Samples set was analyzed on a ProteinChip SAX array at a ph of 9. Both single (Biomarker Wizard) and multi-marker analysis (Biomarker Patterns Software) was performed on the data
30 Single marker analysis using the Biomarker Wizard Analysis of all markers using PEAKS software stats analysis Analysis of the NNNN marker in Excel Control Breast cancer Analysis of the NNNN marker in Excel Control Breast cancer The The upregulation upregulation of of these these proteins proteins was was confirmed confirmed in in both both PEAKS PEAKS software software and and Excel Excel stats stats analysis analysis
31 Multi-marker analysis using the Biomarker Patterns Software Mass Mass Mass 4 Mass 3 The tree with the second smallest relative cost (.873) consisted of four proteins Classification: Control: 8/33 = 85% sensitivity Cancer: 7/9 = 93% specificity
32 Scientific Conclusions The power of ProteinChip technology was illustrated in picking up both single marker and multiple markers from serum Using Biomarker Patterns Software several diagnostic trees were generated that distinguished healthy and breast cancer patients with specificities ranging up to 93%
33 Developing a novel CSF-based multi-marker diagnostic test for Alzheimer s Disease Anja Hviid Simonsen Huw Davies Kaj Blennow
34 Overview Multiple forms of dementia Alzheimer s Disease most common form of dementia Complex diagnostic situation Unambiguous diagnosis is currently only possible through examination of brain pathology post-mortem Several existing CSF-based biochemical markers Differentiation of related dementias generally quite poor Post mortem data has shown -% of diagnosis on living patients incorrect
35 Clinical and commercial needs.. Increase confidence in diagnosis Earlier diagnosis of disease to improve patient treatment Shorten and improve drug development process New therapeutic leads
36 Materials and profiling methods Human CSF from 3 probable AD cases vs. 35 age-matched normal individuals AD phenotype previously assessed using MMSE scores, ApoE genotype, T-Tau, P-Tau and A-beta -4 levels Total of 3 spectra per sample
37 Data mining P-values of candidate univariate markers were calculated (Mann- Whitney) from mean peak intensity values derived from averaged duplicates. Graphing included cluster plots and box and whisker plots Discriminatory peaks with P <. were subsequently used for multivariate BPS analysis
38 NNNN m/z Biomarker P value <. AD Normal Peak intensity Peak intensity 3 3 AD AD Normal Normal AD4 AD6() AD9() AD AD5() N3() N9 N N N7()
39 NNNN m/z Biomarker.5 P value <. AD Normal Peak intensity..5.5 AD3.5 K9.5 AD8().5 N. AD Normal.5 AD8.5 N4.5 Peak intensity AD AD7().5.5 N5 N6. AD Normal
40 NNNN3 m/z Biomarker P value <. AD Normal Peak intensity Peak intensity AD Normal AD AD5 AD8 AD8() AD() N6 N6() N N() N7() AD Normal
41 Biomarker Patterns Software Model Node M77 Mass N = 65 Node M539 Mass N = 44 Terminal Node 6 AD=, N= Node 3 M7466 Mass 3 N = 9 Node 5 M3444 Mass 5 N = 5 Terminal Node AD=5, N= Node 4 M9787 Mass 4 N = 4 Terminal Node 4 AD=, N= Terminal Node 5 AD=, N=3 Terminal Node Terminal Node 3 AD=, N=9 AD=4, N= AD Normal
42 Results summary 39 peaks were found with P values <. 4 of the markers increased in Alzheimer s Disease, 5 decreased None of the peaks showed complete separation of the groups Biomarker Patterns Software Five-peak pattern of markers identified Correctly grouped all Alzheimer s Disease samples (%) and 3 out of 35 normal (9.5%)
43
44 Seeking alternates to PSA test 89, new prostate cancer cases and 3, deaths in PSA limited because of high false-positive rates up to 75% of patients with elevated serum PSA may be negative for cancer Objective of experiment: determine if serum protein profiling will discriminate between patients with prostate cancer and unaffected individuals
45 Methods Serum from 6 cancer patients and 6 controls profiled Two array types WCX: weak cation exchange IMAC3-Cu: metal affinity capture copper Pattern detection Peak detection and clustering with Biomarker Wizard Pattern detection with Biomarker Patterns Software
46 WCX Array Profiles Yes WCX 567Da<=.444 Cancer 6 Control 6 No Yes WCX 687Da<=.64 No Yes WCX 395Da<=5.378 No Cancer 4 Control Cancer Control 5 CONTROL Cancer 4 Control Yes WCX 86Da<=.57 Cancer 36 Control No CANCER Cancer 4 Control 3 Yes WCX 395Da<=3.97 Cancer 8 Control No CANCER Cancer 8 Control CONTROL Cancer 8 Control Yes WCX 337Da<=.8 Cancer 7 Control No CANCER Cancer Control Yes WCX 397Da<=.67 Cancer Control No CANCER Cancer 5 Control Accuracy=67% CANCER Cancer Control CONTROL Cancer Control
47 IMAC3-Cu Array Profiles Yes IMAC3-Cu 396Da<=8.64 Cancer 6 Control 6 No IMAC3-Cu IMAC3-Cu Yes 83Da<=.97 No Yes 4469Da<=3.945 No Cancer 59 Control 6 Cancer 3 Control Yes IMAC3-Cu 973Da<=4.345 Cancer 45 Control 3 No CONTROL Cancer 4 Control 3 CANCER Cancer 3 Control 5 CONTROL Cancer Control 5 Yes IMAC3-Cu 66Da<=3.97 Cancer 44 Control 3 No CONTROL Cancer Control CANCER Cancer 38 Control CONTROL Cancer 6 Control Accuracy=4%
48 Combined profiles Yes WCX 567Da<=.444 Cancer 6 Control 6 No WCX IMAC3-Cu Yes 687Da<=.64 No Yes 483Da<=3.37 No Cancer 4 Control Cancer Control 5 CONTROL CANCER CANCER CONTROL Cancer 4 Control Cancer 36 Control Cancer 7 Control 3 Cancer 5 Control Accuracy=85%
49 Conclusions Comparative serum proteomic profiling provides improved diagnostic potential from single protein targets in traditional blood-based diagnostics Serum profiling using SELDI-TOF-MS supplemented by bioinformatics is a viable tool for detecting prostate cancer
50 Summary ProteinChip Technology is a powerful tool for identifying biomarkers Biomarker Patterns Software provides a method for identifying diagnostic patterns of multiple biomarkers Determines multiple biomarker correlation with phenotype in clinical research studies to improve sensitivity and specificity over single marker methods Automatically and rapidly finds hidden patterns in SELDI data sets Focuses further protein characterization analysis on proteins providing the most definitive splitting rules Models can be tested with additional samples to validate or refine the models
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