Differential Protein Expression Analysis for Biomarker Discovery

Biomarker discovery phase n Develop strategy tailored to samples under study n Broad range of conditions n Fractionation n Test multiple chip binding conditions n Each sample generates >36 spectra n Data analysis n Univariate: Biomarker Wizard n Multivariate: Biomarker Pattern Software

Cancer Biomarker Discovery Sample Sources – Dilution of Markers with Distance from Tumor Sample Type: Neoplastic Tissue CytologyBody Fluids Biomarker ConcentrationHighMediumLow Examples Analyzed by ProteinChip® Technology Biopsy LCM Seminal Plasma Nipple Aspirates Fine Needle Aspirates Serum Plasma Urine

Biomarker discovery phase n Develop strategy tailored to samples under study n Broad range of conditions n Fractionation n Test multiple chip binding conditions n Each sample generates >36 spectra n Data analysis n Univariate: Biomarker Wizard n Multivariate: Biomarker Pattern Software

Strong anion exchange resin Fx1Fx2Fx3Fx4 Serum sample + Urea/CHAPS/TrisHCl pH 9 Protein Profiling of Serum Flow- through pH 7 pH 5 pH 3 Fx5Fx6 pH 4 ACN

Fractionation increases peak count

Biomarker discovery phase n Develop strategy tailored to samples under study n Broad range of conditions n Fractionation n Test multiple chip binding conditions n Each sample generates >36 spectra n Data analysis n Univariate: Biomarker Wizard n Multivariate: Biomarker Pattern Software

Crude Serum Samples Flow-Through pH 9 pH 7 pH 6 pH 4 Organic Wash Anion Exchange Fractionation H50 1%TFA 1%TFA+10%MeOH 1%TFA+25%MeOH 1%TFA+1M KCl 1%TFA+0.4M KCl 1%TFA+0.1M KCl WCX2 pH4 pH4+0.1M KCl pH4+0.4M KCl pH6 pH7 pH9 IMAC-Cu PBS PBS+500mM KCl PBS+20mM immidazole SAX2 pH8 pH8+0.1M KCl pH8+0.4M KCl pH6 pH4 pH3 NP20 Water PBS+0.5M KCl ProteinChip ® System Serum Fraction for High Resolution Profiling

Combined Peak Counts (unique per Surface) Array Type H50 (RP)252 IMAC-Cu317 SAX2416 WCX2365 NP20N/D Unique Peak Count ProteinChip ® System High Resolution Serum Profiling

Reversed-Phase (water wash) Strong Anionic Exchanger (pH 8.5 wash) Weak Cationic Exchanger (pH 4.5 wash) Protein Profiling: Crude liver extracts from treated animals profiled on Reversed-Phase, Strong Anionic, or Weak Cationic Exchange Surfaces

SAX2 Chip 1 (Day 1) SAX2 Chip 2 (Day 2) Spot 2 Spot 1 Spot 2 Assay Reproducibility: Crude Rat Liver Lysate Profiled on a Strong Anion Exchange ProteinChip Array

Standard Error 10-25% n=8 19 representative peaks were picked at at random for the analysis Assay Reproducibility: Crude Rat Liver Lysate Profiled on a Strong Anion Exchange ProteinChip Array

Reproducibility of assays # of Exp.

The ProteinChip ® Bioprocessor Biomek 2000

Biomarker discovery phase n Develop strategy tailored to samples under study n Broad range of conditions n Fractionation n Test multiple chip binding conditions n Each sample generates >36 spectra n Data analysis n Univariate: Biomarker Wizard n Multivariate: Biomarker Pattern Software

Multi-marker Analysis using a Large Patient Population – A Prostate Cancer Study Dr. George Wright, Jr., Virginia Cancer Center, EVMS Early Detection Research Network (NCI) Biomarker Center™ (Ciphergen Biosystems)

Prostate Cancer Serum Analysis Study n Clinical Question n To classify patient groups based on serum sample analysis n Elucidate important peaks used in the classification schema n Study design n Sample size sufficient to generate good statistics (n = 385 patient samples) n Study included samples that should be easy to classify (late stage cancer versus normal elderly patients) n Study additionally included difficult to classify benign prostatic hyperplasia (BPH) cases

Biomarker Profiles Normal Cancer Normal Cancer Sampling of spectra from 6 samples at low and high mass. Zoomed in portions showing candidate markers. Low MW range High MW range

Serum Profiling for Prostate Cancer Like peaks chosen across all samples are analyzed “Normal” Cancer “Normal” Cancer GelViews of same data

Analysis of Control and Treated Samples on a Strong Anion Exchange Surface Prepared at pH8.0 All 40 spectra saved in the same ‘xpt’ file Normalised on Total Ion Current Peak Clusters detected using Biomarker Wizard

Box and Whisker Plot Linear Normalised Intensity Log Normalised Intensity Clustering: Discovery of Biomarker Candidates by using the Biomarker Wizard Highlights up- and down- regulated proteins between groups of samples

Biomarker Patterns ™ Software Benefits of Multi-marker pattern analysis n a classification software-- determines rules that best group samples of known phenotype n a tree building software-- reports the important variables (proteins) and the rules in creating the decision tree- the hard work it does for you! n a multivariate analysis-- uncover hidden expression patterns in high dimensional data n Improves the predictive value over univariate analysis n Accommodates biological and systemic variations n uses relatively small sample sets (~30 per group) for both model building and cross-validation n Takes direct data import from ProteinChip® Software 3.0 – seamless transition

20 Controls 15 Correctly Classified 20 Disease18 Correctly Classified 20 Controls 15 Correctly Classified 20 Disease18 Correctly Classified Does Peak at 10617Da have an intensity less than ? healthy disease Does Peak at 5051Da have an intensity less than ? healthy disease a BPS Example Yes No 5 Healthy 18 Disease 4 Healthy 0 Disease 11 healthy 2 Disease Terminal Node 1 N = 23 Terminal Node 2 N = 4 Node 2 M5051_83 N = 27 Terminal Node 3 N = 13 Node 1 M10617_6 N = 40 Yes No

Biomarker Patterns ™ Analysis Sample Results from a Prostate Cancer Serum Study For 385 starting samples: 131 of 196 cancer samples classified correctly (sensitivity) 162 of 189 non-cancer samples classified correctly (specificity) Sensitivity: 66.8% Specificity: 85.7% Classification: Correctly Incorrectly Peak A Criterion (n=385) Peak B Criterion (n=203) Non- Cancer (n=182) Cancer (n=122) Peak C Criterion (n=81) Peak D Criterion (n=54) Non- Cancer (n=18) Non- Cancer (n=27) Cancer (n=36) “CANCER” = Stage II or III prostate cancer (196 samples) “NON-CANCER” = patients who were either normal or showing signs of benign prostatic hyperplasia (189 samples) Data courtesy of Dr. G. Wright, Jr., Virginia Prostate Cancer Center. Non- cancer: 123 Cancer: 31Non- cancer: 14 Non- cancer: 25 Cancer: 100 Cancer: 59Non- cancer: 5 Cancer: 4Cancer: 2Non- cancer: 22 vs. PSA Test (Sen./Spe.): 75% / 30% lower PSA threshold 40% / 65% higher PSA threshold

Results for Highly Stratified Data Set Peak A Criterion (n=194) Peak B Criterion (n=102) Peak C Criterion (n=92) Peak D Criterion (n=92) Normal (n=3) Cancer (n=89) Normal (n=10) Normal (n=82) Cancer (n=10) Sensitivity: 98.0% Specificity: 96.9% For the 194 starting samples: 96 of 98 cancer samples classified correctly 93 of 96 normal samples classified correctly Normal: 3Cancer: 87Normal: 9Cancer: 9Normal: 81 Cancer: 0Normal: 2Cancer: 1Normal: 1Cancer: 1 “CANCER” = Stage II or III prostate cancer “NORMAL” = age-matched normal patients Data courtesy of Dr. G. Wright, Jr., Virginia Prostate Cancer Center.

Prostate Cancer Study Conclusions n Multiple biomarkers were identified from serum using only one type of ProteinChip ® array and one wash condition n Biomarker Patterns™ software identified potential biomarkers and classification criteria, and assembled these into a predictive tree n Sensitivity and specificity for both low and high grade prostate cancer were > 90%

Prostate Biomarker Clinical Study – Progress of Clinical Validation and Power of Multimarker Assays Sensitivity (“True Positives”) Specificity (“True Negatives”) Single Markers from Seminal Plasma Study (152 samples) 57% (Range %) 38% (Range %) PSA (total, cutoff = 5 ng/ml) 65%35% Accepted Threshold for Clinical Utility 80% Multivariate Combination of 8 Seminal Plasma Markers 85%83%

Cancer Study Sensitivit y Specificity Institution Ovarian94-100%96%NCI, FDA, Northwestern Prostate93.3%93.8%Eastern Virginia Medical School Breast92%82%Johns Hopkins Medical School Liver92.5%90%Chinese University of Hong Kong Bladder79%81%Eastern Virginia Medical School Rates of cancer detection including sensitivities (true positives) ranging from % and specificities (true negatives)ranging from 81-96%. Highlights of selected papers presented at AACR 2002: