SCIENCE WEBINAR. Autoantibody Biomarkers for Cancer and Autoimmune Disease. Eng M Tan, M. D. The Scripps Research Institute La Jolla, California

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2 SCIENCE WEBINAR Autoantibody Biomarkers for Cancer and Autoimmune Disease Eng M Tan, M. D. The Scripps Research Institute La Jolla, California

3 Systemic Lupus Erythematosus The Prototype Autoimmune Disease In 1948, the Lupus Erythematosus (LE) cell, a diagnostic marker for lupus was discovered by Malcolm Hargraves and colleagues at the Mayo Clinic Until the 1960s, it was the only diagnostic marker available for lupus It was detectable in about percent of patients and often in severe or advanced disease states

4 Clinical Implications Into the 1960s and 1970s, the 10 year survival for a lupus patient after initial diagnosis was a dismal 50% or less The reason: diagnosis was made late, after multi-system organ damage to kidney, central nervous system, heart, lung and other organs

5 Autoantibody Biomarker Discoveries In the late 1950s and early 1960s, autoantibodies primarily to cell nuclear components began to be identified and were called antinuclear antibodies (ANAs) Tests for ANAs became increasingly available in the 1970s and were widely used by clinicians in diagnosis of lupus at early stages of the disease

6 Methods for Discovery of Autoantibody Markers in Lupus Immunofluorescence Imaging using tissue culture cells as substrate historically, the most powerful and informative method Immunodiffusion analysis in agar for antibodyantigen precipitin lines Immunoassays: Western Blotting Enzyme Immunoassays

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9 Importance of Autoantibody Profiles Tan, E.M., Ann NY Acad Sci, 815: 1-14 (1997)

10 Clinical Outcome of Biomarker Discoveries Since the 1960s, treatment for lupus has not changed significantly: the main modalities have continued to be corticosteroids and immunosuppressive agents However, since biomarkers for lupus became available, the 10-year survival for lupus patients after initial diagnosis is now over 90%, a huge improvement over 50% described above Most of this advance is widely attributed to early diagnosis with the use of autoantibody biomarkers, before irreversible organ damage has occurred

11 Autoantibodies as Biomarkers in Cancer The Special Case for Hepatocellular Carcinoma (HCC): In HCC, one can identify a cohort of patients, one-third of whom will eventually develop HCC These are patients with liver cirrhosis or chronic hepatitis due to viral hepatitis or other conditions The autoantibody response of some patients during transition to malignancy appears to be reporting aberrant cellular mechanisms associated with malignant transformation

12 Seroconversion Associated with Malignant Transformation Imai, H. et al, Am J Pathol 140: (1993)

13 Immunological Characterization of Seroconversion in Patient HK Imai, H. et al, Cancer 71:26-35 (1993)

14 Frequency of Autoantibodies to p62 and Koc in ELISA Using Full-Length Recombinant Proteins as Antigens Antibodies to Type of Cancer No. of sera tested p62 (No. (%)) Koc (No. (%)) p62 and/or Koc (No. (%)) Breast Colorectal Esophageal Gastric Hepatocellular Lung Lymphoma Pharyngeal Ovarian Uterine Total (5.1) 6 (9.2)* 20 (16.8)** 10 (7.4)* 8 (10.7)* 15 (17.9)** 8 (11.1)* 9 (16.1)** 3 (9.1)* 6 (15.0)** 90 (11.6)** 12 (12.2)** 9 (13.8)** 15 (12.6)** 22 (16.3)** 13 (17.3)** 6 (7.1) 8 (11.1)* 6 (10.7)* 1 (3.0) 3 (7.5) 95 (12.2)** 16 (16.3)** 13 (20.0)** 28 (23.5)** 26 (19.3)** 18 (24.0)** 20 (23.8)** 13 (18.1)** 13 (23.2)** 3 (9.1) 9 (22.5)** 159 (20.5)** NHS-1 a 82 1 (1.2) 1 (1.2) 2 (2.4) NHS-2 b Autoimmune disease sera c (4.3) 5 (3.6) 6 (4.3) Zhang, J.-Y. et al, Clin Immunol 100: (2001)

15 Frequency of Autoantibodies in Relationship to Cumulative Number of TAAs Antigen Breast 64 Type of Cancer Lung 56 Colorectal 45 IMP1 IMP1 and p62 IMP1 and p62 and Koc IMP1 and p62 and Koc and p53 IMP1 and p62 and Koc and p53 and c-myc IMP1 and p62 and Koc and p53 and c-myc and cyclin B1 IMP1 and p62 and Koc and p53 and c-myc and cyclin B1 and survivin 5 (7.8) 8 (12.5) 14 (12.9) 17 (26.6) 25 (39.1) 26 (40.6) 28 (43.8) Numbers in parenthesis are percent of positive reactors Zhang, J.-Y. et al, Cancer Epidem Biomar 12: (2003) 4 (7.1) 15 (26.8) 19 (33.9) 25 (44.6) 27 (48.2) 36 (64.3) 38 (67.9) 6 (13.1) 8 (17.8) 10 (22.2) 16 (35.6) 17 (37.8) 23 (51.1) 23 (51.1)

16 Evidence for Different Autoantibody Profiles In Different Cancers 1. With EIAs, antibodies to a panel of 7 TAAs: c- myc, cyclin B1, IMP1, Koc, p62, p53 and survivin were examined Cancer patients: breast, colorectal, gastric, HCC, lung, prostate Normal individuals 4. Recursive Partitioning used in Statistical Analysis Koziol, J.A. et al. Clin Cancer Res 9: (2003)

17 Antibody Profiling in Different Cancers 1. Antibody to cyclin B1 was the initial discriminator among the 7 TAAs for gastric, lung and hepatocellular CA 2. C-myc was the initial discriminator in breast CA 3. P62 was the initial discriminator in prostate CA 4. IMP1 was the initial discriminator in colon CA No more than 3 of the 7 TAAs were needed to arrive at sensitivities of 0.77 to 0.92 and specificities of 0.85 to 0.91

18 Technologies for Autoantibody Biomarker Discovery Paul F. Predki, Ph.D. VP, Proteomics R&D

19 Autoantibody Biomarker Discovery: Technology Timeline Molecular Biology Approaches Expression library screening SEREX Phage-display selection Mass Spectrometry Approaches Microarray Approaches 2-D gel MS Reverse phase arrays Antigen arrays Discovery arrays expression libraries phage display high content

20 Molecular Biology Approaches cdna expression libraries The initial approach to discovery SEREX cdna libraries from patient tumors (or tumor cell lines) Phage display selection Phage display libraries from patient tumors (or tumor cell lines) Select autoantibody binders Advantages: Disadvantages: Well-established, simple techniques High High abundance transcripts/redundancy Labor-intensive/not high-throughput friendly

21 Mass Spectrometry Approaches 2D-gel MS Protein lysates (tumors or cancer cell lines) on 2D gels Western transfer Sera screening MS follow-up for antigen ID Reverse phase arrays Fractionated lysate arrays Sera screening MS follow-up for antigen ID From Naour et al., MCP 1.3, p (2002) Advantages: Disadvantages: Proteins extracted from from source: can can detect detect PTMs PTMs Must Must use use MS MS to to identify antigens Biased Biased towards denatured epitopes Reproducibility, sensitivity, labor labor (2D-gel MS) MS)

22 Microarray Approaches Antigen arrays Arrays of known autoimmune antigens Sera profiled Discovery arrays Expression libraries Phage display High content functional protein microarrays From Robinson et al., Nature Medicine 8, p (2002) Advantages: Disadvantages: High-throughput possible Simple Simple & fast fast (once (once arrays arrays are are in in hand) hand) Proteins may may be be folded, some some PTMs PTMspresent Proteins may may not not be be folded, some some PTMs PTMsabsent Robust manufacture of of arrays arrays challenging

23 ProtoArrays : High Content Protein Microarrays >8,000 purified human proteins relatively unbiased collection expressed in Sf9 GST tagged >90% pure (nondenaturing conditions) Control spots in each subarray MFG/QC controls Application-specific controls Protein Class Protein Kinases Transcription Factors Membrane Proteins Nuclear Proteins Signal Transduction Secreted Proteins Cell Communication Metabolism Cell Death Protease/peptidase activity Protein Count

24 ProtoArray Manufacturing ProtoMine LIMS Tracks Process Ultimate Human ORF Clone Collection Bac-to-Bac Baculovirus Expression System Purification Process Protein Arraying High-throughput cloning of full-length open reading frames. Completely sequenced High-throughput expression of proteins with high functionality. Expression QCs High-throughput purification of proteins under native conditions. Purification QCs High content protein array. Array QCs Highly automated and controlled manufacturing process

25 Generic Protocol / Workflow 1. Sample Acquisition 2. Sample Processing 3. Data Analysis Controls Controls Block, Probe autoantibody Secondary Ab fluorescent secondary Ab Comparison Image Diseased or Treated Diseased or Treated

26 Data Analysis 1. Normalization Required when measuring relative signals Data correction (background ) Intra-slide normalization Inter-slide normalization ProtoArray Prospector 2. Training Algorithm to differentiate control from test samples Typically requires a panel of biomarkers Neural networks, hierarchical clustering 3. Testing Independent test of Training Algorithm using new samples Determines sensitivity & specificity Normalization Algorithms Data correction (bkg ) Intra-slide (CI-p-value) Inter-slide (quantile)

27 High Sensitivity & Reproducibility signal-background Sensitivity NY-ESO1 positive sera sera dose-response curves detection sensitivities down to 1/50, ,000 10,000 1, increasing [array protein] Reproducibility 1/500 serum dilution Test CV R 2 Intra-Assay 12.0 %.933 Inter-Lot 16.0 %.910 Inter-Operator 16.7 %.972 Intra-Assay Reproducibility /50,000 1/10,000 sera dilution 1/5,000 1/ /150

28 Broad Study Types Clinical Conditions Studied auto-immune cancer inflammatory therapeutic response transplantation Sample Types Tested serum, plasma urine tears aqueous humor CSF saliva Antibody Types Studied IgG IgA IgM IgA R 2 = IgG

29 Example: Cancer 86.8% Sensitivity 84.4% Specificity AUC Trained classifier based on 9 biomarkers training set data test set in progress Normal Sera Cancer Sera

30 Example: SLE (Autoimmune Disease) Hierarchical Clustering Sample Markers Established SLE diagnostic biomarker Controls Normal, RA & ANCA normalized signal Novel SLE biomarker candidate SLE normalized signal marker panel can differentiate SLE from controls *p-value <.02 0 Normal RA ANCA SLE

31 Identification of Proteins Differentially Expressed in Ovarian Cancer Using Protein Microarrays Michael Snyder Research of Mike Hudson in collaboration with Gil Mor

32 Ovarian Cancer -4 th most common cancer in women in US; 15K deaths/yr - Diagnosis at early stages leads to 95% 5-year survival and most are cured - Diagnosis at late stages leads to 15%-30% 5-year survival Current Test: CA-125 Biomarker - Elevated in ~80% of women with advanced EOC - Not present in early stages of diseases - Positive Predictive Value of ~10%

33 Goal Can we use protein chips to identify ovarian cancer markers? Can we determine the prognosis and track disease progression? Can we determine most effective treatment strategy? Hypothesis Cancer Patients Produce Autoantibodies to Antigens that Reflect the Disease State

34 Approach Sera from 30 Ovarian Cancer Patients Sera from 30 Matched Healthy Women Probe Invitrogen Protein Chip Containing 5005 Human Proteins Protein Chip

35 Differential Reactivity Healthy Cancerous Cancer Specific Healthy Se` Specific Secondary 94 Proteins Higher Reactivity in Cancer

36 94 Candidate Markers: Diverse Types of Proteins Protein GO Process Frequency Healthy Frequency Cancer lamina/c Nuclear Envelope 3/30 8/30 2 Regulation Of Transcription 7/30 9/30 3 Regulation Of Transcription 0/30 5/30 4 Unknown 5/30 17/30 5 Unknown 22/30 26/30 6 Unknown 10/30 22/30 7 RNA Splicing 23/30 25/30 8 Signal Transduction 8/30 15/30 9 Regulation Of Cell Cycle 0/30 4/30

37 Lamin A/C Reactivity Healthy Tumor Control Ovarian Healthy Ovarian Tumor Lamin A Lamin C Control Control Brain N Colon N Kidney N Lung N Prostat N Skin N ThyroiN Healthy Tumor Adren. N Brain C Colon C Kidney C Lung C Prostat C Skin C Thyroi C 175- Adren. C Breast N Esoph N Liver N Ovary N Rectum N Stom. N Uterus N Blad. N Breast C Esoph C Liver C Ovary C Rectum C Stom. C Uterus C 48- p53 Blad. C Cervix N Cervix C Cell Line Cell Line Control Cell Line Cell Line

38 Expression of Lamin A Ovarian Tumor Tissue

39 Expression of Lamin A Ovarian Normal Tissue

40 Expression of Lamin A protein Matched Normal/Tumor Tissue Well Differentiated Cancerous * Poorly Differentiated Healthy Cancerous Healthy

41 Expression of CA-125 protein Ovarian Tumor Tissue

42 Expression of CA-125 protein Ovarian Normal Tissue

43 Expression of Marker B protein Ovarian Tumor Tissue

44 Expression of Marker B protein Ovarian Normal Tissue

45 Expression of Marker B protein Matched Normal/Tumor Tissue Well Differentiated Cancerous * Poorly Differentiated Healthy Cancerous Healthy

46 Averaged Lamin A/ Marker B 4 Score Patient

47 Stage 2 EOC Lamin A Stage Specific Staining Stage 3 EOC Stage 4 EOC

48 Expression of Lamin A protein Multiple Tissue Types Cancerous Healthy Ovary Uterus Lung Breast Kidney Liver

49 Conclusions 1) Autoantibody screening of protein microarrays can be used to find differentially expressed proteins. 2) Five promising ovarian cancer biomarkers were identified. 3) In tissue microarrays they appear to detect most late stage and a subset of early stage cancers; 2 are better than CA ) They may prove to be useful markers for tissues, particularly when used in combination.

50 Conclusions 1) Autoantibody screening of protein microarrays can be used to find differentially expressed proteins. 2) Five promising ovarian cancer biomarkers were identified. 3) In tissue microarrays they appear to detect most late stage and a subset of early stage cancers; 2 are better than CA ) They may prove to be useful markers for tissues, particularly when used in combination. Acknowledgements Mike Hudson and Li Kung performed this research. Collaboration with Gil Mor Protein Arrays From Invitrogen

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