SCIENCE WEBINAR

Autoantibody Biomarkers for Cancer and Autoimmune Disease

Eng M Tan, M. D.The Scripps Research InstituteLa Jolla, California

Systemic Lupus ErythematosusThe 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 30-40 percent of patients and often in severe or advanced disease states

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

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

Methods for Discovery of Autoantibody Markers in Lupus

Immunofluorescence Imaging using tissue culture cells as substrate – historically, the most powerful and informative method

Immunoassays:Western Blotting Enzyme Immunoassays

Immunodiffusion analysis in agar for antibody-antigen precipitin lines

Importance of Autoantibody Profiles

Tan, E.M., Ann NY Acad Sci, 815: 1-14 (1997)

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

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

Seroconversion Associated with Malignant Transformation

Imai, H. et al, Am J Pathol 140:859-870 (1993)

Immunological Characterization of Seroconversion in Patient HK

Imai, H. et al, Cancer 71:26-35 (1993)

Frequency of Autoantibodies to p62 and Koc in ELISA Using Full-Length Recombinant Proteins as Antigens

6 (4.3)5 (3.6)6 (4.3)139Autoimmune disease serac

00030NHS-2b2 (2.4)1 (1.2)1 (1.2)82NHS-1a

159 (20.5)**95 (12.2)**90 (11.6)**777Total9 (22.5)**3 (7.5)6 (15.0)**40Uterine

3 (9.1)1 (3.0)3 (9.1)*33Ovarian13 (23.2)**6 (10.7)*9 (16.1)**56Pharyngeal13 (18.1)**8 (11.1)*8 (11.1)*72Lymphoma20 (23.8)**6 (7.1)15 (17.9)**84Lung18 (24.0)**13 (17.3)**8 (10.7)*75Hepatocellular26 (19.3)**22 (16.3)**10 (7.4)*135Gastric28 (23.5)**15 (12.6)**20 (16.8)**119Esophageal13 (20.0)**9 (13.8)**6 (9.2)*65Colorectal16 (16.3)**12 (12.2)**5 (5.1)98Breast

p62 and/or Koc (No. (%))

Koc(No. (%))

p62 (No. (%))

No. of sera testedType of Cancer

Antibodies to

Zhang, J.-Y. et al, Clin Immunol 100:149-156 (2001)

Frequency of Autoantibodies in Relationship to Cumulative Number of TAAs

Numbers in parenthesis are percent of positive reactors

23 (51.1)38 (67.9)28 (43.8)

IMP1 and p62 and Koc and p53 and c-myc and cyclin B1 and survivin

23 (51.1)36 (64.3)26 (40.6)IMP1 and p62 and Koc and p53 and c-myc and cyclin B1

17 (37.8)27 (48.2)25 (39.1)IMP1 and p62 and Koc and p53 and c-myc

16 (35.6)25 (44.6)17 (26.6)IMP1 and p62 and Koc and p53

10 (22.2)19 (33.9)14 (12.9)IMP1 and p62 and Koc

8 (17.8)15 (26.8)8 (12.5)IMP1 and p62

6 (13.1)4 (7.1)5 (7.8)IMP1

Colorectal 45

Lung 56

Breast 64

Type of CancerAntigen

Zhang, J.-Y. et al, Cancer Epidem Biomar 12:136-143 (2003)

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

2. 527 Cancer patients: breast, colorectal, gastric, HCC, lung, prostate

3. 346 Normal individuals4. Recursive Partitioning used in Statistical

Analysis

Koziol , J.A. et al. Clin Cancer Res 9: 5120-5126 (2003)

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 CA4. 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

Antibody Profiling in Different Cancers

Technologies forAutoantibody Biomarker Discovery

Paul F. Predki, Ph.D.VP, Proteomics R&D

Autoantibody Biomarker Discovery: Technology Timeline

SEREX

Expression library screening

Mass Spectrometry Approaches

Microarray Approaches

Phage-display selection

1995 2000 2001 2005

Discovery arrays• expression libraries• phage display• high content

Antigen arrays

Molecular Biology Approaches

Reverse phase arrays2-D gel MS

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: Well-established, ‘simple’ techniques

Disadvantages: High abundance transcripts/redundancy Labor-intensive/not ‘high-throughput friendly’

Advantages: Well-established, ‘simple’ techniques

Disadvantages: High abundance transcripts/redundancy Labor-intensive/not ‘high-throughput friendly’

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

Advantages: Proteins extracted from source: can detect PTMs

Disadvantages: Must use MS to identify antigensBiased towards denatured epitopesReproducibility, sensitivity, labor (2D-gel MS)

Advantages: Proteins extracted from source: can detect PTMs

Disadvantages: Must use MS to identify antigensBiased towards denatured epitopesReproducibility, sensitivity, labor (2D-gel MS)

From Naour et al., MCP 1.3, p197-203 (2002)

Microarray Approaches

Antigen arrays– Arrays of known autoimmune

antigens– Sera profiled

Discovery arrays– Expression libraries– Phage display– High content functional

protein microarrays

Advantages: High-throughput possibleSimple & fast (once arrays are in hand)Proteins may be folded, some PTMs present

Disadvantages: Proteins may not be folded, some PTMs absentRobust manufacture of arrays challenging

Advantages: High-throughput possibleSimple & fast (once arrays are in hand)Proteins may be folded, some PTMs present

Disadvantages: Proteins may not be folded, some PTMs absentRobust manufacture of arrays challenging

From Robinson et al., Nature Medicine 8, p295-301 (2002)

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

138Protease/peptidase activity

145Cell Death

2166Metabolism

863Cell Communication

100Secreted Proteins

710Signal Transduction

830Nuclear Proteins

1085Membrane Proteins

188Transcription Factors

378Protein Kinases

Protein CountProtein Class

ProtoArray™ Manufacturing

Ultimate™ Human ORF Clone Collection

High-throughput cloning of full-length open reading frames.Completely sequenced

Highly automated and controlled manufacturing process

Bac-to-Bac®

BaculovirusExpression System

High-throughput expression of proteins with high functionality.Expression QCs

Purification ProcessHigh-throughput purification of proteins under native conditions.Purification QCs

Protein ArrayingHigh content protein array.Array QCs

ProtoMine LIMS Tracks Process

Generic Protocol / Workflow

Controls

Diseased or Treated

Comparison

Controls

1. Sample Acquisition 2. Sample Processing 3. Data Analysis

Diseased or Treated

Block, Probe

Secondary Ab

Image

autoantibody

fluorescentsecondary Ab

Data Analysis

1. Normalization– Required when measuring relative signals

Data correction (background…) Intra-slide normalization Inter-slide normalization

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 AlgorithmsData correction (bkg…)Intra-slide (CI-p-value)Inter-slide (quantile)

ProtoArray™ Prospector

10

100

1,000

10,000

100,000

1

High Sensitivity & Reproducibility

incr

easi

ng [a

rray

pro

tein

]

sign

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ackg

roun

d

1/50

,000

1/10

,000

1/5,0

00

1/1.0

00

1/15

0

sera dilution

Sensitivity• NY-ESO1 positive sera• sera dose-response curves• detection sensitivities down to 1/50,000

Reproducibility• 1/500 serum dilution

Test CV R2

Intra-Assay 12.0 % .933Inter-Lot 16.0 % .910Inter-Operator 16.7 % .972

IntraIntra--Assay ReproducibilityAssay Reproducibility

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

-5

0

5

10

15

20

-5 0 5 10 15 20 25

R2 = .16IgA

IgG

Example: Cancer

Cancer SeraCancer SeraNormal SeraNormal Sera

Trained classifier• based on 9 biomarkers• training set data• test set in progress

86.8% Sensitivity84.4% Specificity

0.866 AUC

Example: SLE (Autoimmune Disease)

64-marker panel can differentiate SLE from controls

SLE

ControlsNormal,

RA & ANCA

*p-value <.02

0

10000

20000

30000

40000

50000

60000

70000

norm

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ed s

igna

l

0

5000

10000

15000

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25000

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Normal RA ANCA SLE

Established SLE diagnostic biomarker

Novel SLE biomarker candidate

Hierarchical Clustering Sample Markers

Identification of Proteins Differentially Expressed in Ovarian Cancer Using Protein

Microarrays

Michael Snyder

Research of Mike Hudson in collaboration with Gil Mor

- Elevated in ~80% of women with advanced EOC

- Not present in early stages of diseases

- Positive Predictive Value of ~10%

Current Test: CA-125 Biomarker

- 4th 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

Ovarian Cancer

GoalCan 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?

HypothesisCancer Patients Produce Autoantibodies to Antigens

that Reflect the Disease State

Approach

Sera from 30 Ovarian Cancer Patients

Sera from 30 Matched Healthy Women

Probe Invitrogen Protein Chip

Containing 5005 Human Proteins

Protein Chip

Healthy

Cancerous

Se` SecondaryHealthySpecific

CancerSpecific

Differential Reactivity

94 Proteins Higher Reactivity in Cancer

4/300/30Regulation Of Cell Cycle9

15/308/30Signal Transduction8

25/3023/30RNA Splicing7

22/3010/30Unknown6

26/3022/30Unknown5

17/305/30Unknown4

5/300/30Regulation Of Transcription3

9/307/30Regulation Of Transcription2

8/303/30Nuclear EnvelopelaminA/C

Frequency CancerFrequency HealthyGO ProcessProtein

94 Candidate Markers: Diverse Types of Proteins

Cell LineCell LineControlCell LineCell LineCervixC

CervixN

Blad.C

Uterus C

Stom.C

RectumC

OvaryC

LiverC

EsophC

BreastC

Blad.N

UterusN

Stom.N

RectumN

OvaryN

LiverN

EsophN

Breast N

Adren.C

ThyroiC

SkinC

ProstatC

LungC

KidneyC

ColonC

BrainC

Adren.N

ThyroiNSkinN

ProstatN

LungN

KidneyN

ColonN

BrainN

Control

Ovarian Healthy

Ovarian Tumor

Control

Control

Lamin A/C Reactivity

175-

83-

62-

48-

Lamin ALamin C

Health

yTu

mor

175-

83-62-48-

Tumor

Health

y

p53

Expression of Lamin A – Ovarian Tumor Tissue

Expression of Lamin A – Ovarian Normal Tissue

Cancerous

Healthy

Cancerous

Healthy

Expression of Lamin A protein – Matched Normal/Tumor Tissue

*

Well Differentiated

Poorly Differentiated

Expression of CA-125 protein – Ovarian Tumor Tissue

Expression of CA-125 protein – Ovarian Normal Tissue

Expression of Marker B protein – Ovarian Tumor Tissue

Expression of Marker B protein – Ovarian Normal Tissue

Cancerous

Healthy

Cancerous

Healthy

Expression of Marker B protein – Matched Normal/Tumor Tissue

*

Well Differentiated

Poorly Differentiated

Averaged Lamin A/ Marker B

2

3

4

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76

Patient

Scor

e

Stage 2 EOC

Stage 3 EOC

Stage 4 EOC

Lamin A Stage Specific Staining

Cancerous Healthy

Lung

Kidney

Breast

Uterus

Ovary

Expression of Lamin A protein – Multiple Tissue Types

Liver

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 125.

4) They may prove to be useful markers for tissues, particularly when used in combination.

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 125.

4) They may prove to be useful markers for tissues, particularly when used in combination.

AcknowledgementsMike Hudson and Li Kung performed this research.

Collaboration with Gil MorProtein Arrays From Invitrogen

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