DNA-methylation- and autoantibody-

biomarker development strategies for

minimal invasive diagnostics

Andreas Weinhäusel, Istvan Gyurjan, Johana Luna, Regina Linhart, Manuela Hofner, Gordana Wozniak-

Knopp1, Florian Rüker1, Friedrich Längle2, Johann Hofbauer3, Nina Pecha4, Christian Singer4, Robert

Zeillinger4, Andrea Gsur5, Christa Noehammer, Johannes Söllner6

1 Life Sciences Univ, Vienna, 2 LKH Wr Neustadt, Surgery, Wiener Neustadt, 3 LKH Wr Neustadt, Urology, Wiener

Neustadt, 4 Medical Univ Vienna, Senology, Vienna,5 Medical Univ Vienna, Inst, for Cancer Research, Vienna, Austria, 6 emergentec biodevelopment GmbH, Vienna, Austria,

& Molecular Diagnostics, Health and Environment Department , AIT- Austrian Institute of Technology GmbH, Vienna,

Translational Medicine-2014

3-5 November, 2014, Las Vegas

AIT Austrian Institute of Technology

5 academic departments

2 daughters

AIT-Molecular Diagnostics

AIM: improving cancer diagnostics

Breast, 4.570

Colon, 4461

Lung, 4,521

Prostate, 4.402

Other, 16,914

2%

33%

65%

13630 Colonoscopies of FOBT-positive Patients

carcinoma

polyps

healthy

Gsur A, Mach K. (2011) „Burgenländische

Dickdarmkrebs –Initiative“

„the big 4“ breast, colon, lung, prostate cancer

incidence EU25 1.2mio/y (2006)

incidence AUSTRIA 18000/y (2008)

= 4x13% = 52%

low therapeutic treatment success

early diagnosis improves survival

„personalised medicine“

classical diagnostic methods are „inefficient“

Breast: Mammography (60€), MRT (400€),

US (60€): ≈80% can be detected

Colon: FOBT

Lung: chest X-ray (78.3% sensitivity)

Prostate: PSA test – AUC 0.66

Diagnostic Biomarkers - only a few since recent years available

The biomarker development pipeline

4

The 4 main phases of biomarker development

- to move from one phase to another it needs to overcome multiple challenges at different levels.

- the phases are not always distinct from each other and the pipeline is not always linear. Each phase can involve multiple studies

performed at various time points throughout the biomarker's developmental lifecycle.

Scheme according to: Pavlou, M.P., Diamandis, E.P., and Blasutig, I.M. (2013). The Long Journey of Cancer Biomarkers from the Bench to the

Clinic. Clinical Chemistry 59, 147–157.

Discovery

assay development & analytical validation

retrospective validation

prospective

longitudinal study

bio

mark

er

- v

alu

e

BENCH

BEDSIDE

time, costs, number of clinical samples

Cancer „DIAGNOSTIC“ Biomarkers

in clinical use

RNA PCA3- expression in urine – prostate cancer

DNA Methylation - testing biopsies for reconfirmation & also for serum-

testing

SEPT9 – Colon (Epigenomics)

SHOX2 – Lung (Epigenomics)

GSTP1 - prostate (CLIA laboratory testing- e.g http://mdxhealth.com)

PROTEIN (serum assays)

protein -“serum”- cancer biomarkers

• 9 protein cancer biomarkers that have been approved by the FDA for clinical use

• only PSA as a DIAGNOSTIC BM (AUC 0,66)

Autoantibody based lung cancer test EarlyCDT®-Lung test (Oncimmune):

• seven antigens (p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, and MAGE A4)

• sensitivity of 41% with a specificity of 91%;

5 23.10.2014

•Autoantibodies in serum against tumor-associated antigens (TAAs)

have been found in asymptomatic stage of cancer

•early / non invasive / simple == best suited for diagnosis

DISCOVERY

A) SEREX B) phage display based discovery

C) proteins form human clone libraries

• PROTEIN micro-arrays enable discovery of candidates antigens

with low sample (10-30µl) volumes

use micro-arrays to discover & refine marker selection

Autoantibody based serum diagnostics

antigen

Y Y anti-human IgG

IgG – patient

Breast Cancer: TAA discovery

7

2. ) 16k protein micro-array

15286 „UNIPEX-clones“ (his-tag) purified

& duplicates printed – 32000 spots/slide

purified IgG & screen:

72 malignant, 60 healthy, 48 benign

processing of 6x36 = 216 -16k arrays

BrCa- Serex results 194 clones 107 genes

21 already known in SEREX DB

7 thereof described for BrCa/OvCa

22 clones represent

potential „novel fusion genes“

Syed, P., etal (2012). Evaluation of auto-antibody

serum biomarkers for breast cancer screening and in

silico analysis of sero-reactive proteins. Journal of

Molecular Biochemistry; Vol 1, No 2 (2012).

1. ) DISCOVERY using human fetal brain–38k macromembranes &

SEREX dervied clones test on targeted protein µ-arrays

Syed, P (2012). In silico

design and performance

of peptide microarrays

for breast cancer

tumour-auto-antibody

testing. Journal of

Molecular Biochemistry;

Vol 1, No 2 (2012).

AUC ≈ 0.75

16k BrCa TAA analyses

8

IgG, 0.3 mg/mL - from 30 µL of

serum / chip

Cross-validation receiver

operating characteristic (ROC)

curves of antigen-classifiers

derived from the 16k protein-chip

screening for different sets of

samples (Bayesian Compound

Covariate Predictor, p<0.001)

A: AUC 0.956 for malignant vs.

“benign&control” samples (100

classifiers).

B: AUC 0.84 for malignant vs. benign

samples (94 classifiers).

C: ROC curve for malignant vs.

control samples with an AUC of 0.98

(108 classifiers).

D: AUC of 0.855 was obtained from

the classification between benign vs.

control samples (46 classifiers).

“Malignant vs.

Benign&Control“

AUC = 0.956

A

“Malignant vs. Benign“

AUC = 0.84

B

C

“Malignant vs. Control“

AUC = 0.98

D

“Benign vs.

Control“

AUC = 0.855

BIG-4 cancer entities TAA candidate markers

9

Breast Cancer

AUC = 0.938

Carcinoma vs. control

Colon Cancer

Lung Cancer

AUC = 0.924

“40 greedy pairs“

carcinoma vs BPH

ProstateCa

Phage display based discovery (BreastCa)

~50 serum-samples per group Control, Malignant, Benign - 100µl of each patient pooled

random 12aa M13-phage library (from NEB) – subtractive panning

ProteinA & ProteinG - IG isolations >> 106000 reads 3325 different peptides

repeat - using MelonGel for IgG selection >> 67126 reads 8976 different peptides

subtractive panning: non-binders from patient group

“MAL” are enriched on “BEN/CTRL” column

172.596

179k peptide array design

11

PEPTIDE DESIGN

Phage display

16k Screens

SEREX & „642“

membrane derived

12mer peptide

↑ 1 aa overlap ↑

ANTIGENIC-PROTEINS http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

PROCESSING of 12 samples /

array: 179K peptides

BrCa-Serum IgG -179k peptide array analyses

72 samples

24 malignant

24 benign

24 healthy

MAL vs Ctr 54 peptides

AUC = 0,977

Mal vs Ben: 20 greedy pairs

AUC=0,988

Ben vs Ctr 20 greedy pairs

AUC=1

Mapping peptides to biology- KEGG pathway analyses

all 170k unique

peptides have been

mapped back to 21657

distinct proteins in

UNIPROT matching 4139

genes which served as

background for pathway

enrichment analysis and are

obviously cancer related; :

Class distinction via genes/proteins

mapped back from peptides

CONTRAST distinct gene-symbols

distinct proteins

Diseased vs (Benign & Healthy) 473 1149

Benign vs (Diseased & Healthy) 81 171

Healthy vs (Diseased & Benign) 27 60

Diseased vs Benign 2217 6766

Diseased vs Healthy 2280 7394

Benign vs Healthy 480 1113

Pathway analyses - Diseased vs(Benign and Healthy)

- based on 473 distinct genes

Motif enrichment search:

E-value 3.4e-015

E-value 1.7e-005

E-value 2.6e-003

Zn-finger proteins

diverse proteins (e.g. MUC5),

redundant sequences

Zn-finger proteins

enriched in Malignant samples:

healthy

Discovery Set

Discovery & Training

& Validation-SET

Bioinformatics

Y Y Fluoreszently anti-human IgG

detection antibody

IgG – Patient vs. Control

immobilised Antigen

custom 179.000

peptide array

200+ plex Luminex-

bead array

16k Protein-chip Classifiers,

Serex

COSMIC

Phage-Display Peptides patient

PR

OT

EIN

P

eptides confirm

ation & validation 2x 100-plex Luminex-ASSAYS – using

biotinylated peptides on avidin coated

magnetic beads … almost ready

Antigenic / Immunomics -profile based Biomarkers

microarrays improve discovery

30µl serum / plasma are sufficient… SALIVA works also

IgG preparation from serum/plasma wide range of linearity

microarrays & bioinformatics - well established & suited for discovery

bead arrays established for validation studies

from proteins to peptides

direct peptide based screening - using phage display & NGS

tiling design and high density peptide arrays are efficient for defining

chemically synthesizable peptides

specifying antigenic epitopes

peptide arrays improve classification success

peptide array data are biologically meaningful

….The technologies are ready.

18

….The technologies are ready …

& cooperations are welcome….

analyse 92 markers

from 1µl of serum

Oncology

Cardio-Vascular / CVD

Inflammation

….The technologies are ready …

& cooperations are welcome….

analyse 92 markers

from 1µl of serum

Oncology

Cardio-Vascular / CVD

Inflammation

Acknowledgement

AIT‘s –“AUTOANTIBODY“ team: Istvan Gyurjan, Johana Luna, Steffanie Brezina, Regina Soldo, Tina Malovits, Regina

Linhard, Roman Kreuzhuber, Lisa Milchrahm, Olga Peinando, Sandra Rosskopf, Manuela Hofner, Christa Noehammer,

Roni Kulovics, Oskar Morgenbesser, Gabriel Beikircher, Michael Stierschneider, Klemens Vierlinger

see also poster:

#522. Identification of differentially reactive antigen biomarkers for prostate cancer screening using protein

microarrays. J.A. Luna-Coronell, M. Gamperl, R. Kulovics, P. Hofer, A. Gsur, J. Hofbauer, F. Längle, A. Weinhäusel

Cooperations:

C. Singer, R. Zeillinger, C. Rappaport, N. Picha, ………MUW Vienna

F. Längle, J. Hofbauer, I. Berger, B. Hameed, LKH Wr. Neustadt

A. Gsur. & P. Hofer, ICR – MUW

G. Leeb & K. Mach, LKH Oberpullendorf

C. Jungbauer, Austrian Red Cross

J. Söllner, Emergentec

Funding & Support: