DNA-methylation- and autoantibody- biomarker development strategies for

minimal invasive diagnostics

Andreas Weinhäusel, Matthias Wielscher, Johana Luna , Istvan Gyurjan, Walter Pulverer, Klemens Vierlinger, Christa Noehammer, Johannes Söllner* and Albert Kriegner

Molecular Diagnostics, AIT- Austrian Institute of Technology GmbH, Vienna & * ..emergentec biodevelopment GmbH, Vienna, Austria,

4th Munich Biomarker Conference November 25th-26th, 2014

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

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%;

4 25.11.2014

Aim: Improve cancer diagnostics

DNA methylation marker-developments • Big-4, thyroid ca, childhood ALL, Uveal Melanoma • Lung-cancer initial diagnostics as an example

Immuno-profiling on protein-& peptide-microarrays

• the big-4 cancer entities: breast-, colon-, lung-, and prostate cancer

Biomarkers for minimal invasive testing

6 25.11.2014

CpG360 uncovers Lung cancer marker panels

Bioinformatics

group samples (N=108)

AdenoCa 19 TU + 19 N

SqCCL 29 TU + 29 N

PB controls 8

Techn Contr. 4

select „top“ 24

for qPCR

Pyro-Sequencing

MSRE–qPCR independent samples

Weinhaeusel A, Pichler R, Nohammer C.

Lung Cancer Methylation Markers (2010).

EP2391728 (A1); WO2010086388 (A1)

100% correct

100% correct

70% correct

work out minimal invasive test for serum a/o sputum analyses

Isolation of cell-

free DNA

Volume reduction

and purification

Methylation sensitive

restriction digest

preAmplification

48x48 high-throughput

µ-fluidic qPCR (9 nl)

Dataanalysis

and

interptetation

MSREqPCR based marker validation

cut 10ng AciI(C^CGC= , Hin6I (G^CGC);

HpaII (C^CGG), HpyCH4IV (A^CGT)

48-plex - 22x

EvaGreen – ct & Tm

paralleled methylation analyses of 48

candidate markers x 48 samples

using cfDNA (400µl Heparin-plasma)

Lung Cancer plasma samples for cfDNA methylation analyses

conducted MSREqPCR (48.48) using 10ng of cfDNA setup: case vs control – at each array „cases – of a single specified

subtype“ vs „matched controls“ - 17 runs 48 analytically qualified markers 38 were suitable for cfDNA testing

10 25.11.2014

ENTITY patients gender age smoking female male <64 >64 current former never

AdenoCa 100 0.41 0.59 0.56 0.44 0.41 0.41 0.17 Large Cell 48 0.00 1.00 0.65 0.35 0.48 0.48 0.04 Small Cell 100 0.37 0.63 0.55 0.45 0.49 0.43 0.08 SquamousCa 100 0.15 0.85 0.48 0.52 0.44 0.55 0.01 CASE total 348 0.28 0.72 0.54 0.46 0.45 0.46 0.08 Controls 332 0.16 0.84 0.59 0.41 0.28 0.66 0.06

Subtype specific analyses

11 25.11.2014

• 17x 48.48 MSRE-qPCR cfDNA methylation data • data analysed using „penalized logistic regression” (Friedman, Hastie, Tibshirani; 2008)

ENTITY patients

AdenoCa 100

Large Cell 48

Small Cell 100

SquamousCa 100

CASE total 348

Controls 332

all cases vs. contr: Plasma: AUC=0.85 Deep sputum DNA: AUC =0.77 n=96 (48 TU vs 48 contr.; Liverpool - J. Field / T Liloglou)

GENOME-wide discovery - Lung tissues Illumina 450k methylation analyses

bioinformatic analysis for biomarker-selection univariate - cut off: adj. p-value < 0,005 & methylation difference >7.5%; & AUC >0,85 multivariate – using class prediction analyses

LuCa, 18

IPF, 30

HP, 8

COPD, 42

normal LU tissue,

34

blood DNA, 24

Lung cancer (n=18) - AdenoCa (9), SqCCL.(9) IPF - Idiopathic pulmonary fibrosis (n= 30) HP - Hypersens. pneumonia (n= 8) COPD (n=42) - GOLD 0-3, - n = 8-13 per group Normal lung tissue (n=34) Healthy blood DNA (n=24)

n=156

152 samples corr. classified: 95% 98% 92% 97% patient group: IIP HSP COPD Cancer # methylation-sites: 132 92 613 48

design and setup MSRE-qPCR assays for 222 candidates for cfDNA testing, using AIT‘s XworX design pipeline

450k derived candidate-markers enable correct classification

cfDNA „Lung cancer“ case-control pilot study using first 96 out of 222 assays in 96.96 MSRE-HTqPCR

Spec Sens AUC

Cand 1 0.88 0.5 0.8

Cand 2 0.88 0.61 0.85

Cand 3 0.88 0.51 0.81

Cand 4 0.88 0.35 0.74

SHOX2* 0.9 0.62 0.78

* EpiProLung test marketed by Epigenomics

Combination of Candidates use logistic regression on 21 markers

AUC = 0.91-0.99 …

confirms suitability of tissue derived

candidate-methylation markers

Single Candidates

Summary - DNA Methylation biomarkers Lung-Cancer Methylation markers

Tumor tissue methylation analyses enables almost perfect classification Plasma testing plasma samples up to 15y old are useful (age had no effect)

SUBTYPING lung caner entity based on cfDNA feasible AUC≈0.8-0.92 (using classifiers from targeted CpG360 screening) AUCs up to 0.99 based on genomic screening

option for improvements using more sample ≥ 10ng DNA; we used cfDNA from 400µl plasma Illumina‘s 450k array is currently most cost effective tool for BM development

sample size >20 per group needed for discovery

MethPipe: efficient combination of methylation analyses tools for BM development 450k discovery, BSP based confirmation (pyroseq / IonTorrent), MSRE-qPCR enables confirmation and classifier-refinement

LC480 (EvaGreen) & 48.48 / 96.96 high-throughput 0.1-1% LOD & paralleled analyses of 48 (96) markers works with bisulfite based discovery efficient tool (costs & time) for qualification of methylation markers

and bringing these forward to serum-cfDNA testing

•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 Macromembranes (38k fetal brain library) based serological identification of antigenic clones, SEREX, phage display (M13 random peptide phage library & NGS)

AIT‘s 16k protein micro-array

15286 „UNIPEX-clones“ (His-tag) purified duplicates printed – 32000 spots/slide enable discovery of candidates antigens

with low sample (10-30µl) volumes use micro-arrays to discover antigenic markers

“Autoantibody” based serum diagnostics

antigen

Y Y anti-human IgG

IgG – patient

BIG-4 cancer entities – protein chip classifiers

18

Breast Cancer 72 malignant,

60 healthy 48 benign

AUC = 0.938 Carcinoma vs. control

Colon Cancer 32 cancer vs 32 contr. 18 HR & 17 LR polyps

Lung Cancer 25 pts x4 entities & 100 controls

AUC = 0.924 “40 greedy pairs“ carcinoma vs BPH

ProstateCa 50 cancer vs 49 BPH

172.596

179k peptide array design - for generation of „peptide based assays“

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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

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

….The technologies are ready … cooperations are welcome….

analyse 92 markers from 1µl of serum

Oncology

Cardio-Vascular / CVD

Inflammation

DNA Methylation work Matthias Wielscher, Rudolf Pichler, Elisabeth Reithuber, Markus Sonntagbauer, Walter Pulverer, Manuela Hofner, Christa Noehammer Immunoprofiling work: Istvan Gyurjan, Johana Luna, Stefanie Brezina, Regina Soldo, Tina Malovits, Regina Linhard, Roman Kreuzhuber, Lisa Milchrahm, Olga Peinando, Sandra Rosskopf, Roni Kulovics, Gabriel Beikircher, Silvia Schönthaler, Michael Stierschneider Assay design pipelines and bioinformatics Business development; IPR and technology marketing Albert Kriegner, Ilhami Visne, Rainer Kallmeyer, Uwe von Ahsen, Stefan Pabinger, Fabian Schröder, and Klemens Vierlinger Head of AIT-Molecular Diagnostics: Martin Weber Cooperations: Funding & Support:

Acknowledgement

T. Liloglou, J. Field; Roy Castle Lung Cancer Foundation, Liverpool, UK

M. Hajduch, Molecular and Translational Medicine, University Hospital Olomouc, Czech Republic

C. Singer, A. Gsur. & P. Hofer, R. Ziesche Medical Univ. Vienna F. Längle, J. Hofbauer, LKH Wr. Neustadt G. Leeb & K. Mach, LKH Oberpullendorf C. Jungbauer, Austrian Red Cross J. Söllner, Emergentec

Antigenic / Immunomics -profile based Biomarkers

microarrays improve discovery

30µl serum / plasma are sufficient…

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.

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DNA methylation results (MSREqPCR) – Plasma & Sputum

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• Deep Sputum DNA: n=96 (48 TU vs 48 contr.; Liverpool - J. Field / T Liloglou)

• (Heparin)-Plasma cfDNA: n=200 (100 LuCa vs. 100 matched controls)

AUC values depicted:

high AUC in all Lung cancer entities

performance of plasma-DNA better than „deep sputum“-DNA

SPUTUM (n=96)

PLASMA (n=200) genes

Cases vs Controls

Cases vs Controls

SCLC vs. controls

SqCC vs. controls

LCLC vs. controls

AdCa vs. controls

0.759 0.802 0.898 0.834 0.857 0.797 WT1,SALL3, TERT, ACTB, CPEB4

0.772 0.846 0.953 0.863 0.938 0.884 new classifer