Systems Approaches to Blood BasedC Bi kCancer Biomarkers

S HanashS. Hanash

shanash@mdanderson.org

Why so few biomarkers to date?y f

- Developing biomarkers shares some of the samechallenges as developing drugs, yet the investment inbiomarkers is far, far less!

- Requires a road map from discovery to validation for specific/intended clinical applicationsspecific/intended clinical applications

- Need to establish a mechanistic link to the diseaseprocess beyond statistical associations

Targeted cancers

If we screen for it and catch it early does it save lives?does it save lives?

Challenges with CT based screeningChallenges with CT based screening

• High percentage of false positives. 96.4% of pulmonary nodules identified by LDCT p y yare non-malignant unnecessary work-upsups.

• If all smokers and former smokers do C Sthree CT scan per NLST protocol, the

health care system can’t afford it.y

LUNG CANCERNumber of lives that could be saved 

h heach year with CT screening

8 1008,100

Estimated today’s costs per life saved:

$ 100,000 ‐ 240,000$ 100,000  240,000

Goulart et al J Natl Compr Canc Netw 2012;10:267‐275

CONCEPTUAL FRAMEWORKCONCEPTUAL FRAMEWORK

TheTUMOR

INFILTRATING The MICROENVIRONMENT

In tumor initiation, d l d

TUMOR CELLSINFILTRATING

CELLS ANGIOGENESIS

development and progression

FIBROBLASTS

S t i h tSystemic host response

Mouse models

GenomicsGenomics

GlycomicsGlycomics ProteomicsProteomicsBi k lBi k l

yyBiomarker panels Biomarker panels

Cancer cells Human studiesl /h l ll

MetabolomicsMetabolomicsImmunomicsImmunomics

♦ plasma/serum♦ tissues

♦ whole cell extracts♦ secretome/exosome♦ surface proteins♦ nuclear proteins MetabolomicsMetabolomicsImmunomicsImmunomics

From the tumor to bloodFrom the tumor….to ….. blood

Mass Spectrometry Capability: 30,000 LC/MS runs

3,000 proteins 8‐10,000 proteins 4‐6,000 proteins3,000 proteins 8 10,000 proteins 4 6,000 proteins

Proteomic signatures

ChemicalChemicalModifications eg

altered glycosylationProtein Cleavages eg

Alternative Splicing Isoforms

Protein Cleavages egshed receptors andadhesion molecules

Formation ofAltered dynamics of

i iFormation of complexes eg

immune complexes

protein sorting egrelease of

chaperone proteinsTranslationalTranslationalTranslationalTranslationalImplicationsImplications

+/- various treatments O

NH2

NH2 OHCells In Culture HeavyLYS

LightLYS

2

Cell surfaceProtein biotinylation Cell culture media

CELL SURFACE mix heavy and lightSECRETOME

TOTAL EXTRACT concentration

Lysis and affinity t Gl copeptide

Cell Lysis

capture

Elution

GlycopeptideCapture

PHOSPHOPROTEINS NUCLEAR PROTEINS

Conditioned media Proteins localization

Source of conditioned media proteins

2%

Conditioned media Proteins localization using Uniprot Database

19374• Pre-

filtration• Pre-

filtration29%

7%20%

42%Cytoplasm

Nuclear

Surface

Extracellular space2480• Post-

filtration• Post-

filtrationUnknown

Hi h V l t t61%

Ratio S/M >2 32%

Ratio: 1-2

7%Ratio <1

176 High Value targets

Cell line Compartment MS2 counts

H1993 Media 65

1 913

Cytoplasmic domainT b d i

DDR1 protein: epithelial discoidin domain-containing receptor 1

H1373 Media 51

HPC9 Media 37

H1993 Surface 103

H1373 Surface 73

Transmembrane domainExtracellular domainSignal Peptide

HPC9 Surface 78

I. Babel

A B

Function of cytosolic and nuclear proteins released into conditioned media

Biological Process (Gene ontology Term)

Number of proteins

p‐value Bonferroni

Macromolecule metabolic process

48 1.88E‐02

Cellular component organization 30 2 84E 02

A B

p gat cellular level

30 2.84E‐02

Cellular localization 22 9.99E‐03Establishment of localization in 

cell 21 5.58E‐03

Protein transport 19 1.72E‐03

P i l li i 19 4 95E 02Protein localization 19 4.95E‐02

Intracellular transport 18 1.99E‐03

Intracellular protein transport 17 9.94E‐07

Cellular protein localization 17 3.05E‐05

Carbohydrate metabolic process 15 2.73E‐02Oli h id b liOligosaccharide metabolic 

process8 2.79E‐05

Polysaccharide catabolic process 5 3.88E‐02

Oligosaccharide catabolic process 3 8.64E‐03Glycosphingolipid catabolic 

process3 4.28E‐02

p

Macromolecule metabolic processLysosomal enzymes

Cellular localization

Nuclear localization

Component organizationp g

H920A B

Nuclear proteins are released by lung cancer cells in exosomes

H920

Exo

FT Med

iaN

ucle

usTC

E

A B

XPO1

XPOT

ALIXExosome

marker

TNPO1

200 nm

Note: from 300mL of media: 20x more exosome fraction from cancer cell lines than transformed control cell

Blood Based biomarkers

Newly Diagnosed and post Rx for predictive markers

0 2 yrs pre diagnostic3+ yrs pre diagnostic 0-2 yrs pre-diagnostic for early detection

3+ yrs pre-diagnosticFor risk markers

Blood Based biomarkers

Newly Diagnosed and post Rx for predictive markers

0 2 yrs pre diagnostic3+ yrs pre diagnostic 0-2 yrs pre-diagnostic for early detection

3+ yrs pre-diagnosticFor risk markers

Intact Protein Analysis System (IPAS)Case Control

Immunodepletion(Top six proteins)

Concentration, buffer

Immunodepletion(Top six proteins)

Concentration, buffer

Ig bound fraction: MSbound fraction: MS

,exchange and

labeling

SAMPLE ALight Acrylamide

SAMPLE BHeavy Acrylamide

Reduction with DTT and Alkylation

,exchange and

labeling

SAMPLE ALight Acrylamide

SAMPLE BHeavy Acrylamide

Reduction with DTT and Alkylation

SAMPLES MIXED

ANION EXCHANGECHROMATOGRAPHY

SAMPLES MIXED

ANION EXCHANGECHROMATOGRAPHY

REVERSE-PHASECHROMATOGRAPHY

REVERSE-PHASECHROMATOGRAPHY

Shotgun LC/MS/MS96 fractions

Shotgun LC/MS/MS96 fractions

Mouse models

♦ Lung cancerEGFR: TetO-EGFRL858R/CCSP-rtTA (H. Varmus/K. Politi)K T O K 4bG12D/CCSP TA (H V /K P li i)Kras: TetO-Kras4bG12D/CCSP-rtTA (H. Varmus/K. Politi)Urethane: introperitoneal injection of urethane (C. Kemp) SCLC: Trp53F2-10/F2-10; Rb1F19/F19 (J. Sage)

♦ Breast cancer♦ Breast cancerHER2: MMTV-rtTA/TetO-NeuNT (L. Chodosh)PyMT 0.5cm: Tg(MMTV-PyMT)634Mul (C. Kemp)PyMT 1.0cm: Tg(MMTV-PyMT)634Mul

♦ Pancreas cancerPanIN: Pdx1-Cre; LSL-KrasG12D; Ink4a/Arflox/lox (R. DePinho, N. Bardeesy)PDAC: Pdx1-Cre; LSL-KrasG12D; Ink4a/Arflox/lox

C♦ Colon cancer: ApcΔ580/+ (R. Kucherlapati, K. Hung)Kras model (R. DePinho)Mlh1 and Msh2 mutant models (R. Kucherlapati)

♦ Ovarian cancer: LSL K G12D/+ Pt loxP/loxP (D Di l T J k )♦ Ovarian cancer: LSL-KrasG12D/+; PtenloxP/loxP (D, Dinulescu, T. Jacks)♦ Prostate ca (Strain comparison): Ptenpc-/-, Ptenpc-/-;Smad4pc-/- (R. DePinho)♦ Inflammation

A t C i l t ti (C K )Acute: Carrageenan-sponge implantation (C. Kemp)Chronic: intradermal injection of type II collagen (C. Kemp)

Plasma signature for lung adenocarcinoma driven in part by the master regulator NKX2‐1

Validation in humans of mouse lung adenocarcinoma blood markers

Newly diagnosed set

81.

0

y g

0.6

0.8

eFr

actio

n

Assay Normal Cancer T-test Mann Whitney testNewly diagnosed set

0.4

0e

Posi

tive y y

EGFR 1.00 ± 0.067 0.77 ± 0.041 0.0094 0.004SFTPB 1.00 ± 0.135 1.43 ± 0.205 0.0708 0.0332WFDC2 1.00 ± 0.233 4.70 ± 1.145 0.0005 < 0.0001

ANGPTL3 1.00 ± 0.073 1.53 ± 0.205 0.008 0.0038

00.

2True

Combined (AUC= 0.882 )EGFR (AUC= 0.708 )SFTPB (AUC= 0.654 )WFDC2 (AUC= 0.864 )

0.0 0.2 0.4 0.6 0.8 1.0

0.0

False Positive Fraction

ANGPTL3 (AUC= 0.709 )

ase os t e act o

Is there evidence that blood markers could be useful? Validation in the pan Canadian Lung Ca screening study

Red: Base model”|: AUC= .642Blue: Base model + 1 marker (AUC = 736Blue: Base model + 1 marker (AUC .736Difference in AUCs p = .0002

Sin et al JCO in press

Harnessing the immune response to tumor antigens for lung cancer early detection

• Humoral immune response to tumor antigens occurs early during tumor developmentg p

• Immune response is not limited to mutated or otherwise lt d t ialtered proteins

• Implementation of a proteomic strategy to identify proteinsImplementation of a proteomic strategy to identify proteins in lung cancer that induce an autoantibody response

Search for antigens that induce an antibody response in lung cancer

• Recombinant protein arrays

• Natural protein arrays

M t t f i l ti ti• Mass spectrometry of circulating antigen‐antibody complexes

• Peptide arrays (in collaboration with Roche)

Search for antigens that induce an antibody response in cancer

The complete repertoire of peptides encoded inThe complete repertoire of peptides encoded in the human genome on a chip 

+

(Mutant peptides and pathogen peptides)(Mutant peptides and pathogen peptides)

Potential of marker panels to detect l llung cancer early

.0.0.0

Pre-diagnostic set

0.8

1.

ctio

n 0.8

1.

ctio

n 0.8

1.

ctio

n

40.

6

sitiv

e Fr

act

40.

6

sitiv

e Fr

act

40.

6

sitiv

e Fr

act

0.2

0.4

True

Pos

0.2

0.4

True

Pos

0.2

0.4

True

Pos

Combined all (AUC= 0.898 )Combined ELISA (AUC= 0.808 )

t tib d l (AUC 0 828 )

00 02 04 06 08 10

0.0

0

0 0 02 04 06 08 10

0.0

0

0 0 02 04 06 08 10

0.0

0 autoantibody panel (AUC= 0.828 )EGFR (AUC= 0.677 )SFTPB (AUC= 0.74 )WFDC2 (AUC= 0.632 )ANGPTL3 (AUC= 0.615 )

0.0 0.2 0.4 0.6 0.8 1.0False Positive Fraction

0.0 0.2 0.4 0.6 0.8 1.0False Positive Fraction

0.0 0.2 0.4 0.6 0.8 1.0False Positive Fraction

A validation study of biomarkers involvingA validation study of biomarkers involving  10,000 subjects at risk for lung cancer in USA

Primary ObjectivePrimary Objective: Develop and test a biomarker panel in combination with CT to reduce the false positive rate of CT and reduce unnecessaryreduce the false positive rate of CT and reduce unnecessary invasive work‐up by 30%Secondary Objective:Assess need for CT screening based on biomarker panel. Target performance PPV equivalent of better than LDCT (>= 3 6% which corresponds to 40% sensitivity at 95 % specificity)3.6% which corresponds to 40% sensitivity at 95 % specificity)

Paradigm shift in the development of biomarkers

Partnership between academiaPartnership between academia, philanthropy, government and p py, g

industry

Pre-validation of markers(construct refine and nail down panel)(construct, refine, and nail down panel)

MDACC MarkersPre-Dx cohorts: (eg PLCO…) Retrospective Screening

Cohorts (eg PanCan, NLST)Other markers Other markers

Marker panel + Imaging + risk modelProspective Screening Cohorts (USA, China)

validated panel for intended clinical application

Unbiased, in-depth,quantitative

High throughput, Sensitive,affordableq

Cancer Biomarker platforms

Oncoproteome databaseOncoproteome database

• Proteomics technologies have matured to the point of significantly impacting clinical p g y p gapplications

• A collaborative integrative effort with• A collaborative integrative effort with adequate resources and rigorous experimental 

l f h l fdesign is critical for the development of biomarkers

Acknowledgements• Hanash Lab: Ingrid Babel, Clayton Boldt, Muge Celiktas,Tim

Chao, Alice Chin, Lili Chu, Dilsher Dillon, Vitor Faca, Sandra Faca Song Gao Rebecca Israel Askandar Ikbal MelissaFaca, Song Gao, Rebecca Israel, Askandar Ikbal, Melissa Johnson, Hiroyuki Katayama, Jon Ladd, Min-Hee Lee, Amin Momin, Sophie Paczesny, Sharon Pitteri, Ji Qiu, Mark Schliekelman Melissa Silva Jinfeng Suo Ayumu TaguchiSchliekelman, Melissa Silva, Jinfeng Suo, Ayumu Taguchi, Allen Taylor, Sati Tripathi, Nese Unver, Hong Wang, Dong Wang, Chee-Hong Wong, Qing Zhang

• Collaborators: Aaron Aragaki, Nabeel Bardeesy, Robert Bast, Ron DePinho, Daniela Dinulescu, Nora Disis, Kim-Ahn Do, F i E t Zidi F Oli Fi h S G bhiFrancisco Esteva, Ziding Feng, Oliver Fiehn, Sam Gambhir, David Gandara, Guillermo Garcia-Mareno, Adi Gazdar, Gary Goodman, Bill Hancock, Kenneth Hung, Chris Kemp, Raju K h l ti St L P l L C lit L b ill Ch iKucherlapati, Steven Lam, Paul Lampe, Carlito Lebrilla, Chris Li, Karen Lu, Phil Mack, Suzanne Miyamoto, PeymanMoghaddem, Ed Ostrin, Katerina Politi, Peggy Porter, Ross g ggyPrentice, Julian Sage, Karen Spratt, Martin Tammemagi, Harold Varmus, Shan Wan

FUNDING SUPPORT

Foundations CanaryCanaryUniting Against Lung CancerProtect Your Lungs/Lungevityg g yLustgartenAvonKKomen

GovernmentNational Cancer InstituteNational Cancer InstituteNational Heart Lung and Blood InstituteDOD