Systems Approaches to Blood Based CBi kCancer Biomarkers · at cellular level 2.84E‐ Cellular...
Transcript of Systems Approaches to Blood Based CBi kCancer Biomarkers · at cellular level 2.84E‐ Cellular...
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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
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Targeted cancers
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If we screen for it and catch it early does it save lives?does it save lives?
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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
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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
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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
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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
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From the tumor to bloodFrom the tumor….to ….. blood
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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
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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
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+/- 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
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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
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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
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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
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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
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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
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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
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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)
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Plasma signature for lung adenocarcinoma driven in part by the master regulator NKX2‐1
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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
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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
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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
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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)
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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)
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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
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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)
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Paradigm shift in the development of biomarkers
Partnership between academiaPartnership between academia, philanthropy, government and p py, g
industry
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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
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Unbiased, in-depth,quantitative
High throughput, Sensitive,affordableq
Cancer Biomarker platforms
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Oncoproteome databaseOncoproteome database
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• 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
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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
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FUNDING SUPPORT
Foundations CanaryCanaryUniting Against Lung CancerProtect Your Lungs/Lungevityg g yLustgartenAvonKKomen
GovernmentNational Cancer InstituteNational Cancer InstituteNational Heart Lung and Blood InstituteDOD