Network Modeling of Protein-protein Interactions to Identify and Prioritize Candidate Biomarkers

NETWORK MODELING OF PROTEIN-PROTEIN INTERACTIONS TO IDENTIFY AND PRIORITIZE CANDIDATE BIOMARKERS

Walter J. Jessen, Ph.D.

Systems Biologist, Data Scientist

Covance Informatics

June 25th, 2014

ONE OF THE WORLD'S LARGEST AND MOST COMPREHENSIVE DRUG DEVELOPMENT SERVICES COMPANIES

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• 12,000 employees in 60 countries• Lead Optimization (toxicology, pharmacology, etc)• Preclinical Development• Clinical Services• Commercialization Services• Biorepository Services• Antibody Products (custom development, online store)

Covance has helped pharmaceutical and biotech companies develop one-third of all prescription

medicines in the market today

INTRODUCTION

SYSTEMS BIOLOGY AND BIOINFORMATICS

• Exposure to a wide range of disease biology

• Very interested in the etiology of disease and what goes wrong at a pathway level

• Many opportunities to work with large amounts of diverse data

1. Identification(find, focus or refine)

2. Prioritization (ranking, statistical enrichment)

3. Confirmation(overlaps with existing knowledge)

4. Discovery(developing new hypotheses)

INTRODUCTION

As a laboratory services provider, Covance offers over 740 validated assays

~500 human analytes ~240 analytes in multiple species, including human

5 Central Labs •CAP/CLIA certified•Good Clinical Practice(GCP) compliant

Companion Diagnostics• Currently supports ~20

proprietary client programs

Translational Biomarker Services•SOP-based research environment •Follows “Good Research Practices”

INTRODUCTION

BioPathways visualizes all Covance assay analytes and antibody products onto pathways

INTRODUCTION

In addition to simply making it easier to help clients find products and services that Covance offers, displaying validated biomarker assays on biological pathways allows for the potential "repurposing" of existing biomarkers:• from one disease context to another• from markers useful only in limited research

settings to candidate markers with clinical utility

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Reduces time and costs associated with assay identification and development

BioPathways benefits our clients

INTRODUCTION

HELP SUPPORT THE DEVELOPMENT OF A BIOMARKER STRATEGY

A deeper dive: biomarker identification and prioritization for internal clients

Typically, very little information is provided and data is needed ASAP for teams to respond to client inquiries:

• Drug target• Therapeutic area

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Identify drug target connections and establish disease context to construct network models

INTRODUCTION

Use two types of network models to support client programs and drive decision-making

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DrugTarget

Associatedgenes

Marker(s)

From Through To

DrugTarget

Associated genes

1. Linear model

2. Radial model

INTRODUCTION

Construction of these context-dependent networks enables the identification of

candidate biomarkers

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Working examples using network modeling

INTRODUCTION

1. Example 1: applied to a psuedo-client project

2. Example 2: applied to a biomarker research project

• Client work is confidential; instead, simulate a project based on previously published research.

• Pharmacodynamic (PD) biomarker identification and prioritization for an experimental cancer drug.

• Identification and validation of a diagnostic biomarker for neurodegenerative disease (autophagy)

LINEAR MODEL

RADIAL MODEL

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Human Mouse models

Bioinformatics/Biostatistics

CCHMC (Cincinnati, OH)Nancy Ratner, Jianqiang Wu, Tilat Rizvi

Foundation Jean Dausset (Paris, France)Marco Giovannini, Jan Manent

CCHMC (Cincinnati, OH)Bruce Aronow, Walter Jessen, Sergio Kaiser

University of Alabama (Birmingham, AL)Grier Page, Tapan Mehta

CCHMC (Cincinnati, OH)Nancy Ratner, Shrya Miller, Atira Hardiman

MGH/Harvard (Boston, MA)Anat Stemmer-Rachamimov

University of Florida (Gainseville, FL)Margaret Wallace

L’Hospitalet de Llobregat (Barcelona, Spain)Concepcion Lazaro, Eduard Serra

INTRODUCTION LINEAR MODEL

Example 1: simulate a project based on previously published research.

Background: Neurofibromatosis Type 1 (NF1)RAS activation stimulates downstream signaling

Downward J, Cancer: A tumor gene’s fatal flaws. Nature. 2009 Nov 5;462(7269):44-5.

Canonical Ras effector pathway a.k.a. MAP kinase activity pathway

Canonical Ras effector pathway a.k.a. MAP kinase activity pathway

• Genetic disease• Most common hereditary tumor predisposition

syndrome (prevalence 1:3500)• Causes tumors to grow along peripheral nerves• NF1 encodes neurofibromin, a RAS-GAP

(tumor suppressor)• Reduced NF1 expression causes increased

Ras activationDermal neurofibroma95% patients affected

Plexiform neurofibroma25% affected

MPNST 10-13% affected


No effective treatments exist

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Study focus: identify pathways for tumorigenesis and malignancy conserved between 8 mouse NF1 models and human NF1 tumors

Jessen et al. MEK inhibition exhibits efficacy in human and mouse neurofibromatosis tumors. J Clin Invest. 2013 Jan 2;123(1):340-7.

***

*

Data highlighted altered transcriptional regulation of

Raf/MEK/ERK signaling

****


Have negative regulators suppressed ERK signaling?

Paraffin tissue sections: brown staining indicates detection of active (p)-ERK

Despite increased expression of negative regulators, ERK activation is maintained in NF1 neurofibromas and MPNSTs


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Empirical Formula: C16H14F3IN2O4

Perform preclinical trials with the MEK inhibitor PD0325901 in mouse models

• An orally bioavailable, selective and nonATP-competitive MEK inhibitor

• Derivative of ci-1040 developed by Pfizer

• Inhibits both MEK isoforms: MEK1 & MEK2

• Currently in cancer clinical trials


PD0325901 reduces p-ERK and inhibits MPNST cell growth

A-D. p-ERK absent 30 mins after treatment (10 mg/kg)E. Dose response on 5 MPNST cell lines (active at [nM])F. Significantly reduced tumor volume (by day 5)G. MPNST xenograft survival doubled 3 months post treatment


PD0325901 shrinks tumors in >80% of mice testedand maintains MEK inhibition in neurofibromas

Supports MEK signaling as an important clinical target in NF1


Analyze expression of 2 negative regulators: SPRY4 and DUSP6

How to model the question

MEK1/2NF1-associated genes SPRY4, DUSP6

IDENTIFY AND PRIORITIZE BIOMARKERS FOR PD0325901 IN NF1

1. Linear model DrugTarget

Associatedgenes

Marker(s)

From Through To

Data sources used to provide context:• MetaCore• ToppGene at CCHMC (integrates HPRD, GWAS, CTD…)• DisGeNET (a database of gene-disease associations)• Text mine PubMed directly for associations


Target

First-degree proteins

Second-degree proteins

3 in

tera

ctio

ns

2

inte

ract

ion

s1

inte

ract

ion

1 target

16 first-degreeproteins

downstream of MEK1/2

115 second-degreeproteins

downstream of MEK1/2

MetaCore algorithm: shortest-path (maximum number of steps: 3)

Contextualize the network: interactions specific to peripheral nerves

15 biomarker candidates

181818 CLS assay analyte

Marker(s)

LEGEND

Secreted proteins not shown

19

IndexNetwork symbol

NameNetwork object

Entrez ID

StepNumber of

interactionsBack / Up / Down

CLS assay

Integrity biomarker

Secreted protein

1 EGFR Epidermal growth factor receptor

Receptor with enzyme activity 1956 1 1 / 1 / 18 Y Genomic;

Proteomic N

2 APOE Apolipoprotien E Receptor ligand 348 2 3 / 3 / 0 YBiochemical;

Genomic; Proteomic

Y

3 B-Raf Serine/threonine-protein kinase B-raf Protein kinase 673 2 2 / 8 / 2 Y Genomic;

Proteomic N

4 APOC3 Apolipoprotein C-III Transporter 345 2 2 / 2 / 0 YBiochemical;

Genomic; Proteomic

Y

5 MMP-2 72 kDa type IV collagenase Metalloprotease 4313 2 2 / 2 / 1 Y Genomic;

Proteomic N

6 LPL Lipoprotein lipaseGeneric binding protein 4023 2 2 / 2 / 1 Y Genomic;

Proteomic N

7 IL-6 Interleukin-6 Receptor ligand 3569 2 1 / 1 / 1 Y Genomic; Proteomic Y

8 Activin Inhibin Receptor ligand 3624 2 1 / 7 / 3 Y Genomic; Proteomic Y

9 APOA4 Apolipoprotein A-IV Receptor ligand 337 2 1 / 1 / 1 Y Genomic; Proteomic Y

10 Midkine Midkine Receptor ligand 4192 2 1 / 6 / 2 N Genomic; Proteomic Y

11 GDNF Glial cell line-derived neurotrophic factor Receptor ligand 2668 2 2 / 2/ 4 N Genomic;

Proteomic Y

12 GM-CSFGranulocyte-macrophage colony-stimulating factor

Receptor ligand 1437 2 2 / 4/ 1 NBiochemical;

Genomic; Proteomic

Y

13 PCSK9 Proprotein convertase subtilisin/kexin type 9

Generic protease 255738 2 1 / 1 / 1 N Genomic;

Proteomic Y

14 Follistatin FollistatinGeneric binding protein 10468 2 1 / 1 / 1 N Genomic;

Proteomic Y

15 APOA5 Apolipoprotein A-V Transporter116519 2 1 / 1 / 1 N Genomic;

Proteomic Y

Summary

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*

****

• Top biomarker candidate is APOE • 3 interactions back to first-degree proteins

downstream of MEK1/2• CLS assay exists• APOE is secreted• Component of the initial

data set


Example 2: Covance biomarker research project

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Alzheimer’s Neurofibrillary

Tangles Tau Fibrils

Alzheimer’s A-Beta Plaques

Parkinson’s Nigral Lewy Body

immunostain

Parkinson’s Nigral Lewy Bodies

H&E stain

Huntington’sHuntingtin Protein

Alzheimer’sCongophilic Angiopathy

Alzheimer’s Congophilic Angiopathy

Parkinson’s CorticalLewy Bodies

Protein aggregation: common theme in major neurodegenerative diseases

INTRODUCTION LINEAR MODEL RADIAL MODEL

Macroautophagy: a regulated mechanism for clearance of sub-cellular contents

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Macroautophagy is: • Inducible (e.g. starvation, stress response)• Clears cellular components• May be impaired in neurodegenerative diseases

How can autophagy be used therapeutically?• Inducible: a number of drugs can turn on the process

Biomarkers of autophagy:• Intracellular

(maturation of LC3)• Can we establish an

extracellular marker?• Present in CSF?


IS THERE A SECRETED PROTEIN ASSOCIATED WITH AUTOPHAGY THAT CAN BE USED AS A BIOMARKER?

DrugTarget

Associated genes

2. Radial model

Autophagy-associated genes

• 60 genes/proteins in MetaCore (GO:0006914)• Auto-expand algorithm to see neighboring interactions

Process of autophagy


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How to model the question

NPC2: predicted secreted autophagy biomarker

24

MetaCore algorithm: auto-expand (125); evaluate large multi-node network

Contextualize the network: interactions specific to the brain

24

2424 Secreted protein

Associated gene (seeds)

LEGEND


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Niemann-Pick Disease Type C2 (NPC2)• Component of lysosome that binds cholesterol

• Mutant forms are unable to bind lipids causingcholesterol aggregation in cells

Niemann-Pick’s disease• Leads to neurological degeneration

starting early in life

• Causes dementia (similarities with AD)

NPC2 mRNA expression levels change with drug treatments that are linked to autophagy• Is protein expressed?

• Is it secreted?

• Utility as biomarker?


Drugs used to manipulate autophagy

Tested Drug Therapeutic Use Autophagy Action Mechanism

Yes 3-Ma Anti-cancer Inhibitor PI3K

Yes Amitriptyline (AMI) Antidepressant Inducer SNRI, PI3K

Yes Citalopram Antidepressant Inducer SSRI

Yes Loperamide Anti-diarrheal InducerBlocks Ca2+

channels

No Rapamycin* Immunosuppresssant InducermTOR receptor

proteins

Tested 4 standard compounds for autophagy

Antidepressant compounds attractive:• Induce autophagy• Penetrate the brain

Study: Increase NPC2 mRNA levels (12, 72 hrs)

We tested between 12 and 72 hrs

NPC2 protein present in media at 24 hrs

* toxic compound


Neuropsychopharmacology. 2011 Jul;36(8):1754-68.

24 hour AMI treatment in vitro

0uM 1uM 10uM [AMI]

Induction

24hr NPC2 (CM)

0uM 1uM 10uM [AMI]

6h (top) or 24h (bottom) LC3 (Lys)

15kDa -

Shi

ft in

siz

ein

dica

ting

mat

urat

ion

NPC2 Signal Difference

0

20

40

60

80

100

120

140

24 hour

Treatment Length

% C

han

ge

of

Co

ntr

ol

1uM

10uM

Microtubule-associated protein light chain 3 (LC3) is widely used to monitor autophagy:

15kDa -

NPC2 protein is secreted in response to AMI-induced autophagy

20kDa -


Eureka: AMI treatment in vivo

Control Treated

Dosed rats 24 hours and 1 hour priorto collecting samples.

NPC2 protein levels increase in rat CSF in response to AMI-induced autophagy

10ul CSF + 10ul CSF + conj. beads beads alone

IP of NPC2 from rat CSF using anti-NPC-2 conjugated DynaBeads

IP specific for NPC2 in rat CSF


17% increase

Summary

• NPC2 may be useful as a soluble biomarker for macroautophagy

• AMI induces autophagy in human H4 neuroglioma cells• NPC2 secretion was observed in a time- and dose-

dependent manner• NPC2 can be immunoprecipitated from rat CSF• NPC2 levels increase in rat CSF in response to AMI-

induced autophagy

Next steps• Analyze protein levels of NPC2 and LC3 in brain lysate• Increase sample size for in vivo treatment


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Protocol Development: Robert Martone ** Covance Translational Biomarker Services Neuroscience Lead,

now at St. Jude Children's Research Hospital

Cell Culture: Steve Hatch

Animal Dosing: Elizabeth Eberle

Dot Blotting: Jordan Jensen

Western Blotting: Marsha Farkaly, Audra Kuebler

General Lab Techniques/Troubleshooting:Nancy Jackson, Marci Copeland, Erika Troksa

Pathway Analysis: Walter Jessen


Acknowledgements: NPC2 biomarker research project

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DrugTarget

Associatedgenes

Marker(s)

From Through To

1. Identification and prioritization of PD biomarkers for aMEK inhibitor in NF1: top candidate APOE

2. Identification of a diagnostic biomarker for macroautophagy: NPC2 Drug

Target

Associated genes

INTRODUCTION LINEAR MODEL RADIAL MODEL SUMMARY

Two examples identifying and/or prioritizing candidate biomarkers based on content- and context-dependent relationships to drug targets and diseases

Science

Network Modeling of Protein-protein Interactions to Identify and Prioritize Candidate Biomarkers