Blake Anson, PhDMarch 16, 2016

Symposium Session: Pat ient -

Speci f ic Stem Cel ls as Models for

Gene, Drug, and Environment

Interact ions in Disease

Use of Human iPSC-derived Cells as a Means to

Investigate the Relationship Between Genes and Disease

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Agenda and Disclaimer

I. The potential of iPSC technology and requirements for successful utilization

II. Case studies of IPSC use (and potential use) in understanding genetic factors underlying disease

Disclaimer:

The speaker is an employee of Cellular Dynamics International

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Induced pluripotent stem cell (iPSC) technologyMoving from somatic cells to stem cells to tissue cells of choice

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New horizons

iPSC technology is enabling new pathways toward

understanding human biology, pathology, and therapy

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Current pre-clinical modelsUseful with impactful limitations

Primary Human Cells Transformed Cell Lines Animal Models

• Donor variability

• Variable quality

• Limited availability

• Limited characterization

• Inaccessible biology

• Phenotypic instability

• May not recapitulate relevant

cell/tissue biology

• May lack key functional

characteristics

• Cannot adequately represent

human diversity

• May not represent relevant

human biology

• Resource intensive

($ and labor)

• Require significant quantities

of compound

• Animal welfare issues

Induced pluripotent stem cell (iPSC) technology overcomes limitations of

existing cell models - Availability, Functionality, Reproducibility, Translatability

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iPSC technologyProviding human biology from cells to populations

iPSC technology is providing relevant material richer content from targeted populations

Endoderm

Mesoderm

Ectoderm

Terminally differentiated

human cells

…from multiple organs

across the body

…expanding across

diverse populations

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“Disease in a Dish”Using iPSCs to move from Bedside to Bench to Bedside

iPSC technology can be used to

model human innate, induced,

and infectious diseases that

cannot be interrogated using

conventional cell lines, primary

cells or animal models

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iPS Cell TechnologyRevolutionary Access to Human Biology

Differentiate into any cell

type in the human body

208 C

ell

Typ

es

Represent any

individual genotype6 Billion People

Edit any gene in

the genome

Reprogramming

Dif

fere

nti

ati

on

an

d

Man

ufa

tcu

re

To revolutionize life-science

research and medicine,

mastery of all three

technology platforms is

required: reprogramming,

differentiation, and

engineering.

Mastery of one or two is

evolutionary, not

revolutionary

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Contextual Relevance is KeyFunctional cell types provides translatable answers

Neuronal Endpoints

• Developmental,

• Electrical / synaptic

• Degeneration

• Toxicity and DiscoverySynapse formation

Neurite Outgrowth Electrical Activity

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iCell CardiomyocytesContextual Relevance

Cell Signaling• Ca2+ signaling (EC coupling)

• Kinases, transcription factors, etc

• Energetics

Electrical / Membrane

Ion channels, ECM, GPCRs, etc

MechanicalContraction and relaxation

iPSC-Cardiomyocytes can exhibit many

aspects of native human behavior with the

appropriate biological pathways and may

provide more meaningful test substrate.

Cardiac Endpoints

• Electrical

• Biochemical

• Contractile

• Cardiomyopathy

• Toxicity and Discovery

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Why bother?

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Predictive Screens for Functional ToxicityLabel Free Impedance Measurements

Contextual relevance of human iPSC cardiomyocytes provide a more predictive tool for detecting

functional toxicity

Proarrhythmiascreening in 96 wells

> 90% -- QT prolongation> 80% -- Proarrhythmia

• >120 compounds

• ~equal positives and negatives

• beat rate, atypical beats, irregularity

Guo et al., 2013

C. Scott Tox Sci 2014

Contractility screening in

> 96 wells

1 AR Harmer Tox App Pharm (2012)2, A. Pointon Tox Sci (2014)3, C. Scott Tox. Sci (2014)

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KI-induced CardiotoxicityDeconvoluting the problem

FDA approved SMKI show cardiac liabilities

• Preclinical assays were insufficient

• Toxicities arose in late development / clinic

• Difficult to ascribe mechanism

Prediction hindered by :

• Highly conserved site of action-ATP-binding pocket

(on vs off target effects)

• Multiple effects on overlapping endpoints

Model can:

• Determine on-target vs off-target KI toxicity (MARK vs Chk KI)

• Identify KI-related toxicity with p<0.05

(>160 cmpds via Ambit and AZ-proprietary datasets)M. Peters CDI UGM 2014

Contextual relevance provides a predictive

tool for detecting KI toxicity

See also Cohen et al, 2013, Doherty 2013, Talbert 2014, Peters et al, 2015

S. Lamore SOT 2014

Cellular impedance assays with iCell

CMs can predict KI toxicity

Altered beat

phenotype indicates

upstream interaction

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Disease Modeling

Reprogramming to iPSCs

iPSCDifferentiation

INNATE

Healthy DonorDonor with

Genetic Disease

INDUCED

Healthy Conditions

Disease-inducing Conditions

Healthy Donor

ENGINEERED

Healthy DonorDonor with

Genetic Disease

Controls

Engineer Genome

PHENOTYPIC ANDFUNCTIONALANALYSIS

Disease Modeling and Assay DevelopmentDisease in a Dish

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Normal

Diseased

Native Phenotype

Cellular and Molecular Hallmarks

• Increased cell size

• Sarcomeric re-organization

• Increased fetal gene expression

-14 -13 -12 -11 -10 -9 -81000

1100

1200

1300

1400

1500

Log [ET-1] (M)

To

tal A

rea (

m2)

Control

+ET-1 (10 nM)

Control

+ET-1 (10 nM)

Control

ET-1 (10 nM)

Cell Size Cytoskeletal

RearrangementsFetal Gene

Expression

iPSC Cardiomyocytes exhibit classic hallmarks of cardiac

hypertrophy

iPSC Cardiomyocyte Recapitulation

Induced Disease Models – iPSC CardiomyocytesCardiac Hypertrophy

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Case Study #1: Induced Cardiac HypertrophyIn-vitro condition reflects native phenotype

Zhi et al, Front in Genetics, 2012

Aggarwal et al., Plos One 2014

Principal Component Analysis (Expression Array) of Human LV Biopsy

vs iPSC Cardiomyocytes

iPSC Cardiomyocytes provide a relevant inducible model of cardiac

hypertrophy

Biopsy

PC1; Biopsy vs iCell Cardiomyocytes- Difference attributed to heterogeneous

tissue sample vs. pure cardiomyocytes

iCell CM

PC2; control vs hypertrophy- Shift along the axis indicative of pathology

Control

Hypertrophy

Similar location of hypertrophic

samples along PC2 indicates

common pathology components

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NHLBI Next Generation Genetic

Association Studies (RFA-HL-11-066)

250 patient samples – HyperGEN cohort

GWAS – Left Ventricular Hypertrophy (LVH)

Derive iPS cells and cardiomyocytes

Induce hypertrophy, perform molecular analyses

Correlate GWAS findings with in vitro phenotype

Case study #2: Innate disease modelsPopulation and disease diversity

All donors reprogrammed

Optimized differentiation protocol

iPSC Cardiomyocytes in the hands of the PI

Population studies from clinical samples;

Feasible, on-going, and valuable

Identify common and unique pathways across clinical diagnoses

Enable diversity driven drug discovery

Preliminary findings include

Unique and common phenotypes across

disease CMs

Correlation between in-vitro phenotype and

disease progression (Uli Broeckel, MCoW)

Consistent IPSC material

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Case Study #3: Induced Disease ModelDiabetic cardiomyopathy (DCM)

Diabetic media induces DCM disease phenotype in iCell CMs

Functional E-C pathology

Diabetic media induced disease phenotype:

• Sarcomeric disorganization

• Ultrastructural disruption

• Lipid accumulation

• Oxidative stress

• Altered gene expression

• Cellular hypertrophy

• Altered EC functionality

Drawnel et al, Cell Reports, 2014

iPSC Cardiomyocytes model diabetic

cardiomyopathy through environmental induction.

Myofilament remodeling

DM; endothelin, cortisol, glucose)

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Screening with iPSC-CardiomyocytesInduced DCM-disease pathology enables unbiased screen

Diverse set of hits

from iPSC CMs• Lipid synthesis

• DNA synthesis

• Protein control

• PDE inhibitor

• Kinase inhibitors

• Ca2+ signaling

iPSC Cardiomyocytes initial Screen• 480 compounds

• 47 hits

• 28 confirmed DR

• Across a wide MOA

Phenotypic screen enabled large

diversity of hits

iCell DCM Screening Phenotype

Endpoints• Increased CM score

• Decreased BNP secretion

• Decreased nuclear area

iCell DCM Screening Strategy

Drawnel et al, Cell Reports, 2014

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Case Study #4: Innate Disease ModelsPatient derived cardiomyocytes show innate disease phenotypes

• Patient derived CMs show

baseline pathology

• In-vitro phenotype mirrors

clinical presentation

Non-Clinical and Clinical Samples DCM CMs Functional Pathology

Altered EC

function

Increased lipids

Increased ROS

Drawnel et al, Cell Reports, 2014

MyCell-DCM CMs Cellular Morphology

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Screening with clinically-derived CardiomyocytesPhenotype trends with clinical phenotype

Drawnel et al, Cell Reports, 2014

Clinical DCM screening strategy

Source

induced

Innate

Cmpds

14/18

MyCell screening results - Rescue DCM phenotype to control phenotype)

10/18

Clinical DCM - Cardiomyocytes:

• Screen against innate pathology

(no induction)

• Verifies hits across clinical

diversity

• Practical method for bringing the

clinic into screening

Assay Range

MM; control condition

BM = positive control

(rescues baseline pathology)

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MYH7 R403Q iPSC Cardiomyocytes• Show innate and induced signs of cardiac

hypertrophy

• Hypertrophic phenotype can be rescued

Provide another model/example of innate

disease models suitable for discovery

and screening

Case study #5: Innate disease modelsMYH7-R403Q linked hypertrophic cardiomyopathy

MyCell MYH7 R403Q CMFamilial Cardiac Hypertrophy

Baseline Pathology

NPPB 5

ACTA1 4

DUSP4 3

ACTC1 2

ACTN1 1

CREB5 0

MYH7 -1

NPPA -2

MYH6 -3

TRIM63 -4

ADM -5

FBXO32

PDCD4

Relative Expression

ET-1

induced

iCell CM

MyCell

MYH7

R403Q CM

Expression levels normalized

to uninduced iCell CMs

iCell CM

MYH7 R403Q CM

BNP / DAPI 384-well plate.

Phenotype induction and rescue

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Case Study #6: iPSC Neurons and Chemotherapy induced Peripheral Neuropathy (CiPN)

Chemotherapy-induced peripheral neuropathy (CIPN)

• Most common non-hematological side-effect

• Estimated 12 million cancer survivors in 2012

• CIPN can affect 20-40% of patients

• Limits therapeutic options

Can iPSC neurons used as a model to understand the genetics of CiPN?

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Chemotherapeutic-induced NeuropathiesConsistent Material Enables Phenotype Assays

Stem Cell Res, 2016

Different batches of iPSC-Neurons derived from a single iPSC clone showed:

• No significant differences in transcriptomic profiling

• Consistency in gene expression, cytosine modifications, neurite growth

• No significant differences in sensitivities to paclitaxel, vincristine, and

cisplatin

Effect of chemotherapeutic agents on neuronal

outgrowth compared between neuron batches

Profiling of iPSC

differentiated neurons

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iPSC Neurons and Chemotherapy induced Peripheral Neuropathy (CIPN)

Chemotherapeutics affect neurite outgrowth of iPSC neurons

Can this be used as an endpoint to understand the genetics of CIPN?

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Proof of concept for iPSC neurons and CiPN

Paclitaxel changes

neurite morphology siRNA knockdown of the Paciltaxel target

TUBB2A disrupts neurite outgrowth

Target knockdown mimics effect, thus the model

passes the POC assessment

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Proof of concept for iPSC neurons and CiPN (#2)

JAMA, 2015

• Inherited polymorphism in CEP72 is associated with risk and severity

of vincristine-induced neuropathy in children with ALL.

• Polymorphism reduces CEP72 expression

• Knock-down of CEP72 expression in iCell Neurons results in increased

sensitivity to vincristine compared to control iCell Neurons.

POC #2 passed

Can this be extended to

the clinical population?

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Chemotherapeutic-induced Neuropathies Layering in Diversity: Associating population genetics with function

Mean most sensitive: NA12892 and NA12814

Mean most resistant: NA07022 and NA12752

Observed reproducible

differences in morphological

characteristics among different

drugs and genetically diverse

neurons for each drug

Establishes a framework for

functional studies of genes

contributing to CIPN and

identifying drugs to treat or

prevent CIPN

iPSC-Neurons from 4 different unrelated LCLs

with varying degrees of chemotherapeutic toxicity

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Case Study #7: APP Protective and Pathogenic StudiesMyCell Neurons

Maloney JA, et al. (2014) J Biol Chem

Relevant Neuronal Model for Alzheimer’s Disease -

Mechanism of A673T variant of APP for protection against AD

◄ Differentiated cells from

three isogenic lines

appeared morphologically

similar to each other, and

uniformly expressed APP.

▲BACE cleavage of endogenous APP in MyCell

cortical neurons resulted in lower levels of Ab peptides

and sAPPb/sAPPα ratio in the A673T APP variant.

A B C

Disease Modeling using iPSC-Neurons

Induced model: Engineered APP mutations

Disease phenotype: The protective variant

resulted in lower levels of Aβ production and

improved neuronal activity.

Insight to pathology: Opportunity to

discover disease modifying (prevention,

arrest, reverse) targets

▲A673T APP showed improved

electrical and network activity.

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First disease iPSC banked

~800 donor-derived iPSCs banked

~8000 donor-derived iPSCs banked

~2000 donor-derived iPSCs banked

Democratizing access to iPSCs for disease Modeling

2010

2015

2018

2016

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Upcoming Disease and Diversity ProductsDeveloping Banks of Human iPS cells

Jump start your research–Order the differentiated cells you want from newly opened iPSC Banks

CIRM Bank currently contains iPS cell lines from 259 affected donors and 41

control: complete bank will be 3000 donor samples

Neurodevelopment (22 affected, 6 controls*)

Autism (22)

Cerebral Palsy (2)

Epilepsy (2) *

Intellectual disabilities (coming)

Alzheimer’s Disease (1 affected, 16 controls)

Liver Diseases (27 affected, 7 controls)

HCV (17)

NASH (9)

Steatosis (1)

Idiopathic Pulmonary Fibrosis (55 affected. 21 controls)

Blinding Diseases (55 affected, 11 controls*)

AMD (37)

POAG (7)

Diabetic Retinopathy (11)

Cardiomyopathies (102 affected, 0 controls)

LV non-compact CM (2)

HCM (17)

DCM (63)

Other (20)

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Thank-you