Pharmacology Powered by Computational Analysis:

Prediction of Drug-induced Toxicity

Jaehee Shim

Big Data in the Field of Biology:In the Beginning…

Notable Events that led to Big Data Era: Sanger Sequencing(1977) Roger Tsien et al Patented

“Base-by-Base” Technology(1990)

Pyrosequencing Introduced by Nyren &Tsien. (1996)

Human Genome Project(1990-2003)

Big Data Sources: Genome Transcriptome-expressed

genome Proteome Electronic Medical Records

Big Data in the Field of Biology:In the Beginning…

Human Body:

13 organ systems in human body with 4 basic tissue types

15-70 trillion cells

Genome

Transcript(messenger RNA)

Protein

Drawing of woman's torso from Anatomical Notebooks of Leonardo da Vinci(1452-1519)

Complete set of genetic information

Same in every cell

Selectively expressed genes

Specific to the tissue/organ cell type

Proteins are made from transcripts

Multiple versions of protein can arise from one transcript (post-translation modification)

Sequencing Data:How big are they?

Stephens et al.(2015) PLoS Biol 13(7): e1002195

Projected annual storage & computing needs in 2025

…so in 2025, we can expect to see the annual production of

1 X 1021 Bases/Year X 1byte/4bases =2.5X1020 bytes

OR250 Exa-bytes!

Just from sequencing alone!

Twitter Youtube Genomics0

5E+0181E+019

1.5E+0192E+019

2.5E+0193E+019

3.5E+0194E+019

4.5E+019

Series1

Proj

ecte

d An

nual

St

orag

e N

eed

Now that we have covered the basics…

How are we using this BIG DATA approach to predict drug-induced

cardiotoxicity?

Imperfections of Modern Drug Design

Drug Toxicity: Alternative drug targets perturb cellular dynamics and induce adverse event in a patient

How Common are the Drug Toxicity Events?

: 770,000 injuries or deaths in US per

yearper The Agency for Healthcare Research and Quality

By Stephen Jeffrey, The Economist

Cancer Drug Cardiac heath

Prediction of toxicity requires more investigation.

Underlying mechanisms are not clear.

Albini et al. (2009) J. Natl. Cancer Inst. 102:14–25.

Principal Investigators:Marc Birtwistle

Ravi IyengarEric Sobie

Cellular Signatures for Cardiotoxicity of Targeted Cancer Drugs (Protein Kinase Inhibitors)

Can we obtain precise and personalized signatures?

Drug Toxicity Signature Generation Center (DToxS)

Protein kinase inhibition

altered gene expression

cardiomyopathy

Cardiotoxicity

8

Why Do We Want to Personalize Medicine?

If we had to prescribe the same drugs to EVERYONE before…

Now, we can SELECTIVELY prescribe to the ONES who are

expected to respond!

Advantage?

Precise, effective delivery of the treatment for the individual patient

Lower risk of getting unnecessary side-effects

Reducing the unnecessary medical costs for treatments that may not work.

Drug-Induced Toxicity Prediction Strategy

1. Electrophysiological abnormality-- Arrhythmia :

Thinning of the walls

2. Structural abnormality-- Dilated Cardiomyopathy:

Prediction can be made with mathematical modeling

Transcriptome Data

Gene Perturbation Measurements

Mathematical Modeling

Network AnalysisPrediction of abnormalities is assessed through integrating transcriptome data with dynamical models

Upregulated

Downregulated

Experimental & Computational Strategy for Years 1-2

(1) Focus on cardiotoxicity caused by cancer therapeutics, e.g. tyrosine kinase inhibitors (TKIs)

(2) Treat cells with clinically-relevant doses of FDA approved TKIs and mitigating non-cancer drugs as controls.

Mitigators identified from clinical data in the FDA – Adverse Events Database (FAERS)

(3) Measure changes in gene expression and protein levels at 48 hours using mRNA-seq and proteomics

(4) Analyze results to obtain signatures, build biologically-relevant networks, and integrate network analysis data with predictive dynamical models to obtain dynamically ranked signatures

11

SORAFENIB DASATINIBSUNITINIB PAZOPANIB

TOFACITINIB RUXOLITINIBCRIZOTINIB AFATINIB

ERLOTINIB REGORAFENIBGEFITINIB PONATINIB

IMATINIB DABRAFENIB

BOSUTINIB VEMURAFENIB

VANDETANIB CABOZANTINIB

LAPATINIB TRAMETINIBNILOTINIB CERITINIBAXITINIB

Kinase Inhibitors with Cardiac RiskURSODEOXYCHOLIC

ACID PREDNISIOLONELOPERAMIDE DOMPERIDONE

DOMPERIDONE ALENDRONATEAPREPITANT PAROXETINE

DIETHYLPROPION ESTRADIOLENTECAVIR MONTELUKAST

OLMESARTAN CYCLOSPORINE

DICLOFENAC CEFUROXIME

CYTARABINE METHOTREXATE

GRANISETRON LOXAPINE

Control Drugs

Candidates of Cancer drug & Control Drugs

Experimental designCompare cardiotoxic cancer drugs with non-toxic non-cancer drugs and combinations

mRNA-seqProteomics

48 HOURS

Vehicle CTRL Cardiotoxic Drug

non-Cardiotoxic Drug(CTRL Drug)

Combination

Computational analysis to produce precise, personal signatures

13

Generation of Gene Signatures: Computational Pipeline

Mapping/Counting of the Raw Gene Sequences

RAW Sequence in text format(FASTQ file):

Reference Seq.

Schematic representation of how ‘fragments of sequences’ are “aligned” to a reference sequence.

Generation of Gene Signatures: Computational Pipeline

QC: How to Weed Out the Outliers from Replicate Samples

To identify outliers, correlate each pair of samples in the same experimental group

We exclude Control Sample 4 as an outlier

Pearson correlation > 0.98 seems to indicate good reproducibility for this assay; future results will solidify this QC standard

Summary of Signatures and Center Structure

Questions We Can Address With Gene SignaturesWhat patterns are common amongst potentially cardiotoxic protein kinase inhibitors?

PRECISION IN SIGNATURES

What differences are observed between drugs, and can these be connected to differences in drug/target structure, dosing, and clinical data?

PERSONALIZED SIGNATURES

Can differences in signature patterns between human subjects (cell lines) help us to understand inter-individual variability in drug toxicity?

Drug repurposing for cancer chemotherapy?

Can drug combination signatures help us to understand clinically-observed toxicity mitigation?

19

20

Cardiotoxic Cancer Drugs Show a More Consistent Pattern of Differential Expression

Average –log10(p-value) Across Drug Group

Num

ber o

f Gen

es

Cancer Drugs

Non-Cancer Drugs

Mean Log2 Fold Change

Cancer Drug non-Cancer(CTRL)

collagen fibril organizationcellular localization

regulation of cellular component organizationregulation of apoptotic processresponse to organic substance

response to woundingcellular response to chemical stimulus

regulation of cell deathregulation of programmed cell death

regulation of cell migrationregulation of locomotion

regulation of cellular component movementregulation of cell motility

cellular component organizationcellular component organization or biogenesis

negative regulation of cellular processresponse to stress

negative regulation of biological processextracellular structure organization

extracellular matrix organization

0 10 20 30 40 50

protein complex disassemblyestablishment of protein localization to membrane

macromolecular complex disassemblymRNA catabolic process

cellular protein complex disassemblytranslational elongation

nuclear-transcribed mRNA catabolic processtranslational initiation

translational terminationviral life cycle

protein targeting to membranemulti-organism metabolic process

protein localization to endoplasmic reticulumnuclear-transcribed mRNA catabolic process, nonsense-mediated decay

viral gene expressionviral transcription

establishment of protein localization to endoplasmic reticulumprotein targeting to ER

cotranslational protein targeting to membraneSRP-dependent cotranslational protein targeting to membrane

0 10 20 30 40 50

Minus log10(p-value)

Extracellular matrix, Collagen,Response to wounding

Apoptosis, Cell death

Cell migration

Co-translationalprotein targeting,Translation,Ribosomal proteins

(viral) transcriptionand mRNA catabolism

Protein translation andProtein complex assembly/disassembly

Gene

ral

GO b

iolo

gica

l pr

oces

ses

Card

iom

yopa

thy-

rela

ted

GO b

iolo

gica

l pro

cess

es

Cancer Drug Cardiotoxicity Processes are Enriched in the Initial Transcriptomic Signature

Canc

er D

rugs

Non

-Can

cer

Drug

s (CT

RL)

Tanimoto Coefficient for Structural Similarity0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75

W

hole

Tra

nscr

ipto

me

Cor

rela

tion

Coe

ffici

ent

0.7

0.75

0.8

0.85

0.9

0.95

1BOS, AFA

DAS, AFA

DAS, BOS

ERL, AFA

ERL, BOSERL, DAS

PAZ, AFA

PAZ, BOS

PAZ, DAS

PAZ, ERL

RUX, AFARUX, BOS

RUX, DAS

RUX, ERL

RUX, PAZ

SOR, AFA

SOR, BOS

SOR, DAS

SOR, ERL

SOR, PAZ

SOR, RUX

SUN, AFASUN, BOS

SUN, DAS

SUN, ERL

SUN, PAZ

SUN, RUX

SUN, SOR

VAN, AFA

VAN, BOS

VAN, DASVAN, ERL

VAN, PAZ

VAN, RUX

VAN, SOR

VAN, SUN

Differences Between Cancer Drugs—Relationship Between Gene Expression Similarity and Structural SimilarityHigh correlation because small

changes in expression

Correlated structural and gene expression similarity between drugs

Preliminary efforts to define signature precision

Next Step:

Prediction of Phenotypic Changes Based on Gene Expression Data Using Dynamical Modeling with

Differential Equations

Structural Abnormality Prediction : Hypertrophy

Extracellular Stimuli

Interacting Species

Phenotypic OutputsRyall et al. (2012) JBC 287: 42259–42268.

Beta-adrenergic Receptor

Map Kinase Pathway: cascade of phosphorylation reaction to propagate signal from the stimulus

Kraeutler et al. (2012) BMC Sys Biol. 4:157.

Methods: Model implemented using “Normalized Hill” Ordinary Differential Equations Simulations of dynamics with minimal parameterization.

)(1][, DDfw

dtDd

MAXBactBDD

nn

nBMAX

Bact ECBBY

f50

,,

Structural Abnormality Prediction : Hypertrophy

Each arrow represents a generic activation or inhibition reaction.

Structural Abnormality Prediction : Hypertrophy

Quantitative Analysis of Gene Perturbation in the

Network

Transcriptome(~20,000 genes)

Genes in Hypertrophy Network (~106 genes)

Simulate the time course of different pathway activation that leads to hypertrophy

Mathematical Simulation

Trastuzumab

Sorafenib

Sunitinib

Modeling Strategy:

Hypertrophy Signaling Model Simulation

NFAT

BNP

GSK3B

time (minutes)50 100 150 200 250 300 350 400

0

0.5

1

1.5

2

2.5

time (minutes)50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

time (minutes)50 100 150 200 250 300 350 400Nor

mal

ized

act

i vity

0

0.5

1

1.5

2

2.5

time (minutes)50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

CREB

ControlSorafenibSunitinibTrastuzumab

Stimulus given:

Phenylephrine (PE)

No Stimulus

No Stimulus

No Stimulus

Stretch Isoproterenol (ISO)

Fibroblast Growth Factor (FGF)

Nor

mal

ized

act

i vity

Nor

mal

ized

act

i vity

Nor

mal

ized

act

ivity

Different Cancer Drugs Induce Different Responses in Gene Species for

a Given Stimulus

Next Step: How Each Gene Node Contribute to Overall Phenotypic

(Structural) Changes?

Raw Gene Expression Pattern in Hypertrophy Network

Sorafenib Sunitinib Trastuzumab

Log FC in gene expression data

Noticeable genetic perturbation in Sorafenib

Mild induction of gene change in Sunitinib and Trastuzumab

Q. Does this noticeable gene perturbation necessarily mean activation of hypertrophy?

Next Step: Using Hypertrophy Network Model, simulate the projected changes in hypertophic phenotypes by integrating the raw gene expression pattern!

Predicted Pro-hypertrophic Changes Per Drug Condition

phenotypic outputSERCA

aMHC Cell

Area bMHCBNP

ANPsACT

rNo

mali

zed H

yper

troph

ic Re

spon

se

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4SorafenibSunitinibTrastuzumab

Pro-HypertrophicAnti-Hypertrophic

Sunitnib is the most hypertrophic drug!

Instead of looking at overall gene change, we need to look at how each

gene is affected!

Sensitivity Analysis of Hypertrophy Network Model

Serca aMHC CellArea bMHC BNP ANP sACT

Hypertrophy Network has: 106 interacting Nodes 17 stimuli 7 phenotypic outputs

Strategy for simulating the impact of each of 106 interacting species(Sensitivity Analysis) : Given no stimulus Vary each node’s default

parameter by ±10 % Measure the impact of the

variation in relation to each of 7 phenotypic output

Sensitivity Analysis of 106 Nodes

No Significant Changes

Only 5 Nodes are Responsible for Structural Changes!

Sensitive nodes: GSK3B HDAC SERCA aMHC foxo

Sunitinib-induced gene expression changes in the sensitive nodes have complete opposite pattern from the other two drugs

Cancer Drug Induced Changes in the Sensitive Nodes

Does drug treatment change the sensitivity of the node in overall network? (i.e. Given the drug treatment, will the sensitivity pattern

change?)

'aMHC' 'foxo' 'HDAC' 'SERCA'

'aMHC' 'ANP' 'bMHC' 'CellArea' 'CREB' 'foxo' 'GATA4' 'GSK3B' 'HDAC' 'NFAT’ 'sACT' 'SERCA'

'aMHC' 'foxo' 'HDAC' 'SERCA'

Drug specific sensitivity of 106 nodes per phenotypic outputs

Noticeable Increase in the Number of Sensitive Nodes in Sunitinib Treated Cells

Currently in the process of:1. Expanding sensitivity analysis to all drug conditions 2. Integrating sensitivity metrics with hypertrophy index

Conclusions and Future DirectionsSummary:

Gene expression data were integrated with existing network-based models to investigate pathophysiological mechanisms of drug-induced cardiotoxicity.

Simulations were used to show: Time-dependent changes in intracellular signaling Stimulus-dependent phenotypic changes Changes in sensitive nodes in the network

Current Challenges: Integrating additional network-based dynamical models

EGF-induced signaling Apoptosis

Comparing drug classes in depth using simulation results New predictions for which processes/outputs are most

relevant?

AcknowledgementsDr. Eric Sobie LabMegan CumminsRyan Devenyi Elisa Nuñez-AcostaJingqi Gong

Marc BirtwistleRavi IyengarEric Sobie

Evren AzelogluYi-bang ChenSunita D'SouzaJames GalloMilind MahajanChristoph SchanielAvner Schlessinger

Pedro MartinezTina HuPriyanka DhananRick KochGomathi JayaramanJens HansenYuguang Xiong

The Mount Sinai LINCS DSGC team

Sequencing Data:Who is interested in them?

Sequencing Data: Current Computational Approach to Make Sense of Them

Statistical Computation of Differential Expressed Genes(DEGs)

Trastuzumab

Ursodeoxycholic acid

Combination

73/28 (up/down)

22/28 (up/down)

98/43 (up/down)

Differentially Expressed:

Log2 Fold Change: -4 0 4

FASTQ file (Raw data

from Sequencer)

Sequence Alignment with

BWA

QC: Eliminate Outlier Samples

Consolidate and Normalize BWA

output with EdgeR

EdgeR (Trimmed mean of means, TMM) : Normalize based on a weighted average instead of a median.

EdgeR computes statistical significance based on the normalized data using TMM &generates DEGs with p-values

Tras

tuzu

mab

Using DEGs, statistically imporatant cellular pathway list generated