Environmental Cancer Genomicsfrom High-Throughput Assays to Prevention

Stefano MontiArt beCAUSE consortium meeting

8/4/2015 – 9/1/2015

Overall Goal

Predict long-term in vivo carcinogenicity of chemical compounds from

short-term in vitro genomic assays of exposure

Carcinogenicity Screening

Underlying Hypotheses

Short-term exposure assays can predict long-term phenotype

In-vitro assays can predict in-vivo response

GoalsDevelopment of “Carcinogenicity Biomarker(s)”

CarcinogenicityPrediction Model

Chemical

Carcinogen

Non-carcinogen

Pathways affected Driver alterations Biomarkers …

Understand Why

Expression Profilingmeasuring transcriptional “activity” genome-wide

DNA

RNA

mRNA

Proteins

Transcription /Post-transcription Translation

Low High

expression

sort

non-carcinogens carcinogens non-carcinogens carcinogens

Low High

expression

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Expression Profilingto predict chemical carcinogenicity

Stefano Monti − BUSM

Transcriptional Signatures

?

non-carcinogens carcinogens

Expression Profilingto predict chemical carcinogenicity

New Compound

Project Design Overview

…Genotoxicity

Carcinogenicity

Compo

und 1

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und 2

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und 3

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und N…

Prediction EvaluationClassification AccuracySensitivity/SpecificityROC curve…

Biology of ExposureExposure MoAPathways“Drivers”Exposure risk models

Carcinogenicity Prediction

“New” compound

Carcinogen

Non-Carcinogen

Cell lines/iPSC treated w/ compounds …

.. and profiled on L1000/ 3’DGE / SFL

Project depends on high-throughput, cost-effective gene expression assay

Long-term Phenotypes

Short-term Assay

Deliverables

The Carcinogenome DB (CGDB) Genome-wide transcriptional profiles of 10,000s of compounds and

mixtures on multiple cell types and at multiple doses/times

Carcinogenicity Biomarker(s) Predictive models of carcinogenicity from in-vitro profiling

Signatures and Pathways of Carcinogenicity An annotated compendium of biological pathways whose (aberrant)

activation is associated with carcinogenicity/cancer induction

Can Carcinogenicity be predicted from GEP?The DrugMatrix/TG-GATEs answer

Rat-based datasets from NIEHS & Japan (thanks Scott Auerbach & Ray Tice @ NTP)

Can Carcinogenicity be predicted from GEP?The DrugMatrix/TG-GATEs answer

Rat-based datasets from NIEHS & Japan (thanks Scott Auerbach & Ray Tice @ NTP)

…Genotoxicity

Carcinogenicity

Compo

und 1

Compo

und 2

Compo

und 3

Compo

und N…

Prediction EvaluationClassification AccuracySensitivity/SpecificityROC curve…

Biology of ExposureExposure MoAPathways“Drivers”Exposure risk models

Carcinogenicity Prediction

“New” compound

Carcinogen

Non-Carcinogen

Cell lines/iPSC treated w/ compounds …

.. and profiled on Luminex-1000

Rats exposed to compounds …

.. and profiled on Affymetrix

…

Gusentleitner et al., PLoS ONE 2014

Long-Term Carcinogenicity can be Predicted from Short-term Expression Assays

Dose-independent

labelingD

ose-dependent labeling

Gusentleitner et al., PLoS ONE 2014

Long-Term Carcinogenicity can be Predicted from Short-term Expression Assays

Carcinogenicity Prediction can be Improved

by increasing the number of chemicals used to build model

“P

red

icti

ve A

ccu

racy”

Gusentleitner et al., PLoS ONE 2014

Genomic Modeling Helps Identify Pathways of Carcinogenicity

Path

ways

CHEMICALSnon-Carcinogens Carcinogens

Carcinogenicity can be Captured by in-vitro (human) models

Enric

hmen

t Sco

re

p < 0.005

carc non-carc

L1000-based gene ranking

DrugMatrix signature genes

Rat carcinogenicity signature can be mapped to human data

Significant similarity of Rat and Human signatures

36 g

enes

(FDR

≤.05

| FC

≥2)

121 samples (39 C vs. 82 NC)

Human lung cell lines exposed to carcinogens and non-carcinogens

Statistically significant markers identified

Luminex-1000 data

In Progresshigh-throughput data generation

Multi-platform (mirror) experiments

Multiple platform comparison Luminex-1000 (L1000) 3’ Digital Gene Expression (3’DGE) Sparse Full Length/RNA-tag seq (SFL)

Experimental design Chemicals selection (and dose/concentration) Tissue types (liver – HepG2; breast – MCF7, MCF10; lung – A549) challenging set-up (chemical procurement and dose determination)

Multiple funding sources Evans ARC BUSRP admin supplement NIH/LINCS 1-year grant w/ Broad Art beCAUSE

Network-Based Analysis of Chemical Perturbations

Discrimination carcinogens/non-carcinogens

Genes driving the response to chemical exposurePredictive model

• Compare control state to multiple perturbation states

• Capture aggregate differences difficult to see with standard analysisNew

goa

ls

Differential expression(standard)

Differentialconnectivity

controlchemically perturbed control

chemically perturbed

2015 ACS Meeting

Network Analysis Overview

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Annota

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Network

2015 ACS Meeting

Results Summary

Networks structure captures grouping of compounds with similar functions and genotoxicity/carcinogenicity

Differentially connected gene modules enriched for pathways related to chemicals’ action Statins

• Cholesterol biosynthesis, Lipid Metabolism, Steroid biosynthesis, ... Chemoterapeutics

• Cell cycle, DNA replication, DNA damage response (P53)

…

The TeamBroad InstituteAravind SubramanianXiaodong LucMap/LINCS team

NTP/NIEHSScott AuerbachRay Tice

BU CBM/Bioinformatics/SPHDavid SherrAmy LiDaniel GusenleitnerFrancesca Mulas

The End