1 The Application of Genomic Dose-Response Data in Risk Assessment Harvey Clewell and Rusty Thomas CIIT Centers for Health Research

1

The Application of Genomic Dose-Response Data in Risk Assessment

Harvey Clewell and Rusty ThomasCIIT Centers for Health Research

RISKRISK

2

Overview

• Background• Alternative Cancer Risk Assessment Approaches

• Applications of Genomics in Risk Assessment

• Examples• Arsenic

• Issue: location of key dose-dependent transition(s) below observed tumor range

• Formaldehyde

• Issue: relative contribution of genotoxicity vs. cytotoxicity/proliferation

• Genomic Dose-Response Methodology

3

Alternative Dose-Response Approaches under the New EPA Cancer Guidelines

• Linear (default)– Assumes linear relationship of cancer risk to dose

from ED10 (dose associated with 10% increase in tumor incidence) to zero

– Regulation typically based on 1/10,000 to 1/1,000,000 risk

– Most appropriate for directly mutagenic carcinogens with no effect on cell proliferation

– Can greatly overestimate risks for chemicals with a mode of action dominated by cytotoxicity and increased cell proliferation

4


• Biologically Based Dose-Response (BBDR) Model

– Preferred approach in EPA guidelines

– Supports risk estimates below range of observation of tumors

– Requires quantitative data on dose-response for key elements in the mode of action

– Complexity of detailed BBDR description (e.g. formaldehyde) leads to agency concerns about potential uncertainties

5

N I M 1 2

1

A Biologically Motivated Model for Cancer

Biologically Based Cancer Dose Response Model

Goal: capture dose-dependence of critical, rate-limiting processes -- despite a lack of information on the specific biological details

Key Capability:Describes the Interaction of Mutation and Cell Division

- requires data on D/R for- cytotoxicity/apoptosis- cell proliferation- DNA damage

- supports investigation of interactions between key processes

- DNA damage/repair- cell cycle control- cytotoxicity/apoptosis- proliferative pressure

6


• Margin of Exposure (MoE)

– Point of departure can based on LED10 for tumors or obligatory precursor events

– MoE selected to address human inter-individual variability and uncertainties in the underlying data

– Does not provide quantitative risk estimate

– Requires evidence of nonlinear mode of action (the hard part)

• Issues:– Alternative / Multiple modes of action– “Lurking” Genotoxicity

7

Proposed Approach: Biologically Based Dose-Response Modeling of Genomic

Data

• MoE Approach Using Genomic Dose-Response

– It may be a very long time before a fully-developed BBDR model gains acceptance

– Simplified dose-response descriptions that maintain a biological basis may be useful in the near term to inform both mode of action and dose-response

– Basis of simplified approach: nonlinear dose-response analysis of data on genomic alterations in key cell signal pathways

– Combined with quantitative modeling of cell signal pathways, may pave the way to a more detailed BBDR model

8

ConclusionsConclusions• The conduct of quantitative cancer risk assessment that

has minimal reliance on default factors requires knowledge of the key events leading to tumor induction.

• The same sorts of information can lead to the development of informative bioindicators of tumor response.

• Whole-genome approaches appear to offer the best chance for success.

• Computational approaches have to be developed in parallel with the experimental methods.

• These key events can be used for the purpose of extrapolations thereby reducing much of the uncertainty currently handicapping the process.

Excerpt from SOT RASS Tele- Seminar Presented by Julian Preston (EPA/NHEERL)

9

Mode of Action from a Systems Biology Perspective:Mode of Action from a Systems Biology Perspective:Chemical Perturbation of Biological ProcessesChemical Perturbation of Biological Processes

SystemsInputs

BiologicalFunction

ImpairedFunction

Adaptation DiseaseMorbidity &

Mortality

Exposure

Tissue Dose

Biological Interaction

Perturbation

Molecular Target(s) (Chemical Mode of Action Link)

10

Uses of Genomic Data (1):

Hazard Identification – Use of pattern recognition analysis to identify similarity of gene changes from uncharacterized compound with changes produced by compounds with known effects - can provide insights into key elements in mode of action

- essentially qualitative

- typically, little consideration given to tissue dosimetry

11


Functional Genomics – Characterize interactions of compound with gene regulatory network using temporal analysis and iterative gene over-expression / inhibition

- can elucidate key elements of cellular dose-response (e.g., switch-like behaviors)

- time-consuming, requires sophisticated analyses

- modeling of gene regulation is in its infancy

Growth factor

MAPKKK

MAPKK

MAPKPLA2

AA

PKC

PLA2

AA

PKC

MKPMKPIncreasingStimulus

Input Pulse (Conolly 2004)

12


Dose-Response – Collection of data on genomic responses to a compound over a range of cell/tissue exposure concentrations to identify dose-response for key genomic bio-indicators of response

- provides support for mode of action hypothesis

- requires characterization of tissue dosimetry or phenotypic anchoring A

ctiv

ity

/ Am

oun

t (%

con

trol

)

µM AsµM AsIIIIII

(Snow et al. 2002)

0

100

200

300

400

0 5 10 15 20 25

Trx mRNAAPE/Ref-1 mRNA

Pol

Ligase I

13

Heirarchical Model for Cellular Responses Heirarchical Model for Cellular Responses to Stressors (A. Nel)to Stressors (A. Nel)

Normal Epithelia

l Cell

Adaptive

State

StressedState

Pathology

Necrosis

Atrophy

Biochemical effectsGSH/GSSG ratio

Interactions with MM

Stressors (heat, pH change, reactive compounds, etc.)

Genomic alterations

HSP proteinsAnti-Apoptotic

InflammationToxicity

DNA-RepairProliferative

Apoptotic

Goal of Genomic Dose-Response Modeling:To identify Goal of Genomic Dose-Response Modeling:To identify key elements of each state and the points of transitionkey elements of each state and the points of transition

14

Example of Heirarchical Response:Effects of Diesel Exhaust Particles on

Cells:

(Gilmour et al., 2006, EHP)

15

Example 1: Inorganic Arsenic

AsOH

OHOH

O

As

OH

OH OH

AsCH3 OH

OH

O

AsCH3

OH

O

CH3

ARSENATE

ARSENITE

METHYL ARSONIC ACID

DIMETHYL ARSINIC ACID

As

OH

OHCH3

As

CH3

CH3 OH

DIMETHYL ARSINOUS ACID

METHYL ARSONOUS ACID

TRIVALENTSPECIES:

PENTAVALENT SPECIES:

MMA(III)

Metabolism of Inorganic Arsenic

16

Evidence for the Carcinogenicity of Inorganic Arsenic

• Epidemiology: cancer in multiple tissues

• Most common: bladder and lung

• Animal bioassays: equivocal

• Co-carcinogenic

• Mutagenicity:

• Arsenite: clastogenic, co-mutagenic

• MMA(III): genotoxic(?)

• Noncancer toxicity: dermal, vascular

• Proliferation

• Chemical activity: binding to vicinal dithiols

• arsenite, MMA(III)

17

Key Considerations for the Mode of Action of Inorganic Arsenic

• Tumors from inorganic arsenic observed in human populations at around 500 ppb, but animal bioassays at much higher concentrations have been negative

• Increased tumor risk from inorganic arsenic in drinking water correlates with MMA/DMA ratio

• Suggests role for MMA(III)

• Humans exposed to inorganic arsenic in drinking water have higher concentrations of MMA in urine than rodents

• Rodents: higher DMA

• No evidence of endocrine related tumors in chronically exposed human populations

18

Inorganic Arsenic

Exposures

Target TissueConcentrations

of Arsenite(and Trivalent

MMA)

BiochemicalTargets ofArsenic

Increased Mutation Frequencyand Tumors

Dosimetry Modeling Tissue Response Modeling

Biologically Based Dose-Response Modeling of Inorganic Arsenic

Carcinogenicity

Putative Mode of Action: As III / MMA III interactions with key cellular proteins

19

Carcinomain situ

Papillary High-Grade Non-Invasive

Papillary Low-Grade Non-Invasive

Normal Urothelium

Lamina Propria Invasive

Muscle Invasive

Metastases

p53-

9-

p53-

9-

Proposed Model for Bladder Cancer Progression

TCCs CIS

Primary Target Tissue for Arsenic Carcinogenicity: Urinary

Bladder

20

ROSROS

DNA

damage

OxidativeStress

Response

Co-exposure to Mutagens

Apoptosis

Checkpoint

stasis

DNA

damage

DNA repair

Proteotoxicity

OxidativeStress

Arsenite Cell cyclecontrol

Apoptosis

Checkpoint

stasis

Transition

thru cycleCancer

(+)

(+/-)

(+)(-)

(+)

Proliferative signaling

(+/-)

(+)

Arsenite Effects and Biological Responses Arsenite Effects and Biological Responses

21

Review of the Literature on the Dose-Response for the Genomic Effects of

Inorganic Arsenic

• PUBMED literature search concentrating on genomic response in in vitro and in vivo studies

• Prioritization and review of over 300 articles identified

• Population and development of inorganic arsenic genomic database

22

Results of Literature Search

• The database contains information from 161 unique studies evaluating 354 specific genes or proteins.

• 960 specific entries pertaining to specific genes or proteins

• 167 entries pertain to miscellaneous endpoints such as apoptosis, cytotoxicity, or changes in mitotic indexes.

• 1127 total database entries.

23

Summary of the Types of Data Describing Changes in Gene/Protein Levels Following Arsenic Exposure

Type of Data Total Number Percentage of Total

Information for specific genes and/or proteins

354 31%

In vitro data entries 700 62% In vitro gene/protein specific

information measured in immortalized/cancer cell lines

230 33%

In vitro gene/protein specific information measured in normal cells

470 67%

In vivo data entries 427 38% In vivo gene/protein specific

information measured in immortalized/cancer cell lines

44 10%

In vivo gene/protein specific information measured in normal cells

383 90%

24

0.01 uM 0.1 uM 1.0 uM 10 uM 100 uM

Oxidative Stress Trx

Trx Reductase

SOD1

AP-1

HO-1

GSR

TPX-11

MT-1

MT-2

NRF-2

Inflammation COX-2 IL-8

Proteotoxicity HSP-32 HSP-70 HSP-60

HSP-27

Proliferation FGFR4 Fos

Jun

VEGF

Myc

p70

Erk

ERK-1

ERK-2

EGFR

DNA Repair DDB2 Pol beta

Ligase I

PARP-1 Ligase I GADD153

Cell Cycle Control P53 CDC25A p21

CDC25B

CDC25C

Apoptosis p53

EGR-1

p105

p65

NF-kB Casp3

Casp8

Casp9

SRC

JNK

JNK3p53

Gene Expression: Increase Decrease Acute increase, chronic decrease

Dose-Response for the In-Vitro Effects of Arsenic in Normal Cells

25

High-Concentration (1-100 uM) Arsenic Effects on Cells:“Apoptosis” (Anti-Neoplastic Agent)

Arsenite

Non-specific Bindingto Thiols

Specific Binding to Vicinal Di-thiols

Depletion of NPSH

Oxidative StressResponse

Inhibition of DNA Repair Enzymes

(Ligase I)

UbiquitizationOf key proteins

Proteotoxicity Response

Inflammatory ResponseProliferative Signaling

Cell Cycle StasisInduction of Apoptosis

26

Mid-Concentration (0.1-10 uM) Arsenic Effects on Cells:“Toxicity” (Cancer, Blackfoot Disease)

Arsenite



Depletion of NPSH



(Ligase I)



Inflammatory ResponseProliferative Signaling

Cell Cycle DelayInduction of Apoptosis

27

Low Concentration (0.01-1 uM) Arsenic Effects on Cells:“Adaptive Response”

Arsenite



Depletion of NPSH



(PARP-1)



Delay of ApoptosisPre-Inflammatory ResponseGrowth Factor Elaboration

28

EPA / CIIT / EPRI studies on Genomic Dose-Response for Arsenite in Bladder

• In vivo: Drinking water exposures– Female C57Bl/J mouse (bioassay strain)

– 4 concentrations arsenate plus controls (0.05-50 ppm As)

– Genomic analysis of bladder tissues at 1 and 12 weeks

– Concentrations of all relevant arsenic species

• In vitro: Bladder epithelial cell incubations– Primary bladder epithelial cells

– Multiple concentrations, time-points

– Concentrations of all relevant arsenic species

– Compare mouse and human cells

29

Gene Ontology - Biological Process

30

Biological Process Categories Up-regulated by 50 ppm ArseniteFunction Name Unique Gene Total

positive regulation of apoptosis 6

anterior/posterior pattern formation 5

regulation of transcription, DNA-dependent 47

nuclear mRNA splicing, via spliceosome 13

cell cycle 14

intracellular protein transport 11

protein folding 10

mRNA processing 14

ubiquitin cycle 13

transcription 34

mitosis 6

carbohydrate metabolism 7

protein modification 7

cytokinesis 7

response to DNA damage stimulus 6

Preliminary Results of Pilot Study

31

Biological Process CategoriesDown-Regulated by 50 ppm Arsenite

Function NameUnique Input Total

cell-substrate junction assembly 5

collagen catabolism 5

proteolysis and peptidolysis 13

cell-matrix adhesion 5

cell adhesion 11

regulation of cell growth 5

integrin-mediated signaling pathway 5

signal transduction 13

cell differentiation 6

G-protein coupled receptor protein signaling pathway 7

development 7

transcription 11

regulation of transcription, DNA-dependent 13

Preliminary Results of Pilot Study

32

Dose-Response Characterization

• Develop and test dose-response approach with animal data– PK: predict or measure tissue concentrations of active

moieties in both short-term exposures and bioassays

– PD: link tissue concentration to cellular responses using in vitro and iv vivo genomic data

• Apply dose-response approach in human– PK: predict or measure tissue concentrations of active

moieties in exposed populations

– PD: link tissue concentrations to signal pathway alterations using in vitro genomic data from human cells

33

Human in vivo

Mouse in vivo

BladderUrothelial cells

Human in vitro

Bladder cells

Mouse in vitro

Bladder cells

Predict

Compare to understanddifference in response

Validate ability to predict in vivo

Extend dose-response

34

Application of Genomic Dose-Response Data to Refine a

Human Risk Estimate for Arsenic

• Validate in vitro genomic assays by comparison with data from exposed population in Mongolia (study being conducted by Judy Mumford, EPA)

• Apply human genomic dose response to extend tumor dose-response below the region of observation

• Proposed approach:

– Point of departure based on lowest LED10 for genomic response associated with non-adaptive response

– MoE selected to consider uncertainty in genomic data and human interindividual variability

Anchor in vitro dose-response to in vivo tumor incidence:

35

Hypothetical Impact of Population Variability on Cancer Dose-Response for Arsenic in Drinking Water

0.10.010.001 1.0

Concentration in Drinking Water (mg/L)

Ris

k

0.01

0.001

0.0001

0.00001

SusceptibilityFactors:

- Dietary intake- Nutritional status- Other exposures - selenium - mutagens- Genetic factors - metabolism (GST) - cell control (P53)

Linear

Extrapolation

Average Individual Dose-Response

Population Dose-ResponseSensitive / Resistant Individual Dose-Response

36

Example 2: Formaldehyde

0

10

20

30

40

50

60

T

um

or R

espo

nse

(%)

0 0.7 2 6 10 15

Exposure Concentration (ppm)

Kerns et al., 1983

Monticello et al., 1990

Formaldehyde bioassay results: rat nasal tumors

37

Cancer Risk Assessment Considerations

for Formaldehyde

Increased cell proliferation- Secondary to Cytotoxicity

DNA interactions- DNA-protein cross-links - Adduct formation

Tumor

Modes of actionDosimetry Effects

38

Predict decrease in risk at low concentrations using J-shaped dose-response of cell

replication

2.00E-04

3.00E-04

4.00E-04

5.00E-04

6.00E-04

7.00E-04

0 1 2 3 4 5 6 7

1 2 3 4 5 6 710

-4

10-3

10-2

10-1

PPM

DP

X (p

mol

/mm

3 )DPX dose-response for Rhesus monkey

Vmax: 91.02. pmol/mm3/min

Km: 6.69 pmol/mm3 kf: 1.0878 1/min Tissue thickness ALWS: 0.5401 mm MT: 0.3120 mm NP: 0.2719 mm

95% UCL on KMU

39

Final risk assessment model: Hockey stick and 95% upper confidence limit on

mutagenicity

2.0000E-04

2.5000E-04

3.0000E-04

3.5000E-04

4.0000E-04

4.5000E-04

5.0000E-04

5.5000E-04

0 1 2 3 4 5 6 7

1 2 3 4 5 6 710

-4

10-3

10-2

10-1

PPM

DP

X (p

mol

/mm

3 )

DPX dose-response for Rhesus monkey

Vmax: 91.02. pmol/mm3/min

Km: 6.69 pmol/mm3 kf: 1.0878 1/min Tissue thickness ALWS: 0.5401 mm MT: 0.3120 mm NP: 0.2719 mm

95% UCL on KMU

40

Use specific in vivo studies to develop a dose response model for activation of proteotoxic response pathways following formaldehyde exposure and differentiate dose regionsdifferentiate dose regions that activate cell homeostasis pathways vs. DNA-repair delays and proliferative pressure

Tissue Phase ReactionsCl2 HOCl + HCl

Normal Epithelia

l Cell

Adaptive

State

StressedState

Pathology

Necrosis

Atrophy

DosimetryDosimetryInhaled Formaldehyde

(1) (2) (3)

Mechanistic Dose Response Model with Genomic DataMechanistic Dose Response Model with Genomic Data

41

Formaldehyde Genomics Study DesignFormaldehyde Genomics Study Design

• Expose F344 rats to 0, 0.7, 2.0, and 6.00 ppm Expose F344 rats to 0, 0.7, 2.0, and 6.00 ppm formaldehyde for 3 weeksformaldehyde for 3 weeks

• Assess dose- and time-dependent genomic Assess dose- and time-dependent genomic changes using rat gene chips from Affymetrixchanges using rat gene chips from Affymetrix

• Evaluate gene family changes for heat shock Evaluate gene family changes for heat shock response (proteotoxic), oxidative stress, DNA-response (proteotoxic), oxidative stress, DNA-repair, cell cycling, apoptosis, etc.repair, cell cycling, apoptosis, etc.

• Develop qualitative and quantitative models to Develop qualitative and quantitative models to link genomic changes with cell behaviorslink genomic changes with cell behaviors• account for J-shaped cell-proliferation responseaccount for J-shaped cell-proliferation response• incorporate dose response of DNA-damage incorporate dose response of DNA-damage sensors sensors

42

Features of Genomics Study

Hybridized to a Affymetrix Rat Genome 230 2.0 array with over 30,000 probe sets

43

Time Point

Controls 0.7 ppm 2 ppm 6 ppm

6 hours no pathologyno gene changes

at levels 2 to 3

no pathologyno gene changes

at levels 2 to 3

Inflammation (minimal)at level 1

some gene changes at levels 2 to 3

Inflammation (mild)at level 1

Some epithelial hyperplasia of lateral

wall at level 2many gene changes

at levels 2 to 3

1 day Inflammation (minimal)at level 1

no gene changes at levels 2 to 3



Epithelial hyperplasia at level 1


Inflammation at level 1Widespread epithelial hyperplasia of lateral

wall at level 2no gene changes

at levels 2 to 3

5 days no pathologyno increase in

ULLI for any sites at levels 2 or 3


no pathologyno increase in ULLI

for any sites at levels 2 or 3


Inflammation and epithelial hyperplasia at

level 1Increased ULLI on

lateral wall at level 3many gene changes

at levels 2 to 3

Inflammation, hyperplasia, and

squamous metaplasia at level 1

Widespread epithelial hyperplasia of lateral

wall at level 2Increased ULLI for all sites at levels 2 and 3some gene changes

at levels 2 to 3

Results of Formaldehyde Genomics Studyblue: pathology, red: cell proliferation, green: genomics

44

Time Point

Controls 0.7 ppm 2 ppm 6 ppm

8 days Inflammation (minimal)at level 1




wall at level 2


no pathology Some inflammation and epithelial hyperplasia of

lateral wall at level 2


wall at level 2


no increase in ULLI for any sites

at levels 2 or 3no gene changesat levels 2 to 3

no pathologyno increase in ULLI for any sites at levels

2 or 3no gene changesat levels 2 to 3

Some epithelial hyperplasia of lateral

wall at level 2no increase in ULLI for any sites at levels 2 or 3



wall at level 2no increase in ULLI for any sites at levels 2 or 3

many gene changes at levels 2 to 3

Results of Formaldehyde Genomics Studyblue: pathology, red: cell proliferation, green:

genomics

45

-0.2 -0.1 0 0.1 0.2 0.3

-0.2

-0.1

0

0.1

0.2

0.3

Comp1

Comp2

Principal Components Analysis: 0, 0.7, 2 and 6 ppm

Conclusion: essentially no effect of formaldehyde exposure at 0.7 ppm

46

General ObservationsGeneral Observations

• No genes were significantly altered at 0.7 ppm in any of the exposures, nor were there any differences in pathology in the noses of the 0.7 ppm exposed rats.

• Transient gene changes at 2 ppm (at 5 days of exposure only)•Most not altered at the higher concentrations – including circadian rhythm related genes;

• A consistent pattern of genes changed at 6 ppm over time•Most of these genes were part of the group of genes altered immediately after the first 15 ppm, 6 hour exposure.

• Although only a small number genes were affected by the 6 ppm exposure, the GO categories for the longer exposure 6 ppm include gene families related to apoptosis.

47

Next Step: 90 day inhalation Next Step: 90 day inhalation studystudy

• Exposures: 6 hours/day, 5 days/week, 13 weeks

• Concentrations: 0, 0.7, 2, 6, 10, and 15 ppm (Same as in cancer bioassay)

• Endpoints for which dose-responses will be determined:• Genomics• Pathology• Cell proliferation rates • P53 mutation frequency (NCTR)

• Goal: determination of dominant factors in mode of action for carcinogenicity• genomic alterations• cytotoxicity• proliferative pressure• mutagenicity

48

Formaldehyde Genomics StudyFormaldehyde Genomics Study

Applications of ResultsApplications of Results

• Differentiate dose regions for adaptive (survival) Differentiate dose regions for adaptive (survival) responses and overt DNA damage responsesresponses and overt DNA damage responses

• Evaluate hypothesis of enhanced mutagenic potency Evaluate hypothesis of enhanced mutagenic potency at toxic concentrations as compared to lower at toxic concentrations as compared to lower concentrationsconcentrations

• Provide mechanistic basis for U-shaped proliferation Provide mechanistic basis for U-shaped proliferation dose-response noted in bioassay studiesdose-response noted in bioassay studies

• Consider possible implications for low concentration Consider possible implications for low concentration ‘hormesis’ with formaldehyde and other irritants‘hormesis’ with formaldehyde and other irritants

49CIITCenters For Health Research

Experimental Methods: Integrating Genomic Data with Dose-Response Analysis

Gene Expression Dose Response Data

One-Way Analysis of Variance to Identify Genes Changing

with Dose

Power Model

Linear Model

Polynomial Model (2°)


Select Best Model

Remove Genes with BMD > Highest Dose

Group Genes by Gene Ontology Category

Estimate BMD and BMDL for each Gene Ontology Category

Nested test to Select Best Polynomial Model

50

Experimental Results: Benchmark Models Goodness-of-Fit to Transcriptomic Data

0

20

40

60

80

100

120

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Probability Value of Model Fit

Fre

qu

ency

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Cum

ula

tive P

erce

nta

ge (%

)

Frequency

Cumulative %

p > 0.05 for 85% of genes

51

Examples of Individual Gene Dose-Responses

ID: 1383471_atModel: LinearFit p-value: 0.0102

ID: 1368215_atModel: Polynomial 3°Fit p-value: 0.0502

ID: 1370317_atModel: Polynomial 2°Fit p-value: 0.1002

ID: 1371736_atModel: PowerFit p-value: 0.4804

7.4

7.5

7.6

7.7

7.8

7.9

8

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n

Formaldehyde Dose (ppm)

BMDBMDL7.4

7.5

7.6

7.7

7.8

7.9

8

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL

9

9.2

9.4

9.6

9.8

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL

9

9.2

9.4

9.6

9.8

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL

7.2

7.4

7.6

7.8

8

8.2

8.4

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL7.2

7.4

7.6

7.8

8

8.2

8.4

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL9.4

9.6

9.8

10

10.2

10.4

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL9.4

9.6

9.8

10

10.2

10.4

0 2 4 6 8 10 12 14

Lo

g2

Gen

e E

xpre

ssio

n


BMDBMDL

52

Experimental Methods: Integrating Genomic Data with Dose-Response Analysis

Gene Expression Dose Response Data

One-Way Analysis of Variance to Identify Genes Changing

with Dose

Power Model

Linear Model



Select Best Model

Remove Genes with BMD > Highest Dose

Group Genes by Gene Ontology Category

Estimate BMD and BMDL for each Gene Ontology Category

Nested test to Select Best Polynomial Model

53

Defining the Benchmark Response for Gene Expression Changes

Dose (ppm)

Lo

g2

Gen

e E

xpre

ssio

n

0 BMD

μ

0.5%11%

> BMD

1.349*σ μ

0.5%

54

Experimental Results: Benchmark Doses by Gene Ontology Category

Biological process GO Categories with the lowest mean BMD and other selected categories Gene Standard Biological Process GO Category Count Mean BMD Deviation BMD Mean BMDL Regulation of cell size 15 4.12 2.64 2.78 Cell growth 14 4.38 2.52 2.95 Cell division 10 4.46 4.51 3.02 Taxis 12 4.54 1.84 3.23 Chemotaxis 12 4.54 1.84 3.23 Sensory perception 12 4.71 3.53 3.36 Locomotory behavior 15 4.78 3.20 3.26 Pattern specification 11 4.89 4.26 3.46 Wound healing 10 5.05 4.84 3.50 Chromatin modification 10 5.07 2.90 3.39 M phase 11 5.08 5.53 3.68 Monovalent inorganic cation transport 11 5.15 3.41 3.43 Protein import 12 5.22 2.89 3.72 Neurophysiological process 28 5.27 3.15 3.67 Cellular morphogenesis 35 5.29 3.28 3.72 Negative regulation of transcription,

DNA-dependent 19 5.33 4.59 4.02 Cell migration 34 5.33 3.45 3.66 Cellular macromolecule catabolism 22 5.38 3.54 3.60 Establishment and/or maintenance of

chromatin architecture 13 5.43 3.53 3.78 DNA packaging 13 5.43 3.53 3.78 Other Selected GO Categories DNA repair 12 6.81 4.21 5.22 Response to DNA damage stimulus 24 5.99 4.17 4.54 Cell proliferation 73 7.12 4.39 4.96 Apoptosis 71 7.21 4.29 5.12 Inflammatory response 16 7.47 3.75 4.97 Response to unfolded protein 10 7.67 3.56 5.75

BMD for tumors: 6.4 ppm (Schlosser, Risk Anal., 2003)BMD for cell labeling index: 4.9 ppm (Schlosser, Risk Anal., 2003)

55

• Merging genomic tools with BMD analysis allows BMDs to be estimated for individual functional categories.

• Preliminary analysis suggests that the BMD estimates for the genomic effects are similar to those observed for cell labeling and tumor incidence.

• The use of genomic data together with BMD analysis may reduce the need for expensive animal bioassays.

• Challenges will be determining which functional categories represent adverse versus adaptive effects.

• Future and ongoing analyses are being performed on arsenic, chloroform, and a receptor-mediated toxicant.

Conclusions: Genomic Dose-Response Analysis

56

Acknowledgements

Cecilia Tan

Tom Halsey

Todd Page

Linda Pluta

Dana Stanley

Longlong Yang

Ed Bermudez

Jan Yager (EPRI)

Robinan Gentry

Bruce AllenAndy Nong

Tom O’Connell (UNC)

Chris Learn

Frank Boellmann

Annette Shipp

CIIT ENVIRON

Other Collaborators

Mel Andersen

EPA

Mike Hughes

RISKHappy Holidays!

EPRI Formaldehyde Council, Inc.American Chemistry Council

Funding

Rory Conolly

Mike Devito

Russ Wolfinger (SAS)

Jeff Gift

Elaina Kenyon

Documents

1 The Application of Genomic Dose-Response Data in Risk Assessment Harvey Clewell and Rusty Thomas CIIT Centers for Health Research