Application of Genomic Biomarkers to Predict Increased Lung

publicité
TOXICOLOGICAL SCIENCES 97(1), 55–64 (2007)
doi:10.1093/toxsci/kfm023
Advance Access publication February 20, 2007
Application of Genomic Biomarkers to Predict Increased Lung
Tumor Incidence in 2-Year Rodent Cancer Bioassays
Russell S. Thomas,1 Linda Pluta, Longlong Yang, and Thomas A. Halsey2
The Hamner Institutes for Health Sciences, 6 Davis Drive, Research Triangle Park, NC 27709-2137
Received December 23, 2006; accepted February 14, 2007
Rodent cancer bioassays are part of a legacy of safety testing
that has not changed significantly over the past 30 years. The bioassays are expensive, time consuming, and use hundreds of animals. Fewer than 1500 chemicals have been tested in a rodent
cancer bioassay compared to the thousands of environmental and
industrial chemicals that remain untested for carcinogenic activity. In this study, we used existing data generated by the National
Toxicology Program (NTP) to identify gene expression biomarkers
that can predict results from a rodent cancer bioassay. A set of 13
diverse chemicals was selected from those tested by the NTP. Seven
chemicals were positive for increased lung tumor incidence in
female B6C3F1 mice and six were negative. Female mice were
exposed subchronically to each of the 13 chemicals, and microarray analysis was performed on the lung. Statistical classification
analysis using the gene expression profiles identified a set of eight
probe sets corresponding to six genes whose expression correctly
predicted the increase in lung tumor incidence with 93.9% accuracy. The sensitivity and specificity were 95.2 and 91.8%, respectively. Among the six genes in the predictive signature, most
were enzymes involved in endogenous and xenobiotic metabolism,
and one gene was a growth factor receptor involved in lung development. The results demonstrate that increases in chemically induced lung tumor incidence in female mice can be predicted using
gene biomarkers from a subchronic exposure and may form the
basis of a more efficient and economical approach for evaluating
the carcinogenic activity of chemicals.
Key Words: genomics; biomarkers; rodent cancer bioassays.
INTRODUCTION
The primary goal of toxicology and safety testing is to
identify agents that have the potential to cause adverse effects
in humans. Unfortunately, many of these tests have not changed
significantly in the past 30 years, and most are inefficient, costly,
and rely heavily on the use of animals. The rodent cancer bio1
To whom correspondence should be addressed. Fax: (919) 558-1300.
E-mail: [email protected]
2
Present address: Almac Diagnostics, 801-1 Capitola Drive, Durham, NC
27713.
assay is one of these safety tests and was originally established
as a screen to identify potential carcinogens that would be further analyzed in human epidemiological studies (Bucher and
Portier, 2004). Today, the rodent cancer bioassay has evolved
into the primary means to determine the carcinogenic potential
of a chemical and generate quantitative information on the
dose-response behavior for chemical risk assessments.
The experimental design for rodent cancer bioassays involves exposing mice and rats of both sexes for a period of
2 years. Several dosage levels are chosen with the high dose
corresponding to the maximum tolerated dose. Approximately
50 animals per sex per dose level are used in each study. Due
to the resource-intensive nature of these studies, each bioassay costs $2–4 million and takes over 3 years to complete
(National Toxicology Program [NTP], 1996). Over the past
30 years, only 1468 chemicals have been tested in a rodent
cancer bioassay (Gold et al., 1999). By comparison, approximately 9000 chemicals are used by industry in quantities greater
than 10,000 lbs, and nearly 90,000 chemicals have been inventoried by the U.S. Environmental Protection Agency as part
of the Toxic Substances Control Act. Given the disparity between
the number of chemicals tested in a rodent cancer bioassay and the
number of chemicals used by industry, a more efficient and
economical system of identifying chemical carcinogens needs to
be developed.
Despite considerable advances in the biomedical sciences
over the past decade, few viable alternatives to the rodent
cancer bioassay have been identified. An effort by the NTP to
replace the 2-year rodent bioassay with shorter term assessments in transgenic mouse models has yielded limited success
(Pritchard et al., 2003). The predictive accuracy of the individual transgenic mouse models ranged from 74 to 81% and
was as high as 83% when used in combination (Pritchard et al.,
2003). Using a transcriptomic approach, other investigators
have showed that gene expression biomarkers following a 24-h
exposure could predict tumor formation for nongenotoxic hepatocarcinogens with 84% accuracy (Nie et al., 2006). In our laboratory, we have compared transcriptomic and metabonomic
technologies for their ability to identify biomarkers that could
predict increased lung and liver tumor incidence following
a longer, 90-day exposure (Thomas et al., 2006). The results of
Ó The Author 2007. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.
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56
THOMAS ET AL.
the study showed that tissue-specific gene expression biomarkers were generally more accurate than the metabonomic
biomarkers and that increased tumor incidence was predicted
with relatively high accuracy. In the present study, we focused
our efforts on lung tumors and expanded the number of carcinogenic and noncarcinogenic chemicals to assess the ability
of gene expression biomarkers to predict increased tumor
incidence across a more diverse set of chemicals.
Chemicals. 1,5-Naphthalenediamine (NAPD; CAS no. 2243-62-1; purity:
97%), 2,3-benzofuran (BFUR; CAS no. 271-89-6; purity: 99%), N(1-naphthyl) ethylenediamine dihydrochloride (NEDD; CAS no. 1465-25-4;
purity: 98%), pentachloronitrobenzene (PCNB; CAS no. 82-68-8; purity: 99%),
2,2-bis(bromomethyl)-1,3-propanediol (BBMP; CAS no. 3296-90-0; purity:
98%), 1,2-dibromoethane (DBET; CAS no. 106-93-4; purity: 99%), coumarin
(COUM; CAS no. 91-64-5; purity: 99%), benzene (BENZ; CAS no. 71-43-2;
purity: 99%), and 2-chloromethylpyridine hydrochloride (CMPH; CAS no.
6959-47-3; purity: 98%) were purchased from Sigma-Aldrich (St Louis, MO).
N-methylolacrylamide (MACR; CAS no. 924-42-5; purity: 98%) was purchased from Pfaltz & Bauer (Waterbury, CT). 4-Nitroanthranilic acid (NAAC;
CAS no. 619-17-0; purity: 98.5%), diazinon (DIAZ; CAS no. 333-41-5; purity:
98%), and malathion (MALA; CAS no. 121-75-5; purity: 95%) were purchased
from Advanced Technology and Industry (Hong Kong, China).
mice were randomized by weight and divided into treatment groups (Table 1).
The 13 chemicals used in this study have been previously tested by the NTP.
Seven of the chemicals were positive for an increased incidence of primary
alveolar/bronchiolar adenomas or carcinomas and six were negative. Study
results from the NTP for each of the 13 chemicals are summarized in Table 2.
Animal treatment was initiated at 5 weeks of age. Mice were housed five per
cage in polycarbonate cages in a temperature- (17.8°C–26.1°C) and humidity
(30–70%) -controlled environment with a standard 12-h light/dark cycle. All
animals were given access to food (Harlan Teklad; NIH-07 ground meal; Madison,
WI) and water ad libitum. Animal exposures for each chemical were performed
via the route and dose listed in Table 1. Gavage exposures were administered
5 days/week, and feed exposures were provided 7 days/week. The oral route of
exposure was chosen for this study since the majority of chemicals producing lung
tumors in the NTP rodent bioassay were delivered through the oral route.
Animal use in this study was approved by the Institutional Animal Use and
Care Committee of The Hamner Institutes for Health Sciences and was
conducted in accordance with the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals. Animals were housed in fully
accredited American Association for Accreditation of Laboratory Animal Care
facilities.
Following 13 weeks of exposure, the mice were euthanized with a lethal ip
dose of sodium pentobarbital (Abbott Laboratories, Chicago, IL). The four
right lung lobes were isolated by suturing, removed, and minced together in
RNAlater (Ambion, Austin, TX). The left lung lobe was inflated with 10%
neutral-buffered formalin and stored in 10% formalin for further processing.
Following a standard fixation period, the lung tissues were embedded into
paraffin blocks, sectioned at 5 lm, and stained with hematoxylin and eosin.
Histological changes were assessed by an accredited pathologist.
Animals and treatment. One hundred and fifty female B6C3F1 mice were
obtained from Charles River Laboratories (Raleigh, NC). Female B6C3F1 mice
were chosen since they represent the most sensitive model for chemically
induced lung tumor formation in the NTP rodent bioassay. Upon receipt, the
Gene expression microarray analysis. Microarray analysis was performed on three to four animals per treatment group except for the corn oil
and feed control groups (CCON and FCON) where additional animals were
analyzed due to staggered exposures with parallel control groups. A total of
MATERIALS AND METHODS
TABLE 1
Treatment Groups and Abbreviations Used in the 90-Day Exposure to the 13 Chemicals Used in This Study
Chemical
Abbreviation
NTP no.
Route
Dose in study
1,5-Naphthalenediamine
2,3-Benzofuran
2,2-Bis(bromomethyl)-1,3-propanediol
N-Methylolacrylamide
1,2-Dibromoethane
Coumarin
Benzene
N-(1-naphthyl) ethylenediamine
dihydrochloride
Pentachloronitrobenzene
4-Nitroanthranilic acid
2-Chloromethylpyridine hydrochloride
Diazinon
Malathion
NAPD
BFUR
BBMP
MACR
DBET
COUM
BENZ
NEDD
143
370
452
352
86
422
289
168
Feed
Gavagea
Feed
Gavageb
Gavagea
Gavagea
Gavagea
Feed
PCNB
NAAC
CMPH
DIAZ
MALA
61
109
178
137
24
Feed
Feed
Gavageb
Feed
Feed
2000 ppm
240 mg/kg
1250 ppm
50 mg/kg
62 mg/kg
200 mg/kg
100 mg/kg
3000 ppm
(2000 ppm)c
8187 ppm
10,000 ppm
250 mg/kg
200 ppm
16,000 ppmd
(14,800 ppm)
Water
Corn oil
Feed
WCON
CCON
FCON
a
Gavageb
Gavagea
Gavage exposure with a corn oil vehicle (5 ml/kg).
Gavage exposure with a deionized water vehicle (5 ml/kg).
c
The initial dose of 3000 ppm was reduced to 2000 ppm in week 2 of the study due to taste aversion and weight loss. The 2000 ppm dose is the same as the low
dose in the original bioassay.
d
Due to signs of toxicity, the 16,000 ppm dose was reduced to 0 ppm on day 9 for a period of 2 days. The dose was raised to 8000 ppm for a period of 9 days
and returned to 16,000 ppm for the remainder of the study. The time-weighted average dose was 14,800 ppm.
b
57
BIOMARKERS FOR CHEMICALLY INDUCED LUNG TUMORS
TABLE 2
Detailed Testing Results by the NTP among the 13 Chemicals Used in the Study
Genotoxicity results by the NTP
Mouse
CHO cell CHO cell
Chemical Salmonella lymphoma
CAa
SCEb
NAPD
BFUR
BBMP
MACR
DBET
COUM
BENZ
þ
þ, , þ
þ
þ
NEDD
PCNB
NAAC
CMPH
DIAZ
MALA
þ
þ, þ
þ
þ
þ
þ, þ
þ
þ
þ
E
þ
Incidence of alveolar/bronchiolar
adenomas or carcinomas in
female B6C3F1 mice
Control
Low
þ
E
þ
þ
Wþ
þ
þ
þ, , þ
þ
þ
þ
þ
0/49
2/50
5/52
6/50
0/20
2/51
4/49
10/48
9/48
5/50
8/50
11/43
5/49
5/42
þ
þ, þ
þ
þ
þ
þ, þ
þ
þ
0/49
0/20
1/45
1/19
1/23
0/10
2/48
0/23
5/41
1/49
1/46
0/49
Mid
15/51
7/49
10/50
High
5/46
14/47
19/50
13/49
6/46
27/51
13/49
1/31
1/20
1/48
3/48
2/49
0/47
Other tumor sites in Relative dose in
NTP
female micec
present study
classificationd
Thy, Liv
Liv, For
Har, Ski, For
Har, Liv, Ovr
Lym, Sto
Liv
Zym, Ovr,
Mam, Har,
Lym, Liv
None
None
None
None
None
None
High
High
High
High
Low
High
High
Lung
Lung
Lung
Lung
Lung
Lung
Lung
carc
carc
carc
carc
carc
carc
carc
Lowe
High
High
High
Highf
High
Noncarc
Noncarc
Noncarc
Noncarc
Noncarc
Noncarc
Note. þ, positive; , negative; E, equivocal; Wþ, weakly positive.
a
Testing results for chromosomal aberrations (CA) in the Chinese hamster ovary (CHO) cells.
b
Testing results for sister chromatid exchange (SCE) in the Chinese hamster ovary cells.
c
Tumor site abbreviations: Thy, thyroid; Liv, liver; For, forestomach; Har, harderian gland; Zym, zymbal gland; Mam, mammary gland; Ski, skin; Ovr, ovary;
Lym, lymphoma; Sto ¼ stomach.
d
Lung carc ¼ lung carcinogen; Noncarc ¼ noncarcinogen. This definition is based on a statistically significant increase in primary alveolar/bronchiolar
adenomas or carcinomas in female B6C3F1 mice.
e
The initial dose of 3000 ppm was reduced to 2000 ppm in week 2 of the study due to taste aversion and weight loss. The 2000 ppm dose is the same as the low
dose in the original bioassay.
f
Due to signs of toxicity, the 16,000 ppm dose was reduced to 0 ppm on day 9 for a period of 2 days. The dose was raised to 8000 ppm for a period of 9 days
and returned to 16,000 ppm for the remainder of the study. The time-weighted average dose was 14,800 ppm.
70 animals were analyzed. Total RNA was isolated from the lung tissue using
Trizol reagent (Invitrogen, Carlsbad, CA). The isolated RNA was further
purified using RNeasy columns (Qiagen, Valencia, CA), and the integrity of the
RNA was verified spectrophotometrically and with the Agilent 2100 Bioanalyzer (Palo Alto, CA). Double-stranded cDNA was synthesized from 5 lg of
total RNA using the One-Cycle cDNA synthesis kit (Affymetrix, Santa Clara,
CA). Biotin-labeled cRNA was transcribed from the cDNA using the GeneChip
IVT labeling kit (Affymetrix). Fifteen micrograms of labeled cRNA was fragmented and hybridized to Affymetrix Mouse Genome 430 2.0 arrays for 16 h at
45°C. The hybridized arrays were washed using the GeneChip Fluidics Station
450 and scanned using a GeneChip 3000 scanner. Microarray data were processed using Robust Multi-array Average with a log2 transformation (Irizarry
et al., 2003). The gene expression results have been deposited in the National
Center for Biotechnology Information Gene Expression Omnibus (accession
no. GSE6116).
Analysis of chemical diversity. Molecular descriptors representing the
two-dimensional structure of each of the 13 chemical treatments were downloaded from PubChem in the simplified molecular input line entry specification
(SMILES) format. SMILES codes for all single chemicals tested in the NTP
rodent cancer bioassay were downloaded from DSSTox (Richard et al., 2006).
The SMILES code for each chemical was converted into a chemical fingerprint
using the GenerateMD software application (Version 3.1.7.1, JChem, ChemAxon, Budapest, Hungary). The chemical fingerprints were then compared for
structural similarity using the Tanimoto coefficient and the Compr software
application (Version 3.1.7.1, JChem, ChemAxon).
Basic statistical and annotation analysis of microarray data. Basic
statistical differences were analyzed using both a one-way analysis of variance
for differences across the 13 chemical treatments and a linear model (Smyth,
2005) with a contrast between the lung carcinogens and the noncarcinogenic
treatments. In both analyses, probability values were adjusted for multiple comparisons using a false discovery rate (Reiner et al., 2003). Analysis of enrichment within gene ontology (GO) categories was performed using NIH
David (Dennis et al., 2003). Briefly, Affymetrix probe set identifiers for the
genes of interest were uploaded to the DAVID Web site and analyzed based on
the Affymetrix 430_2 reference list. A hypergeometric test was performed to
identify GO categories with significantly enriched gene numbers. The resulting
list of GO categories was refined by selecting categories containing two or more
genes.
Statistical classification analysis. Classification analysis was performed
using a combination of the Golub algorithm (Golub et al., 1999) for feature
selection and a support vector machine model for classification (radial basis
function kernel, C ¼ 17,150, c ¼ 0.0022). The predicted end point was increased lung tumor incidence in female B6C3F1 mice according to the NTP
rodent cancer bioassay (Table 2). To assess the predictive accuracy of the model
on the current data set, 10-fold cross-validation was performed. The crossvalidation process consisted of first randomly dividing all 70 animals into
10 equally sized groups (i.e., seven animals per group). Nine of the groups were
then lumped together to use as a training set (63 animals), and the remaining
group was used as the test set (seven animals). The data for the animals in the
58
THOMAS ET AL.
test set was set aside as if we had never observed them. Feature selection was
then performed on the training set using the Golub algorithm (Golub et al.,
1999), and the genes with the largest Golub statistic were used to build
a support vector machine classification model. The model was then used to
predict the classes for the seven animals in the test set that were held out at
the beginning of the process. The cross-validation process was repeated at least
100 times to obtain a good estimate of the predictive accuracy. Accuracy was
calculated by dividing the number of correct predictions in the test set by
the total number of predictions. Different numbers of genes were evaluated in
the feature selection process to assess the change in predictive accuracy with
gene number. The classification analysis was performed using the PCP software
program (Buturovic, 2006).
RESULTS
Structural and Mechanistic Diversity among Chemical
Treatments
The 13 chemical treatments in the study were intentionally
chosen to be diverse in terms of chemical structure, genotoxicity, and potential modes of action. The structural diversity
among the chemicals was analyzed using a Tanimoto similarity
coefficient with a coefficient of 1.0 being identical molecules
and 0.0 having no structural similarity. The average similarity
among all 13 chemicals in the study was 0.141 with a maximum
similarity of 0.508 between NEDD and NAPD (Table 3).
Among the lung carcinogens, the average similarity dropped to
0.123 with a maximum similarity of 0.327 between DBET and
BBMP. By comparison, the average similarity for all single
chemicals tested by the NTP in a rodent cancer bioassay was
0.155.
To assess potential differences in mode of action, gene expression changes across the 13 chemical treatments were used
as a surrogate of mechanistic diversity. A total of 28,780 probe
sets corresponding to 25,375 unique transcripts were significantly altered among the chemical treatments based on a oneway ANOVA. Given that there are an estimated 39,015 unique
transcripts on the microarray, the number of transcripts altered
by the 13 chemicals corresponds to approximately 65% of the
transcriptome.
Histological Changes
Gross histological examination of the lung tissue identified
treatment-related lesions in only NAPD-treated animals.
Morphological changes were found in all five animals examined
and were limited to the bronchiolar epithelial cells that
exhibited karyomegaly and karyorrhexis. There was occasional
peribronchiolar infiltration by neutrophils and mononuclear cells.
Bronchiolar epithelial cell morphology was suggestive of regenerative hyperplasia. In the 2-year NTP study, the primary
nonneoplastic lesion was adenomatous hyperplasia occurring
in 30% of the animals (NTP, 1978). Histopathological changes
in the 13-week subchronic study were not provided for NAPD in
the original NTP report. Given the absence of lung lesions
among the remaining tumorigenic treatment groups, histological changes alone following a 90-day exposure were not predictive of increased lung tumor incidence in a 2-year bioassay.
This result is consistent with a previous study that reported the
poor predictive properties of histological lesions (Allen et al.,
2004).
Gross Gene Expression Differences between Lung
Carcinogens and Noncarcinogenic Treatments
To obtain an overall sense of key differences between the
lung carcinogens and noncarcinogenic treatments, a twosample statistical comparison was performed between animals
treated with chemicals showing increased lung tumor incidence
in the 2-year bioassay and animals treated with the negative
chemicals plus the vehicle controls. A total of 82 probe sets
corresponding to 75 unique transcripts were significantly altered.
Sixty-five transcripts were significantly upregulated in animals
treated with the lung carcinogens, and 10 transcripts were
TABLE 3
Tanimoto Similarity Coefficients among the Lung Carcinogenic and Noncarcinogenic Treatments
NEDD
PCNB
MALA
NAAC
DIAZ
CMPH
BFUR
NAPD
BBMP
BENZ
COUM
DBET
MACR
NEDD
PCNB
MALA
NAAC
DIAZ
CMPH
BFUR
NAPD
BBMP
BENZ
COUM
DBET
MACR
1.000
0.268
1.000
0.104
0.150
1.000
0.247
0.394
0.202
1.000
0.144
0.170
0.241
0.209
1.000
0.187
0.216
0.127
0.203
0.214
1.000
0.258
0.157
0.083
0.158
0.116
0.130
1.000
0.508
0.267
0.058
0.233
0.104
0.192
0.317
1.000
0.055
0.057
0.094
0.099
0.060
0.060
0.074
0.055
1.000
0.175
0.112
0.034
0.095
0.037
0.105
0.195
0.314
0.046
1.000
0.148
0.151
0.238
0.276
0.234
0.162
0.175
0.102
0.095
0.079
1.000
0.051
0.024
0.031
0.042
0.023
0.051
0.031
0.047
0.327
0.079
0.031
1.000
0.157
0.104
0.108
0.147
0.099
0.071
0.072
0.103
0.178
0.041
0.130
0.100
1.000
BIOMARKERS FOR CHEMICALLY INDUCED LUNG TUMORS
59
FIG. 1. Heat of genes differentially expressed in the lung following a 90-day exposure to lung carcinogenic and noncarcinogenic treatments. Chemical details,
abbreviations, and NTP study results are provided in Tables 1 and 2. Red represents high gene expression and blue is low expression.
significantly downregulated. The gene expression differences
between the lung carcinogens and the noncarcinogenic chemicals are depicted in Figure 1, and a complete list is provided as
supplemental material (Supplemental Table 1). A subset of the
significant gene expression changes were also verified using
quantitative RT-PCR (Supplemental Fig. 1). Notably, there were
a number of highly discriminating gene expression changes that
were common among the lung carcinogens despite the diversity
in chemical structures, genotoxicity categories, and potential
mechanisms.
A GO analysis of the significant gene expression changes
showed enrichment in multiple categories with the majority
related to endogenous and xenobiotic metabolic processes
(Table 4). Changes in glutathione-related processes were consistent with a variety of known biomarkers in both rodent and
human tumorigenesis (Balendiran et al., 2004; Hayes and
Pulford, 1995; Kwak et al., 2004), and a previous study has also
identified gene expression changes related to fatty acid metabolism in human colorectal cancers (Yeh et al., 2006). In
addition, changes in aldehyde dehydrogenase activity have
been associated with experimental and human tumors in a
variety of tissues (Lindahl, 1992).
Statistical Classification Analysis to Predict Increased Lung
Tumor Incidence
To evaluate the ability of the gene expression changes to
predict increased lung tumor incidence in a rodent cancer bioassay, statistical classification analysis was performed using
a combination of the Golub feature selection algorithm (Golub
et al., 1999) and a support vector machine model as the classifier. Ten-fold cross-validation was used to estimate the predictive accuracy. Using this approach, tissue gene expression
profiles were capable of predicting a chemically induced increase in lung tumor incidence with 93.9% accuracy using only
eight probe sets that correspond to six different genes (Fig. 2).
The sensitivity and specificity of the model with the eight biomarkers was 95.2 and 91.8%, respectively. The predictive accuracy of the model declined as more genes were added. The
top gene expression biomarkers were changes in the UDPglucuronosyltransferase 1a (Ugt1a) family, carboxylesterase 1
60
THOMAS ET AL.
TABLE 4
Ontology Analysis of Significant Gene Expression
Changes between Lung Carcinogenic and Noncarcinogenic
Treatmentsa
Biological process
GO category
P-value
Response to chemical
stimulus
Response to abiotic
stimulus
0.0002
Molecular function GO
category
P-value
Glutathione metabolism
Fatty acid metabolism
0.0026
0.0029
Coenzyme metabolism
0.0062
Cofactor metabolism
Lipid metabolism
Sulfur metabolism
0.0108
0.0226
0.0315
Cellular lipid
metabolism
Physiological
process
Carboxylic acid
metabolism
0.0352
Glutathione transferase
activity
Transferase activity,
transferring alkyl or aryl
(other than methyl)
groups
Catalytic activity
Transferase activity,
transferring hexosyl
groups
Transferase activity,
transferring glycosyl
groups
Transferase activity
Oxidoreductase activity
Glucuronosyltransferase
activity
Serine esterase activity
0.0374
Carboxylesterase activity
0.0110
0.0390
0.0148
Organic acid
metabolism
Response to toxin
0.0390
0.0461
Aldehyde metabolism
0.0461
Oxidoreductase activity,
acting on the aldehyde
or oxo group of donors
Carboxylic ester
hydrolase activity
Carbonyl reductase
(NADPH) activity
Aldehyde dehydrogenase
[NAD(P)þ] activity
0.0009
0.0000
(Ces1), fibroblast growth factor receptor 2 (Fgfr2), epoxide
hydrolase 1 (Ephx1), glutathione S-transferase l 1 (Gstm1),
and an unannotated gene (Table 5). A complete ranking is
provided as supplemental material (Supplemental Table 2). For
changes in Ugt1a expression, the three corresponding probe
sets were not specific for a particular isoform. The Ugt1a isoforms are produced through the alternative splicing of variable
exons connected to four constant exons at the 3#-end of the
gene (Zhang et al., 2004).
0.0000
DISCUSSION
0.0000
0.0000
0.0001
0.0001
0.0008
0.0022
0.0110
0.0168
0.0176
0.0220
a
GO analysis was performed using NIH David (http://david.abcc.ncifcrf.
gov/).
The rodent cancer bioassay is part of an established legacy of
safety testing to identify chemical, biological, and physical
agents that have the potential for adverse human effects. The
tests are inefficient, expensive, and involve large-scale animal
exposures. For industry, the inefficiencies of the current safety
testing paradigm lead to increased financial costs to develop
a product and fewer resources to devote to understanding the
underlying mode of action for the toxic effect. For the public,
the inefficiencies result in access to fewer chemicals that may
benefit society and fewer resources to assess the safety of
untested chemicals. Therefore, it is in the interest of society as
a whole to identify more efficient and economical alternatives
to the standard rodent cancer bioassay.
In the search for an alternative to the bioassay, we used
existing data generated by the NTP to assemble a training set of
13 chemicals. Seven of the chemicals were positive for an
increased incidence of primary alveolar/bronchiolar adenomas
or carcinomas and six were negative (Table 2). Animals were
exposed subchronically to each of the 13 chemicals plus
corresponding vehicle controls, and microarray analysis was
performed on the lungs of three to four animals per treatment
group. Gene expression changes were then examined for
TABLE 5
Top Gene Expression Biomarkers That Discriminate between Lung Carcinogenic and Noncarcinogenic Treatments Based
on the Golub Algorithm
Affymetrix ID
Transcript ID
Gene symbol
Gene description
Golub score
Expression in carcinogens
1424783_a_at
Mm.42472.1
Ugt1a
0.9150
Increased
1449486_at
1443996_at
1426260_a_at
Mm.22720.1
Mm.25061.1
Mm.42472.3
Ces1
Fgfr2
Ugt1a
0.8877
0.8550
0.8393
Increased
Decreased
Increased
1422438_at
1426261_s_at
Mm.9075.1
Mm.42472.3
Ephx1
Ugt1a
0.8385
0.8220
Increased
Increased
1424266_s_at
1448330_at
Mm.29110.1
Mm.2011.1
AU018778
Gstm1
UDP-glucuronosyltransferase 1 family, polypeptide Aa
Carboxylesterase 1
Fibroblast growth factor receptor 2
UDP-glucuronosyltransferase 1 family, polypeptide Aa
Epoxide hydrolase 1, microsomal
UDP-glucuronosyltransferase 1 family, polypeptide Aa
Expressed sequence AU018778
Glutathione S-transferase, mu 1
0.7514
0.7419
Increased
Increased
a
Probe set interrogates multiple isoforms of the Ugt1a family.
BIOMARKERS FOR CHEMICALLY INDUCED LUNG TUMORS
FIG. 2. Results from the statistical classification analysis for predicting
chemically induced increases in lung tumor incidence using subchronic gene
expression biomarkers. Accuracy was estimated based on 10-fold crossvalidation and calculated by dividing the number of correct predictions by
the total number of predictions.
potential biomarkers that could discriminate between the lung
carcinogenic and noncarcinogenic treatments.
Based on the analysis of the chemical structures using the
Tanimoto similarity coefficient, the average diversity of the
13 chemicals used in the investigation was similar to the diversity
among all chemicals tested by the NTP in the rodent cancer
bioassay. In addition, the overall changes in gene expression
among the 13 chemical treatments included approximately
65% of the transcriptome, suggesting that the set of chemicals
was also mechanistically diverse. Therefore, the ability to
identify biomarkers and predict increased lung tumor incidence
in a rodent cancer bioassay using these 13 chemicals should
reflect the ability to predict lung tumor incidence across all the
chemicals tested by the NTP.
A formal statistical classification analysis demonstrated that
the tissue gene expression profiles were capable of predicting
chemically induced increases in lung tumor incidence with
93.9% accuracy using eight probe sets that correspond to six
different genes. The ability to predict a complex biological
response such as an increase in tumors with relatively few genes is
not uncommon. A previous study was able to predict chemically
induced rat liver tumors following a 24-h exposure with 84%
accuracy using only six genes (Nie et al., 2006), and an 11 gene
signature was able to predict recurrence, metastasis, and death in
a variety of cancers (Glinsky et al., 2005). The predictive
accuracy of the model in our study also declined as more genes
were added. The decline in the predictive accuracy with increasing gene numbers has been reported previously and is due
to the addition of genes that are treatment specific and not related
to the predicted toxicological end point (Thomas et al., 2001).
The identification of biomarkers that predict an increase in
tumor incidence is fundamentally different than biomarkers
that predict tumor formation in an individual animal. The bio-
61
markers that were identified in this study were likely to be
genes that created a favorable cellular environment for chemically induced lung tumor formation and not those that determined whether a specific animal gets tumors. Among the six
genes in the predictive signature, most were enzymes involved
in endogenous and xenobiotic metabolic processes and one was
a growth factor receptor involved in lung development. The
functional breakdown of these predictive biomarkers was consistent with the established role of metabolism and growth
factor signaling in tumorigenesis. Dysregulation of metabolic
process in preneoplasia and neoplasia is a relatively wellestablished phenomena (Costello and Franklin, 2005; Mazurek
et al., 1997), and many endogenous and xenobiotic metabolizing enzymes have been used as histochemical markers in
early initiation-promotion studies (Hasegawa and Ito, 1994).
Similarly, the disruption of growth factors and developmental
processes related to proliferation and differentiation are believed to be critical steps in carcinogenesis (Breuhahn et al.,
2006; Datta and Datta, 2006; Srinivasan et al., 2005).
Among the most predictive metabolic enzymes was Ugt1a.
Ugt1a is one of a family of enzymes that catalyze the glucuronidation of endogenous and xenobiotic molecules (Tukey
and Strassburg, 2000). The mouse Ugt1 locus produces nine
different genes through the alternative splicing of 14 variable
exons to four constant exons (Zhang et al., 2004). Genomewide scans have identified the Ugt1a locus as playing an important role in chemical carcinogenesis (Tukey and Strassburg,
2000), and various isoforms have been shown to be differentially expressed in human liver cancer (Strassburg et al., 1997).
Another predictive metabolic enzyme was Ces1. Ces1 is part
of a large multigene family of enzymes that hydrolyze ester and
amide bonds and play a role in cellular cholesterol esterification
(Ghosh, 2000; Uphoff and Drexler, 2000). Previous studies have
suggested that Ces1 may play a role in detoxifying ester or
amide containing xenobiotics in the lung (Munger et al., 1991;
Uphoff and Drexler, 2000). Notably, human CES1 was part of an
11 gene transcriptional signature that was used to predict therapy
outcome and malignancy for multiple types of human cancer
including lung cancer (Glinsky et al., 2005). In contrast to our
studies, the downregulation of human CES1 was considered
prognostic (Glinsky et al., 2005). However, the transcriptional
signature in their study was applied to relatively late-stage
tumors and not as early classifier of carcinogenic potential.
The next metabolic enzyme in the predictive set was Ephx1.
Ephx1 has been shown to play a role in the activation and
detoxification of many polyaromatic hydrocarbons (Arand et al.,
2005). In human cancer, one study has noted an increased
expression of human EPHX1 in hepatocellular carcinomas and
variable expression in lung tumors (Coller et al., 2001).
A separate study has identified increased expression of EPHX1
in human glioblastomas (Kessler et al., 2000). The increased
expression in human liver cancer is supported by rodent studies
where expression of Ephx1 was increased in preneoplastic
nodules (Griffin and Gengozian, 1984; Novikoff et al., 1979).
62
THOMAS ET AL.
The fourth most predictive metabolic enzyme was the
relative uncharacterized AU018778 gene. The amino acid
sequence of the AU018778 gene showed significant similarity
to carboxylesterases with approximately 65% identity with
mouse Ces1. On the genomic level, the gene is found in a cluster
of esterases downstream of Ces1 and upstream of Es22 and
Ces3. In normal tissue, AU018778 is predominantly expressed in
kidney, liver, intestine, and adipose tissue (Su et al., 2004). No
reports were found that showed an altered expression in cancer.
The last metabolic enzyme in the predictive set was Gstm1.
Gstm1 is part of a family of glutathione transferases that are
involved in the metabolism of endogenous and xenobiotic
molecules and can modulate cell signaling through a variety of
mechanisms (Hayes et al., 2005). Although the majority of
work on Gstm1 in cancer has focused on associating human
polymorphic differences with susceptibility, increased expression of GSTM1 has been identified as a potential biomarker in
human head and neck tumors (Bongers et al., 1995). In the
lung, a previous study has reported that human GSTM1 was
infrequently expressed in normal tissue and its expression was
not increased in lung tumors (Spivack et al., 2003). In rodent
studies, increased expression of mu class glutathione transferases have been observed in preneoplastic nodules in the rat
liver (Hayes and Pulford, 1995), but not in the mouse liver
(Hatayama et al., 1993).
The only nonmetabolic gene in the predictive set was Fgfr2.
Fgfr2 is part of a family of receptor tyrosine kinases that bind
fibroblast growth factors and initiate cellular signals that affect
proliferation and differentiation (Eswarakumar et al., 2005).
Alternative splicing of Fgfr2 results in two different isoforms,
Fgfr2b and Fgfr2c, that have different ligand binding affinities
(Eswarakumar et al., 2005). The targeted disruption of the
Fgfr2b isoform in mice results in abnormal development of the
lung, pituitary, thyroid, teeth, and limbs (De Moerlooze et al.,
2000), while disruption of the Fgfr2c isoform results in skeletal
abnormalities (Eswarakumar et al., 2002). Additional research
has shown that Fgfr2b plays a significant role in lung development (De Langhe et al., 2006; del Moral et al., 2006). One
study has reported that binding of Fgf9 to Fgfr2b cooperates
with Shh signaling to regulate mesenchymal proliferation in
lung development (White et al., 2006). Notably, expression of
Shh was also found to be one of the top 20 predictive
biomarkers in our study (Supplemental Table 2). In cancer, expression of Fgfr2 has shown different behaviors depending on
tissue and cell type. In human lung and colorectal cancer, increased expression of Fgfr2b was observed in cancer tissue
(Watanabe et al., 2000; Yamayoshi et al., 2004), while in human gastric and bladder cancer, decreased expression of Fgfr2b
was observed in cancer cells and was associated with poor
patient prognosis (Diez de Medina et al., 1997; Matsunobu
et al., 2006). In our study, decreased expression was predictive
of lung tumor formation.
In summary, our results demonstrate that an increase in lung
tumor incidence can be predicted based on gene expression
changes following only a subchronic exposure. Although the
present study was limited to 13 chemicals delivered through an
oral route and the female mouse lung, the results suggest that
this approach has the potential to be more broadly applied to
other organ systems and animal models. To adequately assess
the full potential of the approach, additional studies would need
to be performed and many are currently underway. These
studies include additional chemicals, other routes of exposure
such as inhalation, other organ systems, and equivalent studies
in both sexes and other rodent species. If the approach proves to
be more broadly applicable, it has the potential to be an efficient and economical alternative to the rodent cancer bioassay
and opens the door to a fundamental shift in chemical safety
testing. Based on NTP records, five organ sites (liver, lung,
kidney, mammary, and hematopoietic) account for approximately 50% of the positive chemical responses, and 24 organ
sites have at least five positive chemicals in at least one species
and sex. In the short term, developing gene expression biomarkers for each of the top five tumor sites in both mice and
rats would provide an efficient means to prioritize chemicals.
Within industry, prioritizing chemicals based on short-term gene
expression biomarkers would provide an assessment of product
safety earlier in the development pipeline leading to substantial
monetary savings and reduced time to market. For the public,
the majority of chemicals in the United States are not required
to be tested for carcinogenic activity unless evidence for
adverse health effects is obtained. Therefore, identifying biomarkers for each of the top five organ sites would allow
regulatory agencies to more effectively identify chemicals that
need additional safety testing. For long-term goals, developing
biomarkers for each of the 24 tissues may allow for the eventual replacement of the rodent cancer bioassay, and performing
studies on chemicals that are both rodent and human carcinogens could identify biomarkers with more direct relevance to
human health.
SUPPLEMENTARY DATA
Supplementary data are available online at http://toxsci.
oxfordjournals.org/ and includes (1) a complete list of the genes
identified as being significantly different between animals treated
with chemicals showing increased incidence of lung tumors in
the rodent cancer bioassay and animals treated with negative
chemicals plus the vehicle controls (Supplemental Table 1); (2)
a complete ranking of the most predictive gene expression biomarkers based on the Golub feature selection algorithm (Supplemental Table 2); and (3) qRT-PCR validation of a subset of the
differentially expressed genes (Supplemental Fig. 1).
ACKNOWLEDGMENTS
Research was supported by the American Chemistry Council’s Long Range
Research Initiative under the Improved Methods Focus Area.
BIOMARKERS FOR CHEMICALLY INDUCED LUNG TUMORS
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