mesothelioma cancer

Advances in molecular biology over the past decade have improved our
understanding of genetic, transcriptional, and translational alter¬ations in
human cancers. The sequencing of the human genome has resulted in the
identification of many known and novel genes. Several groups are engaged in
determining the interactions and regulation of all these genes to ascertain
their function in early detection and pre¬vention of cancer. Recent advances in
functional genomic technology have begun to investigate interactive pathways to
elucidate what, where, when, and how these genes are expressed in an
orchestrated fashion. Other groups have concentrated on proteomics, or the study
of proteins, including their relative amount, distribution, posttransla-tional
modifications, functions, and interactions to address fundamen¬tal biologic
questions in the progression of a disease from a normal to a cancerous state.
This chapter discusses the functional genomics and expression proteomics
approaches employed to date in general and their relevance to mesothelioma in
particular. It is our attempt to provide both novice and experienced
investigators in this field with novel methodologies used in other types of
cancers that might ulti¬mately lead to the early detection and treatment of
mesothelioma.

Genomics in Cancer Research

The Human Genome Project analysis has described 30,000 to 50,000 genes after DNA
sequencing analyses. In spite of 20% to 30% differ¬ences observed between the
predicted transcriptomes by International Human Genome Sequencing Consortium
(HGSC) and Celera Genomics, these data have provided a tremendous stimulus for
sys¬tematic analysis of various types of cancer. High-resolution analysis of
chromosomal aberrations, genome-wide mutation screens, and ex¬pression profiling
have given investigators a comprehensive view of genetic alterations in many
cancers. These high-throughput tech¬nologies are being vigorously pursued to
gain a complete list of the molecular and genetic causes that drive malignant
transformation and

the possible therapeutic options that may be exploited for clinical benefit.

Comparative Genomic Hybridization (CGH) Analysis

Comparative genomic hybridization (CGH) analysis is a DNA-based molecular
cytogenetic technique that allows the identification of chromosomal imbalances
(gains, losses, and amplification of DNA sequences) in an entire tumor genome in
a single experiment. An equal proportion of biotin-labeled tumor DNA and
digoxigenin-labeled normal reference DNA is hybridized to normal metaphase
chromosomes. The variance in signal intensities of the two fluo-rochromes
[fluorescein isothiocyanate (FITC) for tumor DNA and the tetrarhodamine
isothiocyanate (TRITC) for the normal DNA] are detected in a fluorescence
microscope, and captured images are then evaluated with a special image analysis
program. The over- and under-represented DNA segments are quantitated by
FITC/TRITC ratios for every single chromosome. The accumulation of various
clonal chro¬mosomal deletions in most malignant mesotheliomas is indicative of a
multistep progression of tumorigenesis, such as gene point muta¬tions, partial
deletion, epigenetic silencing, gene amplification, gene rearrangements, and/or
complete gene loss. All these mechanisms could be involved in the genesis of a
mesothelioma from an occult to advanced stage. Cytogenetic and loss of
heterozygosity (LOH) analy¬ses of malignant mesothelioma have demonstrated
frequent deletions of specific sites within chromosome arms 1p, 3p, 6q, 9p, 13q,
15q, and 22q and trisomies and polysomies of chromosomes 1, 5, 7, 11, 12, and
20. Furthermore, current information has confirmed the involvement of these five
tumor suppressor genes: p16, p15, p53, NF2, and WT1.

Restriction Landmark Genome Scanning (RLGS)

Restriction landmark genome scanning (RLGS) is a highly resolving gel-based
technique in which several thousand fragments in genomic digests are visualized
simultaneously and quantitatively analyzed. The genomic DNA is radioactively
labeled at cleavage sites specific for a rare cleaving restriction enzyme, Not1,
followed by first-dimension electrophoresis. By subjecting separated DNA
fragments to in situ digestion with a frequent cutter prior to a
second-dimension elec-trophoresis, several thousand fragments from the genome
can be resolved and visualized (1). The digestion of genomic DNA by NotI prior
to labeling generates landmarks that allow visualization of DNA fragments that
occur preferentially in CpG islands (2). Because of the localization of CpG
islands in proximity to transcribed sequences (3), there is a strong possibility
that NotI fragments detected in RLGS scans occur in the vicinity of coding
sequences.

There are many applications of RLGS that stem from its quantita¬tive
reproducibility. For instance, RLGS could be useful for studies of restriction
fragment length polymorphisms; for identifying genomic insertions, deletions, or
amplifications; and for identifying somatic methylation changes (4,5). The
widespread use of RLGS has been

hampered by difficulty in deriving sequence information for displayed fragments
and a lack of whole-genome sequence-based framework for interpreting RLGS
patterns. Recently, a collaborative effort among several laboratories has
resulted in the development of bioinformatic tools for comparisons of
sample-derived RLGS patterns with patterns predicted from the human genome
sequence and displayed as virtual genome scans (VGS). The tools developed allow
sequence prediction of fragments in RLGS patterns obtained with different
restriction enzyme combinations. The utility of VGS is demonstrated by the
iden¬tification of restriction fragment length polymorphisms, and of
ampli¬fications, deletions, and methylation changes in tumor-derived CpG islands
and the characterization of an amplified region in a breast tumor that spanned
<230 kilobase (kb) on 17q23 (6). Recently, using RLGS in combination with
promoter methylation studies, a novel lung cancer–related gene, bone
morphogenetic protein 3B (BMP3B) was identified on chromosome 10q11 (7).

Only one study utilizing RLGS in mesothelioma has been described: RLGS analysis
using Not1-EcoRV-Hinf1 restriction enzyme digestion on five malignant pleural
mesothelioma was carried out that showed losses in chromosomal locations
22q13.1, 17q23.2, 4p16.2, 12p13.2 and 1p34.1 (unpublished data).

Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a comprehensive method for analysis
of gene expression patterns (8). There are three main underlying principles that
define SAGE methodology: (1) A short sequence tag [10–14 base pair (bp)]
contains sufficient information to uniquely identify a transcript provided that
the tag is obtained from a unique position within each transcript. (2) Sequence
tags can be linked together to form long serial molecules that can be cloned and
sequenced. (3) Quantitation of the number of times a particular tag is observed
provides the expression level of the corresponding transcript. Recent
technologic advances have made large-scale gene expression measurements routine.
Serial analysis of gene expression counts polyadenylated transcripts by
sequencing a short 14-bp tag at the gene’s 3¢ end, adjacent to the last
restriction site, normally NlaIII. All expressed transcripts with a NlaIII site
can be “tagged” and counted efficiently in large numbers (typically <50,000 per
RNA sample) by using automated sequencing. The tag counts are then archived
elec¬tronically for future analysis and digital comparisons. To provide
quan¬titative expression levels on a genome-wide scale, the Cancer Genome
Anatomy Project (CGAP) uses the SAGE project as one of the largest suppliers of
a public gene expression database (9,10). These data are posted at the National
Center for Biotechnology Information’s SAGEmap Web site
(http://www.ncbi.nlm.nih.gov/SAGE), where SAGE tags are assigned to UniGene
clusters, differentially expressed tags can be identified, and the expression
level of a particular tag can be displayed (11,12). SAGEmap has been quite a
powerful tool, but recently an improvised version has been reported, known as
SAGE

Genie (http://cgap.nci.nih.gov/SAGE). It consists of SAGE Anatomic Viewer, which
allows nearly any gene’s transcript levels to be easily viewed in normal and
malignant tissues. The anatomic view is based on a growing set of over 5.2
million SAGE tags assembled from 114 cell types, plus new Web tools to view
these data. These informatics allow SAGE Genie to automatically identify SAGE
tags from a gene’s primary or alternatively polyadenylated transcript while
screening for experimental artifacts. A large archive of SAGE data is now more
accu¬rately and easily viewed by using SAGE Genie, including a means to see
anatomically based gene expression (13). To date, SAGE analysis has compared the
gene epression from a surgically resected malignant pleural mesothelioma (MPM)
to the patient’s autologous normal mesothelium and the results have been posted
on the Web: (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM727).

SELEX-SAGE

To determine the location and relative strength of all transcription-factor
binding sites within a genome is important not only for a comprehensive
understanding of gene regulation but also for effec¬tive promoter applications.
A bioinformatically driven experimental method has been devised to accurately
define the DNA-binding sequence specificity of transcription factors. Computer
simulations showed that several thousand low- to medium-affinity sequences are
required to generate a profile of desired accuracy. To produce data on this
scale, a method combining systematic evolution of ligands by exponential
enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was
coupled to an automated quality-controlled sequence extraction procedure. This
allowed the sequencing of a data¬base of more than 10,000 potential DNA ligands
for the CTF/NFI transcription factor. The database is publicly available at
http://www.isrec.isb-sib.ch/selex_nf1/. The resulting binding-site model defines
the sequence specificity of this protein with a high degree of accuracy not
achieved earlier, and thereby makes it possible to identify previously unknown
regulatory sequences in genomic DNA (14).

Gene Expression Profile Using DNA Microarrays

Global gene expression profiles for several types of tumors, using DNA
microarrays, have been published recently that delineate distinct pat¬terns of
gene expression among subsets of related tumors (15). These studies have
examined pathologically homogeneous as well as hetero¬geneous set of tumors to
identify clinically relevant subtypes, high-stage versus low-stage tumors of the
same lineage to identify molecular correlates, and tumors of different lineages
for specific molecular sig¬natures. The landmark study that caught much
attention in this area was the uncovering of novel tumor subtypes of diffuse
large B-cell lymphoma (16). Two molecularly distinct forms of this tumor were
uncovered that were reflective of different stages of B-cell differentia¬tion.
One type expressed genes characteristic of germinal center B cells

with significantly better overall survival than the second type, which expressed
genes normally induced during in vitro activation of periph¬eral blood B cells.
This study marked the beginning of identifying pre¬viously undetected and
clinically significant subtypes of lymphoma. Recently, two groups have been able
to show a clear distinction among acute lymphoblastic leukemia (ALL) subtypes
and acute myelogenous leukemia (AML) and the mixed-lineage leukemia (MLL) (17).

Differential Gene Expression Patterns in Mesotheliomas

The differential display technology works by systematic amplification of the 3¢
terminal portions of messenger RNAs (mRNAs) by using anchored primers that bind
5¢ boundary of the poly-A tails by reverse transcription, followed by polymerase
chain reaction (PCR) amplifica¬tion with additional upstream primers of
arbitrary sequences and resolution of those fragments on a DNA sequencing gel.
This method¬ology helps visualize all the expressed genes using side-by-side
com¬parisons between or among related cells (18). To better understand malignant
mesothelioma pathobiology, researchers have used the tech¬nique of differential
display to compare gene expression patterns in mesothelioma, normal pleura, and
normal lung. The human inhibitor of apoptosis protein-1 gene (IAP-1/MIHC/cIAP2)
was discovered to be highly expressed in malignant pleural mesothelioma (MPM)
tumors and cell lines (19). The overexpression of IAP-1 mRNA and protein was
validated by multiple methods, including real-time quantitative
reverse-transcription PCR and Western blot analysis. The main draw¬back of the
above technique has been low specificity, which results in false positives due
to the low annealing temperature of PCR. To circumvent these shortcoming,
modifications with the longer arbitrary primer design (25-mer) and lock-docking
oligo (dT) primers (29-mer) for RNAfingerprinting have yielded high-stringency
PCR products rel-ative to differential display. This modified differential
display method, called RNA fingerprinting, has been used to identify mRNAs that
were differentially expressed during human mesothelial cancer progression. Five
different clones were identified using this procedure. Two clones were expressed
in the metastatic mesothelioma cell line M1A and the malignant mesothelioma cell
line M1, one clone was expressed uniquely in the metastatic cell line M1A, and
one clone was solely expressed in the normal mesothelial cells. Three clones had
no homol-ogy to known genes, whereas the other two clones had a striking
sequence homology to the M130 antigen and rab 12 mRNA, respec¬tively. These
sequence tags may be of interest as a specific mesothe-lioma tumor marker (20).
A mesothelioma cell line with retained ability to differentiate into either
epithelial or fibroblast-like phenotype has been studied to identify the genes
related to tumor cell differentiation using subtractive hybridization. Nine
genes were found to be selectively overexpressed in the epithelial sub-line,
compared to only two genes in the fibroblast-like phenotype. One of the genes
that was differen¬tially expressed by the epithelial cells was thioredoxin, a
small redox-active protein associated with cell growth and differentiation (21).

Subtractive complementary DNA (cDNA) hybridization has been shown to identify
and isolate cDNAs from differentially expressed genes. In general, it involves
hybridization of two populations of cDNAs (test and control) and then separation
of the unhybridized frac¬tion (target) from hybridized common sequences that
would represent the unique differentially expressed genes among the two
populations. An improvisation of this methodology was achieved by a PCR-based
cDNA subtraction method called suppression subtractive hybridiza¬tion (SSH)
(22), which is used to selectively amplify target cDNA fragments (differentially
expressed) and simultaneously suppress nontarget DNA amplification by attaching
long inverted terminal repeats to the DNA fragments. The mRNA expression
patterns at different stages of asbestos-induced carcinogenesis in rats have
been monitored by SSH and array assay. Several genes were found to be
upregulated in pretumorous tissues from asbestos-treated rats, in
asbestos-induced tumors, and in cells treated with asbestos in vitro. The
upregulation of the proto-oncogenes c-myc, fra-1, and egfr in fiber-induced
carcinogenesis was demonstrated at different stages of carcinogenesis. The
upregulation of osteopontin, zyxin, and integrin-linked kinase in this study was
indicative of a possible link between fibers, integrin-linked signal
transduction, and extracellular matrix proteins (23). Recently, these studies
were further substantiated by another group showing induction of CD44 and c-met
were linked to fra-1 expression using microarrays (24).

One of the first mesothelioma cDNA array experiments was carried out on four
malignant mesothelioma (MM) cell lines and two primary mesothelial cell cultures
established from pleural fluid of noncancer patients. Human cancer gene filters
including 588 genes were used for the cDNA array experiments. The study revealed
26 overexpressed genes that play a role in the regulation of cell cycle, cell
growth, and DNA damage repair, and 13 underexpressed genes encoding growth
factors, receptors, and proteins involved in cell adhesion, motility, and
invasion and that are common to three or four MM cell lines. The study presented
gene expression profiles in MM cell lines and showed the involvement of several
genes, such as JAGGED1, ser/thr, protein kinase NIK, Ku80, and cyclin D2, to be
novel in MM (25). Similar conclusions were drawn using high-density filter
arrays of more than 6500 genes to compare constitutive gene expression of
mesothelioma cells to that of pleural cells. Most of the highly expressed
transcripts were common to both cell lines and included genes associated with
stress response and DNA repair. Interestingly, fewer than 300 genes that
differed between cell lines were involved in macromolecule stability, cell
adhe¬sion and recognition, cell migration (invasiveness), and extended cell
division (25–27).

To make a pathologic distinction between MPM and adenocarcinoma (ADCA) of the
lung has been quite challenging given the existing set of markers for this
purpose. Recently, a simple technique, based on the expression levels of a small
number of genes, was designed to accurately distinguish between genetically
disparate tissues using gene expression ratios and rationally chosen thresholds.
The study tested the

fidelity of ratio-based diagnosis in differentiating between MPM and lung cancer
in 181 tissue samples (31 MPM and 150 ADCA). Validation of this microarray data
and ratio-based diagnosis was performed using calretinin/claudin-7 and
VAC-b/TACSTD1 ratios; the two ratios correctly identified 23 of 24 samples.
Using two and three expression ratios, it was found that the differential
diagnoses of MPM and lung ADCA were 95% and 99% accurate, respectively (29,30).

Recently, cDNA microarray filters with 4132 clones were used to identify
differential gene expression profile among four control pleural tissue samples
and 16 mesothelioma patient tumor specimens (31). Use of various normalization
and analysis approaches showed significant induction of 166 genes and
downregulation of 26 genes among these two groups. Expression profiling showed
marked upregulation of genes involved in glucose metabolism, protein
translation, and cytoskeletal remodeling pathways. Prominent upregulated genes
included gp96, lung resistance-related protein, galectin-3-binding protein,
laminin receptor, and voltage-dependent anion channels.

Pass et al (32) have very recently shown that gene expression pro¬files in
malignant pleural mesothelioma can predict time to progres¬sion and survival
patterns among two separate series of patients who underwent cytoreduction at
two different centers in the United States. The study involved gene expression
profile analysis on 21 MPM patient tumor samples that had identical
postoperative adjuvant therapy (Fig. 12.1). Analysis of Affymetrix gene chips
U95, representing 12,000 gene probe sets, was performed using dChip,
significance of analysis (SAM), and GeneSight software packages. A neural
network was constructed using a common set of 27 gene classifiers that could
segregate patient populations based on short or long survival after debulking of
their tumors. The 27 gene classifiers were able to predict actual time to
pro¬gression and survival with 95.2% accuracy in one test set, whereas 76%
accuracy was achieved in the separate validation set of MPMs. These data are
indicative of pretherapy gene expression analysis that can be beneficial in
predicting clinical outcomes of patients undergoing sur¬gical treatments.
Considering all the published literature so far on gene expression profiling on
MPM cases, it is plausible to conceive of having a precise “genetic signature”
among patient populations that can predict the patient’s stage, histology,
response to therapeutic options, and clinical outcome.

Proteomics in Cancer Research

An important step in the postgenomics era has been decoding the func¬tions of
some 30,000 genes that are scattered among 3.2 billion nucleotides. Research in
proteomics involves study of the structure, function, and expression analysis of
all proteins in the normal as well as pathophysiologic conditions during various
stages of development. Cancer proteomics can be defined as the implementation of
proteomic platforms to identify and quantitate differentially expressed proteins
relative to normal tissue from preneoplasia to neoplasia. Recent

methodologies in proteomics also can be used to identify early detec¬tion
biomarkers for cancer, prognostication, and identification of poten¬tial
therapeutic targets. Valuable discoveries of biomarkers have been possible
because of the proteome research efforts that entailed both the interaction
between the functional pathways of a cell and its impact on environmental
milieu. Both posttranscriptional and translational modifications are necessary
in the proper expression and function of proteins. Differential splicing can
yield several RNA transcripts from one gene. Furthermore, many posttranslation
modifications have been shown to alter function, stability, protein–protein and
DNA–protein interactions, and targeting (33), all of which ultimately yield a
poten¬tially large number of protein products from one gene. Prominent changes
have been observed during the conversion of a healthy cell into a neoplastic
cell that could result in altered expression, detri¬mental protein
modifications, and cell localization, which ultimately result in aberrant
cellular function. Identification and in-depth understanding of all these
processes are the main thrust in cancer proteomics.

Technologies Involved in Expression Proteomics

The most widely used techniques for the characterization of proteins are
two-dimensional gel electrophoresis (2DGE), mass spectrometry (MS), amino acid
composition analysis, and peptide sequence tagging. Some approaches include
recombinant proteins obtained using cDNA expression libraries and phage-display
libraries. Some approaches use high-throughput antibody arrays. Matrix-assisted
laser desorption ionization (MALDI), surface-enhanced laser desorption
ionization (SELDI), laser capture microdissection (LCM), and capillary
elec-trophoresis (CE) have been added to the proteomics tool set. Other
techniques include isotope-coded affinity tags (ICAT) and 2D differ¬ential
fluorescence gel electrophoresis (DIGE) in order to quantify relative protein
expressions. We summarize these technologies here in relation to the advances
made in various types of cancers that could be implemented in the field of
mesothelioma carcinogenesis.

Two-dimensional gel electrophoresis (2DGE) has been the method of choice in
quantitative proteomics. In brief, protein samples are dena¬tured and separated
on the basis of their charge through isoelectric focusing. Almost complete
resolution of both basic and acidic proteins have been achieved by the
introduction of immobilized pH gradients into this system (34). The proteins are
further separated by migration in a polyacrylamide gel on the basis of their
molecular weights. Silver-staining protocols have enhanced resolution and
visualization of 3000 proteins on a single gel. Fluorescent dyes have been
developed to make the protein samples accessible to mass spectrometry (35).
Laser densi-tometers with high resolutions have been used in spot detection of
stained gels that can be further analyzed with software packages such as PDQUEST
(36) and Phoretix (37) for all quantitation purposes. Ratio analysis is the most
common form of detecting quantitative changes among different protein samples.
Two-dimensional gel electrophoresis is currently being modified to the point
where it can be used in high-throughput platforms (38).

Mass spectrometry (MS)-based methodologies have proved to be a powerful means
for obtaining peptide mass fingerprints for proteins resolved in 2DGE gels.
Protein databases and reference maps have been generated to document changes in
protein expression resolved by 2DGE from various cell types and different stages
of tumor develop¬ment. Specialized software packages available on the World Wide
Web servers such as ExPaSy and the World 2DGE page (39), have been uti¬lized by
biomarker research investigators to compare 2D gel patterns with one another and
with reference maps on the Internet. Using this techonology, the proteome of 150
bladder tumors was observed to decline in the expression of specific
cytokeratins, psoriasin, galectin 7, and stratifin in tumors with a low degree
of differentiation (40). A map of healthy and renal cell carcinoma (RCC)
proteins has been con¬structed through 2DGE analysis of healthy and RCC kidney
tissue, which led to the identification of ubiquinol cytochrome C reductase as a
potential biomarker (41). A comprehensive analysis of the proteome in lung
cancer has already been undertaken (42). In a recent study

involving a series of 93 lung adenocarcinomas (64 stage I and 29 stage III) and
10 uninvolved lung samples, nine candidate proteins such as antioxidant enzyme
AOE372, adenosine triphosphate (ATP) synthase subunit d (ATP5D),
b1,4-galactosyltransferase, cytosolic inorganic pyrophosphatase,
glucose-regulated Mr 58,000 protein, glutathione-S-transferase M4, prolyl
4-hydroxylase b subunit, triosephosphate isom-erase, and ubiquitin thiolesterase
(UCHL1) were identified as being significantly overexpressed in lung
adenocarcinomas. The expression of these proteins was increased from 1.4- to
10.6-fold as compared with uninvolved lung tissue (43).

To classify human lung cancer tumors, matrix-assisted laser
desorp-tion/ionization mass spectrometry was recently performed on frozen tissue
sections. Proteomic spectra were aligned from 79 lung tumors and 14 normal lung
tissues, and a class-prediction model was built with the proteomic patterns in a
training cohort of 42 lung tumors and eight normal lung samples. To assess
statistical significance, a blinded test cohort of 37 lung tumors and six normal
lung samples was used. A class-prediction model was based on 1600 differentially
expressed peaks that perfectly classified lung cancer histologies, and
distin¬guished primary tumors from metastases with 85% accuracy in the training
cohort. This model nearly perfectly classified samples in the independent
blinded test cohort. Furthermore, a proteomic pattern composed of 15 distinct
mass spectrometry peaks could distinguish between good and poor prognosis among
non–small-cell lung cancer (NSCLC) patients (44). To date, our laboratory is the
only one that has performed 2DGE on four mesothelioma tumors and their
respective cell lines (Fig. 12.2).

Several ionization techniques, such as electrospray ionization and MALDI, have
facilitated the characterization of proteins by MS (45). For sequence-based
determinations by MS, proteins are cleaved with trypsin or cyanogen bromide into
peptide fragments and separated by high-pressure liquid chromatography (HPLC)
followed by tandem MS. The peptides are then subjected to short pulses of
ultraviolet radiation under reduced pressure. Some of the peptides are ionized
and acceler¬ated in an electric field and subsequently turned back through an
energy correction device (46). Peptide mass is derived through a time-of-flight
(TOF) measurement of the elapsed time from acceleration-to-field free drift or
through a quadrupole detector. A peptide mass map is generated with the
sensitivity to detect molecules at a few parts per million. Hence, a spectrum is
generated with the molecular mass of individual peptides, which are used to
search databases to find match¬ing proteins. This approach is termed “peptide
sequence tagging.” The short (2–4 amino acids) sequence can be derived by
fragmentation of the parent ion into three complementary segments: the mass of
an N-terminal fragment, the mass of a C-terminal fragment, and the partial amino
acid sequence between them.

The alternative process of ionization, through the electrospray ionization,
involves dispersion of the sample through a capillary elec-trophoresis (CE TOF
MS) (47). The charged peptides pass through a mass spectrometer under reduced
pressure and are separated accord-

ing to their mass-to-charge ratios through electric fields. After separa¬tion
through 2DGE, digested peptide samples can be delivered to the mass spectrometer
through a “nanoelectrospray” or directly from a liquid chromatography column
(liquid chromatography-MS), allowing for real-time sequencing and identification
of proteins. Some modifi¬cations have led to the MALDI quadrupole TOF
instrument, which combines peptide mapping with the peptide sequencing approach
(48,49). Accurate identification of posttranslational modifications, such as
phosphorylation and glycosylation, have been possible by the assessment of mass
shifts. The MALDI-TOF-MS of 2DGE-separated proteins has been pivotal in the
identification of multimeric isoforms of manganese superoxide dismutase found to
be expressed exclusively in RCCs (47). The MALDI-MS system has also helped to
detect increases in the expression of nuclear matrix, redox, and cytoskeletal
proteins in breast carcinoma compared to benign tumors (36) and iden¬tification
of peroxiredoxin II (Prx II) by peptide mass fingerprinting in MPM (Fig. 12.3).

Amino acid composition analysis is an efficient method for protein
identification (50). The method is complementary to MS and represents a useful
analytical tool for the mapping of proteins of interest.

Direct mapping and imaging of biomolecules present in frozen sec¬tions has been
recently achieved by an innovative technology termed “imaging MS.” For this
analysis, frozen tissue sections mounted on a metal plate, coated with
ultraviolet-absorbing matrix are placed in the MS, and the specimens are
processed as described previously for liquid samples (51). Imaging MS has shown
promise in several applications such as discovery of biomarker tissue
localization, molecular interac¬tions of tumor cells, and assessment of surgical
complexities of tumors (52).

Most of the problems associated with sample preparations with MALDI-MS
have been overcome with the advent of surface-enhanced laser
desorption-ionization (SELDI) (53). The main principle in SELDI is
surface-enhanced affinity capture through the use of specific probe surfaces or
chips. This protein biochip is the counterpart of the array technology in the
genomic arena that forms the basis for Ciphergen’s ProteinChip array SELDI MS
technology (49). A 2DGE analysis sepa¬ration is not required for SELDI analysis
as it can bind protein mole¬cules based on its defined chip surfaces. Chips with
broad binding properties, including immobilized metal affinity capture, and with
bio¬chemically characterized surfaces, such as antibodies and receptors, form
the core of SELDI (49). The advantage of this MS technology involves no
preprocessing of the samples before analysis, and that has expedited both
biomarker discovery and protein profiling in small sample volumes in a short
period of time. After being captured on the SELDI protein biochip array,
proteins are detected through the ionization-desorption, TOF-MS process. A
retentate (proteins retained on the chip) map is generated in which the
individual proteins are dis¬played as separate peaks on the basis of their mass
and charge (m/z). The SELDI technology has been used to provide protein
fingerprints (54), which may provide insights into changing protein expression
from healthy to benign, and subsequently from premalignant to malig¬nant
lesions. Recently, distinct SELDI protein profiles and patterns for individual
cells and cancer type have been evaluated, including prostate, lung, and ovarian
cancer (55). The versatile platform of the ProteinChip SELDI-MS technology has
given a major impetus to the proteomics field, not only for the discovery and
protein profiling appli¬cations, but also as a potential multiplex immunoassay
tool. For this application, antibody rather than a chemical matrix is bound to
the chip array to capture the protein antigen. This format has been successfully
used to develop both single and multiplex versions of the SELDI immunoassay for
detection and measuring prostate-specific antigen (PSA) and prostate-specific
membrane antigen in body fluids (56,57).

Pass et al investigated SELDI-TOF to analyze protein expression pro¬files in
pleural effusions from patients with cytologically documented benign
inflammatory fluid collections, non-MM malignant effusions, and MM effusions.
The 58 discovery samples were randomized in a 96-well format for fractionation,
and a validation set of 50 blinded samples was then randomized and processed in
a similar fashion followed by analysis with classification algorithms defined
with data from the discovery samples.

Spectra from the discovery sample set were analyzed using the Bio-marker Wizard
in ProteinChip Software version 3.0 to detect peak clus¬ters, which were
subjected to univariate statistical analysis using the Mann-Whitney test of
means. All peak clusters were also subjected to multivariate statistical
analysis using Ciphergen’s Biomarker Patterns Software (BPS, 1) and Salford
Systems’ TreeNet. The 58 discovery samples consisted of 30 patients with MM, 22
noncancer control patients, and six control patients with non-MM cancers. Data
from these 58 samples were utilized for the discovery of biomarker candi-

dates and for the determination of classification algorithms to be tested with
data from the validation set of 50 blinded samples.

From the biomarker discovery phase of the project, the six best can¬didates as
determined by univariate analysis and BPS were chosen for purification and
identification. They had approximate molecular masses of 11.6, 10.8, 3.4, 5.0,
6.6, and 4.6kd and were all decreased in amount for the MM patients relative to
the control discovery samples.

Three different predictive algorithms were defined utilizing the TreeNet
software with sets of markers selected from the discovery samples. With the
predictive algorithms, three classification predictions were established for the
50 validation samples using the different marker combinations. The unblinding of
individual patient diagnosis enabled calculation of the accuracy of the three
predictions and this is seen in Table 12.1.

Isotope-coded affinity tags (ICAT) has been the latest addition to the arsenal
of new technologies developed in the field of proteomics (60). This technique
utilizes a thiol-specific reactive group (iodoacetamide) to react with free
cysteine residues in the denatured protein samples via two ICAT reagents, such
as d8-ICAT (X = deuterium) heavy; and d0-ICAT (X = hydrogen) light reagents. A
nonreactive linker incorpo¬rates heavy or light ICAT reagent with the biotin
affinity tag group in that sample. The control and experimental samples are
combined, pro-teolyzed, fractionated, and avidin affinity enriched before LC/MS
is performed to quantify ratios of all peptides having a mass difference of 8
atomic mass unit (amu). The most impressive usage of ICAT tech¬nology has
resulted in quantifying the relative level of expression of 524 proteins that
could be potential serum markers of neoplastic prostate disease (61). Recent
progress with ICAT technology has incor¬porated 13C rather than 8 deuteriums
into the heavy reagent. An alter¬native approach to quantitative protein
profiling has been developed by using Cy-3 and Cy-5 in vitro labeling of control
and experimental proteins, which has increased the magnitude of the linear
response range significantly, compared to existing staining protocols. This
dif¬ferential 2D DIGE has been used in the proteomic expression analysis of
esophageal and breast cancer cell systems (62,63).

Protein and Antibody Microarrays

Biochip-based microarrays containing spotted antigens or antibodies have been
developed to study protein–protein interactions, biomarkers,

and humoral response to cancer (64,65). Arrays of clones from phage-display
libraries have been probed with antigen-coated filters for high-throughput
antibody screening (66). Protein microarrays have been prepared by printing the
proteins on coated microscope slides using purified proteins, recombinant
proteins, and crude mixtures or antibod¬ies with a robotic arrayer. Protein
solutions to be measured are labeled by covalent linkage of a fluorescent dye to
the amino groups on the pro¬teins (67). Protein arrays have been used to
identify and track cancer pro¬gression by immobilization of proteins from pure
populations of microdissected cells from various tissues (68). Synthesis of
protein and antibody arrays has proven to be more costly and labor-intensive
com¬pared to DNA arrays. It is expected, however, that the availability of large
antibody arrays will enhance the discovery of differential bio-markers in normal
and diseased tissue. For excellent reviews on current and innovative
technologies used in protein-antibody array applica¬tions for medical research,
see Cahill (69) and others (64,65).

Tissue Microarrays

Tissue microarrays have become an integral part of high-throughput molecular
profiling of tumor specimens, and are used for rapid vali¬dation of genomic and
proteomic arrays (70). Arrays are generated by robotic punching out of small
cylinders (0.6mm ¥ 3–4mm high) of tissue from thousands of individual tumor
specimens embedded in paraffin and then arrayed in a large paraffin block.
Tissue from as many as 600 specimens can be represented in a single “master”
paraffin block. Subsequent serial sections of the same tissue array helps
analysis of multiple tumor samples in parallel by immunohistochemistry,
fluores¬cence in situ hybridization, and RNA–RNA in situ hybridization. Tissue
arrays have thus expedited the simultaneous analysis of tumors from many
different patients at different stages of disease. Disadvan¬tages of this
technique are that a single core is not representative because of tumor
heterogeneity and uncertainty of antigen stability on long-term storage of the
array. Large-scale validation efforts are cur¬rently ongoing in breast and
prostate cancers that are expected to strengthen protein expression profiling
(71,72). The biggest advantage of this technology has been the rapid turnaround
time to assess expres¬sion profiles and patterns with clinical outcomes from
large cohorts. Our laboratory has produced mesothelioma tumor arrays consisting
of 60 MPM tumors per slide that are being used on a routine basis to validate
other ongoing genomic and proteomic initiatives (Fig. 12.4).

Computational Methods and Bioinformatics

Computation and bioinformatic tools have become an essential com¬ponent of
biologic research. The amount and diversity of the data being generated by
different technologies are challenging, and the data are impossible to organize
or analyze without computational assistance. In functional genomics, a great
deal of effort has been devoted in gen¬erating gene expression profiles using
either Web-based (dchip) or commercially available (GeneSight; Gene Spring)
software packages.

The same holds true for proteomics to validate across the different
technologies. The main databases serving as the targets for MS data searches are
the expressed sequence tag and the protein sequence databases, which contain
protein sequence information translated from DNA sequence data (46). The
2DGE-related protein databases are helping map proteins from specific cells and
from different stages of tumor development (73). Many of these are public
domain, available through the World Wide Web on servers such as ExPaSy and the
World 2DGE page (39). Annotated protein databases, such as SWISS-PROT and
TrEMBL, are fast becoming critical proteome resources (74). Such tools
facilitate the analysis of posttranslational modifications and three-dimensional
structure and physicochemical properties of identified proteins (33). These
databases are becoming invaluable resources of protein maps from such tissues as
breast and bladder transitional cell and squamous cell carcinomas (33).

Future of Genomics and Proteomics in Mesothelioma

The global analysis of gene expression patterns in mesothelioma is likely to
contribute to early detection, better prognostication, and accel¬eration in the
discovery of novel therapeutic options. Moreover, the handling and analysis of
the types of data to be collected in proteomic investigations of mesothelioma
represents an emerging field. New techniques and new collaborations among
surgeons, oncologists, basic research and computer scientists, and
biostatisticians are warranted.

There is a need to develop and integrate database repositories from various
sources, and to develop efficient and valid methods of data analysis. Proteomics
will complement genomic-based approaches in the study of mesothelioma. As new
protein biomarkers will be discov¬ered through proteomic approaches, exciting
information is expected to emerge from various collaborative efforts that can
ultimately pave the way for the early detection, diagnosis, and possible
treatment of this insidious disease.

References

1. Hatada I, Hayashizaki Y, Hirotsune S, Komatsubara H, Mukai T. A genomic
scanning method for higher organisms using restriction sites as landmarks. Proc
Natl Acad Sci USA 1991;88:9523–9527.

2. Lindsay S, Bird AP. Use of restriction enzymes to detect potential gene
sequences in mammalian DNA. Nature 1987;327:336–338.

3. Larsen F, Gundersen G, Lopez R, Prydz H. CpG islands as gene markers in the
human genome. Genomics 1992;13:1095–1107.

4. Kim D, LaQuaglia MP, Yang SY. A cDNA encoding a putative 37kDa leucine-rich
repeat (LRR) protein, p37NB, isolated from S-type neuroblas-toma cell has a
differential tissue distribution. Biochim Biophys Acta 1996; 1309:183–188.

5. Eng C, Herman JG, Baylin SB. A bird’s eye view of global methylation. Nat
Genet 2000;24:101–102.

6. Rouillard J, Erson AE, Kuick R, et al. Virtual genome scan: a tool for
restric¬tion landmark-based scanning of the human genome. Genome Res 2001;
11:1453–1459.

7. Dai Z, Zhu WG, Popkie AP, et al. Promoter methylation and silencing of bone
morphogenetic protein 3B (BMP3B) in non-small cell lung cancer identifies a
novel lung cancer gene on 10q11 [abstract]. Proc Am Assoc Cancer Res
2002;43:2333.

8. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene
expression. Science 1995;270:484–487.

9. Weeraratna AT, Becker D, Carr KM, et al. Generation and analysis of melanoma
SAGE libraries: SAGE advice on the melanoma transcriptome. Oncogene 2004;1–11.

10. Lal A, Lash AE, Altschul SF, et al. A public database for gene expression in
human cancers. Cancer Res 1999;59:5403–5407.

11. Strausberg RL. The Cancer Genome Anatomy Project: new resources for reading
the molecular signatures of cancer. J Pathol 2001;195:31–40.

12. Lash AE, Tolstoshev CM, Wagner L, et al. SAGEmap: a public gene expres¬sion
resource. Genome Res 2000;10:1051–1060.

13. Brentani H, Caballero OL, Camargo AA, et al. Proc Natl Acad Sci USA 2003;
100:13418–13423.

14. Roulet E, Busso S, Camargo AA, Simpson AJ, Mermod N, Bucher P.
High-throughput SELEX SAGE method for quantitative modeling of
transcription-factor binding sites. Nat Biotechnol 2002;20:831–835.

15. Friedrich MJ. Genomics and proteomics may help clinicians individualize
cancer treatment. JAMA 2002;287:1931–2932.

16. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large
B-cell lymphoma identified by gene expression profiling. Nature 2000;403:
503–511.

17. Armstrong SA, Staunton JE, Silverman LB, et al. MLL translocations specify a
distinct gene expression profile that distinguishes a unique leukemia. Nat Genet
2002;30(1):41–47.

18. Liang P, Pardee AB. Differential display of eukaryotic messenger RNA by
means of the polymerase chain reaction. Science 1992;257:967–971.

19. Gordon GJ, Appasani K, Parcells JP, et al. Inhibitor of apoptosis protein-1
promotes tumor cell survival in mesothelioma. Carcinogenesis 2002;23: 1017–1024.

20. Frank S, von Specht BU, Farthmann EH, Hirsch T. Identification of genes
involved in human mesothelial cancer progression using a modified dif¬ferential
display technique. Cancer Lett 1998;123:7–14.

21. Sun X, Dobra K, Bjornstedt M, Hjerpe A. Upregulation of 9 genes, includ¬ing
that for thioredoxin, during epithelial differentiation of mesothelioma cells.
Differentiation 2000;66:181–188.

22. Diatchenko L, Lukyanov S, Lau YF, Siebert PD. Suppression subtractive
hybridization: a versatile method for identifying differentially expressed
genes. Methods Enzymol 1999;303:349–380.

23. Sandhu H, Dehnen W, Roller M, Abel J, Unfried K. mRNA expression pat¬terns
in different stages of asbestos-induced carcinogenesis in rats. Car-cinogenesis
2000;21:1023–1029.

24. Ramos-Nino ME, Scapoli L, Martinelli M, Land S, Mossman BT. Microar-ray
analysis and RNA silencing link fra-1 to cd44 and c-met expression in
mesothelioma. Cancer Res 2003;63:3539–3545.

25. Kettunen E, Nissen AM, Ollikainen T, et al. Gene expression profiling of
malignant mesothelioma cell lines: cDNA array study. Int J Cancer 2001;
91:492–496.

26. Rihn BH, Mohr S, McDowell SA, et al. Differential gene expression in
mesothelioma. FEBS Lett 2000;480:95–100.

27. Mohr S, Keith G, Galateau-Salle F, Icard P, Rihn BH. Cell protection,
resis¬tance and invasiveness of two malignant mesotheliomas as assessed by
10K-microarray. Biochim Biophys Acta 2004;1688:43–60.

28. Siddiq F, Wali A, Lonardo F, Carbone M, Pass HI. Downregulation of TIMP-1
and osteonectin gene expression alters cellular properties of MPM-derived cell
line (Abstract). Proc Am Assoc Cancer Res 2004;45:420.

29. Gordon GJ, Jensen RV, Hsiao LL, et al. Translation of microarray data into
clinically relevant cancer diagnostic tests using gene expression ratios in lung
cancer and mesothelioma. Cancer Res 2002;62:4963–4967.

30. Gordon GJ, Jensen RV, Hsiao LL, et al. Using gene expression ratios to
pre¬dict outcome among patients with mesothelioma. J Natl Cancer Inst 2003;
95:598–605.

31. Singhal S, Wiewrodt R, Malden LD, et al. Gene expression profiling of
malignant mesothelioma. Clin Cancer Res 2003;9:3080–3097.

32. Pass HI, Liu Z, Wali A, et al. Gene expression profiles predict survival and
progression of pleural mesothelioma. Clin Cancer Res 2004;10:849–859.

33. Banks RE, Dunn MJ, Hochstrasser DF, Sanchez J-C, Blackstock W, Pappin DJ.
Proteomics: new perspectives, new biomedical opportunities. Lancet
2000;356:1749–1756.

34. Hanash SM. Biomedical applications of two-dimensional electrophoresis using
immobilized pH gradients: current status. Electrophoresis 2000;21: 102–109.

35. Steinberg T, Jones LJ, Haugland RP, Singer VL. SYPRO ruby and SYPRO red
protein gel stains. Anal Biochem 1996;239:223–237.

36. Bergman AC, Benjamin T, Alaiya A, et al. Identification of gel-separated
tumor marker proteins by mass spectrometry. Electrophoresis 2000;21: 679–686.

37. www.nonlinear.com.

38. Lopez MF, Kristal BS, Chernokalskaya E, et al. High-throughput profiling of
the mitochondrial proteome using affinity fractionation and automation.
Electrophoresis 2000;21:3427–3440.

39. Hochstrasser DF, Appel RD, Golaz O, Pasquali C, Sanchez JC, Bairoch A.
Sharing of World Wide Web spread knowledge using hypermedia facilities and fast
communication protocols (Mosaic and World Wide Web): the example of ExPaSy.
Methods Inf Med 1995;34:75–78.

40. Ostergaard M, Wolf H, Orntoft TF, Celis JE. Psoriasin (S100A7): a putative
urinary marker for the follow-up of patients with bladder squamous cell
carcinomas. Electrophoresis 1999;20:349–354.

41. Sarto C, Marocchi A, Sanchez JC, et al. Renal cell carcinoma and normal
kidney protein expression. Electrophoresis 1997;18:599–604.

42. Hanash SM. Global profiling of gene expression in cancer using genomics and
proteomics. Curr Opin Mol Ther 2001;3:538–545.

43. Chen G, Gharib TG, Huang CC, et al. Proteomic analysis of lung
adeno-carcinoma: identification of a highly expressed set of proteins in tumors.
Clin Cancer Res 2002;8:2298–2305.

44. Yanagisawa K, Shyr Y, Xu BJ, et al. Proteomic patterns of tumour subsets in
non-small-cell lung cancer. Lancet 2003;362:433–439.

45. Hillenkamp F, Karas M, Beavis RC, Chait BT. Matrix-assisted laser
desorption/ionization mass spectrometry of biopolymers. Anal Chem
1991;63:1193A–1203A.

46. Andersen JS, Mann M. Functional genomics by mass spectrometry. FEBS Lett
2000;480:25–31.

47. Krutchinsky AN, Zhang W, Chait BT. Rapidly switchable matrix-assisted laser
desorption/ionization and electrospray quadrupole-time-of-flight mass
spectrometry for protein identification. J Am Soc Mass Spectrom 2000;
11:493–504.

48. Schevchenko A, Loboda A, Shevchenko A, Ens W, Standing KG. MALDI quadrupole
time-of-flight mass spectrometry: a powerful tool for pro-teomic research. Anal
Chem 2000;72:2132–2141.

49. Merchant M, Weinberger SR. Recent advancements in surface-enhanced laser
desorption/ionization-time of flight-mass spectrometry. Electro-phoresis
2000;21:1164–1167.

50. Humphery-Smith I, Cordwell SJ, Blackstock WP. Proteome research:
com-plementarity and limitations with respect to the RNA and DNA worlds.
Electrophoresis 1997;18:1217–1242.

51. Stoeckli M, Chaurand P, Hallahan DE, Caprioli RM. Imaging mass
spec-trometry: a new technology for the analysis of protein expression in
mam¬malian tissues. Nat Med 2001;7:493–496.

52. Chaurand P, Stoeckli M, Caprioli RM. Direct profiling of proteins in
biological tissue sections by MALDI mass spectrometry. Anal Chem
1999;71:5263–5270.

53. Hutchens TW, Yip T-T. Rapid Commun Mass Spectrom 1993;7:576–580.

54. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in
serum to identify ovarian cancer. Lancet 2002;359:572–577.

55. Kozak KR, Amneus MW, Pusey SM, et al. Identification of biomarkers for
ovarian cancer using strong anion-exchange ProteinChips: potential use in
diagnosis and prognosis. Proc Natl Acal Sci USA 2003;100:12343–12348.

56. Xiao Z, Cazares LH, Wright GL. A novel biochip SELDI mass spectrome-try
immunoassay for quantitation of prostate specific membrane antigen (PSMA) in
body fluids [abstract]. Proc Am Assoc Cancer Res 2000;41: 316.

57. Xiao Z, Jiang X, Beckett ML, Wright GL Jr. Generation of a baculovirus
recombinant prostate-specific membrane antigen and its use in the devel¬opment
of a novel protein biochip quantitative immunoassay. Protein Exp Purification
2000;19:12–21.

58. Adam B-L, Davis JW, Cazares LH, Schellhammer PF, Lynch DF, Wright GL Jr.
Identifying the signature proteins of prostate cancer in seminal plasma by SELDI
affinity mass spectrometry [abstract]. Proc Am Assoc Cancer Res 2000;41:564.

59. Moody TW, Walters J, Zakowicz H, et al. Surface enhanced laser
desorp-tion/ionization analysis of human lung cancer specimens [abstract]. Proc
Am Assoc Cancer Res 2001;42:59.

60. Aebersold R. Quantitative proteome analysis: methods and applications. J
Infect Dis 2003;187 (suppl 2):S315–S320.

61. Martin DB, Gifford DR, Wright ME, et al. Quantitative proteomic analysis of
proteins released by neoplastic prostate epithelium. Cancer Res 2004; 64:347–55.

62. Zhou G, Li H, DeCamp D, et al. 2D differential in-gel electrophoresis for
the identification of esophageal scans cell cancer-specific protein markers. Mol
Cell Proteomics 2002;1(2):117–124.

63. Gharbi S, Gaffney P, Yang A, et al. Evaluation of two-dimensional
differ¬ential gel electrophoresis for proteomic expression analysis of a model
breast cancer cell system. Mol Cell Proteomics 2002;1(2):91–98.

64. James P. Chips for proteomics: a new tool or just hype? BioTechniques 2002;
33:S4–S13.

65. Kusnezow W, Hoheisel JD. Antibody microarrays: promises and problems.
BioTechniques 2002;33:S14–S23.

66. De Wildt RMT, Mundy CR, Gorick BD, Tomlinson IM. Antibody arrays for high
throughput screening of antibody-antigen interactions. Nat Biotech
2000;18:989–994.

67. Haab BB, Dunham MJ, Brown PO. Protein microarrays for highly parallel
detection and quantitation of specific proteins and antibodies in complex
solutions. Genome Biol 2001;2:1–13.

68. Paweletz CP, Charboneau L, Bichsel VE, et al. Protein microarrays which
capture disease progression show activation of pro-survival pathways at the
cancer invasion front. Proc Am Assoc Cancer Res 2001;42:55.

69. Cahill DJ. Protein and antibody arrays and their medical applications. J
Immunol Methods 2001;250:81–91.

70. Kononen J, Bubendorf L, Kallioniemi A, et al. Tissue microarrays for
high-throughput molecular profiling of tumor specimens. Nat Med 1998;4: 844–847.

71. Camp RL, Carette LA, Rimm DL. Validation of tissue microarray technol¬ogy in
breast cancer. Lab Invest 2000;80:1943–1949.

72. Mucci NR, Akdas G, Manely S, Rubin MA. Neuroendocrine expression in
metastatic prostate cancer: evaluation of high throughput tissue micro-arrays to
detect heterogeneous protein expression. Hum Pathol 2000;31: 406–414.

73. Oh JMC, Hanash SM, Teichroew D. Mining protein data from two-dimensional
gels: tools for systematic post-planned analyses. Elec-trophoresis
1999;20:766–774.

74. Bairoch A, Apweiler R. The SWISS-PROT protein sequence data bank and its
supplement TrEMBL in 1998. Nucleic Acids Res 1998;26:38–42.

75. Shetty S, Kumar A, Pueblitz S, et al. Fibrinogen promotes adhesion of
monocytic to human mesothelioma cells. Thromb Haemost 1996;75: 782–790.

76. Kinnula VL, Lehtonen S, Sormunen R, et al. Overexpression of peroxire-doxins
I, II, III, V, and VI in malignant mesothelioma. J Pathol 2002;196:316– 323.

77. Kahlos K, Paakko P, Kurttila E, Soini Y, Kinnula VL. Manganese superoxide
dismutase as a diagnostic marker for malignant pleural mesothelioma. Br J Cancer
2000;82:1022–1029.