proteomic profiling of cancer biomarkers

Hungyi Shau

was trained as a cancer

immunologist with interest in

tumour biology, gene medicine

and cancer biomarkers.

G. Scott Chandler

is involved in the development

and preclinical testing of breast

cancer vaccine therapy.

Julian P. Whitelegge

was trained in biochemistry and

molecular biology. His research

interest is proteomics of

membrane proteins.

Jeffrey A. Gornbein

has been a key designer in the

planning and analysis of cancer

studies of Revlon/UCLA Breast

Cancer Center. His research

interests are clinical trial

design, repeated measure

analysis and statistical

discrimination methods.

Kym F. Faull

has worked and published on

CG/MS of biogenic amines and

related compounds, and on

FAB, electrospray and MALDI

of peptides, proteins and other

biomolecules.

Helena R. Chang

is a surgical oncologist and

immunologist focused on

developing early breast cancer

detection methods and breast

cancer vaccine therapy.

Keywords: analysisalgorithm, biomarkers, cancerdiagnosis, matrix assistedlaser desorption (MALDI),proteomics, surface-enhancedlaser desorption/ionisation(SELDI)

Helena R. Chang,

Director and Professor,

Revlon/UCLA Breast Center 200

UCLA David Geffen Medical Plaza,

Suite B265-1,

Los Angeles, CA 90095-7028, USA

E-mail: [email protected]

Additional Paper

Proteomic profiling ofcancer biomarkersHungyi Shau, G. Scott Chandler, Julian P. Whitelegge, Jeffrey A. Gornbein,Kym F. Faull and Helena R. ChangDate received (in revised form): 17th March 2003

AbstractEarly detection and correct diagnosis are essential for effective treatment of cancer and patient

survival. Complete sequencing of the human genome, and the genomes of other species,

provides valuable tools for discerning the genetic abnormalities in cancer. However,

differences between cancerous and normal cells reflect more than variations in genetic

sequences and abundance of transcribed RNA. Many cancer biomarkers are manifestation of

differences in post-transcriptional splicing and/or post-translational modifications. Thus,

proteomic tools are being increasingly utilised in the post-genomic era for discovery of new

cancer biomarkers. In this paper we will provide an overview of the biomarker discovery

process from the proteomic profiling point of view, with emphasis given to the principles that

are involved in the process, including the protein identification strategies, and how surface

enhanced laser desorption ionisation mass spectrometry fits into the picture. The aim is to

provide a resource for the experimental practitioner seeking awareness of the analytical tools

that are now available in contemporary cancer research.

INTRODUCTIONCancer claims a half million lives each

year in the USA. Surgery, radiotherapy

and chemotherapy are effective treatments

for most localised early cancers. Because

there can be significant differences in

survival rates between local and advanced

disease, detection of cancer at an early

stage can be life saving in many cases. For

example, five-year survival rates for

localised breast or ovarian cancers (Stages

I–II) can exceed 80 per cent whereas the

five-year survival rates for advanced breast

or ovarian cancers (Stages III–IV) can be

as low as 20 per cent.1,2 The conventional

methods for detecting cancer include

assessment of symptomatic complaints of

patients, findings of the physical

examination, imaging abnormalities and

various laboratory tests. Although these

are the gold standards of today’s medicine,

many cancers are found in the advanced

stage where the current treatment is

frequently ineffective in halting the

disease. Biomarkers that are characteristic

of cancer growth may allow cancer

discovery at an earlier stage than our

current practice.

Many new technologies are being

introduced to identify biomarkers

associated with cancer. These

technologies may mark the beginning of a

new era in cancer screening and diagnosis.

The ultimate goal of biomarker discovery

is to be able to establish a panel of

biomarkers with sufficient and reliable

predictive value for diagnosing a specific

type of cancer.

One of the most promising of the new

biomarker discovery strategies involves

proteomic profiling by mass spectrometry.

This development has been made possible

through recent advances in mass

spectrometry of biopolymers which were

acknowledged by the 2002 Nobel

committee with the recognition of the

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discovery of electrospray ionisation (ESI)

mass spectrometry3,4 and for the discovery

of soft laser desorption (SLD) ionisation,5

which led to the development of matrix-

assisted laser desorption ionisation

(MALDI).6 Proteomic profiling for cancer

detection and diagnosis began to emerge

after the development of convenient

methods for displaying a wide range of

proteins in convenient one- and

two-dimensional formats. Most notable in

this regard was the development of

isoelectric point focusing in one

dimension followed by polyacrylamide

gel electrophoresis (PAGE) for molecular

weight filtering in the second dimension7

and visualisation of the display with

protein dyes (Coomaasie Blue, elemental

silver, SyproRubyTM, etc). ESI and

MALDI mass spectrometry became

important in this process as the preferred

methods for identifying the displayed

proteins (see below).

SELDI – surface-enhanced laser

desorption ionisation mass spectrometry8,9

– is an acronym coined to describe an

extension of theMALDI process that is

currently receiving careful scrutiny

because it combines in a convenient one-

step process a method for both protein

isolation from biological samples and

presentation of the isolated proteins for

laser-induced mass spectrometric analysis.

SELDI has several advantages including

elimination of the requirement for off-line

protein isolation (such as PAGE and liquid

chromatography) and sample transfer and

manipulation, and it is potentially

compatible with automation, a necessary

requirement for any high-throughput

process.

Any biomarker profiling strategy

requires the application of appropriate

statistical methods for making

comparisons between complex data sets

and for assigning reasonable probabilities

that any one profile is in some way

abnormal. So the development of the

proteomic profiling strategy for disease

biomarker recognition involves the

merger of technologies from widely

disparate disciplines including physical

chemistry, engineering, biochemistry and

statistics.

CURRENT CANCERBIOMARKERSAttempts to develop new and improved

biomarker screenings for cancer have

focused on all levels of biochemistry

including the gene, RNA, protein and

metabolites, extending early attempts in

other disease detections. Early efforts

included one by Linus Pauling and

associates to profile metabolites in urine

and other body fluids using gas

chromatography and combined gas

chromatography-mass spectrometry.10,11

Today, metabolite screening strategies for

cancer detection are all but abandoned. At

the nucleic acid level, karyotyping was

one of the early approaches to detect the

Philadelphia chromosome as a marker for

the bcl-abl gene abnormality in chronic

myeloid leukaemia. Fluorescence in situ

hybridisation and real time polymerase

chain reaction have been developed to

detect amplification and overexpression of

genes. At the protein level,

immunohistological staining and

fluorescence-activated cell sorting are

techniques that use antibodies for

detection of specific cancer biomarkers.

However, all these techniques are time

consuming and, with the exception of

metabolite profiling, represent single

marker-based approaches.

The inability to unequivocally screen

for cancer is due, in part, to the fact that

most of the cancer biomarkers currently

used are also expressed by normal cells.

These are considered cancer markers

because they are produced at the wrong

time of one’s life, in excessive amounts

and/or in an altered conformation (ie in

incorrectly spliced or post-translationally

modified forms).

Unfortunately, most of these markers

have not been useful as independent

methods for the screening or diagnosis of

cancer. However, they still play an

important role in the diagnostic process

and the overall management of the

disease. Most markers are used in patients

Most markers are usedin patients withestablished cancers forthe purpose ofmonitoring response totherapy or insurveillance forrecurrence of disease

SELDI is promising indetecting cancer-related biomarkers

SELDI’s advantagesinclude elimination ofoff-line proteinisolation, sampletransfer andmanipulation

The limited use ofbiomarkers in cancerscreening is due, in part,to the fact that most ofthe cancer biomarkerscurrently used are alsoexpressed by normalcells

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with established cancers for the purpose of

monitoring response to therapy or in

surveillance for recurrence of disease.

Recently, advances in cancer

immunobiology have resulted in the

development of immunotherapeutic

agents directed at certain cancer

biomarkers which has resulted in some

very significant, but limited, new cancer

vaccine therapies. Some of the more

common cancer biomarkers currently

used with an impact on clinical practice

are described below.

Carcinoembryonic antigen (CEA) is an

example of a molecule expressed at the

wrong time because it is normally

expressed in significant amounts only

during embryonic life. In adults, CEA is

only expressed in small amounts in the

large intestine. Elevated levels of CEA are

frequently associated with an advanced

stage of colon cancer, but may also be

expressed in other epithelial-derived

tumours such as pancreas, stomach and

breast.12 These levels may also be elevated

in some benign conditions.

Consequently, CEA has not been useful

in screening or diagnosis. However, once

elevated, CEA is very useful in

monitoring response to therapy. It is also

useful in the surveillance for recurrence,

particularly metastatic disease.13

Prostate specific antigen (PSA) is an

example of a molecule expressed in

abnormally elevated amounts. Because

PSA is unique to the prostate gland,

elevated levels may indicate cancer of the

prostate, but because PSA may also be

elevated in benign states, it cannot be

used as a sole criterion for screening.

Therefore, PSA should be used in

conjunction with digital rectal

examination in order to screen for

prostate cancer.14 CA 125 is also expressed

in abnormally elevated amounts and has

been important in the management of

ovarian cancer. The use of CA 125 may

be much more effective in identifying

patients at high risk for ovarian cancer

when elevated CA 125 levels are

combined with findings on transvaginal

ultrasound.14 Like CEA, elevated PSA

and CA 125 levels are also being used in

the surveillance and monitoring of

patients for recurrence of disease.15–17

Her2/neu, a growth factor receptor of

normal cells, is an example of a molecule

expressed in an excessive amount, in

certain subsets of cancers, notably breast

and ovarian. Infrequently, Her2/neu gene

amplification with overexpression of

protein represents an aggressive form of

breast cancer. Although Her2/neu is not

useful as a screening biomarker for breast

cancer, Her2/neu overexpression is

prognostically valuable because it

indicates the presence of a more

aggressive form of breast cancer and helps

to identify patients with a more

unfavourable prognosis. Recent advances

in vaccine research have resulted in the

development of anti-Her2/neu receptor

antibodies for target specific treatment of

breast cancer.18

CD44 is an example of a molecule with

incorrect splicing. CD44 is a cell surface

protein and certain lymphoma and

leukaemia cells express the CD44v6

variant whose exon conformation differs

from that of normal lymphocytes.19,20

Because incorrect splicing results in

expression of an abnormal protein, such a

molecule may prove to be a target for

immunotherapeutic agents in addition to

playing a diagnostic role. In cases of non-

Hodkin’s lymphomas, another type of cell

surface protein known as CD20 provides

such an immunological target on these

malignant cells. Development of specific

monoclonal antibodies directed against

these malignant, CD20-expressing cells

has played a significant role in elimination

of these lymphoma cells.21

An example of a cancer biomarker

resulting from post-translational

modifications is mucin (MUC1), also

known as CA 15-3 and CA 27-29.

MUC1 is a high molecular weight

glycoprotein having oligosaccharides

attached to the 20 amino acid repeats of

apomucin protein backbone by O-

glycosidic linkages.22 Breast and

pancreatic cancers are associated with

high surface expression of the MUC1

Her2/neuoverexpression isprognostically valuablebecause it indicates thepresence of a moreaggressive form ofbreast cancer and helpsto identify patients witha more unfavourableprognosis

Like CEA, elevated PSAand CA 125 levels arebeing used in thesurveillance andmonitoring of patientsfor recurrence ofdisease

CD20 is a biomarkerand immunologicaltarget for Non-Hodgkin’s Lymphoma


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molecules that are improperly

glycosylated and antigenically different

from the normal cells.23,24 Measuring

MUC1 mucin levels does not have an

accepted role in screening or diagnosis,

but monitoring levels has been used for

surveillance in patients with advanced

disease.25 The use of MUC1-specific

vaccines has become a topic of great

interest as efforts are underway to design

and develop cancer vaccine therapy

against MUC1-expressing tumours.23,24,26

LikeMUC1, other biomarker and

therapeutic target candidates are being

discovered and tested. One area of

research which shows particular promise is

that of matrix metalloproteinases (MMPs).

Significant advances have been made in

understanding the role of MMPs in cancer

development and metastasis. These

molecules are believed to play an

important role in tumour invasion,

angiogenesis and metastasis.27 MMPs have

been detected in tumour tissues of

multiple types and endogenous tissue

inhibitors of metalloproteinases (TIMPs)

have also been identified. Owing to the

nature of theMMP’s role in tumour

invasion, propagation and spread, levels of

MMPs and TIMPs may provide a marker

for the presence of certain cancers and the

associated aggressiveness or prognosis.27,28

Although these levels may prove useful in

detecting cancer or determining prognosis,

their use is not currently accepted as

standard practice. Furthermore, clinical

trials usingMMP inhibitors have shown

mixed results and further design,

development and testing will be necessary

before these agents can be fully evaluated

and applied clinically.27,28

Tumour markers may be useful tools in

the fight against cancer, but the limitations

of their use must be understood.

Specifically, a given tumour marker may

be elevated in persons without a specific

malignancy (false positive) while some

tumour markers may not be elevated in all

patients with a given cancer (false

negative). In order to improve the

predictive value for tumour markers, their

measurements can be combined or used

with other diagnostic tests. As described

previously, examples of these combined

screening methods include the use of PSA

and digital rectal exam in prostate cancer;

the use of CA 125 and pelvic ultrasound in

ovarian cancer; and measurement of both

AFP and hCG in testicular cancer.While

the majority of tumour markers are used as

Monitoring MUC1mucin levels by CA 27-29 and CA 15-3 havebeen used forsurveillance in patientswith advanced breastcancer

In order to improve thepredictive value fortumour markers, theresults should beinterpreted inconjunction with otherdiagnostic tests

MMPs and TIMPs arecandidate markers forcertain cancers and mayreflect aggressivenessand poor prognosis

Table 1: Common tumour biomarkers and clinical uses

Cancerbiomarker

Cancer typea Clinical applicationb Therapeutic application Reference

AFP Liver Screening, diagnosis, prognosis,surveillance

N/A Johnson29

AFP/hCG Germ cell Diagnosis, prognosis, surveillance N/A Labdenne and Heikinheimo 30

CA 15-3CA 27-29

Breast Surveillance Anti-MUC1 vaccines (experimental) He et al., 26 Duffy, 25 Finn et al. 23

CA 19-9 Pancreatic Diagnosis, surveillance N/A Riker and Bartlett 31

CA 125 Ovarian Screening, diagnosis, surveillance N/A Menon and Jacobs, 32 Rosenthal andJacobs15

CD20 Non-Hodgkinlymphoma

Diagnosis, prognosis, surveillance Anti-CD20 antibodies (rituximab) Press et al. 21

CEA Colon Prognosis, surveillance Anti-CEA vaccines (experimental) Macdonald, 13 Berinstein 33

ER/PR Breast Prognosis Estrogen antagonists (tamoxifen)/aromatase inhibitors(anastrazole)

Duffy 25

Her2/neu Breast Prognosis Anti-Her2/neu antibodies(trastuzumab)

Pegram and Slamon 18

PSA Prostate Screening, diagnosis, prognosis,surveillance

N/A Small and Roach,17 Smith et al. 16

aMost common cancer/biomarker pairing.bScreening/diagnostic role limited.AFP, alpha-fetoprotein; CEA, carcinoembryonic antigen; ER/PR, oestrogen receptor/progesterone receptor; hCG, human chorionic gonadotropin; Her2/neu, human epidermal growth factor receptor 2/neuroblastoma antigen; PSA, prostate specific antigen.


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surveillance tools to monitor clinical

response and the recurrence or spread of

disease in patients following cancer

diagnosis and treatment, efforts to find

tumour markers effective in the diagnosis

of cancer continue to evolve. Table 1 lists

common cancer biomarkers and their

clinical uses.

Mircroarray and gene profilingWith the advent of complete human

genome sequences, profiling cancer genes

with DNA microarrays has been pursued

vigorously. Gene profiling has not only

been successfully applied to cancer

diagnosis, but has also been used to sort

out subtypes of the cancers with different

phenotype.34,35 With its potential to

separate normal condition from a cancer

condition and cancers of different

biologies, DNA microarray may play an

important role in cancer diagnosis and

predicting prognosis in the future.

However, the current technology of

DNA profiling does not always reflect the

post-transcriptional modification such as

splicing and does not distinguish post-

translational modifications of the protein

products. Thus, besides direct enzyme

assays, other high-throughput

technologies in proteomic analysis may

bridge gaps in addition to validating the

gene findings in cancer detection.

PROTEOMICS ANDBIOMARKERSSince cellular behaviour is influenced by a

large number of possiblemolecules, normal

growth and differentiation dependon the

presence of appropriate proteins in the right

context. Through signal transduction

cascades and transcriptional networks,

alterations of one protein can affect a large

number of cellular pathways and result in

global effects on cell behaviour.Regulation

of translation and post-transcriptional

modification clearly play significant roles.

For instance,while loss ofBRCA1 gene

expression is often the cause of breast

cancer, post-translational phosphorylation

at vital sites is also essential for its tumour-

suppressing function.Thus, analysing

protein post-translational differences can

be informative. Since abnormal growth and

de-differentiation are hallmarks of

malignancy, it is reasonable to speculate

that cancer cells express a different

repertoire of proteins comparedwith their

‘normal’ counterparts. It is also likely that

other biological differences, such as tumour

sensitivity or resistance to chemotherapy,

are also reflected by the activity of discrete

but unique sets of proteins.These

differences can bemanifested not only

qualitatively but also quantitatively. These

protein profile changes are tightly coupled

with clonal expansion of particular cancer

cells. Tumour classifications based on the

protein profiles therefore should bemore

predictive of outcome andprovide a better

guidance for appropriate treatment.

Finally, defining biomarkers that are

involved in the tumour response to

chemotherapymay provide strategic

insight for the development of novel

therapeutic agents.

Mass spectrometry is one of many

available methods that can be used for

biomarker screening. Both ESI and

MALDI can be used in this process

although MALDI may have an advantage

because the spectra are simpler and thus

more complex mixtures can be tolerated.

One advantage of mass spectrometry is

that it can also be used for chemical

identification of the biomarkers once they

are recognised.

Mass spectrometry of proteinsThe discovery of ESI and MALDI mass

spectrometry for peptides, proteins and

other macromolecules has been

revolutionary, providing easy

measurements of molecular weight with

unprecedented accuracy. Measurement

accuracy for larger proteins typically falls

into the range 1–0.001 per cent of the

molecular weight, depending upon the

method used, while measurements of

smaller peptides can achieve 0.0001 per

cent (or 1 ppm) accuracy. Important

applications of these techniques have been

for protein identification (the so-called

mass tag or peptide mass fingerprinting,

Since abnormal growthand de-differentiationare hallmarks ofmalignancy, it isreasonable to speculatethat cancer cellsexpress a differentrepertoire of proteins

The current technologyof DNA profiling doesnot always reflect thepost-transcriptionalmodification and doesnot distinguish post-translationalmodifications of theprotein products

One of massspectrometry’sadvantages inbiomarker screening isthat it can also be usedto identify thebiomarkers once theyare recognised





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and sequence tag protein identification

strategies). Another important application,

particularly relevant in the current

context, is for making protein displays.

SLD and MALDIBecause the dominant ions generated

under SLD and MALDI conditions are

singly charged, the technique is most

often used in combination with a time-

of-flight (TOF) analyser to extend the

m/z range to 100,000 Da and beyond.5,6

A dramatic improvement in the technique

resulted from the discovery that a UV

absorbing matrix which co-crystallised

with the analyte increased the efficiency

of ionisation.5 Thus MALDI (or MALDI-

TOF) has become widely used for protein

and peptide measurements. Solutions of

the sample and matrix are co-spotted on a

surface. The matrix is in sufficient

concentration to crystallise, and in so

doing is thought to trap some of the

analyte within the crystal lattice. The

dried spot is then inserted into the mass

spectrometer and irradiated with laser

energy. The laser pulse ablates a plume of

material from the sample surface. In this

plume conditions exist which permit ion

formation to occur, and proteins and

peptides emerge typically carrying a single

charge (a proton). The m/z values of the

ions are determined in the mass analyser

attached to the ion source.

SELDIThe idea of covalently modifying a

surface to enhance ionisation by atom and

ion bombardment and laser ablation

originated in the 1980s and possibly even

earlier. However, this approach did not

blossom until the chemistry for covalently

attaching functional groups to a metal

surface was developed and perfected.8

Today this chemistry is marketed as a

SELDI and ProteinChip1 array

technology for proteomics and biomarker

discovery by Ciphergen Biosystems,

Fremont, California.9 As already stated,

SELDI is an acronym coined to describe

an extension of the MALDI process. The

surface used in SELDI is covalently

modified with one of a number of

functionalities to impart specific

properties to the surface (ProteinChips1).

The available surfaces include anion and

cation exchange (for selectively retaining

negatively and positively charged

proteins, respectively), metal affinity (for

capturing His-tagged recombinantly

expressed proteins, for example), reverse

phase (immobilised hydrophobic surface)

and normal phase (immobilised

hydrophilic surface). The functionalised

surfaces are used to capture specific classes

of analytes and concentrate them from

impure extracts. The surfaces can then be

washed to remove non-absorbed

components, and following the addition

of an appropriate matrix, the surfaces are

then subjected to laser desorption mass

spectrometry. There is no evidence to

indicate that the ionisation processes

taking place during SELDI from a

ProteinChip1 surface are any different

from those taking place from a MALDI

surface, and it seems that the adsorption of

analytes to the immobilised surfaces does

not hinder the ionisation process during

laser desorption. The advantage provided

by the ProteinChip1 surface can be

attributed to the ability to selectively

capture classes of proteins from complex

biological matrices thus eliminating the

need for off-line sample purification,

manipulation and transfer.

SELDI has an advantage in cancer

research where the quantity of the

available specimen may be a limiting

factor. Using crude preparations in sub-

microlitre volumes containing less than a

picomole of analyte, SELDI can produce

information on specific components such

as molecular mass, surface affinity and pI.

Thus the technique can be used to

capture and partially purify a specific

component or group of components from

a complex mixture, and simultaneously

provide a convenient display of the

captured components. At the same time

the technique yields information about

the components that can be used for their

off-line should that be necessary. This

information may eliminate most of the

The surface used inSELDI is covalentlymodified with one of anumber offunctionalities to impartspecific properties tothe surface(ProteinChips1)


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preliminary work involved in

identification and characterisation by

traditional methods. One drawback of

many screening procedures is that the

most abundant molecules in the

specimens may overshadow the subtle

signals of interest. SELDI, however, may

overcome this problem by selectively

eliminating abundant molecules using

differences in surface affinity. Although

intact protein molecular mass alone can

rarely be used to identify proteins, the

screening strategy does not depend upon

the identification of the signals that

discriminate between subjects or groups

of subjects. However, the questions that

arise from the screening strategy

concerning the actual identification of the

proteins of interest, will require extension

of the SELDI technique to embrace either

the mass or sequence tag experiments.

Despite the promise of the technique,

the published work using SELDI for

biomarker discovery, and the information

available from the manufacturer, is lacking

in some important specifications. There is

no information available on the dynamic

range and capacity of the ProteinChips1

for protein capture, or for the percentage

recovery of proteins of interest from

complex samples. It will be necessary for

this type of data to be made available

before the technique can be confidently

used in a routine clinical setting which

demands reliability and accuracy for

unambiguous interpretation of results.

STATISTICAL METHODSWhen using protein profiles to

discriminate/classify between cancer and

non-cancer groups, the number of m/z

‘variables’ greatly exceeds the number of

samples, so classical discriminant or cluster

analysis methods cannot be used directly.

There are also specific problems including

profile standardisation, ‘baseline removal’

and the definition of ‘noise’.

Discrimination/classificationwhen cancer groups are knownRepeated measure ANOVA

Classic repeated measure analysis of

variance (ANOVA) is used to compare

mean protein profiles when group

membership (cancer/no cancer) is

known. This method does not require

finding individual peaks in each sample

and takes into account correlations

between multiple profiles from the same

patient and between adjacent values in the

profile. It assumes there is a ‘true’

underlying mean profile for each group

and individual profiles are composed of

the underlying mean profile plus within

patient and between patient error.

Peak consolidation and alignment

As outlined by Adam et al.36 individual

peaks are found for each sample where a

‘true peak’ is any peak value larger than a

given ‘noise’. Peaks from all samples in a

group are combined and ‘aligned’ where

two adjacent peaks whose intensities differ

by less than a specified percentage (the

authors say , 0.18 per cent) are

consolidated. Then mean comparisons are

made as in ANOVA.

Uniform Maximum Separability Analysis

(UMSA)

This is a recent method developed by

Zhang et al.37 UMSA is similar to

principal component analysis (PCA) as it

takes linear combinations of the molecular

weight ‘variables’ and creates a smaller

number of components. Unlike PCA, the

UMSA components are designed such

that the mean difference for any

component between cancer and non-

cancer (or across cancer/non-cancer

groups) is maximised rather than

maximising the variance of each

component as in PCA. In this regard

UMSA is similar to linear discriminant

analysis. As with PCA, UMSA reduces

high dimensions to low dimensions. If

UMSA is successful, the most important

part of the information contained in the

original variables can be visualised.

UMSA identifies molecular weight

markers that ‘load’ heavily on the first few

components (particularly the first

component) as providing the best

discrimination. After ‘markers’ have been

Rules for classificationare more difficult toobtain when the truecancer status isunknown





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identified using any of the above, a

decision rule for predicting cancer or

non-cancer groups is needed. Since the

number of variables is now manageable,

classical methods such as logistic

regression or classification tree (CART)38

analysis may be used. In addition, the data

can be broken up into training and

validation sets for validation.

‘Genetic’ algorithm

In addition to the methods above, the so-

called ‘genetic’ algorithm was developed

by the Correlogic group and the NIH

using cluster analysis.39 A training data set

is obtained and molecular weight variables

are initially arbitrarily placed in one of

1,500 ‘logical chromosomes’, each

composed of a fixed number of molecular

weight variables. For each, a Euclidian

distance-based cluster analysis using the

variables in the chromosome creates

cancer or non-cancer clusters depending

on the majority of samples that are one

type or the other. The proportion of

samples correctly classified

(‘homogeneity’) is a measure of the

‘fitness’ of the chromosome. Those with

poor ‘fitness’ are eliminated with higher

probability than those with good fitness.

Random ‘crossovers’ (exchange of

molecular weight variables) are also made

among the remaining chromosomes and

fitness of each new ‘generation’ of logical

chromosomes are evaluated iteratively

until a final logical chromosome (set of

Figure 1: Detection of differential biomarker expressions in cancer and control nipple aspirates (NA) with (left panel)strong anion exchange, and (right panel) silica gel surface chips by SELDI assays. Relative intensities (heights in y-axes)indicate that some (indicated number in x-axis) proteins are overexpressed in NA of cancerous (Ca) breasts, but othersare decreased relative to a volunteer (Vol) and patients with benign (Ben) breast disease as well as the contralateral (Con)breast of patients


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SELDI can be used todiscover biomarkers ofvarious cancers withoutprior knowledge aboutthem or the need forpurification

SELDI protein spectrafrom cancerous nippleaspirates are distinctfrom those of non-cancerous nippleaspirates

markers) is generated whose clusters best

separate the cancer from the non-cancer

samples. These markers are validated on a

separate data set.

Classification when cancer groups are not

known

The problem is much more difficult when

one is not certain how many groups exist

a priori. One approach is to tentatively

classify samples into groups, apply UMSA

procedures and then examine the low

dimensional plots. More formally,

hierarchical or k-means cluster analysis

methods37 can be applied using the

UMSA components. Validation of the

results is made using bootstrap

(resampling) methods.

SELDI AND PROTEOMICPROFILINGBecause of the advantages afforded by

SELDI, NCI has sponsored an Early

Detection Research Network to utilise

SELDI as a major tool for biomarker

searching and screening.40 SELDI is able

to screen proteins across wide range of

molecular weights (MW) without prior

knowledge of their biological or

biochemical characteristics. This unique

feature has enabled investigators to

compare the qualitative and quantitative

difference for each individual protein.

We are one of the early groups to

embark on SELDI for discovery of breast

cancer-related biomarkers. Our

examination of nipple aspirates and

comparison of their SELDI protein

spectra allows us to distinguish cancerous

nipple aspirate profiles from that of non-

cancerous nipple aspirates (Figure 1).

From SELDI-TOF we have found several

protein peaks that are specifically present

in cancerous nipple aspirates. On the

other hand, several protein peaks are

more abundant in non-cancerous nipple

aspirates. We are currently identifying

those potential breast cancer-associated

biomarkers as well as those that are down-

regulated in breast cancer. Similarly,

comparison between extracts of cancerous

breast biopsies with normal counterparts

of the same patient by SELDI-TOF

shows differences in their protein profiles

(Figure 2).

This technology resulted in detection

of new candidate markers in the

prostate,41–44 ovarian,45,46 pancreatic47 and

Figure 2: Massspectrometry profile ofprotein extracts frompaired cancerous (BrCa)and normal breast (NBr)specimens of the samepatient. X-axis indicatesthe molecular mass indaltons, y-axis indicatesthe relative intensity ofthe peak signals





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breast48–50 cancers using minute amounts

of tumour biopsies or body fluids. Table 2

gives a summary of the types of cancer,

specimens used and the molecule masses

of potential biomarkers detected in

published reports so far. Investigators

were able to quickly identify the markers

without prior knowledge about them or

the need for purification. Thus, SELDI

signature of tumour specific biomarkers

can be developed into routine screening

for the presence of the malignancies.

FUTURE DIRECTIONS ANDAPPLICATIONSAttempts to develop new and improved

biomarker screening for cancer have a

long history and have focused on all levels

of the biochemical process including the

gene, RNA, protein and metabolites.

With the recent development of more

efficient screening techniques at all these

levels, the role of biomarkers detected by

high-throughput technologies are being

re-examined. The ProteinChip1

technology using mass spectrometry for

protein separation and display offers a new

and useful slant on the proteomics

profiling strategy. New data indicate that

this screening process may be an

important addition to the available tools

for detecting a variety of cancers.

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