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Genomics and proteomics—the way forward

J. P. A. Baak 1,2,3, E. A. M. Janssen1, K. Soreide1 & R. Heikkilæ4

Departments of 1

Pathology and 4 Medical Hemato-Oncology, Stavanger University Hospital, Stavanger, Gade Institute,

2University of Bergen,

Norway;3

Free University, Amsterdam, The Netherlands

The Post-Genomic Era is characterized by huge numbers. This will only increase in the future when

more will become known about proteins, which not only outnumber the genes, but also can be func-

tionally altered in various ways. These "big numbers" have two consequences. Studies, which were

despisingly called Fishing Expeditions only a few years ago are now respectfully dubbed Discovery

Science, which answers questions and generate new hypotheses. Secondly, it is not always easy to

select the way forward in Genomics and Proteomics. Therefore emerging technologies are updated

and the hallmarks of cancer cells briefly discussed. Promising applications for screening/early

disease detection, diagnosis and tumour classification, prognostication, predictive response and

tailoring therapy are described.

Strategic choices as to certain types of study, which diseases or organ sites should be analyzed or

techniques used, are mainly political and very much determined by loco-regional and national inte-rests. However, in post-genomic research the following practical points must be considered. To

avoid non-informative noise, homogeneous groups (preferably of small lesions) should be analyzed.

Adequate sampling will be even more important than in the past to minimize the risk of non-infor-

mative benign cells inclusion. Accurate, biologically relevant, well reproducible quantitative mor-

phological information is of the utmost importance to define the lesions, as mixtures of functionally

different cells will obscure important signals. RNA and proteins degrade quickly under hypoxic con-

ditions, so that for reliable results the interval between tumour excision and freezing must be mini-

mized and standardized. Promising results must always be independently validated as the use of

relatively few patients combined with the enormous number of variables analyzed, carries a serious

risk of selection bias and too optimistic results. An integrated collaboration structure of multiple

disciplines becomes increasingly important.

Introduction

A mere 50 years have passed since the 1953 landmark

description of the DNA double helix [1], yet the parallel

efforts of the Human Genome Project and Celera Genomics

have successfully sequenced the human genome [2, 3]

(officially completed on April 14, 2003), thus introducing the

‘post-genomic era’ [4]. These are major achievements in

biology and medicine in general, contributing not least to the

understanding of carcinogenesis. New molecular insights and

technologies in oncology have given clues to the initiation,

early detection and possible prevention of neoplasia, prog-

nostic and predictive markers, and targets for early detection

and therapy.

The search for cancer-causing alterations within the cur-

rently known genes of the whole genome (‘genomics’, the

study of the human genome) is complicated by the different

ways the genes may be transcribed (transcriptomics) into a

variety of functionally different proteins (‘proteomics’, the

analysis of the protein complement of the genome), which can

themselves undergo essential functional changes.

The definition of proteomics has changed greatly over time

[5]. Originally, it was coined to describe the large-scale, high-

throughput separation and subsequent identification of proteins

resolved by 2-dimensional poly-acrimide gel electrophoresis

(2DE). Currently, proteomics denotes nearly any type of tech-

nology focusing upon proteins analysis, ranging from a single

protein to thousands in one experiment. Proteomics thus has

replaced the phrase ‘protein science’.

The post-genomic era is characterized by the generation of

massive data numbers from studies with descriptive

approaches. Although previously despisingly called ‘fishingexpeditions’, today the term ‘discovery science’ is respectfully

used for such large-scale studies, crediting the answers and

new hypotheses being generated. Alongside the optimism

fuelled by new discoveries, however, there is also the sen-

sation of being overwhelmed by their complexity and size.

Such a sensation is underlined by the notion that according to

our existing knowledge, only 1.1% of the genome consists of

exons coding for proteins, 24% is intronic sequences and the

remaining 75% consists of intergenic DNA, currently without

a known function in RNA transcription or protein translation.

Annals of Oncology16 (Supplement 2): ii30–ii44, 2005

doi:10.1093/annonc/mdi728

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Moreover, compared with evolutionary lower organisms,

human beings have only twice to three times as many genes

as the fruit fly and the mustard plant. This indicates that the

functional complexity, rather than the absolute number of

genes, is essential for the human phenotype.

To clarify where we are in the post-genomic era, we could

say that we now have the letters (the sequence) and have thus

far found a few sentences (genes that we know of), but we

have only merely begun reading the chapter contents (how

genes may be transcribed), while the books (the proteins and

the metabolites) will keep human mankind reading for many

centuries to come. In more scientific terms, the outline of the

genome has enabled the study of gene products that are the

focal point of proteomic studies, the effectors of the DNA [6].

Thus, considering the complexities of human nature and dis-

ease processes, to illustrate where cancer science is today: we

have just become aware of the alphabet-code for a vast library

of knowledge.

We must therefore accept the idea that we are only at the

beginning of a scientific discovery journey, a journey that may

not be finished for many decades, or even centuries, to come.

One may ask, how do we grasp this unfolding knowledge?

This article will attempt to give a short overview of where we

are and how the way forward might be chosen.

The genome lays a foundation for theunderstanding of cancer as a genetic disease

The completion of the Human Genome Project gives us more

insight into the genetic variations that are causally implicated

in oncogenesis. A 2004 consensus report described that more

than 1% of genes are causally involved in oncogenesis [7].Some of these genetic mutations can be transmitted through

the germ-line and result in hereditary cancers (5–10% of all).

Many hereditary cancers have been extensively described, so

that the focus now is more on the susceptibility for cancers in

certain families or populations. This has resulted in a new

approach, molecular epidemiology: searching for low pene-

trant cancer susceptibility genes that may give rise to much

smaller increases in individual risk [8]. These genes may

interact with environment and lifestyle factors and as a result,

cancer risk is not equally elevated in all persons exposed to an

environmental factor, and the risk can change. One example

is the change in cancer incidence in Chinese and Japanese

immigrants to the USA [9]. Another example is relatives of patients with early-onset lung cancer, who have a genetic

predisposition and elevated risk for developing lung cancer, in

contrast to relatives of patients that develop lung cancer later

in life [10]. It was recently suggested that a T27C polymorph-

ism in CYP17 (cytochrome P450 c17a) is associated with

elevated sex hormone levels, and interacts with insulin levels

and diet to affect breast density levels and potential breast

cancer risk [11]. Such genetic susceptibility is currently being

intensively investigated by DNA fingerprinting with the use of

single nucleotide polymorphisms (SNPs) in large populations.

From genotype to phenotype: the hallmarks of human cancer cells

Research over the past decades has provided us with increased

insight that cancer is a genetic disease. Knudson [12] formu-

lated the so-called ‘two-hits hypothesis’ to explain the devel-

opment of retinoblastoma. A larger, stepwise number of

acquired genetic mutations were later detected in the early

colorectal cancer studies [13–15]. Today, cancer is often per-ceived as a genetic disease driven by the time sequence of

these DNA changes and is phenotypically determined by a

limited number of underlying rules [16, 17]. These genetic

alterations and rules are not the same for all tumors even

within a certain organ site (e.g. lung cancer). This functional

perception of cancer cells has clinical implications concerning

the biological behavior, natural history and potential thera-

peutic tumor targets.

Briefly, the acquired set of functional capabilities of a can-

cer cell are: (i) self-sufficiency in growth signals; (ii) insensi-

tivity to anti-growth signals; (iii) evasion of apoptosis; (iv)

limitless replicative potential; (v) sustained angiogenesis; and

(vi) tissue invasion and metastasis [16].

Furthermore, extracellular matrix and its components (fibro-

blasts, stromal cells, signal substances, inflammatory cells)

influence cancer cell proliferation, invasion and metastasis

[18–21]. Fibroblasts have a more profound influence on the

development and progression of carcinomas than was pre-

viously appreciated [19], e.g. in oral epithelium differentiation

[22].

Molecular techniques

The molecular techniques for genomics and proteomics

studies are developing rapidly (for an overview, see Baak et al.[23]). A short description follows here. Table 1 shows import-

ant widely established techniques with their respective

strengths and weaknesses. Some newer promising methods

will be described in detail.

Established DNA techniques

Conventional karyotyping. The development of chromosome

banding techniques in 1969 was a breakthrough, as all

chromosomes could be individually recognized (‘conventional

karyotyping’) and chromosomal rearrangements characterized

(a classical example is the Philadelphia chromosome in

chronic myeloid leukaemia).Fluorescence in situ hybridization. A small DNA fragment of

known origin (a probe) is fluorescently labeled and hybridized

to a metaphase chromosome spread or interphase nuclei. The

probe binds to homologous sequences within the chromo-

somes and this can be visualized by fluorescence microscopy

to identify chromosomes, centromeres, aberrations in inter-

phase tumor nuclei and others (Figure 1).

Comparative genomic hybridization (CGH). CGH provides

information on the number of copies of chromosome parts

throughout the whole tumor genome to identify genetic

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Table 1. Molecular techniques, their strong and weaker points

Advantages Limitations Provides data on

Genomic techniques

Karyotyping Whole chromosomeanalysis

Laborious Structural large chromosomal changes

Fluorescence in situhybridization

Gene specific Maximum four genes ata time

Gains and losses of specific genes in cellsand tissue

Comparative genomehybridization

Whole genomeanalysis

Resolution limited to $10Mbp Gains and losses throughoutthe whole genome

Spectral karytyping Whole genomeanalysis

Laborious Structural chromosomal changes

Microarray Whole genomeanalysis

Fresh tissue necessary forcDNA isolation

Differential expression of thousandsof genes

Microsatelli te instabili ty Allele specific Large numbers of microsatellitesneeded to be informative

Chromosome stability, effectiveness of DNA repair mechanisms

Loss of heterozygosity Allele specific No correlation with mRNAexpression

Losses at gene level

Single nucleotidepolymorphism (SNP)

Stable Large number of SNPsneeded to be informative

Susceptibility for disease developmentand therapy response

Abundant through

genome

SNPs map not finished

yetRNA interference Gene specific Many false-negative and -positive

resultsGene function under well

described circumstances

Proteomic techniques

Two-dimensionalSDS–PAGE

Separation of complexprotein mixtures

Hydrophobic and membrane proteinsdo not enter well

Differential protein expression profilingof complex protein mixtures

Sensitivity(micromolar, 10

À6)

Only 2000 spots pergel

Posttranscriptionalmodifications can bedetected

Not suitable for smallclinical samples

Yeast two-hybrid Detects new proteininteractionsin vivo

False positives Protein –ligand interactions

Laborious

Protein microarray High throughput Data analysis Protein –ligand interactions and proteinexpression in complex proteinmixtures

Very diverse both functionand detection

Limited amount of antibodies andrecombinant proteins available

Fluorescence microscopy(FRET, FLIP, FRAP,FLIM, BRET, PRIM)

Very specific, under welldescribed circumstancesin vivo and in time

Laborious Spatial and temporal dynamicsof protein–ligand interactions in theliving cell

Mass spectometry andcombinations with TOF

High specificity and sensitivity(femtomolar, 10À

15)

Often extensive sample preparation Protein identification/characterization,mass fingerprinting

Surface-enhanced laserdesorption/ionization

High speed, high resolutionseparations of complexprotein mixtures

Detection >30kDa Differential protein expression profilingof complex protein mixtures

Small samples Low mass reproducibility

Little sample preparation Large protein profile CVs

Tissue microarray High throughput Formalin-fixed material Protein expression and locationin correlation with patientand therapy data

Morphologic information intact

Possible on archival material

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changes (deletions or amplifications). The resolution of

chromosome CGH is limited to $10 million base pairs

(Mbp).

Array CGH . Array CGH does not require karyotyping and the

resolution can be 0.5 Mbp or better. Figure 2 explains array

CGH and shows the results of chromosome and array CGH of

the same chromosome combined in one figure.

Spectral karyotyping (SKY). SKY gives all chromosomes a

unique fluorescent color by hybridizing with chromosome-

specific probes each labeled with a different combination of

Table 1. (Continued )

Advantages Limitations Provides data on

Liquid chromatographynuclear magneticresonance

Quantitative Only metabolites [amino acids,organic acids and bases,nucleotides, carbohydrates,osmolytes, lipids (broad,non-specific resonances)]can be detected

Metabolite target analysis, metaboliteprofiling, metabolic fingerprinting,metabolicprofiling

High throughput Expensive

Complete samplerecovery

Little samplepreparation

Complex proteinmixtures

Figure 1. Fluorescence in situ hybridization (FISH) examples. Top, left: interphase cytogenetics. Normal diploid (left, two spots per chromosome) and

malignant (right, several spots per chromosome, indicating its aneuploidy) urothelial cells from a urinary bladder washing hybridized with centromere

probes to chromosomes 3 (red), 7 (green) and 17 (white), and a locus-specific probe for p16 at 9p21 (yellow). Bottom: FISH using whole chromosome X

and chromosome 3 probe on tumor cell line scc040, showing that a part of chromosome X is attached to chromosome 12, and showing that the addition

on chromosome 3 is in fact other chromosome 3 material. Reprinted with permission from Baak et al. [23].

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Figure 2. Top left: schematic view of the array comparative genomic hybridization (CGH) technique and higher sensitivity and resolution of array CGH

over chromosome CGH. Top right: example of a hybridization of green-labeled tumor DNA and red-labeled normal reference DNA onto an array of 2400

BAC clones representing the whole genome with an average spacing of 1 Mbp. Bottom: chromosome CGH detected low level gain of a large part of

chromosome arm 20q; array CGH identified a narrow high level amplification in band 20q13.2. Reprinted with permission from Baak et al. [23].

Figure 3. Spectral karyotyping (SKY) karyogram of tumor cell line scc040. For every pixel in the image the spectrum from 450 to 750 nm is measured,

and with this spectral information the unique color combination of each chromosome is identified. The addition on chromosome 3 is composed of chromo-

some 3 material, the addition on chromosome 12 comes from a part of chromosome X, and the addition on chromosome 18 consists of chromosome 8

material (the third chromosome 9 is lying in overlap with chromosome 4). Reprinted with permission from Baak et al. [23].

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fluorochromes, and is useful for the genome-wide detection of

structural chromosomal changes. Translocations are readily

visible (Figure 3). SKY does not require prior knowledge of

chromosomal breakpoints.

Gene expression (cDNA) arrays. High-density oligonucleotide

cDNA microarrays measure many thousands of gene-specific

mRNAs in a single tissue sample in parallel (Figure 4). Large-

scale gene expression analysis has proved to be a valid strat-egy for developing gene expression profiles, or ‘signatures’, to

classify prognostic subgroups. Unfortunately the method

requires fresh tissue for optimal results.

Loss of heterozygozity (LOH), microsatellite instability (MSI).

One strategy for screening the genome used a panel of 150

polymorphic microsatellite markers from throughout the

whole genome with LOH. By using microsatellites one can

also check the ability of cells to repair DNA replication errors.

In tumors with MSI, both genetic and epigenetic modifications

of mismatch repair genes were identified.

Proteomics techniques

Two-dimensional electrophoresis. 2DE [16] is a powerful

technique for protein separation. The digestion of spots in the

gel makes it possible to further analyse proteins with mass

spectrometry. Peptide spectra obtained in this way can be used

to search protein sequence databases.

Yeast two-hybrid system. The two-hybrid system can generate

vast amounts of new data, but each and every complex has to

be tested and confirmed separately afterwards.

Protein microarrays. Most protein microarrays are affinity-

based. Although powerful, these techniques have several

drawbacks: lack of available antibodies, lack of purified

recombinant proteins and cross-reactivity with affinity agents.

Figure 4. Genes which distinguish normal from malignant endometrium (P, proliferative; S, secretory; T, tumors) endometrium (Permax <0.50, three-fold,

100 difference). Columns show individual tissues, rows represent genes. Color scale shows standard deviation from the mean expression value for each

gene. Dendrograms on the margin show agglomerative hierarchical clustering (Wards linkage, euclidean distances) of genes (right) and tissues (bottom)

(courtesy of Dr G. L. Mutter). Reprinted with permission from Baak et al. [23].

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Fluorescence resonance energy transfer (FRET). FRET moni-

tors macromolecular interactions and resolves the spatial and

temporal dynamics of protein–protein interactions in the liv-

ing cell, but in principle it can also be applied to tissue sec-

tions of fixed material.

Surface-enhanced laser desorption/ionization (SELDI). SELDI

is an affinity-based mass spectometry method in which pro-

teins (<20 kDa) are selectively absorbed to a chemically modi-

fied surface, and impurities are removed by washing with

buffer.

Tissue microarrays (TMAs). TMAs (Figure 5) consist of hun-

dreds of small core (0.6– 2.0 mm) tissue sections arrayed on a

glass slide for immunohistochemistry (IHC) (protein), in situ

hybridization (DNA, RNA), and are useful for evaluation of

new antibodies and large-scale outcome studies. Because of

the very small sample size per case (with two cylinders of

1 mm diameter per tumor of 2Â2 cm; typically <0.5% per

section), TMAs only give a reliable impression of a tumor

characteristic if large numbers of cases are analyzed, and even

then, the significance of rare events may easily be overlooked

(see below under ‘Critical remarks and need for validation’).

Evolving molecular methods

Single nucleotide polymorphisms. SNPs are the most abundant

form of DNA polymorphisms in the human genome. Each

SNP has a defined position in a chromosome at which base

pairs differ among individuals with significant frequency

(>1%). Human SNPs are not very polymorphic, contrasting

CA repeats (see below), and therefore SNPs are often less

informative than other genetic markers such as simple

sequence-length polymorphisms and microsatellites. On the

other hand, SNPs occur abundantly in the whole genome and

provide great potential for automated genotyping. SNPs can

therefore be used as genetic markers to detect disease genes in

genetic linkage studies. Thus high-density SNP mapping can

be as informative as current strategies with simple sequence-

length polymorphisms and microsatellites. As an example,

genetic polymorphisms in T27C in CYP17 have been linked

to the risk of developing breast cancer [24].

Loss of heterozygosity. Huang et al. [25] recently developed a

high-density oligonucleotide array-based SNP genotyping

method, whole genome sampling analysis (WGSA), to identify

genome-wide chromosomal gains and losses at high resol-

ution. WGSA simultaneously genotypes over 10 000 SNPs by

allele-specific hybridization to perfect match and mismatch

probes synthesized on a single array. The coupling of LOH

analysis, via SNP genotyping, with copy number estimations

using a single array provides additional insight into the

structure of genomic alterations. With median inter-SNPeuchro- matin distances of 199 kilobases, this method affords

a resolution that is not easily achievable with non-oligonucleo-

tide-based experimental approaches.

RNA interference (RNAi). RNA silencing [26] is a posttran-

scriptional gene silencing process that is based on sequence-

specific interactions of small interfering RNAs with targeted

mRNA molecules. Silencing is initiated when double-stranded

Figure 5. An example of a tissue microarray (TMA) consisting of 1 mm diameter cylinders. Ki-67 staining. (A) Paraffin block for TMA; (B) histological

TMA section, with three control markers at the upper left; (C, D) examples of different immunostains.

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RNA is processed into small RNAs, which triggers a number

of enzymatic reactions resulting in the degradation of the tar-

geted mRNA molecules [27]. RNAi-based genome-wide func-

tional analysis for mammalian cells [28] is well underway.

Combining RNAi with transfected cell array (TCA) holds

enormous potential for drug-discovery oriented research and

therapy.

Transfected cell array. The principle of TCA is based on the

transfection of DNA or RNA molecules. These are immobi-

lized on a solid surface upon which cells are cultured. Cells

growing on top of the DNA/RNA spots become transfected,

resulting in the expression or silencing of specific

genes/proteins in spatially distinctive groups of cells. Conse-

quently, the physiological effects caused by the introduction of

these foreign nucleic acids can be studied [29]. Densities of up

to 8000 cell clusters per standard slide can be achieved [30].

Microfluidics. Microfluidics technology utilizes a network of

channels and wells that are etched onto glass or polymer chips

to build ‘laboratories-on-chips’. Pressure or electrokinetic

forces move pico- or nanoliter volumes in a finely controlled

manner through the channels. These microfluidic circuits can

be designed to accommodate virtually any analytic biochemi-

cal process. For example, a lab-on-a-chip for immunological

assays could integrate sample input, dilution, reaction and sep-

aration; or one designed to map restriction enzyme fragments

might have an enzymatic digestion chamber followed by a

separation column. Labs-on-chips are well suited for high-

throughput analyses. Their small dimensions reduce proces-

sing times and the amount of reagents necessary per assay,

and have high reproducibility owing to standardization and

automation [31].

Applications of genomics and proteomics

The long interval between a discovery and a clinical

application

It takes a long time before the real impact of a new discovery

is realized. An example is that the invention of the microscope

by Leeuwenhoeck occurred in the 17th century, yet the era of

‘cellular pathology’ was not fully introduced until the teaching

of Virchow, nearly 200 years later! Similarly, even though

p53 was discovered two decades ago, for the first 10 years it

was believed to be an oncogene. Only later was it vindicated

as a tumor-suppressor gene and the ‘guardian of the genome’

[32]. DNA contains four variables only: adenine, cytosine,guanine and thymidine, yet it took many decades to under-

stand its biology, and that process is far from finished. Pro-

teins are much more complex: they contain 20 different amino

acids and the number of possible permutations thus is expo-

nentially larger. In addition, there are more than 100 known

different possible posttranslational modifications [33] and

each one will be able to change the function and location of a

protein drastically over time and under different conditions.

The interval between the development of genomic and

proteomic technologies, and the actual paradigm shift in

the understanding of disease and development of new treat-

ments may be very long indeed!

The conclusion of the above is that we are at the very

beginning of clinical genomic and proteomic applications.

Nonetheless, the enormous expansion of molecular cancer

research to date has created applications that are useful in the

daily care of patients. The following description can by no

means be complete, but aims to mention typical examples,

including weak points, and how new discoveries have been

made by the combined use of different technologies. Here we

will concentrate on some established or promising applications

in (i) early disease detection/screening, (ii) diagnosis and

tumor classification, (iii) prognostication, (iv) prediction of

response, and (v) tailoring of therapy.

Screening

In many countries, classical cytological screening for cervical

cancer is being amplified with testing for human papilloma

virus [34]. International genomic collaborations to detect

minimal residual cancer have produced important results in

leukemia [35]. Proteomics technologies have also been

announced as future biomarkers for screening and early

detection of cancer disease [36–38]. However, the early prom-

ising results with near 100% sensitivity and specificity for

detection of ovarian cancer with proteomics [39] have been

seriously criticized, as Baggerly et al. [40] showed that the

method performed no better than chance for classifying the

second dataset. In agreement with this, the reproducibility of

the proteomic profiling approach remains to be established.

Yet, others still hope that proteomic profiling will enable

protein-screening in adjunct with endoscopic surveillance for

improved and more effective detection of early (pre)malignant

disease [36, 41].

Tumor classification

Histological typing and grading of tumors is widely used, but

is notoriously subjective and lacks intra- and inter-observer

reproducibility [42, 43]. This is unacceptable, as the thera-

peutic and social consequences of neighboring grades for the

individual patient are often enormous: e.g. adjuvant

chemotherapy or radiotherapy in FIGO 1 ovarian cancer ‘grade

I–II’; and total colectomy in ulcerative colitis patients ‘with

some epithelial dysplasia’ [44]. Molecular analysis allows for

subgrouping based on genomic or proteomic (including IHC)

profiles together with histopathology evaluation in colorectalcancer, breast cancer, lung cancer, lymphomas and others

[45–52]. A well known example is that BCL-2 (an anti-apopto-

tic protein) is overexpressed in follicular lymphomas, princi-

pally as a result of the t(14;18)(q32;q21), and useful in

distinguishing follicular lymphoma (usually BCL-2-positive)

from follicular hyperplasia (BCL-2-negative), although with

certain exceptions [53]. Multiplex polymerase chain reaction

(PCR) assays have successfully been developed and standar-

dized for the detection of clonally rearranged immunoglobulin

(Ig) and T-cell receptor (TCR) genes and the chromosome

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aberrations t(11;14) and t(14;18) [54], and clonality testing in

lymphomas has been widely adopted as a routine diagnostic

method. Moreover, ‘undifferentiated’ (poor prognosis) laryn-

geal cancers and gastric appeared to be large-cell lymphomas

(with good prognosis) when IHC panels were applied. The

classification of the diagnosis ‘unknown primary’ (in the past

applied to 20% or more of metastases) can be reduced con-

siderably with the today’s application of accurate multi-panel

immunopathology. Hopefully, molecular techniques will ren-der even more exact subclassifications in adjunct to histomor-

phological evaluation. Eventually, the diagnosis ‘unknown

primary’ may become obsolete [55]. An example is DNA

methylation mapping, which showed that unique profiles of

hypermethylated CpG islands exist, defining each neoplasia

[56], as in, for example, prostate cancer [57]. These results for

subclassification of tumors are promising, but validation and an

appropriate link to clinical outcomes is always mandatory.

Prognostic value, prediction of response and subgroup

analysis

Many articles have had a prognostic goal, as staging (like

grading and typing) is practically useful but may have a low

prognostic accuracy. Prognostic gene expression signatures

are promising, as, for example, in breast cancer [48]. CGH

analysis in lymph node-negative breast cancers led to the dis-

covery that a gain in chromosome 3q is a much stronger prog-

nosticator than classical features [58]. The most common

region of overlap in this study was 3q26. The PIK3CA gene

[59] located here is mutated in up to 40% of primary breast

cancers [60, 61] (Figure 6). Overexpression of PIK3CA can

result in an increased activation of the Akt pathway after

activation of ErbB2 upon binding with a growth factor

(e.g. estradiol or heregulin) [28], and lead to excessivelyincreased proliferation, invasion and cell motility (Figure 8).

Proliferation is the strongest prognosticator in node-negative

breast cancer [62–64]. However, many other factors, like

noey2 deletion, may also cause increased proliferation [65].

Noey2 is located on chromosome 1p, but 1p deletion was not

prognostic [58]. We hypothesize that there are two types of

high proliferation in breast cancer, a PIK3CA-dependent one

(strongly prognostic) and a second caused by genetic changes

in other genes (which are much less, or not at all, prognostic).

It is important that the tumor suppressor gene PTEN opposes

the action of PIK3CA. PTEN deficiencies occur in up to 35%

of breast tumors, and may be an important predictor of trastu-

zumab resistance in breast cancers with ErbB2 overexpression[66]. Loss of PTEN tumor suppressor function is observed in

tumors of breast, prostate, thyroid and endometrial origin

[67–69]. Allelic losses in the proximity of the PTEN locus

(10q23) also occur in sporadic colorectal cancers [70]. PTEN

therefore may be a general key progression determinant in

(pre)cancers. In endometrial hyperplasias (EHs), a frequent

pre-neoplastic lesion, progression to cancer only occurs in

PTEN-negative and not in the PTEN-positive EHs [68]

(Figure 7). However, only those PTEN-null EHs with an unfa-

vorable morphometric D-Score progressed [71], greatly

increasing the positive predictive value of PTEN-null glands

(Figures 8 and 9). This again shows the importance of

combined molecular and morphological technology to get the

strongest prognostic information.

Targeted tumor therapies

Targeted therapies are now being developed that work at

the level of the proteome. Examples of these agentsinclude imatinib mesylate (Gleevec

w) targeting the Bcr-ABL

tyrosine kinase in chronic myelogenous leukemia and mutated

c-KIT - or PDGFRa-tyrosine kinase in gastrointestinal stromal

tumors [72], trastuzumab (Herceptinw

) for (her2-neu amplified)

breast cancer [73, 74] and gefitinib (Iressaw

) for EGFR-mutated

lung cancers [75]. These are all targeted therapies in that they

are monoclonal antibodies (imatinib, trastuzumab) or small

molecules (gefitinib) directed at distinct defects in the tumor

cells. Even though early clinical trials have shown

positive results, resistance patterns have been reported for gefi-

tinib [76, 77], imatinib [78, 79] and trastuzumab [66, 67]. Con-

sequently, several pathways should perhaps be simultaneously

addressed if long-term response or remission is to be achieved.

The problem is that such combinations would be expected to

increase the toxicity, which undermines the whole idea of

targeted therapy, but combination analysis may also prevent

overtreatment. Her2-neu and proliferation testing is a good

example. Volpi et al. [64] showed that in node-negative breast

cancer patients, HER2 expression was a significant discriminant

of prognosis, but only in the subgroup of patients with rapidly

proliferating cancers. PTEN-negative patients are more prone

to trastuzumab resistance [66]. A problem with Her2-neu is the

testing method and which patients to select for treatment. Gen-

erally accepted is to treat all IHC strongly positive (3+) cancers,

although several have no gene overexpression while others with

gene amplification do not show IHC [80]. The cause and the

clinical value of these discordances are not yet known.

The way forward

What course shall we take with genomics and proteomics in

cancer research and treatment? Which strategic and practical

choices should be made?

Strategic choices

Just as ‘All roads lead to Rome’, so the answer to ‘Genomics

and Proteomics—The Way Forward’ may be similarly easy,

but at the same time complex, for there are certainly manypossible ways to go in the post-genomic era. Eventually,

many will hopefully lead to a better understanding of human

disease, its causes, prevention and potential therapeutic

targets. Clearly, one way will lead there sooner than another;

we do not yet have a final blueprint and need to rely on

cooperation and experience in getting to our target. Strategic

choices regarding types of study, which diseases or organ sites

should be analyzed or which techniques used, are mainly

political and very much determined by locoregional and

national interests.

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However, the following practical points must be considered

in research on ‘The way forward’.

Analyze homogeneous groups

Large tumors are often geno- and phenotypically very hetero-

geneous, hypoxic and necrotic. The essential information one

is searching for can be heavily blurred by the additional noise

coming from these epiphenomenal properties. Thus, the

utmost care is needed with the interpretation of results from

large tumors. It is not always immediately evident that the

material presented in an article is biased for large tumors, as,

for example, in studies using frozen tissue for RNA and pro-

tein chips. With many years of daily practical experience in

pathology laboratories, we can guarantee that frozen tumor

material often comes from large tumors! Other examples

where heterogeneity has a major influence are when tumors

from different stages are included, or mixtures of carcinoma

in situ, small and large cancers, or different histological sub-

types or different age groups with varying genetic and

prognostic information. Unfortunetely, including such mix-

tures may be the rule rather than the exception.

Sample accurately

Adequate sampling is always important, but even more so

when the cancers are small, as is increasingly the case. In

small samples there is the added risk of including propor-

tionally significant numbers of benign epithelial and

other cells. Consequently, accurate sampling by means of, for

Figure 7. PTEN-expressing (brown) and -null glands (blue) in (A) ‘normal’ proliferative and (B, C) hyperplastic endometrium. Note that adjacent glandu-

lar cells can be positive and negative (B). Stroma cells are invariably positive (reprinted from Baak et al. [104]).

PIP3Tyr

Kinase

Receptor

AKT

GrowthFactor

P-Tyr p110PIK3CA

p85SH2

Ras

GTPPTEN

PIP2

Cell MotilityRAC, BTK kinaseNFkappaB, uPa

Phosporylationof BRCA1

ApoptosisCaspase 9, BAD

MitogenesisACG kinases

Figure 6. Schematic overview of some of the Akt pathway effectors. Overexpression of the catalytic subunit of PI-3 kinase may result in an increased

activation of the Akt-pathway, in reaction to a growth factor binding to cell surface receptor ErbB2. PTEN promotes the transition from PIP3 to PIP2,

thereby suppressing PIK3CA.

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example laser dissection, becomes even more important than

in the past. In pre-cancers, the precise localization in the

mucosa of a prognostic factor is often highly important to

extract its value. In a cervical squamous intraepithelial lesion,

for example, the concentration of retinoblastoma protein is

predictive of progression, but only in the deep layer of the

epithelium. Mixtures of superficial, deep and basal cells (as

occurs with biochemical tests) will blur the results [81].

Morphology, accurate quantitation and keeping the

definitions

It is important to emphasize that in many areas, molecular

insights are only possible with very careful morphological ana-

lyses, as for example in gastrointestinal lymphomas [52] and

endometrial hyperplasias [82]. Accurate quantitation of micro-

scopic features is also essential; qualitative impressions are

often simply too indefinite [83]. Not adhering to definitions of

certain features may have enormous consequences for the prog-

nostic value of a prognostic factor, where for example formal

mitotic activity counts versus general mitotic impressions in

breast cancer. A formal count is strongly prognostic but

impressions are not (Baak J.P.A., van Diest P.J., Janssen

E.A.M. and Voorhorst F., 2005, unpublished results).

Poor reproducibility owing to different techniques or

poor test methods

Up to 50% of breast cancers have been reported to present

with loss of PTEN function [66], but others using IHC [84]

found just 8% PTEN negativity and almost 70% showed weak

positivity. This raises the important question: which determi-

nation technique best predicts the clinical outcome? A typical

example of a poorly defined test method is the use of IHC

without strict quality and assurance control or standardization;

the international NEQAS network for IHC quality testing has

shown that a high percentage of IHC determinations in differ-

ent laboratories were suboptimal [85]. In the future, aninternational quality standard for IHC cancer studies should be

introduced.

Minimize and standardize the interval between excision

of a tumor and freezing

This is essential for RNA and proteins, but less urgent for

DNA (which is much more resistant to degradation) (Figure 9).

Critical remarks and need for validation

We have seen that post-genomic era studies are characterized

by huge numbers [86– 91]. The traditional reductionistapproach with hypothesis-driven research that focuses on one

gene at a time is now being challenged by high-technology,

hypothesis-generating ‘Omic’ approaches [92]. The discovery-

based research currently applied to the Omic-technologies

calls for a change in reasoning [93]. The steps in development

(‘Will it work?’), evaluation (‘Does it work?’), and

explanation (‘How does it work?’) are separate. Many of the

current Omic-discoveries are based on large-scale detection of

genes/gene products from where we do not yet know. One

might argue that a test may be useful if patterns can reliably

Figure 10. The bench-to-bedside approach.

Figure 9. RNA and proteins are very sensitive to hypoxia, caused by

excision of the tumor.

Follow Up (months)

120967248240

% W i t h o u t C a n c e r

I n

F o l l o w - U p

100

80

60

40

20

0

P<0.00001

D-Score>1 orD-Score<1/PTEN+Progression 0/89

D-Score<1 &PTEN NullProgression 7/14

Figure 8. Cancer-progression free survival curves of endometrial hyper-

plasia, according to combined application of the two different techno-

logies: the morphometric D-Score and PTEN expression (reprinted from

Baak et al. [104]).

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discriminate disease from no disease, regardless of whether

they can be understood or explained. However, before such

profiles are to be clinically implicated the results need to be

validated, according to good laboratory practice guidelines

[44, 94].

In other words, microarray techniques have made it easier

to find ‘the needle in the haystack’. However, as these results

often come up with tens or hundreds of genes that are differ-

entially expressed in tumors compared with normal cells, we

now find ourselves looking at a haystack of needles. The chal-

lenge for the future is to sift out the genetic information from

non-informative noise and find clinically useful data by means

of sound experimental design [95]. Testing very many vari-

ables at the same time has a serious risk, i.e. that the learning

(training) sets are very small compared with the large number

of features analyzed. This can result in far too optimistic

results that cannot be reproduced in subsequent new patient

groups (a phenomenon called ‘overtraining’ of the initial

learning set). For example, the differences in the same

tumor types can be more striking than similar, as shown in

microarray studies for lung cancer [46, 96]. We have men-tioned above that serum proteomics tests [39] reported to be

nearly 100% sensitive and specific for ovarian cancer, 3 years

later are yet to be duplicated. Verification by an independent

method [92] or in independent patient groups (see Baak [44])

is mandatory. Recently, Michiels et al. [97] reanalyzed data

from the seven largest published studies that have attempted

to predict prognosis of cancer patients on the basis of DNA

microarray analysis. By using multiple random sets the stab-

ility of the molecular signature and the proportion of mis-

classifications were studied. They found that the list of genes

identified as predictors of prognosis was highly unstable;

molecular signatures depended strongly on the selection of patients in the training sets. For all but one study, the

proportion misclassified decreased as the number of patients

in the training set increased. Because of inadequate validation,

the chosen seven studies published overoptimistic results. Five

of the seven studies did not classify patients better than chance

[97]. In agreement with Baggerly et al. [40] and Michiels et al.

[97] we believe that any genomic and proteomic study should

be considered prone to error before it is further validated by

methods or patient groups that are independent of the original

study.

Large scale analyses on many patients may be required.

One such analysis used a statistical method, ‘comparative

metaprofiling’, which identifies and assesses the intersection

of multiple gene expression signatures from a diverse collec-

tion of microarray datasets [98]. The results were encouraging.

From 40 published cancer microarray datasets, comprising 38

million gene expression measurements from >3700 cancer

samples, a common transcriptional profile emerged that is

universally activated in most cancer types relative to the

normal tissues from which they arose, likely reflecting

essential transcriptional features of neoplastic transformation

[98].

Consequences of the post-genomic era

The variety of consequences of the post-genomic era is not

yet clear, but certain points have emerged.

Will genomics and proteomics replace morphological

science? When gene expression arrays became available some

6 years ago, it was predicted that the end of morphological

science was near, but it now seems unlikely that this will hap-

pen soon. The studies by Mutter et al. on endometrial pre-can-cers are classical examples of the development as follows. In

1985, the basis was laid for the WHO94 classification of endo-

metrial hyperplasias, but a molecular basis was lacking. By

1995, studies could not detect a strong correlation between

WHO94 and genetic clonality. However, subsequent compu-

terized morphometrical analysis revealed a strong correlation

between clonality and the prognostic morphometric D-Score

[82]. Further analyses comparing the prognostic value of the

WHO94 and D-Score showed that the latter was superior

[71, 99], but the morphometric technology required for the

D-Score is not widely available. This led to a subjective var-

iant, the Endometrial Intraepithelial Neoplasia (EIN) classifi-

cation [100], which in our study is well reproducible [101].

Thus, pathologists come back to the standard microscopic

image, but with renewed thinking, and are now armed with

new knowledge about reactive endometrial changes (called

benign hyperplasias) versus genetically monoclonal expansive

growing lesions with a high cancer risk (called EIN). This

new approach greatly strengthens the prognostic evaluations

and therapeutic choices. Of course, compared with

genetic testing microscopic evaluations are currently very

cost-effective and time-efficient, but this may change over

time when more efficient automated molecular tests become

available.

It is beyond the purpose of this article to discuss in detailthe expected changes in the curricula of medical students,

basic scientists, clinicians and technicians, and the conse-

quences for the staff members of cancer-related departments.

Bioinformatics (the application of computer science and

informatics to molecular biology) becomes essential for the

study of DNA, RNA and proteins, and will help to map

sequences to databases, create models for molecular inter-

action, evaluate structural compatibility, find differences

between host and pathogen DNA and identify conservation

motifs in protein structure [102].

For research, the development of a cooperative framework

among basic researchers, medical specialists, bio-mathemati-

cians, technology experts and producers is essential for realiz-

ing the revolutionary promise that genomics and proteomics

hold for drug development, regulatory science, medical

practice and public health.

The hospital structure may also change from speciality

driven (surgery, radiology, medical oncology, pathology) to

specialized multidisciplinary treatment teams encompassing

certain technologies, e.g. anti-angiogenesis or gene transfer.

The legal aspects of biobanks are becoming rapidly more

important. Publications on obscure material may soon no

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longer be acceptable. The ownership of the material and per-

mission from the patient to use the material is already lega-

lized in a number of countries.

Concluding remarks

It follows from the above that we agree with Hall and Lowe

[103] that modern cancer research is about the interplay

between disciplines. New findings must be carefully validated.

A lesson from several successful studies is that genomic and

proteomic insights only came through very careful morpho-

logical studies, analysis of homogeneous groups and the use

of accurate, reproducible quantitative methods.

Finally, a piece of sound advice to everyone on the way for-

ward: don’t sell your microscope!

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