Genomics and proteomics—the way forward

Annals of Oncology16 (Supplement 2): ii30 – ii44, 2005 doi:10.1093/annonc/mdi728

Genomics and proteomics—the way forward J. P. A. Baak1,2,3, E. A. M. Janssen1, K. Soreide1 & R. Heikkilæ4 Departments of 1Pathology and 4Medical Hemato-Oncology, Stavanger University Hospital, Stavanger, Gade Institute, 2University of Bergen, Norway; 3Free University, Amsterdam, The Netherlands

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, prognostic and predictive markers, and targets for early detection and therapy. The search for cancer-causing alterations within the currently 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. q 2005 European Society for Medical Oncology

The definition of proteomics has changed greatly over time [5]. Originally, it was coined to describe the large-scale, highthroughput separation and subsequent identification of proteins resolved by 2-dimensional poly-acrimide gel electrophoresis (2DE). Currently, proteomics denotes nearly any type of technology 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 ‘fishing expeditions’, 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 sensation 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.

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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 functionally 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 interests. 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-informative benign cells inclusion. Accurate, biologically relevant, well reproducible quantitative morphological 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 conditions, so that for reliable results the interval between tumour excision and freezing must be minimized 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.

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The genome lays a foundation for the understanding 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 penetrant 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 polymorphism 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] formulated the so-called ‘two-hits hypothesis’ to explain the development 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 perceived 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 therapeutic tumor targets. Briefly, the acquired set of functional capabilities of a cancer cell are: (i) self-sufficiency in growth signals; (ii) insensitivity 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 (fibroblasts, 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 previously 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 important 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 chromosomes and this can be visualized by fluorescence microscopy to identify chromosomes, centromeres, aberrations in interphase 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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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 disease 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.

ii32 Table 1. Molecular techniques, their strong and weaker points Advantages

Limitations

Provides data on

Karyotyping

Whole chromosome analysis

Laborious

Structural large chromosomal changes

Fluorescence in situ hybridization

Gene specific

Maximum four genes at a time

Gains and losses of specific genes in cells and tissue

Comparative genome hybridization

Whole genome analysis

Resolution limited to
10 Mbp

Gains and losses throughout the whole genome

Spectral karytyping

Whole genome analysis

Laborious

Structural chromosomal changes

Microarray

Whole genome analysis

Fresh tissue necessary for cDNA isolation

Differential expression of thousands of genes

Microsatellite instability

Allele specific

Large numbers of microsatellites needed to be informative

Chromosome stability, effectiveness of DNA repair mechanisms

Loss of heterozygosity

Allele specific

No correlation with mRNA expression

Losses at gene level

Single nucleotide polymorphism (SNP)

Stable

Large number of SNPs needed to be informative

Susceptibility for disease development and therapy response

Abundant through genome

SNPs map not finished yet

Gene specific

Many false-negative and -positive results

Gene function under well described circumstances

Separation of complex protein mixtures

Hydrophobic and membrane proteins do not enter well

Differential protein expression profiling of complex protein mixtures

Sensitivity (micromolar, 106) Posttranscriptional modifications can be detected

Only 2000 spots per gel Not suitable for small clinical samples

Detects new protein interactions in vivo

False positives

Genomic techniques

Proteomic techniques Two-dimensional SDS–PAGE

Yeast two-hybrid

Protein–ligand interactions

Laborious Protein microarray

High throughput

Data analysis

Very diverse both function and detection

Limited amount of antibodies and recombinant proteins available

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

Very specific, under well described circumstances in vivo and in time

Laborious

Spatial and temporal dynamics of protein–ligand interactions in the living cell

Mass spectometry and combinations with TOF

High specificity and sensitivity (femtomolar, 1015)

Often extensive sample preparation

Protein identification/characterization, mass fingerprinting

Surface-enhanced laser desorption/ionization

High speed, high resolution separations of complex protein mixtures

Detection >30 kDa

Differential protein expression profiling of complex protein mixtures

Small samples

Low mass reproducibility

Tissue microarray

Little sample preparation

Large protein profile CVs

High throughput

Formalin-fixed material

Morphologic information intact Possible on archival material

Protein–ligand interactions and protein expression in complex protein mixtures

Protein expression and location in correlation with patient and therapy data

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RNA interference

ii33 Table 1. (Continued)

Liquid chromatography nuclear magnetic resonance

Advantages

Limitations

Provides data on

Quantitative

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

Metabolite target analysis, metabolite profiling, metabolic fingerprinting, metabolic profiling

High throughput

Expensive

Complete sample recovery Little sample preparation Complex protein mixtures

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 chromosomespecific probes each labeled with a different combination of

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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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

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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 chromosome 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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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].

ii35 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 affinitybased. 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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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). Largescale gene expression analysis has proved to be a valid strategy 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.

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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 sequencelength 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 resolution. 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-SNP euchro- matin distances of 199 kilobases, this method affords a resolution that is not easily achievable with non-oligonucleotide-based experimental approaches. RNA interference (RNAi). RNA silencing [26] is a posttranscriptional gene silencing process that is based on sequencespecific 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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Fluorescence resonance energy transfer (FRET). FRET monitors macromolecular interactions and resolves the spatial and temporal dynamics of protein–protein interactions in the living cell, but in principle it can also be applied to tissue sections of fixed material. Surface-enhanced laser desorption/ionization (SELDI). SELDI is an affinity-based mass spectometry method in which proteins (<20 kDa) are selectively absorbed to a chemically modified surface, and impurities are removed by washing with buffer. Tissue microarrays (TMAs). TMAs (Figure 5) consist of hundreds 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 2  2 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’).

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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 understand its biology, and that process is far from finished. Proteins are much more complex: they contain 20 different amino acids and the number of possible permutations thus is exponentially 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 treatments 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 promising 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 therapeutic 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 colorectal cancer, breast cancer, lung cancer, lymphomas and others [45–52]. A well known example is that BCL-2 (an anti-apoptotic protein) is overexpressed in follicular lymphomas, principally 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 standardized for the detection of clonally rearranged immunoglobulin (Ig) and T-cell receptor (TCR) genes and the chromosome

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RNA is processed into small RNAs, which triggers a number of enzymatic reactions resulting in the degradation of the targeted mRNA molecules [27]. RNAi-based genome-wide functional 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 immobilized 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. Consequently, 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 biochemical process. For example, a lab-on-a-chip for immunological assays could integrate sample input, dilution, reaction and separation; 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 highthroughput analyses. Their small dimensions reduce processing times and the amount of reagents necessary per assay, and have high reproducibility owing to standardization and automation [31].

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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 discovery that a gain in chromosome 3q is a much stronger prognosticator 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 excessively increased 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 trastuzumab 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 unfavorable 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 agents include imatinib mesylate (Gleevecw) 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 gefitinib [76, 77], imatinib [78, 79] and trastuzumab [66, 67]. Consequently, 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. Generally 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 many possible 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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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) laryngeal 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 considerably with the today’s application of accurate multi-panel immunopathology. Hopefully, molecular techniques will render even more exact subclassifications in adjunct to histomorphological 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.

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Growth Factor

Receptor

Tyr Kinase

PIP3

Ras GTP

PIP2 PTEN

P-Tyr

p85 SH2

AKT

Mitogenesis ACG kinases

Apoptosis Caspase 9, BAD

Cell Motility RAC, BTK kinase NFkappaB, uPa

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.

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 heterogeneous, 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 protein 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 subtypes or different age groups with varying genetic and prognostic information. Unfortunetely, including such mixtures 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 proportionally 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 glandular cells can be positive and negative (B). Stroma cells are invariably positive (reprinted from Baak et al. [104]).

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Phosporylation of BRCA1

p110 PIK3CA

ii40 100 D-Score>1 or D-Score<1/PTEN+ Progression 0/89

% Without Cancer In Follow-Up

80

60 P<0.00001 40 D-Score<1 & PTEN Null Progression 7/14

20

0 0

24

48

72

96

120

Follow Up (months) Figure 10. The bench-to-bedside approach.

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

Figure 9. RNA and proteins are very sensitive to hypoxia, caused by excision of the tumor.

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 analyses, as for example in gastrointestinal lymphomas [52] and endometrial hyperplasias [82]. Accurate quantitation of microscopic 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 prognostic value of a prognostic factor, where for example formal mitotic activity counts versus general mitotic impressions in

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 determination 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 different laboratories were suboptimal [85]. In the future, an international 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 reductionist approach 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 discoverybased 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

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Figure 8. Cancer-progression free survival curves of endometrial hyperplasia, according to combined application of the two different technologies: the morphometric D-Score and PTEN expression (reprinted from Baak et al. [104]).

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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 happen soon. The studies by Mutter et al. on endometrial pre-cancers are classical examples of the development as follows. In 1985, the basis was laid for the WHO94 classification of endometrial hyperplasias, but a molecular basis was lacking. By 1995, studies could not detect a strong correlation between WHO94 and genetic clonality. However, subsequent computerized 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 variant, the Endometrial Intraepithelial Neoplasia (EIN) classification [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 detail the expected changes in the curricula of medical students, basic scientists, clinicians and technicians, and the consequences 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 interaction, 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-mathematicians, technology experts and producers is essential for realizing 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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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 differentially expressed in tumors compared with normal cells, we now find ourselves looking at a haystack of needles. The challenge 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 variables 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 mentioned 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 stability of the molecular signature and the proportion of misclassifications 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 collection 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].

ii42 longer be acceptable. The ownership of the material and permission from the patient to use the material is already legalized 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 morphological studies, analysis of homogeneous groups and the use of accurate, reproducible quantitative methods. Finally, a piece of sound advice to everyone on the way forward: don’t sell your microscope!

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