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doi:10.1016/S1470-2045(04)01382-8

  1. 1. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 89 No currently available chemotherapy seems likely to substantially improve outcome in most patients with brain tumours. Several resistance-associated cellular factors, which were discovered in other cancer models, have also been identified in brain tumours. Although these mechanisms play some part in resistance in brain tumours, they are not sufficient to explain the poor clinical response to chemotherapy. There could be other brain-tumour-specific genetic profiles that are associated with tumour sensitivity to chemotherapy. There is increasing awareness that drug resistance in brain tumours is not a result of changes in single molecular pathways but is likely to involve a complex network of regulatory dynamics. Further insights into chemoresistance in brain tumours could come with comprehensive characterisation of their gene expression, as well as the genetic changes occurring in response to chemotherapy. Recent progress in high-throughput bioanalytical methods for genome-wide studies has made possible a novel research model of initial hypothesis generation followed by functional testing of the generated hypothesis. Lancet Oncol 2004; 5: 89–100 Despite the well-established use of chemotherapeutic approaches in the treatment of malignant brain tumours, prognosis has not improved much over the past 20 years and continues to be dismal for the majority of patients. Although chemotherapy prolongs survival for some types of brain tumours, such as medulloblastoma, primitive neuroectodermal tumour, oligodendroglioma, germ-cell tumour, and primary central-nervous-system lymphoma, for most histological types chemotherapy is applied as a last resort rather than as an established beneficial component of a multimodality treatment regimen. The most common type of brain tumour, the large group of high-grade gliomas, tends to be resistant to chemotherapy, and long- term tumour control is rarely achieved. Chemotherapy for brain tumours poses a special challenge owing to the existence of a blood–tumour barrier, which is intact in those tumour regions that are biologically and clinically most important—namely in areas of the brain infiltrated with tumour cells. However, there is growing consensus that, in addition to difficulties related to the blood–tumour barrier and pharmacokinetic issues, the modest effect of chemotherapy in brain tumours is mainly linked to tumour-cell resistance as shown by certain genetic and epigenetic factors.1,2 This review discusses the potential usefulness of recent progress in high-throughput bioanalytical methods for genome studies as tools to identify determinants of response and to characterise the genetic changes associated with chemotherapeutic perturbation in brain neoplasms (figure 1). Drug resistance in human cancer Most cancers have heterogeneous cell populations. Goldie and Coldman3 postulated a mathematical model for relating drug sensitivity of human tumours to gene mutation rate and hypothesised that tumours undergo spontaneous genetic changes that enable development of resistance to cytotoxic agents to which the tumours have never been exposed. Selection of mammalian cells in vitro for resistance to cytotoxic agents via exposure to incrementally increased sublethal drug concentrations commonly results in cross-resistance to many other drugs, which share little structural similarity with the primary selective agent and act at different intracellular targets. This pleiotropic process, which is a major impediment to ReviewGenomics and brain-tumour drug resistance MB is Assistant Professor of Experimental Neurooncology, Department of General Neurosurgery at the Neurocenter, University of Freiburg, Germany; and visiting Assistant Professor, Division of Oncology, Stanford University School of Medicine, CA, USA. CB is a molecular biologist in the Department of General Neurosurgery at the Neurocenter, University of Freiburg, Germany. BIS is Professor of Medicine (Oncology and Clinical Pharmacology) at Stanford University School of Medicine, CA, USA. Correspondence: Dr Markus Bredel, Division of Oncology, Stanford University School of Medicine, 269 Campus Drive, CCSR-1110, Stanford, CA 94305-5151, USA. Tel: +1 650 498 6949. Fax: +1 510 438 8830. Email: mbredel@stanford.edu Genomics-based hypothesis generation: a novel approach to unravelling drug resistance in brain tumours? Markus Bredel, Claudia Bredel, Branimir I Sikic Figure 1. Images of a cDNA microarray slide profiling gene expression. For personal use. Only reproduce with permission from The Lancet.
  2. 2. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com90 treating patients with cancer, has become known as multiple-drug (or multidrug) resistance (MDR). Resistance mechanisms are expressed constitutively either as genes normally expressed by the tissue of origin of the tumour (eg, MDR1 in colon cancer) or as genetic alterations during tumorigenesis (eg, p53 mutations). Such intrinsic (or de novo or constitutive) resistance results in little treatment response or failure of initial treatment (figure 2). In many tumours, early drug treatment can achieve substantial cell killing, though there might be selection of a clonal variant of cells that confers acquired resistance to subsequent treatment, leading via clonal expansion, to repopulation and tumour recurrence (drug selection; figure 2). Two principles help to explain the acquired resistance phenotype (figure 2). First, the inherent genetic instability of tumour cells can lead pre-existing resistant cell clones that are present before initial therapy to expand under long-term chemotherapy. Second, drug resistance can be acquired through induction of resistance pathways in cancer cells during chemotherapy. Anticancer agents have mutagenic potential and could cause mutations in key cellular target genes (genetic level); and chemo- therapy can cause surviving cells to induce the coordinated expression of protective stress response genes (epigenetic level). The resistant clones formed could be selected by subsequent therapy and expand, eventually leading to disease relapse (figure 2). Drug-resistance in brain tumours Several mechanisms of drug resistance discovered in other tumour models have also been implicated in brain tumours (figure 3). They include ATP-dependent efflux of cytotoxic agents by transmembrane transporter proteins encoded by the genes multiple-drug resistance 1 (MDR1, ABCB1) and multidrug-resistance-associated protein (MRP1, ABCC1); DNA damage caused by quantitative changes in expression of DNA topoisomerase II␣; increased detoxification of alkylating agents by glutathione and the glutathione-linked enzyme system, particularly glutathione-S-transferases; and increased activity and expression of members of the protein kinase C family, causing changes in cell-cycle transition and in the expression status of various resistance markers that are relevant to drug action.1,2 In addition, a deficiency in pathways of DNA mismatch repair (which render tumour cells tolerant to methylation) and increased nucleotide excision repair of DNA adducts owing to altered activity of poly(ADP)-ribose polymerase and the product of the excision-repair cross-complementing rodent repair deficiency gene 2 (ERCC2) could be involved in the MDR of brain-tumour cells.1,2 Since most chemotherapeutic agents kill tumour cells via apoptosis, dysfunction of genes involved in apoptotic pathways and resultant impaired ability to commit to apoptosis can also contribute to chemotherapy resistance of brain-tumour cells (figure 3). In addition to MDR phenotypes, resistance to chemotherapeutic drugs can affect single agents or a class of related drugs that share structural similarity. This type of resistance, which is broadly referred to as individual drug resistance and has also been associated with resistance to some chemotherapies in brain tumours (figure 3), might be caused by raised concentrations of enzymes involved in intracellular drug metabolism, for example O6 -methyl- guanine-DNA methyltransferase (MGMT), thymidylate synthase, and dihydrofolate reductase.1,2 Further- more, metallothioneins can function to Review Genomics and brain-tumour drug resistance Anticancer drug treatment Initial response Anticancer drug resistance No response Remission Relapse First drug exposure Clonal selection and expansion by prolonged therapy Resistant clones Inherent genetic instability Mutagenesis Drug sensitivity De novo Intrinsic (epi-)genetic profile Pre-existing resistant clones Acquired Epigenetic stress response Figure 2. Relation between drug effect and drug resistance/sensitivity of human cancers. For personal use. Only reproduce with permission from The Lancet.
  3. 3. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 91 inactivate anticancer drugs in brain-tumour cells. In oligodendroglioma, loss of heterozygosity (LOH) on chromosomes 1p and 19q, a unique cytogenetic profile, is related to responsiveness to chemotherapy. Importance of the hypothesis-driven research model The presence of resistance factors in a subset of brain tumours has generally been associated with the prevalence of clinical resistance to chemotherapy. However, substantial uncertainty remains about whether the expression of these factors in brain tumours is the cause of the low response to chemotherapy in patients with brain tumours. Apart from the DNA-repair gene MGMT—for which expression and hypermethylation-induced gene inactivation have been related to response of brain tumours to nitrosoureas1,2,4 —no causal link between these markers in brain tumours and failure of chemotherapy has been found. The traditional research model has dictated that when a gene is newly found to be related to drug resistance in one type of tumour, the hypothesis that the same gene is important in drug resistance in brain tumours is formulated and experiments are done to test this hypothesis. Although some advances have been made with this approach, others are needed to elucidate the polygenetic basis of resistance to chemotherapy in brain tumours (figure 4). Many known mechanisms of drug resistance are expressed constitutively in brain tumours and could be the cause of intrinsic resistance to therapy.1,5 However, other mechanisms are likely to be either induced by drug exposure or selected as mutations that occur during the evolution of a tumour-cell population. The observation of intrinsic expression in brain tumours could also reflect the known physiological role of some of these genes in non- neoplastic brain, for example the contribution of the MDR1 ReviewGenomics and brain-tumour drug resistance Drug Brain tumour blood vessel lumen Brain tumour cell Basal membrane Nucleus Pgp GST GSH GSH MRP Pgp PKC M S G1 G2 Lethal mitosis DNA damage DNA repair Apoptosis Anti-apoptopic Pro-apoptopic Apoptopic Components MGMT Alkyl G T TII␣ DHFR dUMP dTMP DNA TS MT Figure 3. Subcellular location and possible mechanism of action of previously identified resistance markers in brain tumours. The MDR1-encoded P-glycoprotein (Pgp) and members of the multidrug-resistance-associated protein (MRP) family act at the cell membrane as ATP-driven drug-efflux pumps. Glutathione-S-transferase (GST) mediates conjugation of anticancer agents with reduced glutathione (GSH). MRP1 mediates excretion of glutathione–drug conjugates. Quantitative changes in topoisomerase II␣ (TII␣) can withdraw the cellular target of agents targeting this enzyme. Nuclear O6 -methylguanine-DNA methyltransferase (MGMT) may act to remove drug-induced O6 -alkylguanine-DNA. Increased expression of nuclear thymidylate synthase (TS) and dihydrofolate reductase (DHFR), which are involved in the conversion of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP), can result in maintenance of free enzyme when challenged with drugs that deplete these enzymes. Metallothioneins (MT) can inactivate drugs either by chelation or by intracellular sequestration. Protein kinase C (PKC) induces a cell-cycle arrest to allow repair of drug-induced DNA damage before the cell enters lethal mitosis, can modulate the expression of a wide variety of resistance markers, and influences apoptotic pathways. Changes in the balance of proapoptotic and antiapoptotic factors prevent drug-induced apoptosis. The yellow arrows indicate increased or decreased marker expression associated with drug resistance. For personal use. Only reproduce with permission from The Lancet.
  4. 4. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com92 (ABCB1) gene encoding P-glycoprotein to the formation of a blood–brain barrier. The dual roles of other genes, such as those for thymidylate synthase, dihydrofolate reductase, and topoisomerase II␣, in drug resistance and cell proliferation raise uncertainty over whether changes in expression of some of these genes in brain tumours are related to drug resistance, cell-cycle turnover, or both. Changes in the expression state of some of these genes might be completely independent of any role in drug resistance. For example, the locus for topoisomerase II␣ is close to that of the oncogene HER2/neu on chromosome 17q. Overexpression of HER2/neu has been shown in a subset of brain tumours,6 and the accompanying increased expression of topoisomerase II␣ might be a consequence of malignant transformation in these neoplasms. The search for brain-tumour type-specific resistance fingerprints Evidence for the existence of a tumour type-specific resistance profile to a certain anticancer agent in the form of a resistance “fingerprint” comes from recent investigations that have used large-scale gene expression profiling and have shown substantially differing genetic response patterns to a defined chemotherapeutic agent for distinct histological types of cancer cells.7–10 There is growing awareness that drug resistance in human cancer is probably dictated in a combined way by the abnormal expression of groups of coregulated genes; this association suggests that many more genes are involved in the development of resistance phenotypes in brain tumours than the gene expression changes described thus far for a limited number of known resistance genes (figure 5). The phenotypes of benign and malignant cells and tissues ultimately depend on the types and amounts of proteins present at any given time. Translational and post- translational biochemical modifications exert decisive effects in regulating the amounts of active forms of proteins involved in pathological and pharmacological conditions. However, a primary mechanism by which protein expression is regulated and by which cells adjust to varying conditions is through variation of the abundance of mRNA present in the cell. Accordingly, a primary event in the development of tumour-cell resistance could be a change in degree of gene transcription. Analysis of the pattern of variation in expression of genes could therefore be useful in assessment of variation in the resistance characteristics of tumour cells and tissues (figure 4). Pharmacogenomics is predicated on the concept that cellular responses to anticancer drugs are analogous to a higher gated logic circuit, in which, in some cases, many contradictory inputs modulated by features such as feedback, feed-forward, error checking, and redundancy, are summed to produce a response (figure 5). Genomics-based generation of drug-resistance hypotheses There is increasing recognition of the value of comprehensive approaches to the molecular characteri- sation of biological phenotypes such as drug resistance. This appreciation has highlighted the need for biotechnology that allows parallel, large-scale assessment of many genes. The emergence of high-throughput genomics platforms has enabled genome-wide studies of gene expression. The key feature of successful genomic characterisation of a drug-resistance state is the ability to measure changes in mRNA abundance accompanying the formation of this state, or differences between sensitive and resistant phenotypes. Various strategies have emerged for gene expression profiling, including serial analysis of gene expression (SAGE), differential display, subtractive hybridisation approaches, and massively parallel signature sequencing. Microarray technology that uses densely spotted cDNAs or oligonucleotides allows the large-scale analysis of gene expression in a high-throughput way (figure 1). There are two main types of microarray systems. In the single-colour Affymetrix system, short oligonucleotide probes are synthesised in situ on a chip. Each gene is represented by a Review Genomics and brain-tumour drug resistance Research paradigm Resistance mechanism Brain tumour resistance phenotype Traditional Genomics-based Observation in other tumours Hypothesis testing Hypothesis generation Genomics ? ? Hypothesis formulation Empirical reductionism Functional genomics Figure 4. Traditional versus genomics-based research approaches. For personal use. Only reproduce with permission from The Lancet.
  5. 5. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 93 set of probe pairs that correspond to different gene regions. Each probe pair consists of an exact-match base-pair sequence probe and a mismatch sequence probe. Gene expression is measured absolutely by the brightness of hybridisation to the multiple probes representing a gene or relatively to the hybridisation signal of a reference RNA on a separate chip. By contrast, cDNA microarrays use a dual- colour hybridisation scheme for the comparison of gene expression in two or more samples. Typically, mixtures of (m)RNA reverse-transcription-generated Cy5-labelled “red” cDNA from one condition (eg, resistant or sensitive cell line) and mixtures of Cy3-labelled “green” cDNA from another condition (eg, reference sample) are combined and allowed to hybridise to the glass microarray slide (figure 1). The common reference control—for example, a pooled sample of (m)RNAs from a set of cancer cell-lines— generally has no biological significance and simply contributes a consistently measurable signal in the denominator of the assayed ratios. The amount of red and green fluorescence (R/G ratio) at each spot allows the amount of (m)RNA for each gene in the test sample to be compared with that in the common reference sample, avoiding the need for absolute measurements of (m)RNA (figure 1). An increasing number of studies have used microarray methods to examine gene expression patterns characteristic of sensitive or drug-resistance phenotypes in various human cancers.7–25 Many of these studies have used the in vitro resistance selection/induction model, which means that a more or less sensitive parental cell line is exposed to (sub)lethal drug concentrations until a sufficient degree of resistance is achieved (figure 6). These studies have provided preliminary insights into how changes in the sensitivity state of a cancer cell are reflected in changes in overall gene expression. Other genomic studies have examined the sensitivity state of various established and low-passage parental cancer cell lines.26–28 Scherf and colleagues29 first integrated large databases on gene expression and chemosensitivity. They linked the expression profiles of about 8000 unique genes assessed in a set of 60 human tumour cell-lines, termed the NCI60, with the activity patterns of more than 70 000 anticancer drugs obtained from a database of the Developmental Therapeutics Program (DTP) of the US National Cancer Institute. Many gene–drug relations were identified. Similar analysis in the NCI60 by Staunton and co-workers30 with 6817-gene microarrays revealed gene-expression-based classifiers of sensitivity or resistance for 232 compounds. Butte and colleagues31 identified gene regulatory networks determining chemotherapeutic susceptibility of the NCI60 to 5084 agents by use of 7245-gene oligonucleotide arrays. Dan and associates24 have used 9200-gene cDNA microarrays to compare the sensitivity state of 39 human cancer cell-lines to 55 anticancer agents. Pearson correlation and clustering analysis identified genes with expression patterns that showed significant association with patterns of drug responsiveness. Some genes were commonly associated with response to various drug classes, whereas others were associated only with response to drugs with a similar mechanism of action. Similarly, Zembutsu and colleagues20 analysed the expression profile of more than 23 000 genes in a panel of 85 cancer xenografts, derived from nine human organs and implanted into nude mice, along with chemosensitivity to nine differing anticancer drugs. This approach identified various genes that were significantly associated with sensitivity to one or more drugs examined. In addition, gene expression profiles have been linked to drug response in clinical tumour samples (figure 6). Kihara and co-workers15 used a cDNA microarray ReviewGenomics and brain-tumour drug resistance Response to chemotherapy Genomic reponse fingerprint Gene expression Microarray Gene copy number Array CGH Polygenetic network (including contradictory inputs) genome-wide mRNA abundance genome-wide DNA copy numbers Tumour type-specific co-regulation co-alteration Drug-specific Clinical tumour response Figure 5. The molecular determination of tumour response to chemotherapy, which includes in some cases many contradictory inputs, is a feature of the genome that might be determined by the expression and copy number of many known and unknown genes. Two complementary strategies could be useful in assessing molecular response fingerprints. First, gene expression profiling with microarrays allows characterisation of genome-wide mRNA abundance. Second, microarray-based comparative genomic hybridisation (array CGH) enables assessment of DNA copy numbers throughout the genome. For personal use. Only reproduce with permission from The Lancet.
  6. 6. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com94 containing 9216 genes to predict the sensitivity of oesophageal tumours to adjuvant chemotherapy. They identified 52 genes that were linked to prognosis and possibly to chemosensitivity and/or chemoresistance, and a drug response score based on differential expression of these genes was predictive of outlook for individual patients. Sotiriou and associates17 investigated the response to systemic chemotherapy of fine-needle aspirates of breast Review Genomics and brain-tumour drug resistance Established cell line Stable step-wise treatment Transient single-step treatment cDNA microarray Mock treatment Surgical tumour sample Outlier genes Gene(s) of clinical interest Patient Histological tumour type In vitro system In vivo system sample pair Resistant daughter cell line Stressed daughter cell line Distinct gene expression profiles Candidate marker validation Sensitive parental cell line Purified resistant tumour cells Purified sensitive tumour cells Biostatistic algorithms Post-chemotherapy sample Pre-chemotherapy sample Laser microbeam microdissection Real-time quantitative PCR Pharmacogenomics Candidate markers Laser pressure catapulting Low passage cell line Magnetic cell sorting drug Resistance induction/ selection Clinical tumour response data Figure 6. Genomics-based resistance research can pursue a dual strategy of assessing tumour (cell) responsiveness in vitro and in vivo. In vitro resistance research commonly involves the selection/induction model in which a parental cell line is selected/induced for resistance via chronic exposure to sublethal drug concentrations until a sufficient degree of resistance is achieved. Comparison of the gene expression of the parental line and the resistant subline can identify subsets of genes that are differentially expressed. Inclusion of stress response data by transient single-step drug exposure can help in dissecting gene expression changes primarily linked to drug resistance from secondary genetic changes that may be only downstream and marginally relevant to resistance formation. Ideally, genomics-based in vivo approaches to resistance research are combined with purification techniques that allow for enrichment of tumour cells. Laser-assisted tissue microdissection has emerged as the method of choice to limit tissue heterogeneity. Gene expression profiling in surgical tumour samples can use a dual strategy. On the one hand, the expression pattern of prechemotherapy samples is directly compared with that of postchemotherapy samples; on the other hand, with a pharmacogenomics strategy, expression data are directly correlated with clinical tumour response data to identify response-associated patterns of gene expression. For personal use. Only reproduce with permission from The Lancet.
  7. 7. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 95 cancers with expression profiles from 7600-feature cDNA microarrays. Candidate gene expression profiles were identified that distinguished tumours with complete response to chemotherapy from those with no response. Application of genomics methods Gene expression profiling has been used for molecular genetic characterisation, classification, and subclassification of intracranial neoplasms, including meningiomas32 and various histological types of glioma.33–46 Such strategies have provided initial evidence to offer the promise of refined prognosis prediction in brain tumours such as diffuse astrocytoma,42 malignant glioma,37,47 and medulloblastoma.48,49 Microarray analysis has also identified a potential serum marker for glioblastoma multiforme.50 The potential value of microarrays as a tool for assessing variation in the response characteristics and resistance state of brain tumours is just beginning to be appreciated. Rhee and colleagues51 characterised cellular pathways involved in the response of glioblastoma multiforme cells to the nitrosourea carmustine. Their array contained only 588 genes, of which 17 had differential expression between a carmustine-sensitive glioblastoma multiforme subline and a resistant parental subline. Most (13) of these genes showed lower expression in the resistant variant. Expression of MGMT, which was previously thought to be the primary mechanism of resistance of glioblastoma multiforme cells to carmustine, was similar in both sublines and not altered by carmustine treatment, which suggests that the MGMT pathway did not contribute to the carmustine resistance phenotype. By contrast, six other DNA repair genes, including ERCC2, were downregulated in the sensitive subclone. Bacolod and co-workers52 used microarrays with 12 000 elements to study changes in gene expression accompanying carmustine resistance selection in medulloblastoma cells. Gene expression profiling identified 89 genes with upregulated or downregulated expression. These included the changed expression of genes related to various biological functions, including increased expression of several metallothionein genes and reduced expression of several proapoptotic genes. Although MGMT activity of one resistant clone was twice that of the sensitive parental cells, a second clone selected with carmustine and O6 -benzylguanine (an irreversible inhibitor of MGMT) did not show changes in MGMT, which implies that other mechanisms must have had a role in the resistance phenotype of these cells. Baseline information about differences in chemo- sensitivity between genetic subsets of oligodendroglioma has been provided by gene expression profiling. Mukasa and colleagues53 used oligonucleotide microarrays with more than 12 600 human genes and expressed sequence tags to examine 11 tumour specimens of two cytogenetic subsets of oligodendroglioma, (characterised by the presence or absence of loss of heterozygosity [LOH] of chromosome 1p), which show profoundly different response rates to chemotherapy.1,2 The researchers identified 209 genes expressed differentially between the tumour subsets, with 86 genes showing higher expression and 123 lower expression in tumours with 1p LOH. Notably, only 60% of the 123 genes with reduced expression in tumours with 1p LOH were located on chromosomes 1 and 19 (oligodendrogliomas with 1p LOH commonly also show 19q deletion). Potential pitfalls of comprehensive gene expression profiling strategies The strength of microarray technology in research on drug resistance is the ability to assess the expression of many genes at the same time and subsequently, by use of sophisticated computer-driven pattern recognition and ReviewGenomics and brain-tumour drug resistance Functional testing via conventional biochemistry for logical coherence Prioritisation of probable hypotheses Many hypotheses Raw microarray data Bioinformatics Identification of co-regulated genes or gene families Data mining for meaningful information Multiple upregulated and downregulated genes hiearchical clustering relevance networks self- organising maps gene shaving principal component analysis terrain maps quality threshold clustering Figure 7. Hypotheses generated from a microarray experiment require sophisticated data mining for meaningful information to be obtained. In general, coregulated genes and families of genes are identified via biostatistical approaches with the assumption that genes that are similarly altered in expression are likely to be functionally related. Functional testing by conventional biochemical approaches is essential for logical coherence and to ascertain a causal link between a resistance phenotype and altered gene expression. For personal use. Only reproduce with permission from The Lancet.
  8. 8. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com96 map building, to cluster those that are simultaneously upregulated or downregulated when a tumour becomes chemoresistant (figure 7). The studies on drug resistance that have used microarray methods provide evidence that genomic responses of a tumour cell to a chemotherapeutic agent can include altered expression of a substantial number of genes that might well be in the range of 5–10% of the human genome; a large proportion of these changes have been confirmed by traditional methods of measuring mRNA.7–9 In addition, the intrinsic sensitivity of a tumour might be reflected by the expression of several genes or a group rather than by single genes. For example, in the study by Zembutsu and colleagues,20 comparison of the gene expression profiles of 85 human cancer xenografts with sensitivities to various drugs identified 1578 genes for which degree of expression was significantly related to chemosensitivity; 333 of these genes showed significant associations with two or more drugs, and 32 genes were linked with sensitivity to six or seven drugs. Microarray technology provides a rapid way of collecting large amounts of data and can generate new hypotheses on mechanisms leading to drug resistance. However, potential problems with the application of this approach to drug resistance research have to be recognised. In particular, biostatistical methods have to be available to analyse the substantial number of hypotheses that might be generated from a microarray experiment (figure 7). This approach does not differentiate between genes that might cause a certain resistance phenotype and secondary genetic changes that might be downstream, marginally relevant, or even irrelevant to the resistance phenotype. In fact, a substantial proportion of transcriptional changes observed during resistance formation probably do not immediately relate to the drug-resistant phenotype. The early changes in resistance selection are likely to be broader than mere resistance formation, representing a stress response of a tumour cell when exposed to a chemotherapeutic agent rather than a targeted tumour-cell response to reduce its vulnerability to this drug. Identification of sets of genes that concomitantly show altered expression during development of drug resistance could directly relate to regulation and coregulation for families of genes. Therefore, the most common approach to organisation of microarray data is hierarchal clustering54 (figure 7). It relies on the basic premise that genes with similarly altered expression profiles are likely to be coregulated and functionally related and uses a “guilt by association” strategy to identify functional clusters. Hypotheses generated by such associations require confirmation by conventional biochemical approaches, to establish logical coherence between the resistance state of a brain-tumour cell and an altered state of expression of single genes or a group of genes (figures 4 and 7). Alternative statistical approaches for data mining include the application of self-organising maps,55 the construction of relevance networks,27,31 principal component analysis,56 terrain maps,57 quality threshold clustering,58 and a mathematical strategy called “gene shaving” that differs from hierarchical clustering in that it takes into account that genes may belong to more than one cluster59 (figure 7). In addition, computational algorithms allow organisation of upregulated and downregulated genes on the basis of their ontology. For example, sophisticated software can be used to view and analyse gene expression data according to biological pathways and gene relations can be directly explored and annotated.60 Ontology-based arrangement of differential gene expression can provide immediate clues about the potential involvement of certain signalling and biochemical pathways in drug resistance states of tumour cells. A further problem of microarray technology is its low sensitivity to transcriptional changes in the baseline range, which complicates the identification of modest changes in gene expression that might be relevant to drug resistance. In addition, normal variability in gene expression could lead to differential expression patterns of a gene in a sensitive and resistant sample without reflecting the true effect of the resistance formation process. In general, measurements of gene expression provide direct information on the abundance of the mRNA template only, rather than on the expression of the final gene product, the protein. This point is particularly important, because concentrations of proteins can vary significantly among genes with similar abundance of mRNA. Conversely, there can be substantial variation in the amounts of mRNAs encoding proteins expressed with similar abundance.61 At present, the efficiency of obtaining an overall view of gene expression by measurement of mRNA is better than that for similar proteomic methods, in terms of throughput, reproducibility, and compatibility with clinical material. The degree of protein expression of small numbers of genes potentially associated with response to therapy can be examined in a high-throughput way by tissue-microarray-based immunohistochemistry, which allows rapid screening of large numbers of tumour specimens for candidate marker validation on paraffin- embedded tissue. The usefulness of tissue microarrays for neuropathology research has been readily demonstrated.62 Recent progress in the application of microarrays to cytogenetics—particularly comparative genomic hybridisation—has led to chip-based genome-wide screening for changes in gene copy numbers.63 Since gene dose effects are also relevant to chemosensitivity and changes in gene copy number are frequently observed in tumour cells exposed to chemotherapeutic agents, the application of array comparative genomic hybridisation to research on brain-tumour drug resistance might provide valuable information on codeleted and coamplified genes that are potentially important to resistance formation (figure 5). In vitro drug resistance versus in vivo chemoresistance Experiments in tumour cell-lines are an important first step in characterising genomic changes linked to resistance development in vitro. However, culture-derived patterns of gene expression changes accompanying resistance formation may not correlate with resistance-associated Review Genomics and brain-tumour drug resistance For personal use. Only reproduce with permission from The Lancet.
  9. 9. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 97 gene expression in solid brain tumours. Long-term brain- tumour cell lines differ to some extent from brain-tumour tissue samples in their basic gene expression profile. Hess and colleagues64 showed that in hierarchical cluster analysis of microarray data, three glioblastoma multiforme cell lines formed one main cluster and ten glioblastoma multiforme tissue samples formed a separate cluster. Multidimensional scaling and principal component analysis provided further evidence that the cell lines clearly differed from the tissue samples and that the cell lines differed genetically more between themselves than the tissues did. Sasaki and associates65 showed that gene expression of tissue-cultured meningiomas and corresponding in situ meningiomas differed significantly for a portion of genes. Accordingly, an integrative look at resistance-related changes in gene expression in response to drug application necessarily has to include actual brain tumour specimens (figure 6). One of the confounding factors in studying primary clinical specimens by microarrays is the mixture of neoplastic and various normal cell-types in the tumours. Tissue heterogeneity in brain tumours—such as the presence of non-neoplastic fibroblasts, endothelial cells, immune cells, and necrosis—could substantially interfere with drug-resistance analyses in brain tumour specimens obtained by surgery. Much attention has lately focused on strategies designed to enrich selected cell types from tissue samples to facilitate the use of material from patients in the study of markers or target discovery. In this regard, laser- assisted tissue microdissection has emerged as a method of choice to reduce tissue heterogeneity (figures 6 and 8). Several distinct laser-based techniques for tissue microdissection have been developed. For example, laser capture micro- dissection66 uses a cap coated with a thermolabile film of ethylene vinyl acetate, which is placed in contact with a tissue section stained to enable selection of the relevant cell type. Visualisation is achieved with an inverted microscope. An applied focused laser beam of variable diameter causes localised melting of the film over selected cells and these cells fuse to the cap. The selected tissue is selectively removed when the cap is lifted. Several thousand targeted laser “shots” of material can be made on a single cap, thereby enabling concentration of the cell type of interest. A second technique67 avoids mechanical contact to capture samples of between 1 µm and several hundred micrometers in diameter. In this method, the laser is used not only for microbeam microdissection but also for transfer of the selected clusters of cells or single cells from the object plane directly into the cap of a routine microfuge tube via high photonic pressure force—laser pressure catapulting (figure 8). The feasibility and reproducibility of this approach for subsequent genetic analysis with no adverse effect on mRNA have been clearly demonstrated.68 Laser microdissection has been used for reduction of tissue heterogeneity in gene expression profiling in mammalian brain69 and brain disease, including temporal- lobe epilepsy70 and brain tumours.71 Many of the studies that have combined microdissection and overall gene expression analysis have used mRNA amplification to achieve sufficient amounts of mRNA.72,73 Since amplifi- cation protocols are prone to representative biases, other studies have shown that expression profiles for thousands of genes can be successfully generated with non-amplified mRNA derived from clinical cancer specimens procured by laser microdissection.74–76 Perspective With the perception that gene networks rather than individual genes determine chemoresistance, genomics will have an important role in efforts to unravel how the transcriptome and genome of a brain-tumour cell influence its sensitivity to chemotherapy. Ideally, the genome-wide analysis of gene expression and copy numbers in samples obtained by surgery will be combined with laser microdissection techniques to ascertain that the assessed profiles truly reflect the signature characteristics of the tumour-cell component. Such approaches will become increasingly feasible with the development of microarray techniques that depend on lower amounts of study mRNA and DNA. In addition, further insights into ReviewGenomics and brain-tumour drug resistance 10 ␮m Figure 8. Histological section of a high-grade glioma from a child, imaged by differential contrast and analysed by immunocytochemistry for expression of DNA topoisomerase II␣ protein. The white arrow points to a nucleus positive for expression of the protein. The black arrow points to a microdissected area where a single positive nucleus has been captured by laser pressure catapulting. For personal use. Only reproduce with permission from The Lancet.
  10. 10. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com98 chemoresistance in brain tumours will come with a comprehensive analysis of the epigenetic regulation of gene expression in these neoplasms. Aberrant methylation of cytosine guanine dinucleotide (CpG) islands is a predominant epigenetic mechanism of gene inactivation. Microarrays that allow analysis of CpG island methylation throughout the genome (epigenomics) have recently been developed. Genomics investigations cannot be considered the endpoint in research on brain-tumour drug resistance. Such strategies allow the formulation of hypotheses about the relation between drug sensitivity and transcriptomic and genomic variation at the level of correlation rather than cause and effect. Ascertainment of biological function requires candidate gene validation via conventional molecular biological approaches. The challenge of functional genomics will be to elucidate how validated candidates act and interact as components of complex gene regulatory networks that determine drug sensitivity in brain tumours. Better understanding will ultimately support the search for refined therapeutic strategies that improve the response to cytotoxic agents and thus the mostly poor outlook for patients with brain tumours. Conflict of interest None declared. References 1 Bredel M. Anticancer drug resistance in primary human brain tumors. Brain Res Brain Res Rev 2001; 35: 161–204. 2 Bredel M, Zentner J. Brain-tumour drug resistance: the bare essentials. Lancet Oncol 2002; 3: 397–406. 3 Goldie JH, Coldman AJ. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat Rep 1979; 63: 1727–33. 4 Esteller M, Garcia-Foncillas J, Andion E, et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med 2000; 343: 1350–54. 5 Rieger L, Rieger J, Winter S, et al. Evidence for a constitutive, verapamil-sensitive, non-P-glycoprotein multidrug resistance phenotype in malignant glioma that is unaltered by radiochemotherapy in vivo. Acta Neuropathol (Berl) 2000; 99: 555–62. 6 Schwechheimer K, Laufle RM, Schmahl W, et al. Expression of neu/c-erbB-2 in human brain tumors. Hum Pathol 1994; 25: 772–80. 7 Duan Z, Feller AJ, Penson RT, et al. Discovery of differentially expressed genes associated with paclitaxel resistance using cDNA array technology: analysis of interleukin (IL) 6, IL-8, and monocyte chemotactic protein 1 in the paclitaxel-resistant phenotype. Clin Cancer Res 1999; 5: 3445–53. 8 Kudoh K, Ramanna M, Ravatn R, et al. Monitoring the expression profiles of doxorubicin-induced and doxorubicin-resistant cancer cells by cDNA microarray. Cancer Res 2000; 60: 4161–66. 9 Watts GS, Futscher BW, Isett R, et al. cDNA microarray analysis of multidrug resistance: doxorubicin selection produces multiple defects in apoptosis signaling pathways. J Pharmacol Exp Ther 2001; 299: 434–41. 10 Turton NJ, Judah DJ, Riley J, et al. Gene expression and amplification in breast carcinoma cells with intrinsic and acquired doxorubicin resistance. Oncogene 2001; 20: 1300–06. 11 Robert J. Resistance to cytotoxic agents. Curr Opin Pharmacol 2001; 1: 353–57. 12 Crowley-Weber CL, Payne CM, Gleason-Guzman M, et al. Development and molecular characterization of HCT-116 cell lines resistant to the tumor promoter and multiple stress-inducer, deoxycholate. Carcinogenesis 2002; 23: 2063–80. 13 Dvorakova K, Payne CM, Tome ME, et al. Molecular and cellular characterization of imexon-resistant RPMI8226/I myeloma cells. Mol Cancer Ther 2002; 1: 185–95. 14 Hoshida Y, Moriyama M, Otsuka M, et al. Identification of genes associated with sensitivity to 5-fluorouracil and cisplatin in hepatoma cells. J Gastroenterol 2002; 37 (suppl 14): 92–95. 15 Kihara C, Tsunoda T, Tanaka T, et al. Prediction of sensitivity of esophageal tumors to adjuvant chemotherapy by cDNA microarray analysis of gene-expression profiles. Cancer Res 2001; 61: 6474–79. 16 Sakamoto M, Kondo A, Kawasaki K, et al. Analysis of gene expression profiles associated with cisplatin resistance in human ovarian cancer cell lines and tissues using cDNA microarray. Hum Cell 2001; 14: 305–15. 17 Sotiriou C, Powles TJ, Dowsett M, et al. Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res 2002; 4: R3. 18 Tracey L, Villuendas R, Ortiz P, et al. Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma. Am J Pathol 2002; 161: 1825–37. 19 Weldon CB, Scandurro AB, Rolfe KW, et al. Identification of mitogen-activated protein kinase kinase as a chemoresistant pathway in MCF-7 cells by using gene expression microarray. Surgery 2002; 132: 293–301. 20 Zembutsu H, Ohnishi Y, Tsunoda T, et al. Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Res 2002; 62: 518–27. 21 Vikhanskaya F, Marchini S, Marabese M, et al. P73a overexpression is associated with resistance to treatment with DNA-damaging agents in a human ovarian cancer cell line. Cancer Res 2001; 61: 935–38. 22 Komatani H, Kotani H, Hara Y, et al. Identification of breast cancer resistant protein/mitoxantrone resistance/placenta-specific, ATP-binding cassette transporter as a transporter of NB-506 and J-107088, topoisomerase I inhibitors with an indolocarbazole structure. Cancer Res 2001; 61: 2827–32. 23 Levenson VV, Davidovich IA, Roninson IB. Pleiotropic resistance to DNA-interactive drugs is associated with increased expression of genes involved in DNA replication, repair, and stress response. Cancer Res 2000; 60: 5027–30. 24 Dan S, Tsunoda T, Kitahara O, et al. An integrated database of chemosensitivity to 55 anticancer drugs and gene expression profiles of 39 human cancer cell lines. Cancer Res 2002; 62: 1139–47. 25 Wittig R, Nessling M, Will RD, et al. Candidate genes for cross- resistance against DNA-damaging drugs. Cancer Res 2002; 62: 6698–705. 26 Bao L, Guo T, Sun Z. Mining functional relationships in feature subspaces from gene expression profiles and drug activity profiles. FEBS Lett 2002; 516: 113–18. 27 Moriyama M, Hoshida Y, Otsuka M, et al. Relevance network between chemosensitivity and transcriptome in human hepatoma cells. Mol Cancer Ther 2003; 2: 199–205. 28 Wallqvist A, Rabow AA, Shoemaker et al. Establishing connections between microarray expression data and chemotherapeutic cancer pharmacology. Mol Cancer Ther 2002; 1: 311–20. 29 Scherf U, Ross DT, Waltham M, et al. A gene expression Review Genomics and brain-tumour drug resistance Search strategy and selection criteria Data for this review were identified by searches of PubMed. All papers related to gene expression profiling, drug resistance, and laser microdissection in primary human brain tumours discussed in the article were reviewed. A subset of articles chosen for their importance and the further reading opportunities they provide was included in the review. Search terms included “array CGH”, “astrocytoma”, “brain tumo(u)r”, “cDNA array”, “chemoresistance”, “comparative genomic hybridization”, “drug resistance”, “ependymoma”, “expression array”, “gene expression profiling”, “genomics”, “glioma”, “glioblastoma”, “laser microdissection”, “medulloblastoma”, “microarray”, “microdissection”, “multidrug resistance”, “oligodendroglioma”, “oligonucleotide array”, “PNET”, and “tissue microarray”. For personal use. Only reproduce with permission from The Lancet.
  11. 11. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com 99 database for the molecular pharmacology of cancer. Nat Genet 2000; 24: 236–44. 30 Staunton JE, Slonim DK, Coller HA, et al. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA 2001; 98: 10787–92. 31 Butte AJ, Tamayo P, Slonim D, et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 2000; 97: 12182–86. 32 Watson MA, Gutmann DH, Peterson K, et al. Molecular characterization of human meningiomas by gene expression profiling using high-density oligonucleotide microarrays. Am J Pathol 2002; 161: 665–72. 33 Watson MA, Perry A, Budhjara V, et al. Gene expression profiling with oligonucleotide microarrays distinguishes World Health Organization grade of oligodendrogliomas. Cancer Res 2001; 61: 1825–29. 34 Hunter S, Young A, Olson J, et al. Differential expression between pilocytic and anaplastic astrocytomas: identification of apolipoprotein D as a marker for low-grade, non-infiltrating primary CNS neoplasms. J Neuropathol Exp Neurol 2002; 61: 275–81. 35 Kim S, Dougherty ER, Shmulevich L, et al. Identification of combination gene sets for glioma classification. Mol Cancer Ther 2002; 1: 1229–36. 36 Fathallah-Shaykh HM, Rigen M, Zhao LJ, et al. Mathematical modeling of noise and discovery of genetic expression classes in gliomas. Oncogene 2002; 21: 7164–74. 37 Fuller GN, Hess KR, Rhee CH, et al. Molecular classification of human diffuse gliomas by multidimensional scaling analysis of gene expression profiles parallels morphology-based classification, correlates with survival, and reveals clinically-relevant novel glioma subsets. Brain Pathol 2002; 12: 108–16. 38 Gutmann DH, Hedrick NM, Li J, et al. Comparative gene expression profile analysis of neurofibromatosis 1-associated and sporadic pilocytic astrocytomas. Cancer Res 2002; 62: 2085–91. 39 Ljubimova JY, Khazenzon NM, Chen Z, et al. Gene expression abnormalities in human glial tumors identified by gene array. Int J Oncol 2001; 18: 287–95. 40 Qi ZY, Li Y, Ying K, et al. Isolation of novel differentially expressed genes related to human glioma using cDNA microarray and characterizations of two novel full-length genes. J Neurooncol 2002; 56: 197–208. 41 Rickman DS, Bobek MP, Misek DE, et al. Distinctive molecular profiles of high-grade and low-grade gliomas based on oligonucleotide microarray analysis. Cancer Res 2001; 61: 6885–91. 42 Sallinen SL, Sallinen PK, Haapasalo HK, et al. Identification of differentially expressed genes in human gliomas by DNA microarray and tissue chip techniques. Cancer Res 2000; 60: 6617–22. 43 Sehgal A, Boynton AL, Young RF, et al. Application of the differential hybridization of Atlas Human expression arrays technique in the identification of differentially expressed genes in human glioblastoma multiforme tumor tissue. J Surg Oncol 1998; 67: 234–41. 44 Huang H, Colella S, Kurrer M, et al. Gene expression profiling of low-grade diffuse astrocytomas by cDNA arrays. Cancer Res 2000; 60: 6868–74. 45 Markert JM, Fuller CM, Gillespie GY, et al. Differential gene expression profiling in human brain tumors. Physiol Genomics 2001; 5: 21–33. 46 Mischel PS, Shai R, Shi T, et al. Identification of molecular subtypes of glioblastoma by gene expression profiling. Oncogene 2003; 22: 2361–73. 47 Nutt CL, Mani DR, Betensky RA, et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res 2003; 63: 1602–07. 48 Korenberg MJ. Gene expression monitoring accurately predicts medulloblastoma positive and negative clinical outcomes. FEBS Lett 2003; 533: 110–14. 49 Pomeroy SL, Tamayo P, Gaasenbeek M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002; 415: 436–42. 50 Tanwar MK, Gilbert MR, Holland EC. Gene expression microarray analysis reveals YKL-40 to be a potential serum marker for malignant character in human glioma. Cancer Res 2002; 62: 4364–68. 51 Rhee CH, Ruan S, Chen S, et al. Characterization of cellular pathways involved in glioblastoma response to the chemotherapeutic agent 1, 3-bis(2-chloroethyl)-1-nitrosourea (BCNU) by gene expression profiling. Oncol Rep 1999; 6: 393–401. 52 Bacolod MD, Johnson SP, Ali-Osman F, et al. Mechanisms of resistance to 1,3-bis(2-chloroethyl)-1-nitrosourea in human medulloblastoma and rhabdomyosarcoma. Mol Cancer Ther 2002; 1: 727–36. 53 Mukasa A, Ueki K, Matsumoto S, et al. Distinction in gene expression profiles of oligodendrogliomas with and without allelic loss of 1p. Oncogene 2002; 21: 3961–68. 54 Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95: 14863–68. 55 Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 1999; 96: 2907–12. 56 Hilsenbeck SG, Friedrichs WE, Schiff R, et al. Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 1999; 91: 453–59. 57 Kim SK, Lund J, Kiraly M, et al. A gene expression map for Caenorhabditis elegans. Science 2001; 293: 2087–92. 58 Heyer LJ, Kruglyak S, Yooseph S. Exploring expression data: identification and analysis of coexpressed genes. Genome Res 1999; 9: 1106–15. 59 Hastie T, Tibshirani R, Eisen MB, et al. ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol 2000; 1: RESEARCH0003. 60 Dahlquist KD, Salomonis N, Vranizan K, et al. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 2002; 31: 19–20. 61 Gygi SP, Rochon Y, Franza BR, Aebersold R. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999; 19: 1720–30. 62 Wang H, Zhang W, Fuller GN. Tissue microarrays: applications in neuropathology research, diagnosis, and education. Brain Pathol 2002; 12: 95–107. 63 Pollack JR, Sorlie T, Perou CM, et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA 2002; 99: 12963–68. 64 Hess KR, Fuller GN, Rhee CH, Zhang W. Statistical pattern analysis of gene expression profiles for glioblastoma tissues and cell lines. Int J Mol Med 2001; 8: 183–88. 65 Sasaki T, Hankins GR, Helm GA. Comparison of gene expression profiles between frozen original meningiomas and primary cultures of the meningiomas by GeneChip. Neurosurgery 2003; 52: 892–99. 66 Emmert-Buck MR, Bonner RF, Smith PD, et al. Laser capture microdissection. Science 1996; 274: 998–1001. 67 Schutze K, Lahr G. Identification of expressed genes by laser- mediated manipulation of single cells. Nat Biotechnol 1998; 16: 737–42. 68 Fink L, Seeger W, Ermert L, et al. Real-time quantitative RT-PCR after laser-assisted cell picking. Nat Med 1998; 4: 1329–33. 69 Bonaventure P, Guo H, Tian B, et al. Nuclei and subnuclei gene expression profiling in mammalian brain. Brain Res 2002; 943: 38–47. 70 Becker AJ, Wiestler OD, Blumcke I. Functional genomics in experimental and human temporal lobe epilepsy: powerful new tools to identify molecular disease mechanisms of hippocampal damage. Prog Brain Res 2002; 135: 161–73. 71 Mariani L, Beaudry C, McDonough WS, et al. Death-associated protein 3 (Dap-3) is overexpressed in invasive glioblastoma cells in vivo and in glioma cell lines with induced motility phenotype in vitro. Clin Cancer Res 2001; 7: 2480–89. 72 Luzzi V, Holtschlag V, Watson MA. Expression profiling of ductal carcinoma in situ by laser capture microdissection and high-density oligonucleotide arrays. Am J Pathol 2001; 158: 2005–10. 73 Kitahara O, Furukawa Y, Tanaka T, et al. Alterations of gene expression during colorectal carcinogenesis revealed by cDNA microarrays after laser-capture microdissection of tumor tissues and normal epithelia. Cancer Res 2001; 61: 3544–49. 74 Alevizos I, Mahadevappa M, Zhang X, et al. Oral cancer in vivo gene expression profiling assisted by laser capture ReviewGenomics and brain-tumour drug resistance For personal use. Only reproduce with permission from The Lancet.
  12. 12. THE LANCET Oncology Vol 5 February 2004 http://oncology.thelancet.com100 microdissection and microarray analysis. Oncogene 2001; 20: 6196–204. 75 Leethanakul C, Patel V, Gillespie J, et al. Distinct pattern of expression of differentiation and growth-related genes in squamous cell carcinomas of the head and neck revealed by the use of laser capture microdissection and cDNA arrays. Oncogene 2000; 19: 3220–24. 76 Sgroi DC, Teng S, Robinson G, LeVangie R, Hudson JR, Jr., Elkahloun AG. In vivo gene expression profile analysis of human breast cancer progression. Cancer Res 1999; 59: 5656–61. Review Genomics and brain-tumour drug resistance Call for papers The Lancet Oncology would like to invite all potential authors to consider submitting articles to the Advances in research section of the journal. Further details on this section can be found in the Instructions to authors, and all enquiries and submissions should be sent to the Editor at: david.collingridge@lancet.com Advances in research We consecutively imaged tumour proliferation and glucose utilisation in a L5178Y lymphoma-bearing mouse. This was done by the use of a high resolution, small-animal PET scanner, and the administration of [18 F]FLT and [18 F]FDG. Currently, [18 F]FDG is the most commonly used tracer for cancer detection with PET, and reflects the activity of glucose transporters and hexokinase (panel a). By contrast, tumour detection by [18 F]FLT is thought to depend on thymidine kinase 1 (TK1) activity—the key enzyme of the DNA salvage pathway (panel b). In mice with bilateral variants of L5178Y tumours (TK1 -/- variant implanted in lower left dorsal region, TK1 +/- variant implanted in lower right dorsal region), the +/- tumours produced 48% more TK1 enzyme on average, and also grew faster (27% shorter volume doubling time), compared with -/- tumours. Uptake of [18 F]FLT by PET was higher for the +/- tumour, compared with the -/- tumour. A converse pattern, however, was found in the [18 F]FDG image. These findings support the conclusion that the imaging of DNA synthesis is more accu- rate for the assessment of the proliferative potential of tumours, compared with the imaging of glucose consumption. This example highlights the unique possibility of modern PET techniques for non-invasive investigation of different tumour properties in small laboratory animals with ultra- high spatial resolution (~1 mm). Such techniques should encourage oncological research because they could potentially support drug development and also shorten the time between translation of preclinical research into a clinical setting. With respect to the new tumour proliferation marker, [18 F]FLT, our PET imaging experiments provide clarity on the uptake mechanism of this radiotracer by tumours. Clinical testing of this promising new tumour proliferation marker for PET is already underway and may provide a new more sensitive, non-invasive approach for monitoring treatment response and improving the accuracy of clinical staging. Small-animal imaging of tumour proliferation with PET Henryk Barthel, Pat Price, and Eric O Aboagye TK1Ϫ/Ϫ TK1ϩ/Ϫ TK1Ϫ/Ϫ TK1ϩ/Ϫ Kidney Kidney Brain A B Correspondence: Dr Henryk Barthel, Molecular Therapy and PET Oncology Research Group, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK. Tel: +49 (0)341 9718082. Fax: +49 (0)341 9718009. Email: barh@medizin.uni-leipzig.de For personal use. Only reproduce with permission from The Lancet.

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