213 Profiling cancer Marco Ciro, Adrian P Bracken and Kristian Helin In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer. parameters, such as patient history, tumour histology and the expression of often not very reliable tumour markers. Third, since cancer therapy is based on cancer classification, it is a difficult task to choose the optimal treatment. In the future, if oncologists are to optimally tailor the treatment of each patient, they will need information regarding the diagnosis and prognosis of the cancer based on an understanding of specific genetic alterations. Addresses European Institute of Oncology, Department of Experimental Oncology, via Ripamonti 435, 20141 Milan, Italy Correspondence: Kristian Helin; e-mail: [email protected] Since cancer is a genetic disease, and it is believed that the specific genetic changes in the cancer determine the phenotype, it should be possible to base cancer classification on the specific expression pattern of cellular genes (the transcriptome). The invention of DNA microarray technology [3–5] has made it possible to test this prediction and, as we will describe in this review, several studies have shown that gene-expression profiling can be used to accurately diagnose tumours and to predict clinical outcome. In a few cases, the use of microarray technology has been instrumental in identifying novel genes involved in cancer, and although the technology as such may not give us an understanding of the molecular mechanisms that result in cancer, it will provide us with several testable hypotheses. The hope is that the use of microarray technology in combination with classical gene function characterisation will result in the development of highly specific drugs tailored to the treatment of specific subsets of cancers. Current Opinion in Cell Biology 2003, 15:213–220 This review comes from a themed issue on Cell regulation Edited by Pier Paolo di Fiore and Pier Giuseppe Pelicci 0955-0674/03/$ – see front matter ß 2003 Elsevier Science Ltd. All rights reserved. DOI 10.1016/S0955-0674(03)00007-3 Abbreviations ALL acute lymphoblastic leukaemia AML acute myelogenous leukaemia BRCA1 breast cancer 1 CGH comparative genomic hybridisation DLBCL diffuse large-B-cell lymphoma EWS/FLI Ewing’s sarcoma transforming fusion protein EZH2 enhancer of zeste homologue 2 PDGFRa platelet-derived growth factor receptor a pRB retinoblastoma protein siRNA small interfering RNA In this review, we briefly summarise the major recent advances obtained through the determination of cancer gene-expression patterns, and discuss how these advances could result in a better understanding of tumour biology. Classifying cancer Introduction Cancer occurs because of genetic alterations that select for cells that escape the regulatory mechanisms restricting normal cell growth [1]. These genetic alterations include point mutations, and chromosomal deletions, amplifications and translocations [2]. The large clinical heterogeneity of these alterations poses several challenges to cancer researchers, pathologists and oncologists. First, it is an enormous task to identify the molecular mechanisms underlying the genesis of cancer, and although a large number of genes and molecular pathways central to the biology of cancer have been identified, there is an urgent need to accelerate the discovery of the key events leading to cancer. Second, it is difficult to accurately classify cancer, and currently used classifications are often not based on an understanding of the genetics of cancer. Instead, cancer classification is mostly based on empirical www.current-opinion.com Accurate cancer classification is essential since any effective cancer treatment is based on a detailed knowledge of the primary tissue of origin and its histopathological appearance. Until now, cancer classification methods have been based on the morphology of the tumour samples, the presence or absence of metastases and the degree of differentiation. In some rare cases, tumour subclasses have been delineated, for example the division of acute leukaemia into acute myelogenous leukaemia (AML) and acute lymphoblastic leukaemia (ALL). However, these classical methods are often inadequate, since many morphologically similar tumours classified in this way are found to have dramatically different clinical outcomes and responses to treatment. Recent reports have shown that the determination of the gene-expression profiles of tumours using microarrays is a promising alternative approach. Current Opinion in Cell Biology 2003, 15:213–220 214 Cell regulation The first reports to show that gene-expression profiling can be used for the discovery and prediction of relevant tumour classes came from studies of leukaemias and lymphomas [6,7]. Golub and colleagues [6] applied microarrays to a set of leukaemia samples belonging to the AML or ALL classes. Solely on the basis of gene-expression profiles, they found that samples were clustered into two groups, corresponding to the known AML and ALL classes. In addition, they identified a cluster of 50 genes that best distinguished AMLs from ALLs. Significantly, this cluster of 50 genes was sufficient to classify AMLs and ALLs in an independent set of leukaemia samples. This study therefore demonstrated for the first time that gene-expression profiling could be applied to predict tumour classes in the absence of any previous knowledge. of co-ordinately expressed genes well correlated with the mitotic index. They also found clusters of genes related to specific cell types, such as stromal cells, B and T cells, endothelial cells, and macrophages, reflecting the potential of microarray screenings in uncovering the histological complexity of breast tumours. Importantly, the same group recently showed that these classes could be correlated to different patients’ survival [8] and their work therefore demonstrated that breast tumours could be potentially classified into clinically relevant classes based on gene expression analysis. More recently, several other groups have applied microarray technology to study different types of tumours, including melanomas, lymphomas and leukaemias, and breast, lung, brain, and prostate cancers [6–9,10,11,12, 13,14,15,16,17,18,19,20]. Because of space limitations in this review, we will illustrate some of the more interesting recent advances by focusing on a few studies relating to breast cancer. In breast cancer patients, the detection of lymph node metastases at the time of surgery is currently used to determine whether the cancer has spread or not [21]. The determination of metastases or the likelihood of its occurrence is an essential parameter for oncologists to decide whether a breast cancer patient should receive further (‘adjuvant’) treatment following removal of the primary tumour. Since adjuvant therapy is associated with several toxic side effects, it is extremely valuable to identify markers that can reliably predict whether an in situ breast cancer will develop metastases or not. This prompted van’t Veer and colleagues [14] to perform gene-expression profiling of 78 breast cancers from lymph-nodenegative patients who had not received adjuvant therapy. The authors looked for differences in the gene-expression profiles between patients who subsequently developed metastases and patients who did not. Then, they generated a list of 70 differentially expressed genes, which they termed a ‘poor prognosis signature’. Significantly, they validated this prognosis signature on an independent group of lymph-node-negative patients and found that the predictor identified the disease outcome with high accuracy. They therefore concluded that a signature for poor prognosis already exists in primary breast tumours at the time of surgery and that it can be used to precisely predict survival. Importantly, the predictive power of the proposed ‘prognosis classifier’ is much stronger than currently used methods, especially for identifying those patients who did not relapse after surgery, but would have been unnecessarily treated with adjuvant therapy. Breast cancer patients have different clinical outcomes and vary in their responsiveness to treatment, despite an overall similarity in tumour morphology. Therefore, gene-expression profiling could represent a very promising tool, not only in delineating breast cancer molecular profiles as compared with normal cellular profiles, but also in deriving new clinical and therapeutically relevant classes. Perou et al. [9] derived a molecular profile of breast cancer by characterising 65 tumour samples from 42 different patients. Hierarchical clustering allowed the identification of groups of samples based on gene expression only. They termed the largest cluster of genes identified as the ‘proliferation cluster’, defined as a set As a final example of the power of microarray technology in the classification of breast cancer, Hedenfalk and colleagues demonstrated that there are distinct geneexpression profiles that differentiate tumour samples with mutations in the BRCA1 (breast cancer 1) and BRCA2 tumour suppressor genes, as compared with sporadic cancers lacking these mutations [10]. Interestingly, one tumour sample defined as a ‘sporadic tumour’ by classical diagnostic methods was by contrast classified, according to its molecular profile, as a carrier of a BRCA1 mutation. Further controls revealed that although the BRCA1 gene was intact in this tumour, the promoter was in fact silenced by DNA methylation. These Subsequently, another study showed that gene-expression profiling could in addition be used to predict clinical outcome [7]. In this study, gene-expression profiles were determined on samples from diffuse large-B-cell lymphoma (DLBCL) patients, who are known to have highly variable clinical outcomes combined with varying therapeutic responses. On the basis of gene-expression profiling, the tumour samples were clustered into two classes related to the different stages of B cell differentiation, one resembling normal germinal centre B cells, and the other sharing the molecular profile of in-vitro-activated B cells. Significantly, the two classes showed strong correlation with clinical outcomes, demonstrating that patients with germinal centre B-like DLBCL had a better overall survival. On the basis of these results, the authors proposed that the two classes could be referred to as two separate diseases with probably different clinical outcomes. Current Opinion in Cell Biology 2003, 15:213–220 www.current-opinion.com Profiling cancer Ciro, Bracken and Helin 215 observations strongly point to the efficacy of expression profiling in detecting changes in gene expression in the absence of germline information. This is of particular interest, as it is becoming increasingly clear that epigenetic events are very important determinants of tumour development [22]. Identifying key players in human cancer Although several studies, including those mentioned above, have shown the potential use and power of gene-expression profiling for the classification of cancer, there are significantly fewer reports in which geneexpression profiling has been applied successfully to identify key genes critical for tumourigenesis. Indeed, thus far we have learned relatively little about the molecular mechanisms leading to cancer by studying the prognosis classifiers identified in the various studies. Despite this limited success, the hope is that geneexpression profiling will contribute to the identification of the key genes critical for tumourigenesis. Identification of such genes is of great importance, since these genes are potential targets for the development of tumour-specific drugs with fewer toxic side-effects. Moreover, the identification of these genes will lead to a better understanding of tumour biology. Below, we describe a few success stories in which microarray technology has led to the identification of potential key regulators of human cancer. The first story comes from the work of Trent and colleagues. A new taxonomy of melanomas was recently proposed, with possible clinical and therapeutic implications [11]. Cluster analysis based on gene-expression data classified 31 melanoma samples into two main groups. These groups were very different in the expression of genes involved in cell motility and invasion, suggesting that the new classes differ in their metastatic potential, as was subsequently confirmed by in vitro scratch healing assays. Interestingly, Wnt5a, a member of the Wnt family of ligands [23], was among the genes that strongly correlated with enhanced motility and invasiveness. In agreement with a possible involvement of Wnt5a in metastasis, the same laboratory showed that the overexpression of Wnt5a increased the metastatic phenotype of melanoma cells and that an antibody to the Wnt5a receptor, Frizzled-5, was effective in inhibiting the invasive capacity of aggressive melanoma cells in vitro [24]. Therefore, in addition to suggesting a gene-expression profile for metastatic melanomas, these studies also pointed to the Wnt5a signalling pathway as an attractive target for therapeutic inhibition of tumour invasivity in melanomas. Medulloblastoma is a highly metastatic brain tumour of childhood that is difficult to distinguish from other brain tumours using the classical histopathological methods. The main predictor of patient survival is the metastatic spread and, as in the cases of melanoma and breast cancer, www.current-opinion.com there is an urgent need for reliable genetic markers that can clearly differentiate the metastatic from the nonmetastatic states. In a recent study, MacDonald et al. used gene-expression profiling to identify 85 genes differentially expressed between metastatic and non-metastatic medulloblastomas [25]. They found that the PDGFRa (platelet-derived growth factor receptor a) and members of the Ras/MAPK (mitogen-activated protein kinase) downstream pathway were slightly overexpressed in metastatic tumours, both at the mRNA and protein levels. Furthermore, they showed that an antibody against PDGFRa or specific inhibitors of the Ras/ MAPK pathway reduced the cell adhesion and migration properties of one medulloblastoma cell line in vitro. These findings suggest that this signalling pathway is involved in metastatic spread and may suggest that inhibitors of PDGFRa or of the Ras/MAPK pathway can be used in the treatment of medulloblastoma. As a final example of how microarray studies have helped the identification of key markers in cancer progression, Dhanasekaran et al. have successfully applied geneexpression profiling to the study of prostate cancer [20]. Prostate tumours are not only one of the most common malignancies in males, but are also among the most clinically and morphologically heterogeneous cancers, being almost incurable if they present metastatic spread. The authors described a molecular signature of prostate cancer progression by analysing cDNA arrays from normal prostate and from localised and metastatic prostate specimens. They found 55 genes to be significantly upregulated in metastatic prostate cancer. Interestingly, one of the strongest markers in the list was the Polycomb protein enhancer of zeste homologue 2 (EZH2). In a follow-up to this study, the same group confirmed that EZH2 is highly expressed in metastatic prostate cancer, as compared with benign tissues [26]. Significantly, high EZH2 expression was found to correlate with poor clinical outcome. In addition, inhibition of EZH2 expression by small interfering RNA (siRNA) resulted in a significant impairment in cellular growth of prostate cancer cells. These findings strongly suggest that EZH2 is a marker for prostate cancer progression with a potential role in the regulation of cell growth. Further studies will be required to determine if EZH2 is actually causal of this cancer progression in addition to being an excellent marker. Current limitations in gene-expression profiling It is clear that gene-expression profiling has been highly successful in predicting the tumour gene subclasses and in identifying potential novel key regulatory genes involved in the genesis of cancer. However, it is important to realise that the technique is also very limited, since it exclusively relies on measuring changes in gene expression. For example, none of the above screens identified Current Opinion in Cell Biology 2003, 15:213–220 216 Cell regulation Figure 1 Uncontrolled proliferation Cancer phenotypes Resistance to apoptosis and growth arrest New Metastasis RAS p16 BMI1 CycD TBX2 CDK4 ? WNTs E-cadherin CycE CDK2 p14 ? Pathway pRb MDM2 E2F p53 β-catenin TCF ? PKC ? Motility Target genes CDC6 p21 CCND1 ... CCNE1 FAS MYC ... CDC25A BAX ... ... MCMs MDM2 ... ... p14 APAF1 ... APAF1 ... Current Opinion in Cell Biology From gene expression to functional pathways? The acquisition of specific cancer phenotypes, such as uncontrolled proliferation and resistance to apoptosis, is a consequence of specific genetic alterations that determine a global change in gene expression. In the past decade, classical biology and genetics have shown that alterations in the ARF ! MDM2 ! p53 and the p16 ! Cyclin D ! pRB ! E2F pathways are common in most human cancers, resulting in insensitivity to antigrowth stimuli and aberrant proliferation, respectively. The critical players of these pathways are shown. Genes that may be found to have altered gene-expression levels or already have been detected as overexpressed by microarray analysis are shown in red. Significantly, important oncogenes such as RAS or key tumour suppressors such as p53, pRB or p14ARF (p14) have not been identified by geneexpression analysis of tumours. Moreover, alterations in the levels of genes such as APAF1 or CDKN1A (p21), whose expression is suppressed in certain cancers, are difficult to detect by microarray analysis. The limitation of microarray technology in identifying key players in cancer is due to the fact that the technology relies on detection of gene expression only. Oncogenes can be activated by point mutations without affecting the level of expression, and point mutations or subtle changes in the level of tumour suppressor gene expression are sufficient to result in tumourigenesis, but will be undetected. Therefore, we believe that the major contribution of microarray technology in the current effort to identify key molecular players in cancer will be in the identification of functional clusters of genes shared between different cancer types. By surveying these gene clusters, traditional biology and genetic approaches may gain insights into novel cancer phenotypes and key pathways central to human cancer, with new potential oncogenes and tumour suppressor genes. As an example, the WNT5a gene, initially identified by gene-expression profiling as a robust marker of metastatic behaviour in melanomas, has been proposed recently as a critical regulator of tumour invasiveness in melanoma cells through the activation of the protein kinase C (PKC) pathway. CCND1, cyclin D1; CCNE1, cyclin E1; CDK, cyclin-dependent kinase; CycD, cyclin D; MCM, mini chromosome maintenance deficient; TCF, T-cell-specific transcription factor. the RAS oncogene as being an important player in the genesis of tumours (see also Figure 1). The reason for this is that RAS is activated by point mutations, and not overexpression [27], and the microarrays currently in use will not pick up such changes. Decreased levels of tumour suppressor genes such as TP53 (encoding p53) or RB1 (encoding retinoblastoma protein [pRB]) have also not been identified in expression profiling, since often only one of the alleles of these genes is lost in cancer and the other mutated allele is expressed. Current Opinion in Cell Biology 2003, 15:213–220 Although gene-expression profiling is sensitive enough to detect even twofold changes in expression levels, most studies have been performed using heterogeneous tumour tissue, which contains non-tumour cells in addition to the tumour cells. Therefore, most gene-expression profiles published so far have not been able to detect twofold changes in the tumour cells themselves. To solve this problem several researchers are in the process of using microdissected tumour tissue [28], and it will be interesting to see whether gene-expression www.current-opinion.com Profiling cancer Ciro, Bracken and Helin 217 profiling of the isolated tumour cells will lead to the identification of the classical tumour suppressor genes as part of prognosis classifiers. Another problem for the identification of the critical alterations of important pathways in cancer is posed by the high degree of genetic instability observed in the tumours that results in a global change in gene expression, affecting many cellular processes at a time. This makes the finding of potential key genes even more difficult. Therefore, to make sense of the complex molecular profiles of cancers, researchers need to build cellular models that reflect what might occur in the tumour. In this way, it could be possible to identify potentially interesting genes, which subsequently can be tested in functional assays to assess their importance in cancer progression. To illustrate this point, Lessnick and colleagues [29] have recently demonstrated the utility of linking data from transcriptional profiling to traditional functional assays in Ewing’s sarcoma. They performed microarray expression profiling of a cell line expressing an inducible form of the EWS/FLI (Ewing’s sarcoma transforming fusion protein) oncogene fusion protein. In this setting, cells underwent growth arrest upon induction of the oncogene, presumably due to the activation of a tumour suppressor. Interestingly, many genes linked to growth suppression were upregulated by EWS/FLI. Among these genes, TP53 was identified as the most likely candidate to mediate growth arrest. Subsequently, the authors demonstrated that the EWS/FLI oncogene stress response caused an early p53-dependent but pRB-independent growth arrest. Therefore, they proposed p53 as a potential tumour suppressor in Ewing’s sarcoma. In addition, since the EWS/FLI-expressing cells were found to eventually undergo a late growth inhibition — even if the p53 or pRB pathways were impaired — the authors also speculated that additional growth inhibitory pathways, and therefore tumour suppressors, remain to be identified. It is likely that in the very near future the genome-wide approach to gene expression will be improved by new array-derived technologies [30], overcoming some of the current limitations. In particular, new technologies are being developed that will allow screening for global changes in protein expression [31,32] or for protein– ligand interactions [33]. Furthermore, the combination of existing techniques will most probably result in a better understanding of cancer genetics. For example, by implementing comparative genomic hybridisation (CGH) with cDNA microarray profiling, Pollack and colleagues were able to link alterations in copy number to specific geneexpression profiles in breast tumours [34,35]. Finally, a new array-based technique has been developed that allows for the simultaneous analysis of alterations in gene www.current-opinion.com expression and promoter methylation on the basis of the identification of expressed CpG-island sequence tags (ECIST) [36]. This technique is important, since DNA methylation is a common epigenetic event that affects gene transcription in cancer cells. Conclusions Undoubtedly, the ability of microarray technology to survey the complex molecular profiles of cancers has enormous potential to improve the methods currently used by clinicians to classify cancers and to predict patient survival. However, the potential application of gene-expression profiling as a routinely used diagnostic method is an open question, in part because of feasibility and cost. Carefully designed and standardised experimental approaches together with the generation of special facilities to which hospitals can send tumour tissues for gene-expression analyses should make gene-expression profiling feasible. Meanwhile, the development of mass-produced arrays for specific tumours and more competition among the producers of chips should dramatically decrease the cost. Another issue that still needs to be addressed is the precision of the various ‘prognosis clusters/signatures’. This will require the testing of larger numbers of samples, the development of common protocols for sample preparation and data mining, and the possibility for researchers to compare independent datasets from different laboratories. There are several essential steps to reach this goal which, in addition to better interactions between clinicians, pathologists and researchers, would require the generation of databases where researchers are invited to deposit their primary datasets. From these public databases, scientists will be able to derive invaluable clues for their research. To obtain more knowledge regarding the molecular mechanisms that lead to cancer, gene-expression profiling will certainly have an important role in the coming years. When any biologist considers the fact that all cancer gene-expression profiles show high levels of proliferation-related genes, it is of course not surprising that the growth-control pRB pathway is found deregulated in most human cancers. However, in addition to uncontrolled growth, it is becoming increasingly evident that additional common sets of altered phenotypes are shared among all cancer forms, for instance the ability to metastasise and resistance to apoptosis. This is despite the fact that thousands of different cancers exist with complex morphologies and various genetic changes [1]. Therefore, it is logical to think that by surveying the available gene-expression databases, new functional gene clusters can be identified that may reveal new cellular pathways that are important for cancer progression (Figure 1). Upon further study using classical biological and genetic approaches, these new Current Opinion in Cell Biology 2003, 15:213–220 218 Cell regulation monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14:1675-1680. pathways will probably reveal new oncogenes and tumour suppressors. 6. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537. 7. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403:503-511. 8. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001, 98:10869-10874. 9. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA et al.: Molecular portraits of human breast tumours. Nature 2000, 406:747-752. Update To further validate the prognostic potential of the breastcancer-specific gene-expression profile identified by van’t Veer et al., the same group recently extended the study to a cohort of 295 patients [37]. This time, both lymphnode-negative or lymph-node-positive breast cancer were analysed in patients younger than 53 years. The authors classified 115 patient to the good prognosis group and 180 patients to the bad prognosis group, based on the expression of the 70 predictor genes previously identified. The prognosis signature best performed in predicting the risk of distant metastases and the overall survival within the first five and ten years. In addition, Kaplan-Meyer analysis of the probability of remaining metastasis-free showed that the gene-expression profiling is a far better predictor than other currently used criteria based on histological and clinical characteristics. Interestingly, the predictive power of the molecular prognosis profile didn’t correlate with the lymph-node status. This remarkable observation implies that the ability of a breast tumour to develop distant metastasis is an earlyacquired capability detectable in the primary tumour by gene-expression profiling, and that lymph-node positivity at the time of diagnosis is not a good indicator of later metastasis. These data strongly suggest that gene-expression profiling at the moment of surgery is a far better outcome predictor for breast cancer than any other currently used criteria and may also be a very powerful tool for forecasting patients who would benefit most from adjuvant therapy. Acknowledgements We thank Claire Attwooll and Fraser McBlane for helpful comments on the manuscript. The work in the authors’ laboratory is supported by grants from the Italian Association for Cancer Research (AIRC), the Italian Foundation for Cancer Research (FIRC), the Human Science Frontiers Science Programme, the EU’s Fifth Framework Programme, the Association for International Cancer Research, and the Italian Health Ministry. References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100:57-70. 2. Lengauer C, Kinzler KW, Vogelstein B: Genetic instabilities in human cancers. Nature 1998, 396:643-649. 3. Schulze A, Downward J: Navigating gene expression using microarrays — a technology review. Nat Cell Biol 2001, 3:E190-E195. 4. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270:467-470. 5. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H et al.: Expression Current Opinion in Cell Biology 2003, 15:213–220 10. Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP et al.: Geneexpression profiles in hereditary breast cancer. New Engl J Med 2001, 344:539-548. The authors performed gene-expression profiling of 21 primary breast carcinomas and identified 176 genes that were differentially expressed in tumours with BRCA1 mutations and tumours with BRCA2 mutations. Fifty-one of these genes were found to best differentiate BRCA1-mutation-positive, BRCA2-mutation-positive and sporadic cases of primary breast cancer. Although a limited number of specimens were used and independent datasets were not analysed, the study indicates that geneexpression profiles can increase the specificity of the molecular classification of breast cancer. 11. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A et al.: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000, 406:536-540. 12. Schoch C, Kohlmann A, Schnittger S, Brors B, Dugas M, Mergenthaler S, Kern W, Hiddemann W, Eils R, Haferlach T: Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles. Proc Natl Acad Sci USA 2002, 99:10008-10013. 13. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C et al.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002, 415:436-442. This is an outstanding study, in which the authors first showed the feasibility of distinguishing medulloblastomas from other embryonal CNS (central nervous system) tumours on the basis of gene-expression profiling. They also demonstrated that expression analysis could be used to identify the desmoplastic medulloblastoma subclass among other medulloblastomas. Finally, they performed gene-expression profiling on 60 medulloblastomas with the aim of identifying a prognosis classifier. By a supervised learning approach they identified a prognosis classifier containing eight genes that with high significance could predict patient survival (only 13 out of 60 classification errors). Using the same set of medulloblastomas, the authors showed that the prognosis classifier substantially improved the currently available prognostic parameters for survival. An independent dataset confirming these studies could result in a very useful clinical tool to predict patient survival. 14. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536. This outstanding paper offers a wonderful example of the enormous potential of gene-expression profiling for the prediction of clinical outcome of cancer patients. van’t Veer and colleagues performed geneexpression profiling of 78 lymph-node-negative primary breast cancers and identified a group of 70 genes, which they called a ‘poor prognosis’ signature. They showed that this signature outperforms all currently used clinical parameters in predicting disease outcome and they suggested that if independent studies confirm their data, the poor prognosis signature will dramatically help in the determination of patients who would benefit from adjuvant therapy. www.current-opinion.com Profiling cancer Ciro, Bracken and Helin 219 15. Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS et al.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002, 8:68-74. Similar to [7], this study used gene-expression profiling to subclassify B-cell lymphomas. A reliable 30-gene predictor was shown to correctly classify 71 out of 77 tumours (58 DLBCLs and 19 follicular lymphomas). Furthermore, the authors identified a 13-gene predictor that was able to predict five-year overall survival rates with high significance. Interestingly, three genes from this predictor were also identified in [7] to correlate with poor prognosis. 16. Armstrong SA, Staunton JE, Silverman LB, Pieters R, den Boer ML, Minden MD, Sallan SE, Lander ES, Golub TR, Korsmeyer SJ: MLL translocations specify a distinct gene expression profile that distinguish a unique leukemia. Nat Genet 2002, 30:41-47. This paper shows that the subset of human acute leukaemias with a translocation of the mixed-lineage leukaemia gene (MLL) have a specific gene-expression signature significantly different from the AML and ALL profiles. Gene-expression profiling was applied to 20 ALL, 20 AML and 17 MLL patients. The authors showed that MLL has a distinct molecular profile from AML and ALL and expresses many myeloid- and lymphocytespecific genes. In addition, the authors developed a three-class predictor based on 100 genes that correctly classified 10 out of 10 independent samples. The authors suggested that MLL leukaemia should be referred to as a distinct disease. 17. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D’Amico AV, Richie JP et al.: Geneexpression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1:203-209. 18. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG et al.: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002, 8:816-824. The importance of this study is the identification of a prognosis identifier for early-stage lung adenocarcinomas that can predict patient survival. This prognosis identifier would, when introduced in the clinic, allow the oncologist to selectively propose adjuvant therapy for patients with poor prognosis. 19. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001, 7:673-679. 20. Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature 2001, 412:822-826. Prostate-specific antigen (PSA) is currently used as a prognostic marker for the diagnosis of prostate cancer. However, nonmalignant conditions such as benign prostatic hyperplasia (BPH) also result in elevated levels of PSA. To identify better markers for prognosis, the authors of this paper performed gene expression profiling and successfully identified several genes that were specifically expressed in metastatic prostate cancer. 21. Shek LL, Godolphin W: Model for breast cancer survival: relative prognostic roles of axilliary nodal status, TNM stage, estrogen receptor concentration, and tumor necrosis. Cancer Res 1988, 48:5565-5569. 22. Jones PA, Baylin SB: The fundamental role of epigenetic events in cancer. Nat Rev Genet 2002, 3:415-428. 23. Polakis P: Wnt signaling and cancer. Genes Dev 2000, 14:1837-1851. 24. Weeraratna AT, Jiang Y, Hostetter G, Rosenblatt K, Duray P, Bittner M, Trent JM: Wnt5a signaling directly affects cell motility and invasion of metastatic melanoma. Cancer Cell 2002, 1:279-288. In previous work from the same group [11], WNT5a was identified as one of the best markers for the metastatic behaviour of human melanomas. In this paper the authors show that the Wnt5a/Frizzled-5 signalling pathway is a critical determinant of the motility and invasion capabilities of melanoma cells. In fact, WNT5a overexpression in melanoma cell lines caused alteration of cell morphology with increased cell motility and invasion, and increased levels of activated protein kinase C (PKC). Furthermore, an antibody raised against Frizzled-5 was effective in inhibiting PKC activation and cell motility. These findings were supported by the observation that high levels of WNT5a protein were found in high-grade tumours. www.current-opinion.com 25. MacDonald TJ, Brown KM, LaFleur B, Peterson K, Lawlor C, Chen Y, Packer RJ, Cogen P, Stephan DA: Expression profiling of medulloblastoma: PDGFRA and the RAS/MAPK pathway as therapeutic targets for metastatic disease. Nat Genet 2001, 29:143-152. In this paper the authors analysed 10 metastatic and 13 non-metastatic medulloblastomas and built a class predictor based on the 85 genes that best discriminated between the two groups. Once tested on an independent set of samples, four out of five medulloblastomas were correctly classified according to their diagnosis. Despite the low change in gene expression, members of the PDGRA signalling pathway were picked up for further studies and shown to be essential for the invasive potential in a medulloblastoma cell line. 26. Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP et al.: The Polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 2002, 419:624-629. In this paper, which is a continuation of [20], the authors confirmed that the levels of one of the genes identified in their previous screen, EZH2, are highly expressed at both the mRNA and protein levels in metastatic prostate cancer as compared with the benign state. They correlated the high levels of EZH2 in clinically localised prostate cancers with poor prognosis. In agreement with a causal role of EZH2 in the formation of metastatic tumours, abrogation of EZH2 expression by small interfering RNA resulted in less proliferation of the prostate cancer cell line. However, control normal cells or non-metastatic prostate cancer cells were not tested in the experiments, and it is therefore not known if EZH2 is specifically required for metastatic prostate cells to grow or if it is required for normal cell growth. 27. Bos JL: The ras gene family and human carcinogenesis. Mutat Res 1988, 195:255-271. 28. Maitra A, Wistuba II, Gazdar AF: Microdissection and the study of cancer pathways. Curr Mol Med 2001, 1:153-162. 29. Lessnick SL, Dacwag CS, Golub TR: The Ewing’s sarcoma oncoprotein EWS/FLI induces a p53-dependent growth arrest in primary human fibroblasts. Cancer Cell 2002, 1:393-401. This paper is a good example of how microarray technology can be applied to model cellular systems with the aim of identifying cooperating mutations in human cancer. Ewing’s sarcomas contain a small group of chromosomal translocations, of which the most common gives rise to the EWS/FLI fusion protein. Gene-expression profiling was performed using human diploid fibroblasts expressing EWS/FLI, and the authors found that the profiles resembled closely the profile of the cancer itself. The expression of EWS/FLI induced a growth arrest, suggesting that other genetic changes were required for tumourigenesis. Significantly, their gene-expression profiles revealed that p53 is transcriptionally upregulated upon EWS/FLI expression. Inhibition of p53 resulted in the abrogation of the growth arrest, supporting a role for p53 as a tumour suppressor in Ewing’s sarcoma. This approach may be applied to several cancer models with the hope of identifying several other key cooperative mutations. 30. Mohr S, Leikauf GD, Keith G, Rihn BH: Microarrays as cancer keys: an array of possibilities. J Clin Oncol 2002, 20:3165-3175. 31. Han DK, Eng J, Zhou H, Aebersold R: Quantitative profiling of differentiation-induced microsomal proteins using isotopecoded affinity tags and mass spectrometry. Nat Biotechnol 2001, 19:946-951. 32. Shiio Y, Donohoe S, Yi EC, Goodlett DR, Aebersold R, Eisenman RN: Quantitative proteomic analysis of Myc oncoprotein function. EMBO J 2002, 21:5088-5096. 33. Mirzabekov A, Kolchinsky A: Emerging array-based technologies in proteomics. Curr Opin Chem Biol 2002, 6:70-75. 34. Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, Williams CF, Jeffrey SS, Botstein D, Brown PO: Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 1999, 23:41-46. 35. Pollack JR, Sørlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO: 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, 99:12963-12968. In this paper the authors show a global analysis of genomic alterations across 6691 genes in 44 breast tumour and 10 breast cancer cell lines using CGH analysis. This approach allowed for the precise mapping of recurrent regions of DNA amplification and loss. In addition, by combining Current Opinion in Cell Biology 2003, 15:213–220 220 Cell regulation these data with gene-expression levels from a previous report [9], they showed that 62% of the amplified genes were also highly expressed. Furthermore, they report that 12% of all the variations in mRNA levels among breast tumours could be attributed to variation in gene copy number. Therefore, these data point to a stronger than previously expected influence of DNA copy number on the overall deregulated levels of gene expression in tumours. 36. Shi H, Yan PS, Chen CM, Rahmatpanah F, Lofton-Day C, Caldwell CW, Huang TH: Expressed CpG island sequence tag microarray for dual screening of DNA hypermethylation and gene silencing in cancer cells. Cancer Res 2002, 62:3214-3220. Current Opinion in Cell Biology 2003, 15:213–220 37. van de Vijver M, He YD, van’t Veer LJ, Dai H, Hart AAM, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ et al.: A geneexpression signature as a predictor of survival in breast cancer. New Engl J Med 2002, 347:1999-2009. This study is an extension of van’t Veer et al. (2002) [14]. The expression profiles of tumours from 295 patients were analysed (including the 78 used in [14]), and the gene-expression ratios for the 70 poor prognosis genes were examined in more detail. This study shows that the previously identified 70 poor prognosis genes are more reliable as a prognosis predictor than previously used criteria, and suggest that the determination of the expression of these 70 genes might be a useful tool to decide on which patients would benefit from adjuvant therapy. www.current-opinion.com
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