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Integrated genomics of chemotherapy resistant ovarian cancer: A role for extracellular matrix, TGFbeta and regulating microRNAs
The International Journal of Biochemistry & Cell Biology 42 (2010) 25–30
Contents lists available at ScienceDirect
The International Journal of Biochemistry & Cell Biology journal homepage: www.elsevier.com/locate/biocel
Signalling networks in focus
Integrated genomics of chemotherapy resistant ovarian cancer: A role for extracellular matrix, TGFbeta and regulating microRNAs Jozien Helleman a , Maurice P.H.M. Jansen a , Curt Burger b , Maria E.L. van der Burg a , Els M.J.J. Berns a,∗ a b
Department of Medical Oncology, Erasmus MC/JNI, Rotterdam, The Netherlands Department of Gynaecology, Erasmus MC, Rotterdam, The Netherlands
a r t i c l e
i n f o
Article history: Received 14 July 2009 Received in revised form 9 October 2009 Accepted 13 October 2009 Available online 23 October 2009 Keywords: Ovarian cancer Chemotherapy resistance Extracellular matrix TGFbeta Epithelial to mesenchymal transition miRNAs
a b s t r a c t Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world. Our explorative pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer reveals TP53 and transforming growth factor beta as key genes. Furthermore, the extracellular matrix was associated with chemotherapy resistance in ovarian cancer as well as endocrine resistance in breast cancer. Pathway analysis again revealed transforming growth factor beta as a key gene regulating extracellular matrix gene expression. A model is presented based on literature linking transforming growth factor beta, extracellular matrix, integrin signalling, epithelial to mesenchymal transition and regulating microRNAs with a (bivalent) role in chemotherapy response. © 2009 Elsevier Ltd. All rights reserved.
1. Background Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world (Parkin et al., 2005). The management of ovarian adenocarcinoma has improved over the last 20 years due to better-quality debulking surgery and chemotherapy: i.e. platinum-based drugs and later the addition of taxanes (van der Burg et al., 1995; Harries and Gore, 2002). Despite these treatment improvements, 20–30% of the patients never have a clinical remission and the majority of women eventually relapse with generally incurable disease (Cannistra, 2004). These treatment difficulties are due to the advanced stage of the disease at diagnosis and platinum-based chemotherapy resistance resulting in a poor overall 5-year survival of only 30% (Moss and Kaye, 2002). The specific genes and pathways involved in platinum-based chemotherapy resistance as well as their role in the resistance seen in the clinic have yet not been clearly established. However, a number of molecular profiling studies including ours, have revealed gene-sets that are associated with resistance to platinum-
∗ Corresponding author at: Department of Medical Oncology, Erasmus MC, Josephine Nefkens Institute, Room Be424, P.O. Box 1740, 3000 CA Rotterdam, The Netherlands. Tel.: +31 107044370; fax: +31 107044377. E-mail address: [email protected] (E.M.J.J. Berns). 1357-2725/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocel.2009.10.016
based chemotherapy in ovarian cancer (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006; Dressman et al., 2007; Bachvarov et al., 2006). Besides prediction of response, these microarray data could also give us more insight in the mechanisms involved. In this review, we describe a pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer, with focus on the extracellular matrix, TGFbeta and regulating miRNAs. Through integration of the data we aim to gain more insight in platinum-based chemotherapy resistance mechanisms in human ovarian cancer.
2. Pathway analysis of published gene-sets associated with platinum resistance 2.1. Gene-sets Retrospective expression profiling studies resulted in the discovery of gene-sets associated with platinum-based chemotherapy resistance (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006; Dressman et al., 2007; Bachvarov et al., 2006). However, an overlap of only seven genes was observed, each between only two gene-sets. Furthermore, only one of these genes, galectin 1 (LGALS1), showed a similar
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expression, i.e. underexpressed in the resistant when compared to the sensitive carcinomas, for both studies (Bernardini et al., 2005; Peters et al., 2005). However, these studies are challenged by several different clinical and experimental characteristics that could have resulted in the observed lack of overlap between the discovered genes: (1) small number of ovarian cancer specimens analysed and mostly no independent validation. (2) Heterogeneity of ovarian cancer (even when mostly advanced stage and serous histology are included). (3) Variety of therapy modalities and used response criteria. (4) Microarray platforms (statistical) data analysis and techniques used (most of these studies did not use whole genome microarrays resulting in a limited overlap from the start). Thus overlapping pathways may be present but overlooked since each gene signature could contain different genes from the same pathway.
3.2. TGFbeta In normal ovarian surface epithelial cells autocrine growth inhibition and differentiation is sustained via TGFbeta and loss of these responses is often seen in cancer (Massague and Gomis, 2006). Interestingly, this is also observed in about 30–40% of ovarian cancer cases while the receptors and the downstream Smad signalling are generally intact (Landen et al., 2008; Bast et al., 2009). This suggests inhibition of the signalling pathway downstream of the Smad components. Indeed, expression of genes that bind to and inhibit Smads have been shown to be altered in ovarian cancer, for instance DACH1, EVI1, SKILL and RUNX (Bast et al., 2009; Sunde et al., 2006). However, this is more profound in early compared to late stage ovarian cancer which might suggest a role in early stage of carcinogenesis (Sunde et al., 2006). Besides the lost cytostatic effect, TGFbeta profoundly alters the tumour microenvironment including the ECM that possibly causes the therapy resistance.
2.2. Pathway analysis Gene Ontology (http://www.geneontology.org) was used to make a functional subdivision of the genes for subsequent pathway analysis by selecting the functional processes that were common in the seven published gene-sets (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006). A process was selected if more then 10% of the genes listed by that study were involved in the process and if it was present in more than one of the studies (described previously by us (Helleman, 2006)). The six most prevalent processes were cell growth and-or maintenance (84 genes), transcription (53 genes), protein metabolism (53 genes), signal transduction (45 genes), organismal physiological process (35 genes) and response to external stimulus (31 genes). With the genes belonging to these processes (so-called focus genes), networks were generated using Ingenuity Pathway Analysis (IPA). The genes linked to at least two of the focus genes were labelled as key genes. Remarkably, tumour necrosis factor (TNF) was a key-gene for all six processes, TP53 for five processes and transforming growth factor beta (TGFbeta) for four of the processes. A similar analysis with four mock data sets resulted in the selection of less key genes and less networks per process. Although clearly less prominent, TNF was again found as a key gene in the mock analyses indicating this might be by chance. However, this was not the case for TP53 and TGFbeta indicating the validity of these key genes. This suggests that gene expression data through pathway analysis could lead to the identification of key genes that may show posttranslational modifications or genetic defects and were therefore not discovered in the gene expression analysis.
3. TP53 and TGFbeta 3.1. TP53 The role of the tumour suppressor gene TP53 in the development of cancer and platinum-based chemotherapy resistance has been extensively discussed (Wang and Lippard, 2005; Schuijer and Berns, 2003) and will therefore not be further addressed here. Although 43% of 215 published analyses from 64 papers reported a correlation between TP53 mutation and a clinical end point that was relevant to chemotherapy resistance, none of the six most crucial studies showed a statistical significant correlation (Hall et al., 2004). Interestingly, TP53 mutations have been associated with a short term survival benefit (Havrilesky et al., 2003).
4. TGFbeta and ‘extracellular matrix’ gene cluster We observed a tight set of eight extracellular matrix (ECM) related genes after clustering of our 69-gene set associated with platinum-based chemotherapy resistance, the ‘extracellular matrix gene cluster’ (Helleman et al., 2006). These eight genes showed a remarkably similar expression and were all expressed at higher levels in resistant compared to the sensitive ovarian carcinomas (Fig. 1A). Interestingly, we also observed an ECM cluster of six genes in our parallel study on tamoxifen resistance in advanced breast cancer (Fig. 1B) (Jansen et al., 2005) and fibronectin was found in both clusters. Thus, ECM gene clusters are related to resistance to very different kind of therapeutics (hormonal and chemotherapeutics) and in two different tumour types (ovarian and breast cancer). Next, pathway analysis was done to determine whether there was a common key gene causing the similar expression pattern of these two clusters of ECM-related genes. The unique identifiers representing the ECM genes published in these studies, were linked to Unigene identifiers (Hs. numbers) and subsequently analysed separate using Ingenuity Pathways Analysis (IPA). Intriguingly, IPA generated one network for both the ovarian and the breast cancer ECM-related gene clusters with TGFbeta as the key gene in both networks (Fig. 1C/D). 5. TGFbeta, epithelial to mesenchymal transition (EMT) and miRNAs TGFbeta, present in the tumour microenvironment, induces epithelial to mesenchymal transition (EMT) which is an early step in carcinogenesis. EMT, a process in which cell adhesion properties are altered, describes the molecular reprogramming and phenotypic changes involved in the conversion of polarized immotile epithelial to motile mesenchymal cells. In addition, the TNF produced by activated macrophages in the tumour stroma, accelerates the TGFbeta mediated EMT (Bates and Mercurio, 2003; DeClerck et al., 2004). ZEB1 (a transcriptional repressor of E-cadherin and polarity factor genes) is a crucial EMT activator in human colon and breast cancer (Burk et al., 2008) (Fig. 2). Conversely, repression of ZEB1 and its family member ZEB2 as well as TGFbeta2 by the miR-200 family (miR-200a, miR-200b, miR-200c, miR-141 and miR-429) and miR205 is responsible for the maintenance of the epithelial phenotype (Burk et al., 2008; Gregory et al., 2008). Enforced expression of the miR-200 family alone has been reported to be sufficient to prevent TGFbeta-induced EMT. Indeed, when looking at microRNA signatures in human normal ovaries compared to ovarian cancer, Iorio et al. (2007) discovered that the most significant overexpressed
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Fig. 1. Pathway analysis of the ECM-related gene cluster. The expression profiles of 24 ovarian carcinomas in duplicate (A) and 46 breast cancers (B) with the ECM-related gene clusters within the red box. Columns: tumours, rows: gene expression levels, red colour: overexpressed genes, green colour: underexpressed genes (adapted from Helleman et al., 2006; Jansen et al., 2005). Ingenuity Pathway Analysis networks for the eight ovarian cancer (C) and the six breast cancer (D) ECM-related genes. These genes are shown in grey. The asterisk (*) indicates that FN1 and COL3A1 were represented by two spots per gene.
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Fig. 2. A model of the described interaction of TGFbeta, ECM, EMT and miRNAs with therapy response.
miRNAs were miR-200a, -b, -c and miR-141. MiR-141 and 200c, that are closely linked on human chromosome 12 (Burk et al., 2008), have been reported to have the strongest inhibitory effect on TGFbeta2 and ZEB1 expression respectively (Cochrane et al., 2009). Moreover, in vitro restoration of miR200c reduces class III beta-tubulin (TUBB3) expression resulting in an 85% increase in sensitivity to microtubule targeting agents (Fig. 2) (Cochrane et al., 2009). Another putative target of miR200c, fibronectin, was found by us in both ECM gene clusters associated with therapy resistance. Fibronectin binds to the cell membrane via integrin receptor and is upregulated during epithelial to mesenchymal transition (Yang et al., 2004). In addition, the ECM gene Tenascin C (TNC) associated with tamoxifen resistance in breast cancer (Jansen et al., 2005; Helleman et al., 2008), is also involved in EMT. Interestingly, miR335 regulates TNC expression (Tavazoie et al., 2008; Negrini and Calin, 2008) and was down regulated in three ovarian cancer cell lines resistant to paclitaxel and one resistant to cisplatin (Sorrentino et al., 2008). This illustrates a possible connection between TGFbeta-induced ECM alterations leading to EMT and therapy resistance.
6. Extracellular matrix and therapy resistance in cancer The ECM acts as a substrate to which cells can adhere and it serves as a reservoir for growth factors. As a result, the ECM, for instance via binding to integrins, has a major role in regulating both cell proliferation and migration, and consequently, metastasis. Several studies have suggested that the adhesion of diverse type of tumour cells to the ECM could play a role in resistance to a range of therapeutics, the so-called cell adhesion-mediated drug resistance (CAM-DR). The adhesion of hematopoietic, pancreatic or lung cancer cell lines to the ECM proteins fibronectin, laminin, or collagen yields resistance to a number of drugs like etoposide, melphalan,
mitoxantrone, cisplatin, and cyclophosphamide as well as radiation (Hodkinson et al., 2006; Hazlehurst and Dalton, 2001; Sethi et al., 1999; Miyamoto et al., 2004). Furthermore, this effect was shown to be dependent on the interaction of the ECM proteins with the cell membrane receptor 1-integrin (Hodkinson et al., 2006; Hazlehurst and Dalton, 2001; Sethi et al., 1999). This suggests that activation of the ECM-integrin-mediated signalling might be involved in and might therefore serve as a marker for resistance to several chemotherapeutics. For instance, focal adhesion kinase (FAK) downstream of integrin, is overexpressed in 70% of ovarian cancer patients (Landen et al., 2008) which could indicate an active integrin-mediated signalling pathway possibly related to chemotherapy resistance. Unexpectedly, recent data suggests an activated integrin signalling pathway could also be related with sensitivity to paclitaxel. Ahmed et al. (2007) showed in ovarian and breast cancer cell lines that the ECM protein TGFbeta-induced (TGFBI) mediates specific sensitization to paclitaxel. TGFBI induces stabilization of microtubules via integrin-mediated signalling pathways dependent on FAK and RhoA that are both downstream of integrin (Ahmed et al., 2007) (Fig. 2). In addition, they observed a significant lower TGFBI mRNA expression in paclitaxel resistant patients (in a total of only 20 patients, thus more validation will be needed). Interestingly, Palazzo et al. showed that an integrin-mediated adhesion of cells to fibronectin also induces microtubule stabilization (Palazzo et al., 2004). Furthermore, the expression of TGFBI showed a very strong correlated with ECM genes and in particular fibronectin in the expression data of Ahmed et al. (2007) as well as in an additional ovarian (Spentzos et al., 2005) and breast cancer (Naderi et al., 2007) data sets. TGFBI as well as fibronectin are regulated by TGFbeta, therefore the possibility that the sensitization for paclitaxel seen for TGFBI might also be due to the interaction of integrin with fibronectin induced by TGFbeta, cannot be excluded.
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This implies that the TGFbeta-induced ECM-integrin signalling is related with chemotherapy resistance but could at the same time sensitize for paclitaxel therapy. The model based on all data described above (Fig. 2), illustrates the complex interactions. In summary, the depicted signalling pathways can lead to two states, the epithelial and the mesenchymal state. The signalling pathway leading to the epithelial state includes the inhibition of EMT by the miR200 family via repression of ZEB1, TGFbeta and possibly FN, resulting in sensitivity to chemotherapy. In addition, miR200c also represses TUBB3 thereby sensitizing cells to microtubule targeting agents. The signalling pathway leading to the mesenchymal state begins with the TGFbeta-induced ECM alterations in the absence of miR200 family, resulting in EMT and chemotherapy resistance. In addition, the absence of miR200c will also result in loss of repression of TUBB3 associated with resistance to microtubule targeting agents. In contrast, the integrin signalling, possibly induced by FN, results in stabilization of microtubules, which renders the cells sensitive to microtubule targeting agents. One could imagine that in the mesenchymal state the stabilization of microtubules through integrin signalling might overrule the effect of an increased expression of TUBB3 thereby rendering the cells sensitive to microtubule targeting agents. However, this needs to be further investigated. Thus, whether the mesenchymal state is associated with the contradictory effects of these signalling pathways on the response to microtubule targeting agents need further investigation. These results illustrate the complexity of resistance mechanisms that can also vary from patient to patient. Therefore, integrated genomics will be imperative to tackle the complex problem of therapy resistance and to determine for each patient which mechanism will be dominant.
7. Do ECM and TGFbeta play a role in therapy resistance as seen in the clinic? Very recent, Bowtell and colleagues have identified a so-called “molecular subtype” characterized by ECM, matrix remodelling and stromal response that was strongly associated with desmoplastic response and poor survival (Tothill et al., 2008). Furthermore, in a separate paper they described two subsets within the chemotherapy resistant patients, one with a high cyclin E (CCNE1) copy number (encompassing approximately one quarter of resistant tumours), and the other without CCNE1 amplification but characterized by overexpression of genes involved in the ECM structure and cell adhesion (Etemadmoghadam et al., 2009). Thus a molecular subgroup characterized by ECM gene expression is present in ovarian cancer associated with chemotherapy resistance. The TGFbeta-induced ECM/integrin-mediated pathway seems to be a good target for therapy to sensitize this subgroup of ovarian carcinomas to a range of therapeutics. In conclusion, the ‘seeds and soil’ hypothesis originating from 1889, in which the English surgeon Stephen Paget compared tumour cells with seeds and its environment with soil, still seems relevant in cancer research. The interplay between epithelial tumour cells, fibroblasts, inflammatory cells, the ECM as well as EMT are interesting topics in relation with therapy resistance. The complex integration of chemotherapeutic response and pathway analysis with tumour gene and miRNA expression as well as sequencing data provides a promising strategy to optimise patient outcome in ovarian cancer. Finally, together with restoration of miRNAs, it could refine chemotherapeutic protocols on a patient tailored basis.
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Contents lists available at ScienceDirect
The International Journal of Biochemistry & Cell Biology journal homepage: www.elsevier.com/locate/biocel
Signalling networks in focus
Integrated genomics of chemotherapy resistant ovarian cancer: A role for extracellular matrix, TGFbeta and regulating microRNAs Jozien Helleman a , Maurice P.H.M. Jansen a , Curt Burger b , Maria E.L. van der Burg a , Els M.J.J. Berns a,∗ a b
Department of Medical Oncology, Erasmus MC/JNI, Rotterdam, The Netherlands Department of Gynaecology, Erasmus MC, Rotterdam, The Netherlands
a r t i c l e
i n f o
Article history: Received 14 July 2009 Received in revised form 9 October 2009 Accepted 13 October 2009 Available online 23 October 2009 Keywords: Ovarian cancer Chemotherapy resistance Extracellular matrix TGFbeta Epithelial to mesenchymal transition miRNAs
a b s t r a c t Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world. Our explorative pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer reveals TP53 and transforming growth factor beta as key genes. Furthermore, the extracellular matrix was associated with chemotherapy resistance in ovarian cancer as well as endocrine resistance in breast cancer. Pathway analysis again revealed transforming growth factor beta as a key gene regulating extracellular matrix gene expression. A model is presented based on literature linking transforming growth factor beta, extracellular matrix, integrin signalling, epithelial to mesenchymal transition and regulating microRNAs with a (bivalent) role in chemotherapy response. © 2009 Elsevier Ltd. All rights reserved.
1. Background Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world (Parkin et al., 2005). The management of ovarian adenocarcinoma has improved over the last 20 years due to better-quality debulking surgery and chemotherapy: i.e. platinum-based drugs and later the addition of taxanes (van der Burg et al., 1995; Harries and Gore, 2002). Despite these treatment improvements, 20–30% of the patients never have a clinical remission and the majority of women eventually relapse with generally incurable disease (Cannistra, 2004). These treatment difficulties are due to the advanced stage of the disease at diagnosis and platinum-based chemotherapy resistance resulting in a poor overall 5-year survival of only 30% (Moss and Kaye, 2002). The specific genes and pathways involved in platinum-based chemotherapy resistance as well as their role in the resistance seen in the clinic have yet not been clearly established. However, a number of molecular profiling studies including ours, have revealed gene-sets that are associated with resistance to platinum-
∗ Corresponding author at: Department of Medical Oncology, Erasmus MC, Josephine Nefkens Institute, Room Be424, P.O. Box 1740, 3000 CA Rotterdam, The Netherlands. Tel.: +31 107044370; fax: +31 107044377. E-mail address: [email protected] (E.M.J.J. Berns). 1357-2725/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocel.2009.10.016
based chemotherapy in ovarian cancer (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006; Dressman et al., 2007; Bachvarov et al., 2006). Besides prediction of response, these microarray data could also give us more insight in the mechanisms involved. In this review, we describe a pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer, with focus on the extracellular matrix, TGFbeta and regulating miRNAs. Through integration of the data we aim to gain more insight in platinum-based chemotherapy resistance mechanisms in human ovarian cancer.
2. Pathway analysis of published gene-sets associated with platinum resistance 2.1. Gene-sets Retrospective expression profiling studies resulted in the discovery of gene-sets associated with platinum-based chemotherapy resistance (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006; Dressman et al., 2007; Bachvarov et al., 2006). However, an overlap of only seven genes was observed, each between only two gene-sets. Furthermore, only one of these genes, galectin 1 (LGALS1), showed a similar
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expression, i.e. underexpressed in the resistant when compared to the sensitive carcinomas, for both studies (Bernardini et al., 2005; Peters et al., 2005). However, these studies are challenged by several different clinical and experimental characteristics that could have resulted in the observed lack of overlap between the discovered genes: (1) small number of ovarian cancer specimens analysed and mostly no independent validation. (2) Heterogeneity of ovarian cancer (even when mostly advanced stage and serous histology are included). (3) Variety of therapy modalities and used response criteria. (4) Microarray platforms (statistical) data analysis and techniques used (most of these studies did not use whole genome microarrays resulting in a limited overlap from the start). Thus overlapping pathways may be present but overlooked since each gene signature could contain different genes from the same pathway.
3.2. TGFbeta In normal ovarian surface epithelial cells autocrine growth inhibition and differentiation is sustained via TGFbeta and loss of these responses is often seen in cancer (Massague and Gomis, 2006). Interestingly, this is also observed in about 30–40% of ovarian cancer cases while the receptors and the downstream Smad signalling are generally intact (Landen et al., 2008; Bast et al., 2009). This suggests inhibition of the signalling pathway downstream of the Smad components. Indeed, expression of genes that bind to and inhibit Smads have been shown to be altered in ovarian cancer, for instance DACH1, EVI1, SKILL and RUNX (Bast et al., 2009; Sunde et al., 2006). However, this is more profound in early compared to late stage ovarian cancer which might suggest a role in early stage of carcinogenesis (Sunde et al., 2006). Besides the lost cytostatic effect, TGFbeta profoundly alters the tumour microenvironment including the ECM that possibly causes the therapy resistance.
2.2. Pathway analysis Gene Ontology (http://www.geneontology.org) was used to make a functional subdivision of the genes for subsequent pathway analysis by selecting the functional processes that were common in the seven published gene-sets (Selvanayagam et al., 2004; Bernardini et al., 2005; Hartmann et al., 2005; Jazaeri et al., 2005; Peters et al., 2005; Spentzos et al., 2005; Helleman et al., 2006). A process was selected if more then 10% of the genes listed by that study were involved in the process and if it was present in more than one of the studies (described previously by us (Helleman, 2006)). The six most prevalent processes were cell growth and-or maintenance (84 genes), transcription (53 genes), protein metabolism (53 genes), signal transduction (45 genes), organismal physiological process (35 genes) and response to external stimulus (31 genes). With the genes belonging to these processes (so-called focus genes), networks were generated using Ingenuity Pathway Analysis (IPA). The genes linked to at least two of the focus genes were labelled as key genes. Remarkably, tumour necrosis factor (TNF) was a key-gene for all six processes, TP53 for five processes and transforming growth factor beta (TGFbeta) for four of the processes. A similar analysis with four mock data sets resulted in the selection of less key genes and less networks per process. Although clearly less prominent, TNF was again found as a key gene in the mock analyses indicating this might be by chance. However, this was not the case for TP53 and TGFbeta indicating the validity of these key genes. This suggests that gene expression data through pathway analysis could lead to the identification of key genes that may show posttranslational modifications or genetic defects and were therefore not discovered in the gene expression analysis.
3. TP53 and TGFbeta 3.1. TP53 The role of the tumour suppressor gene TP53 in the development of cancer and platinum-based chemotherapy resistance has been extensively discussed (Wang and Lippard, 2005; Schuijer and Berns, 2003) and will therefore not be further addressed here. Although 43% of 215 published analyses from 64 papers reported a correlation between TP53 mutation and a clinical end point that was relevant to chemotherapy resistance, none of the six most crucial studies showed a statistical significant correlation (Hall et al., 2004). Interestingly, TP53 mutations have been associated with a short term survival benefit (Havrilesky et al., 2003).
4. TGFbeta and ‘extracellular matrix’ gene cluster We observed a tight set of eight extracellular matrix (ECM) related genes after clustering of our 69-gene set associated with platinum-based chemotherapy resistance, the ‘extracellular matrix gene cluster’ (Helleman et al., 2006). These eight genes showed a remarkably similar expression and were all expressed at higher levels in resistant compared to the sensitive ovarian carcinomas (Fig. 1A). Interestingly, we also observed an ECM cluster of six genes in our parallel study on tamoxifen resistance in advanced breast cancer (Fig. 1B) (Jansen et al., 2005) and fibronectin was found in both clusters. Thus, ECM gene clusters are related to resistance to very different kind of therapeutics (hormonal and chemotherapeutics) and in two different tumour types (ovarian and breast cancer). Next, pathway analysis was done to determine whether there was a common key gene causing the similar expression pattern of these two clusters of ECM-related genes. The unique identifiers representing the ECM genes published in these studies, were linked to Unigene identifiers (Hs. numbers) and subsequently analysed separate using Ingenuity Pathways Analysis (IPA). Intriguingly, IPA generated one network for both the ovarian and the breast cancer ECM-related gene clusters with TGFbeta as the key gene in both networks (Fig. 1C/D). 5. TGFbeta, epithelial to mesenchymal transition (EMT) and miRNAs TGFbeta, present in the tumour microenvironment, induces epithelial to mesenchymal transition (EMT) which is an early step in carcinogenesis. EMT, a process in which cell adhesion properties are altered, describes the molecular reprogramming and phenotypic changes involved in the conversion of polarized immotile epithelial to motile mesenchymal cells. In addition, the TNF produced by activated macrophages in the tumour stroma, accelerates the TGFbeta mediated EMT (Bates and Mercurio, 2003; DeClerck et al., 2004). ZEB1 (a transcriptional repressor of E-cadherin and polarity factor genes) is a crucial EMT activator in human colon and breast cancer (Burk et al., 2008) (Fig. 2). Conversely, repression of ZEB1 and its family member ZEB2 as well as TGFbeta2 by the miR-200 family (miR-200a, miR-200b, miR-200c, miR-141 and miR-429) and miR205 is responsible for the maintenance of the epithelial phenotype (Burk et al., 2008; Gregory et al., 2008). Enforced expression of the miR-200 family alone has been reported to be sufficient to prevent TGFbeta-induced EMT. Indeed, when looking at microRNA signatures in human normal ovaries compared to ovarian cancer, Iorio et al. (2007) discovered that the most significant overexpressed
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Fig. 1. Pathway analysis of the ECM-related gene cluster. The expression profiles of 24 ovarian carcinomas in duplicate (A) and 46 breast cancers (B) with the ECM-related gene clusters within the red box. Columns: tumours, rows: gene expression levels, red colour: overexpressed genes, green colour: underexpressed genes (adapted from Helleman et al., 2006; Jansen et al., 2005). Ingenuity Pathway Analysis networks for the eight ovarian cancer (C) and the six breast cancer (D) ECM-related genes. These genes are shown in grey. The asterisk (*) indicates that FN1 and COL3A1 were represented by two spots per gene.
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Fig. 2. A model of the described interaction of TGFbeta, ECM, EMT and miRNAs with therapy response.
miRNAs were miR-200a, -b, -c and miR-141. MiR-141 and 200c, that are closely linked on human chromosome 12 (Burk et al., 2008), have been reported to have the strongest inhibitory effect on TGFbeta2 and ZEB1 expression respectively (Cochrane et al., 2009). Moreover, in vitro restoration of miR200c reduces class III beta-tubulin (TUBB3) expression resulting in an 85% increase in sensitivity to microtubule targeting agents (Fig. 2) (Cochrane et al., 2009). Another putative target of miR200c, fibronectin, was found by us in both ECM gene clusters associated with therapy resistance. Fibronectin binds to the cell membrane via integrin receptor and is upregulated during epithelial to mesenchymal transition (Yang et al., 2004). In addition, the ECM gene Tenascin C (TNC) associated with tamoxifen resistance in breast cancer (Jansen et al., 2005; Helleman et al., 2008), is also involved in EMT. Interestingly, miR335 regulates TNC expression (Tavazoie et al., 2008; Negrini and Calin, 2008) and was down regulated in three ovarian cancer cell lines resistant to paclitaxel and one resistant to cisplatin (Sorrentino et al., 2008). This illustrates a possible connection between TGFbeta-induced ECM alterations leading to EMT and therapy resistance.
6. Extracellular matrix and therapy resistance in cancer The ECM acts as a substrate to which cells can adhere and it serves as a reservoir for growth factors. As a result, the ECM, for instance via binding to integrins, has a major role in regulating both cell proliferation and migration, and consequently, metastasis. Several studies have suggested that the adhesion of diverse type of tumour cells to the ECM could play a role in resistance to a range of therapeutics, the so-called cell adhesion-mediated drug resistance (CAM-DR). The adhesion of hematopoietic, pancreatic or lung cancer cell lines to the ECM proteins fibronectin, laminin, or collagen yields resistance to a number of drugs like etoposide, melphalan,
mitoxantrone, cisplatin, and cyclophosphamide as well as radiation (Hodkinson et al., 2006; Hazlehurst and Dalton, 2001; Sethi et al., 1999; Miyamoto et al., 2004). Furthermore, this effect was shown to be dependent on the interaction of the ECM proteins with the cell membrane receptor 1-integrin (Hodkinson et al., 2006; Hazlehurst and Dalton, 2001; Sethi et al., 1999). This suggests that activation of the ECM-integrin-mediated signalling might be involved in and might therefore serve as a marker for resistance to several chemotherapeutics. For instance, focal adhesion kinase (FAK) downstream of integrin, is overexpressed in 70% of ovarian cancer patients (Landen et al., 2008) which could indicate an active integrin-mediated signalling pathway possibly related to chemotherapy resistance. Unexpectedly, recent data suggests an activated integrin signalling pathway could also be related with sensitivity to paclitaxel. Ahmed et al. (2007) showed in ovarian and breast cancer cell lines that the ECM protein TGFbeta-induced (TGFBI) mediates specific sensitization to paclitaxel. TGFBI induces stabilization of microtubules via integrin-mediated signalling pathways dependent on FAK and RhoA that are both downstream of integrin (Ahmed et al., 2007) (Fig. 2). In addition, they observed a significant lower TGFBI mRNA expression in paclitaxel resistant patients (in a total of only 20 patients, thus more validation will be needed). Interestingly, Palazzo et al. showed that an integrin-mediated adhesion of cells to fibronectin also induces microtubule stabilization (Palazzo et al., 2004). Furthermore, the expression of TGFBI showed a very strong correlated with ECM genes and in particular fibronectin in the expression data of Ahmed et al. (2007) as well as in an additional ovarian (Spentzos et al., 2005) and breast cancer (Naderi et al., 2007) data sets. TGFBI as well as fibronectin are regulated by TGFbeta, therefore the possibility that the sensitization for paclitaxel seen for TGFBI might also be due to the interaction of integrin with fibronectin induced by TGFbeta, cannot be excluded.
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This implies that the TGFbeta-induced ECM-integrin signalling is related with chemotherapy resistance but could at the same time sensitize for paclitaxel therapy. The model based on all data described above (Fig. 2), illustrates the complex interactions. In summary, the depicted signalling pathways can lead to two states, the epithelial and the mesenchymal state. The signalling pathway leading to the epithelial state includes the inhibition of EMT by the miR200 family via repression of ZEB1, TGFbeta and possibly FN, resulting in sensitivity to chemotherapy. In addition, miR200c also represses TUBB3 thereby sensitizing cells to microtubule targeting agents. The signalling pathway leading to the mesenchymal state begins with the TGFbeta-induced ECM alterations in the absence of miR200 family, resulting in EMT and chemotherapy resistance. In addition, the absence of miR200c will also result in loss of repression of TUBB3 associated with resistance to microtubule targeting agents. In contrast, the integrin signalling, possibly induced by FN, results in stabilization of microtubules, which renders the cells sensitive to microtubule targeting agents. One could imagine that in the mesenchymal state the stabilization of microtubules through integrin signalling might overrule the effect of an increased expression of TUBB3 thereby rendering the cells sensitive to microtubule targeting agents. However, this needs to be further investigated. Thus, whether the mesenchymal state is associated with the contradictory effects of these signalling pathways on the response to microtubule targeting agents need further investigation. These results illustrate the complexity of resistance mechanisms that can also vary from patient to patient. Therefore, integrated genomics will be imperative to tackle the complex problem of therapy resistance and to determine for each patient which mechanism will be dominant.
7. Do ECM and TGFbeta play a role in therapy resistance as seen in the clinic? Very recent, Bowtell and colleagues have identified a so-called “molecular subtype” characterized by ECM, matrix remodelling and stromal response that was strongly associated with desmoplastic response and poor survival (Tothill et al., 2008). Furthermore, in a separate paper they described two subsets within the chemotherapy resistant patients, one with a high cyclin E (CCNE1) copy number (encompassing approximately one quarter of resistant tumours), and the other without CCNE1 amplification but characterized by overexpression of genes involved in the ECM structure and cell adhesion (Etemadmoghadam et al., 2009). Thus a molecular subgroup characterized by ECM gene expression is present in ovarian cancer associated with chemotherapy resistance. The TGFbeta-induced ECM/integrin-mediated pathway seems to be a good target for therapy to sensitize this subgroup of ovarian carcinomas to a range of therapeutics. In conclusion, the ‘seeds and soil’ hypothesis originating from 1889, in which the English surgeon Stephen Paget compared tumour cells with seeds and its environment with soil, still seems relevant in cancer research. The interplay between epithelial tumour cells, fibroblasts, inflammatory cells, the ECM as well as EMT are interesting topics in relation with therapy resistance. The complex integration of chemotherapeutic response and pathway analysis with tumour gene and miRNA expression as well as sequencing data provides a promising strategy to optimise patient outcome in ovarian cancer. Finally, together with restoration of miRNAs, it could refine chemotherapeutic protocols on a patient tailored basis.
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