Molecular basis for therapy resistance

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available at www.sciencedirect.com

www.elsevier.com/locate/molonc

Review

Molecular basis for therapy resistance Per E. Lønninga,b,* a

Section of Oncology, Institute of Medicine, University of Bergen, Norway Department of Oncology, Haukeland University Hospital, Bergen, Norway

b

A R T I C L E

I N F O

A B S T R A C T

Article history:

Chemoresistance remains the main reason for therapeutic failure in breast cancer as well

Received 8 March 2010

as most other solid tumours. While gene expression profiles related to prognosis have been

Received in revised form

developed, so far use of such signatures as well as single markers has been of limited

16 April 2010

value predicting drug resistance. Novel technologies, in particular with regard to high

Accepted 16 April 2010

through-put sequencing holds great promises for future identification of the key “driver”

Available online 24 April 2010

mechanisms guiding chemosensitivity versus resistance in breast cancer as well as other malignant conditions.

Keywords: Breast cancer

ª 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Chemoresistance p53 Microarray Prediction

1.

Introduction

Thirty years ago, breast cancer research much focused on identification of prognostic factors. A seminal discovery was identification of the prognostic role related to number of tumour-infiltrated lymph nodes (Fisher et al., 1983), most importantly to be followed by data from the Oxford metanalysis confirming node status to be without predictive power with respect to endocrine treatment as well as chemotherapy sensitivity (Abe et al., 2005). However, while multiple individual prognostic factors were identified, in general combining these factors into prognostic indexes revealed limited information beyond 3-4 factors (at most) in combination. Second, the identification of most of these factors did not extend our biological understanding of breast cancer significantly.

Finally, for many of these factors knowledge about their potential relationship to therapy sensitivity remains marginal, further limiting their practical role in the clinic. Thus, except for predictive power of the estrogen receptor (ER) with respect to endocrine therapy, no predictive factors related to chemotherapy were identified. The last two decades have provided substantial novel insight into molecular pathology characterizing human breast cancers. This relates to identification of mutations or other genetic lesions affecting genes playing a key role related to cancer risk as well as somatic alterations affecting genes vital to processes like growth, apoptosis, senescence or DNA repair (Horowitz et al., 1989; Lowe et al., 1993; Miki et al., 1994; Slamon et al., 1987; Wooster et al., 1994). Still, we have limited knowledge regarding which processes may control resistance

* Department of Oncology, Haukeland University Hospital, Bergen, Norway. Tel.: þ47 55975000. E-mail address: [email protected] 1574-7891/$ e see front matter ª 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.molonc.2010.04.005

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a

toward endocrine treatment as well as chemotherapy. Thus, the topic for this review is critically to examine different parameters of relevance to therapy resistance in breast cancer.

2. Defining drug sensitivity versus resistance in clinical trials The wording “drug sensitivity” and “resistance” has limited meaning by itself in experimental research. Assessing the potential of different compounds killing cell lines in vitro, most drugs are able to kill most cell lines provided the dose concentration is sufficiently high. Rather, it provides meaning talking about degree of sensitivity comparing different cell lines or, in particular, effect of inhibiting or restoring defect gene pathways on parameters like LD50. While such experiments may provide important information understanding the role of individual pathways, the wide spectrum of genetic alterations involved in immortalized cell lines presents a major problem. Most immortalized cell lines are well characterized with respect to mutations/ deletions in key genes like TP53; they may, however, harbour multiple additional defects, in general poorly characterized. Such differences may account for inconsistent results obtained in different experimental systems. In a clinical perspective, it is imperative to discriminate between prognostication and prediction of therapy efficacy. A detailed discussion of these parameters are presented elsewhere (Lønning et al., 2007). In brief, there are two major models evaluating predictive factors. In the first model (Figure 1), long-term outcome (progression-free or overall survival) is compared between patients treated with a cytotoxic compound versus no treatment, or between two treatment regimens, in the adjuvant setting (Lønning, 2003). Notably, in addition to “classical” prognostic factors like lymph node status, differences with respect to tumour biology may interact with the result. While tumour characteristics like triple-negativity as well as high histologic grade (Carey et al., 2007; Hugh et al., 2009; Lundin et al., 2001) both predicts response to primary chemotherapy, in the same data-sets they also predicted poor long-term outcome. Thus, to infer a difference in chemosensitivity from long-term outcome the tumours should be balanced for all confounding effects across the two treatment arms. Accordingly, such assessments should be performed in the setting of randomized phase III trials. The second possibility is to directly measure changes in tumour size (by calliper or radiological techniques including ultrasound, computer tomography or PET-scan) in response to primary (pre-surgical; previously termed neoadjuvant) therapy or treatment for metastatic disease (Figure 2). An alternative to clinical measurement is assessment of pathological response at surgery; this has become widely used, mainly because a pathological “complete response” (pCR), has been correlated to long-term survival (Rastogi et al., 2008). Recently, a novel classification based on tumour cell density after therapy has been proposed (Ogston et al., 2003). Such a system may correct for potential interactions between tumour size and outcome; the chance of having a pCR is less

Reg A, B Assumed outcome no chemotherapy

Relapse-free survival Time Effect of adjuvant therapy

b Reg A; X + Reg B; X+ XReg A; X Relapse-free survival Time Effect of adjuvant therapy

Figure 1 e Predicting drug sensitivity. (a). Two regimens (A and B) provide similar clinical benefit administered in the adjuvant setting. (b). Analysing patient tumours for a potential predictive factor (Factor “X”), this factor has no predictive power regarding outcome for patients treated with regimen B. In contrasts, patients expressing Factor “X” are highly sensitive to Regimen A, while tumours lacking factor “X” are resistant to that regimen.

for large (T3) and T4 tumours (Harris et al., 2007; Kuerer et al., 1999).

3.

Resistance toward endocrine therapy

Endocrine manipulation (using SERMS, SERDS or estrogen ablation) is effective only in tumours expressing the estrogen receptor (ER). While not all ER positive tumours respond to hormonal therapy, interestingly, beneficial effects of tamoxifen has been recorded among tumours expressing ER positivity among 1% of cells only (Harvey et al., 1999). While the reason for this remains poorly understood; potentially it may involve paracrine loops. If that is the case, disruption of such pathways due to genetic or epigenetic mechanisms may be a cause of therapy resistance.

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Figure 2 e Evaluating predictive factors by direct tumour measurement (“primary therapy”). Assuming each tumour to harbour a heterogeneous cell content, tumours in general composed of cells resistant to therapy (red colour) will continue to grow on therapy, classified as “progressive disease; PD. In contrast, tumours mainly composed of sensitive cells (blue, pink) will shrink, eventually leading to a complete response (CR) or a partial response (PR), while heterogeneous tumours may have a “stable disease” (SD).

Few drugs have been more thoroughly studied with respect to potential mechanisms of resistance as tamoxifen, and the reader is referred to previously published reviews on this topic (Jordan, 2008; Lønning and Lien, 1995; Swaby and Jordan, 2008). In brief, while different mechanisms including receptor mutations, pharmacokinetic alterations and growth factor overexpression have been proposed, so far none of these mechanisms have been shown to explain tamoxifen resistance among ERþ tumours in vivo. Interestingly, novel gene expression signatures have been found correlated to outcome among tamoxifen-treated patients. This relates to the OncotypeDx 21 gene signature (Paik et al., 2004) as well as the 2-gene expression signature by Ma et al (Ma et al., 2004), both revealing prognosis among tamoxifen-treated patients. However, as these signatures were not compared among untreated patients, so far we may not decide whether they should be considered prognostic or predictive factors to tamoxifen sensitivity. The role of OncotypeDX predicting sensitivity to chemotherapy is discussed in a later section of this paper. Regarding estrogen deprivation, data are more limited. One issue in particular relates to the phenomenon of LTED (Long Term Estrogen Deprivation), described by several groups (Detre et al., 1999; Lippman et al., 1976; Masamura et al., 1995). Briefly, estrogen-stimulated MCF-7 cells grown for long-term periods (months) in culture exposed to estrogens at decreasing concentrations develop estrogen “hypersensitivity”, meaning they may be growth stimulated by estradiol at a concentration 1/1000 to 1/10,000 the concentration needed to stimulate wt MCF-7 cells. The growth stimulation curves (with respect to wt as well as to LTED cells) express a bell-shaped profile (Masamura et al., 1995), meaning that estradiol at high concentrations inhibits cell growth; actually, others (Lewis-Wambi and Jordan, 2009; Lewis et al., 2005) have shown estradiol may cause apoptosis in sensitized cells.

In LTED cells, the concentration of estradiol needed for growth inhibition is much lower that the concentration needed to inhibit wt cells. While several mechanisms explaining LTED “estrogen hypersensitivity”, including enhanced MAP-kinase activity, has been proposed (Yue et al., 2007), the phenomenon remains incompletely understood. Yet the fact metastatic breast cancers developing acquired resistance to aromatase inhibitors may respond to additive estrogen therapy (Lønning et al., 2001) reveals a potential clinical importance of this effect, warranting further studies exploring the mechanism of action. An issue of particular interest with respect to treatment with tamoxifen as well as aromatase inhibitors relates to overexpression of members of the HER-2 family. Thus, Ellis and colleagues (Ellis et al., 2001) reported a differential effect of HER-2 status with respect to treatment efficacy of aromatase inhibitors versus tamoxifen administered as primary endocrine therapy. In this study, HER-1 and/or HER-2 overexpressing tumours revealed a particular benefit for the aromatase inhibitor letrozole as compared to tamoxifen. However, studies in the metastatic (Lipton et al., 2002) as well as adjuvant (Dowsett et al., 2008; Rasmussen et al., 2008) setting in general reveal a reduced efficacy of all forms of endocrine therapy among HER-2 overexpressing as compared to HER-2 normal tumours; the hazard ratio revealing the benefit of aromatase inhibitors as compared to tamoxifen is of the same magnitude among HER-2 normal as among HER-2 overexpressing tumours. A topic of current interest is whether HER-2 actually may play a significant role to endocrine resistance also among HER-2 non-amplified breast cancers. Interestingly, Johnston et al (Johnston et al., 2009) reported a benefit of lapatinib not only among HER-2 amplified breast cancers but in addition among HER-2 non-amplified tumours with an early relapse following adjuvant tamoxifen. In a small study, we found primary treatment with aromatase inhibitors to upregulate HER-2 expression (Flageng et al., 2009), suggesting this may be a mechanism of resistance toward estrogen suppression. The clinical relevance of this finding, as well as potential mechanisms (involving other HER-family factors as well?) remains to be studied.

4.

Anti HER-2 therapies

In this section, potential mechanisms of resistance to HER-2 blocking strategies are discussed; the implications of HER-2 overexpression regarding resistance to chemotherapy are dealt with in section 5.3. HER-2 amplification occurs in 15e25% of all breast cancers (Browne et al., 2009; Moelans et al., 2010). The seminal discovery by Dr. Slamon and co-workers (Slamon et al., 1987, 1989) of the prognostic impact of HER-2 amplification in breast cancer lead to development of trastuzumab, a humanized antibody (Ross et al., 2004) targeting HER-2. Causing a modest albeit significant effect in advanced breast cancer administered as monotherapy (Cobleigh et al., 1999) or when added in concert to chemotherapy (Slamon et al., 2001), trastuzumab has been shown dramatically to reduce relapse rate when administered in concert with chemotherapy in the

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adjuvant setting (Piccart-Gebhart et al., 2005; Romond et al., 2005). Similar, lapatinib, a tyrosin kinase inhibitor attacking HER-1 as well as HER-2 revealed significant anti-tumour effects in metastatic disease (Johnston et al., 2009; Kaufman et al., 2009), while gefitinib, a HER-1 inhibitor, was found ineffective (Green et al., 2009; Smith et al., 2007). The mechanisms of resistance toward anti-HER2 therapies remain poorly understood. Notably, patients failing on trastuzumab in advanced breast cancer may subsequently benefit from treatment with either lapatinib (Blackwell et al., 2009; Kaufman et al., 2009) or Neratinib (Wong et al., 2009), the second inhibiting the HER4 thyrosine kinase in addition (Bose and Ozer, 2009). Another treatment option is pertuzumab, a humanized antibody binding HER-2 at and blocking its heterodimer domain (Yao et al., 2009). Thus, preliminary evidence suggests patients becoming resistant to trastuzumab and pertuzumab each administered as monotherapy may benefit from having both compounds administered in concert (Baselga et al., 2009), indicating mutations affecting the extracellular HER-2 domain could be a mechanism of trastuzumab resistance. Also, lapatinib administered in concert with trastuzumab revealed superiority as compared to lapatinib monotherapy in patients with advanced breast cancer progressing on trastuzumab (O’Shaugnessy et al., 2008). Activation of the HER-2 leads to subsequent activation of several downstream pathways including the MAP kinase system as well as PI3K-Akt (Normanno et al., 2003). Notably, there is preliminary evidence suggesting activating PI3K mutations may predict for resistance toward trastuzumab but not lapatinib (Berns et al., 2007; Junttila et al., 2009; Migliaccio et al., 2009). PI3K mutations are detected in 16% to 40% of all breast cancers (Benvenuti et al., 2008; Karakas et al., 2006); in addition, breast cancers may also harbour activating AKT mutations (Bleeker et al., 2008) or reveal lack of PTEN staining (Yonemori et al., 2009), although these mechanisms have been reported not associated with trastuzumab resistance (Yonemori et al., 2009). In contrast, overexpression of AXL has been associated with lapatinib resistance in experimental systems (Liu et al., 2009). If confirmed in vivo, this could be a major mechanism directing resistance to HER-2 blockade. Also, there is evidence use of lapatinib may upregulate HER-2 expression, providing more target molecules for trastuzumab binding (Scaltriti et al., 2009). The importance of these mechanisms however needs to be further elucidated.

5.

Chemotherapy

Different chemotherapeutic compounds work through different mechanisms of action. Ideally, any discussion focusing on chemoresistance should address the issue with respect to distinct compounds or, rather, different classes of compounds. This however is rarely possible in a clinical setting, in as much as most drug regimens contain two or more compounds administered in concert. Here, we will address the issue of drug resistance under 3 major headings; individual predictive factors phenotypically related to drug sensitivity/resistance, gene profiles predicting sensitivity and chemoresistance related to biologically defined targets

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or genes playing a key role to DNA disposition, growth arrest or apoptosis.

5.1. Phenotype and single parameters related to drug sensitivity Over the years, several phenotype characteristics have been discussed with respect to drug sensitivity in breast cancer. A major characteristic of breast cancers is the distinction into ERþ versus ER- cancers; the impact of ER status on endocrine sensitivity is discussed above. However, while ER status clearly predicts sensitivity to endocrine treatment, potential influence on chemosensitivity has remained controversial for decades (Jonat et al., 1980; Lippman et al., 1978; Webster et al., 1978). An important issue relates to endocrine effects of chemotherapy in premenopausal women related to induction of amenorrhea. Notably, different chemotherapy regimens express different effects on ovarian function. While amenorrhea is more likely to become permanent in women >40 years of age as compared to younger ones (Dnistrian et al., 1983), notably there are differences between conventional treatment with cyclophosphamide, metotrexat- and 5-fluorouracil (CMF) and contemporary anthracycline containing regimens with or without taxanes added in concert (Petrek et al., 2006); while on average 50% of women experienced amenorrhea on CMF contrasting >80% during anthracycline þ/ taxane-containing therapy, CMF-induced amenorrhea in general is irreversible, contrasting recovery of ovarian function in most patients having anthracyclines. The actual contribution of ovarian ablation to the efficacy of adjuvant chemotherapy in premenopausals has been difficult to estimate and a subject of controversy over years. Today, this controversy to a large extend has been solved through meticulous analysis of large randomized trials in which patients harbouring ERþ tumours were given optimal endocrine treatment in concert with chemotherapy. While these studies (Berry et al., 2006) reveal an additional benefit for chemotherapy also among patients harbouring ERþ tumours, the benefit is smaller as compared to patients with ER- tumours. Consistent with that, the Oxford metanalysis (Abe et al., 2005) revealed benefit of adjuvant chemotherapy to be larger for women <50 years of age compared to women >50 years of age; moreover, the percentage reduction in relapse rate due to chemotherapy was similar for young patients with ER- and those harbouring ERþ tumours receiving tamoxifen in concert. This contrasts a smaller benefit of chemotherapy among postmenopausal patients with ERþ tumours treated with tamoxifen in concert. Notably, ER status is strongly related to tumour differentiation, with ER negative tumours frequently expressing a high histologic grade (Fisher et al., 1988; Ring et al., 2004), and they both relate to cell growth rate, as evaluated with use of 3H-incorporation, flow-cytometric S-phase fraction or, more recently, immunohistochemical Ki67 status (Burcombe et al., 2005; Di Stefano et al., 1991; Gerdes et al., 1986; Kute et al., 1985; Moran et al., 1984). While these findings represent conventional wisdom, they are imperative to much of our understanding of the predictive power of contemporary gene expression signatures.

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While some studies have reported low Ki 67 (Chang et al., 1999; Aas et al., 2003) to be associated with chance of having response to primary therapy with “low intensity regimens” as the 3M or weekly doxorubicin, in general response rate and, in particular, the chance of having a pCR has been related to a high histologic grade, high Ki67 index, and estrogen receptor negativity (Colleoni et al., 2000; Fisher et al., 2002; Hess et al., 2006; Kuerer et al., 1999; Vincent-Salomon et al., 2004; Wang et al., 2002). Notably, recent studies have confirmed a better effect of anthracycline-containing as well as anthracycline plus taxane-containing adjuvant chemotherapy regarding long-term risk of relapse for ER- as compared to ERþ patients having tamoxifen in concert (Berry et al., 2006). In addition, high expression of Ki67 seems to identify a subgroup of ERþ patients that may benefit from having a taxane in concert with anthracycline-containg adjuvant chemotherapy (Penault-Llorca et al., 2009). This finding seems related to the fact that ERþ tumours expressing a high Ki67 index in general belong to the Luminal B class while those expressing Ki67 at low levels are luminal A tumours,. the second more likely to benefit from adjuvant tamoxifen (Sørlie et al., 2001, 2003); see discussion of these findings in (Lønning et al., 2005).

5.2.

Gene expression profiles

Discussing gene expression profiles, these may be divided into three major groups; gene expression signatures derived by hierachial clustering, by supervised analysis, or by pre-defined phenotypic criteria as expression of genes involved in wound healing or stromal proliferative response. Also, there has been attempts combining hierarchical and supervised analysis (Parker et al., 2009).

5.2.1.

Supervised clustering

Several gene expression signatures have been derived revealing prognostic as well as predictive response to chemotherapy. The issue of prognostication is outside the scope of this review and will only be summarized here, as several “prognostic signatures” also have been evaluated for predictive power. The first supervised signature to be developed was the 70gene signature, currently known as the Mammoprint, developed by the Netherlands Cancer Institute (van ’t Veer et al., 2002). Other gene signatures includes the Rotterdam 76 gene profile (Wang et al., 2005), the PAM50 (Parker et al., 2009), derived as a supervised model based on the hierarchical classification model by Perou et al. (2000), the 21-gene OncotypeDX (Paik et al., 2004) and 2-gene Theros (Ma et al., 2004), the last two signatures revealing risk of relapse among patients on tamoxifen treatment. Notably, it should be mentioned that there is significant overlap between the different platforms (supervised as well as hierarchical) with respect to identifying patients with a poor prognosis (Fan et al., 2006) despite the fact there is little overlap of genes in-between the signatures. The prognostic value of the OncotypeDX signature has been validated and claimed superior to conventional prognostic factors identifying patients at risk of relapse (Goldstein et al., 2008). Further, while the prognostic value of the Mammoprint signature has been validated (Buyse et al., 2006; van de Vijver et al.,

2002), investigators have revealed multiple prognostic signatures may be derived from the original data-set (Ein-Dor et al., 2005), while others (Ede´n et al., 2004) have challenged the value of this signature versus “conventional” prognostic factors. While predictive signatures derived by supervised analysis in general has revealed statistical correlations to efficacy of anthracycline- as well as taxane-containing regimens and their combinations (Ayers et al., 2004; Chang et al., 2003; Dressman et al., 2006; Gianni et al., 2005; Iwao-Koizumi et al., 2005; Thuerigen et al., 2006), with a few exceptions most of them have not been confirmed in independent studies. Interestingly, Hannemann et al. (2005) revealed alterations in gene expression during treatment to predict response to anthracyclinecontaining primary chemotherapy. While Potti and collegues (Potti et al., 2006; Salter et al., 2008)took an interesting approach, developing drug sensitivity signatures across a panel of cell lines, the practical implications of this approach predicting drug resistance in vivo remains to be confirmed. Particular interest relates to the Mammoprint and OncotypeDx signatures as they are increasingly taken into clinical use. The main idea beyond the Mammoprint test is to identify patients at low risk of relapse not in need of chemotherapy. On the contrary, even low risk patients may be suitable candidate for chemotherapy provided the benefit is high; ideally, selection of patients for cytotoxic therapy should be based not on a risk assessment but on the potential benefit derived (Lønning, 2007; Lønning et al., 2007). Regarding the OncotypeDx signature, Paik et al. (2006) reported benefit for CMF-based chemotherapy among estrogen receptor positive patients on tamoxifen with a high risk score for relapse, while patients with a low-risk score experienced no benefit. A similar finding has recently been reported with respect to anthracycline-containing chemotherapy (Albain et al., 2010). Regarding Mammoprint, Straver et al. (2010) found pCR to primary anthracycline-containing chemotherapy to be correlated to a “poor” prognostic signature.

5.2.2. Hierarchical clustering and predictive value of the basal class/BRCA1 mutation status Using hierarchical clustering, Perou and Sørlie with colleagues (Perou et al., 2000; Sørlie et al., 2001) separated breast cancers into 5 distinct classes based on gene expression profiles (Figure 3). This classification has been confirmed in several independent data-sets (Hu et al., 2006; Sotiriou et al., 2003). While the existence of the “Normal breast-like” class as a distinct entity has been questioned (Parker et al., 2009), there is evidence at least a sub-class of these tumours express a distinct gene profile characterized by low expression of several Claudins (Herschkowitz et al., 2007). While the luminal A and B classes in general contains trogen receptor positive tumours, and most tumours of the HER-2 class reveal HER-2 overexpression, this is not a uniform picture. Classification is based on expression of a number of genes; thus, estrogen receptor negative luminal A class tumours exist, and not all tumours belonging to the HER-2 class actually overexpress HER-2 (Parker et al., 2009). Interestingly, the different subclasses are associated with a different spectrum of gene mutations (Hollestelle et al., 2009). A key finding in this classification was identification of the “basal cell-like” class as a distinct tumour entity. Based on

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Figure 3 e Gene expression pattern of 85 experiment samples representing 78 carcinomas, three benign tumors and four normal tissues. Heading Basal [ basal-like cell class, HER-2 [ HER-2 class, Nor [ Normal cell-like class, LUM B and A is the luminal B and C-class, respectively. Notice in the initial work light blue was termed “luminal C” class while yellow was “luminal B”; these two classes together no is grouped as “luminal B”. Adapted form reference (Sørlie et al., 2001) with permission.

cytokeratin immunostaining, these tumours originally were suggested to arise from basal, or myoepithelial, cells (Perou et al., 2000). Although the cellular origin of these tumours remain an issue of debate (Gusterson, 2009), there is little doubt these tumours constitute a separate subgroup based on gene profiling. “Triple negative” breast cancers (lacking expression of the estrogen- as well as progesterone receptor and HER-2) accounts for approximately 12-18% of all breast cancers among whites (Cheang et al., 2008; Hugh et al., 2009; Tan et al., 2008). While most basal cell-like breast cancers are triple negative, pending on additional biomarkers, including p63, EGF-R, HER-3 and HER-4 and ER-beta (Kuroda et al., 2009; Rakha et al., 2009) between 60-80% of triple negative breast cancers may be classified as basal cell-like (Cheang et al., 2008; Rakha et al., 2009). Notably, the incidence of basal cell-like cancers seems higher among young Afro-American women (Bowen et al., 2008; Carey et al., 2006). Basal cell-like tumours in addition overexpress several biomarkers including neuroendocrine markers, the growth hormone-releasing hormone receptor (GHRH) and a Notch gene activation signature (Koster et al., 2009; Lee et al., 2008; Rakha et al., 2009). These tumours reveal a poor prognosis; similar to HER-2 overexpressing tumours, they seems to have a high propensity for metastasizing to the central nervous system (Lin et al., 2008).

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Hedenfalk and colleagues (2001) and Wessels et al. (2002) developed distinct gene expression and comparative genomic hybridisation profiles for BRCA1 mutated tumours. Now, it has become clear that approximately 80% of tumours arising in BRCA1 mutation carriers belong to the basal cell-like class (Carey, 2010; Sørlie et al., 2001), and about 10% of all basal cell-like tumours are diagnosed in BRCA1 mutation carriers (Young et al., 2009). This has raised the question whether spontaneous arising basal-like tumours also may harbour dysfunctions in the “BRCA1 pathway” (Turner et al., 2007). A detailed discussion of BRCA1 and BRCA2 mutation status with respect to chemoresistance is provided in section 5.3. Several studies have evaluated impact of tumour class on response to therapy. Rouzier et al. (2005) correlated pCR to a combined regimen of paclitaxel (12 weeks) followed by four courses of FAC (fluorouracil, doxorubicin and cyclofosphamide) across the different tumour classes, revealing a high chance of having a pCR among tumours of the HER-2 and basal cell-like classes, but a low chance of a pCR among tumours belonging to the luminal classes. In contrast, evaluating response to weekly doxorubicin (Sørlie et al., 2006), we found therapy failure (defined as primary progressive disease on therapy) to be particularly high among tumours belonging to the luminal B class, contrasting a low risk of failure among luminal A class tumours. Classifying tumours based on immunostaining, Carey and colleagues (Carey et al., 2007) confirmed a high pCR as well as clinical response rate in basal cell-like tumours, contrasting a poor response rate in luminal A class tumours. Similar, Liedtke and colleagues (Liedtke et al., 2008) found triple negativity to predict an increased chance of a pCR to different anthracycline- and taxane-containing regimens among more than 1100 patients receiving different regimens as primary medical (pre-surgical) therapy. However, despite being more likely to achieve a pCR, this study (like others) confirmed a general poor prognosis for triple-negative tumours as compared to other breast cancers. Examining tumours from more than 1300 patients participating in the Breast cancer International Research Group (BCIRG) 001 trial comparing adjuvant FAC (5-fluorouracil, doxorubicin and cyclofosfamide) to TAC (exchanging cyclophosphamide by docetaxel), Hugh et al. (2009) reported TAC to improve relapse-free and overall survival compared to FAC among patients harbouring tumours belonging to the luminal B class, HER-2 class, and triple negative tumours in general (covering the basal celllike and normal cell-like subgroups), but not for tumours belonging to the luminal A class. The hazard ratio for a relapse among patients treated with the TAC versus FAC regimen was 0.50, 0.46 and 0.66 with respect to triple negative, HER-2 overexpressing and luminal B groups, respectively. Although the luminal B class in general are estrogen receptor positive, they express receptors at a lower level, reveal a higher Ki67 index, and respond more poorly to tamoxifen as compared to luminal A class tumours (Cheang et al., 2009; Lønning et al., 2005; Sørlie et al., 2003), suggesting many of these tumours actually to be endocrine insensitive. The data accordingly fits to the general observation that ER negative tumours may benefit more from dose-dense treatment as well as the addition of taxanes to anthracycline-

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containing regimens as compared to estrogen receptor positive tumours (Berry et al., 2006). Contrasting the HER-2 overexpressing tumours, for which HER-2 amplification predicts a pCR to primary chemotherapy provided trastuzumab is added in concert (Tan et al., 2009) and, consistently, use of trastuzumab in the adjuvant setting dramatically improves relapse-free survival (Romond et al., 2005), triple negative and basal cell-like tumours remain with a poor prognosis, despite initial responsiveness to therapy (Carey et al., 2007). Similar, breast cancers arising in BRCA1 mutation carriers seems to respond adequately to anthracycline-containg chemotherapy (Byrski et al., 2010; Hubert et al., 2009), but so far we lack evidence of a significantly improved long-term outcome (Keet et al., 2009). Thus, many efforts are spent on improving treatment for this group of tumours and, in particular, the sub-group of tumours harbouring BRCA1 mutations (see section 5.3).

5.2.3.

Gene profiles defined by pre-determined criteria

While gene signatures with respect to TP53 status as well as oncogene profiles have been developed for breast cancer (Bild et al., 2006; Miller et al., 2005; Troester et al., 2006), so far these signatures have not been applied as predictive factors to therapy. With stromal proliferation playing an important role to breast cancer growth, different signatures based on expression of stromal-related genes have been reported as prognostic (Finak et al., 2008; West et al., 2005) but also predictive to resistance to FEC (5-fluorouracil, epirubicin and cyclophosphamide) treatment (Farmer et al., 2009). Further research is warranted to evaluate this interesting approach. Based on the hypothesis that normal wound healing also may be of importance in cancer, a “wound-response” signature was developed and found prognostic with respect to breast cancer survival (Chang et al., 2005). So far this signature has not been evaluated as a predictive factor to drug sensitivity. A major problem with respect to tumour grading relates to inter-individual observer variation regarding classification in particular of grade II tumours. Thus, Sotiriou et al. (2006) developed the Genomic Grade test (Mpquant DX). Based on genes characterizing grade I versus grade III tumours,they showed that grade II tumours could be classified as either grade I or grade III; further, they showed this classification to provide improved prognostic information as compared to “classical” tumour grading. Notably, high grade by this scoring system has been shown to predict for a pCR to treatment with paclitaxel followed by 5-fluorouracil, epirubin and cyclophosphamide (Liedtke et al., 2009). Inflammatory breast cancers harbour a particular poor prognosis (Bertucci et al., 2004b). While prognostic gene profiles specific to inflammatory breast cancer has been developed (Bieche et al., 2004), interestingly, Bertucci and colleagues (Bertucci et al., 2004a) developed a signature predicting pCR among inflammatory cancers treated with anthracycline-based chemotherapy. The findings however were based on a limited number of observations (n ¼ 26 inflammatory breast cancer samples available for post-treatment

histological examinations), and needs verification in larger data sets.

5.3. Chemoresistance related to biologically defined targets or genes playing a key role to DNA disposition, growth arrest or apoptosis Following the seminal study by Muss and colleagues (Muss et al., 1994) revealing a dose-dependent efficacy of anthracycline-based adjuvant treatment in tumours overexpressing HER-2 contrasting HER-2 negative ones, much interest has focused on HER-2 overexpression as well as HER-2 amplification predicting sensitivity to anthracyclines. Substantial evidence now reveals a dose-response effect to anthracyclines in HER-2 positive tumours; thus, HER-2 positive tumours seems to gain an improved benefit as compared to HER-2 negative ones, but at a cost of increased dosing with respect to primary (neoadjuvant) treatment (Petit et al., 2001) as well as in the adjuvant setting (Paik et al., 1998; Pritchard et al., 2006). More uncertainty relates to whether this effect of HER-2 overexpression may be a co-variate to another key target. In vitro experiments have not supported a direct role of HER-2 overexpression conferring drug resistance (Pegram et al., 1997). Notably, about 40% of all HER-2 amplified tumours reveal co-amplification of the topoisomerase-II (Topo-II) gene, located close to HER-2 on the chromosome 17q arm (Jarvinen et al., 2000; Press et al., 2005). Topo-II is a major target for anthracycline therapy; thus, it seems logical postulating overexpression of Topo-II to be related to enhanced anthracycline sensitivity. Thus, several studies now support the hypothesis that anthracycline-containing chemotherapy offers an advantage as compared to other regimens in patients harbouring tumours revealing topo-II amplifications or deletions (Knoop et al., 2005; O’Malley et al., 2009; Tanner et al., 2006), although there are negative studies as well (Harris et al., 2009). Chromosome 17 however contains several additional genes of potential interest to chemoresistance, and chromosome 17 polysomy was reported a better predictor of anthracycline sensitivity as compared to HER-2 as well as Topo-II amplifications (Bartlett et al., 2008; Reinholz et al., 2009). Further work in this area is needed to sort out individual predictive factors. A gene of particular interest is TP53. Thus, we (Geisler et al., 2003, 2001; Aas et al., 1996) and others (Kandioler-Eckersberger et al., 2000) revealed TP53 mutations to be associated with lack of response to anthracycline-containing chemotherapy. In contrast, Bertheau and colleagues (Bertheau et al., 2002) reported TP53 mutations to predict a pCR to a high-dose chemotherapy regimen. So far, we have no explanation to this finding. In a recent study, we confirmed TP53 mutations to predict resistance to epirubicin when administered as a “normal-dose” regimen (Chrisanthar et al., 2008). Importantly, in the same study (Chrisanthar et al., 2008) we detected non-sense mutations in the CHEK2 gene among some of the tumours expressing primary resistance to epirubicin therapy despite harbouring wtTP53, underlining the importance of looking not at individual genes but, rather, trying to explore defects in “genetic cascades” (Lønning, 2004) related to drug resistance (Figure 4).

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Figure 4 e Potential explanation to the finding that gene expression profiles do not consistently reflect drug sensitivity. Figure obtained from (Lønning, 2004) with permission. Postulating drug resistance may be due to genetic disturbances affecting a few, perhaps 2, critical pathways, this figure models 4 tumours expressing a different gene expression profile by microarray. a) Depicting 2 functional cascades critical to execution of apoptosis in response to genotoxic therapy. Red balls illustrated genes inactivated by mutations or epigenetic mechanisms, orange balls indicating downstream genes down-regulated due to such events. b) The effects of these events on gene expression profiles. Notice a greater similarity between tumour B and C (sensitive and resistant to therapy, respectively) than in-between A and B (both sensitive) or C and D (both resistant).

BRCA1 mutations cause a defect in DNA double-strand repair (Martin et al., 2008), and experimental evidence suggest these tumours may be particularly sensitive to drugs like platinum compounds generating interstrand cross-links (Rhiem et al., 2009; Rottenberg et al., 2008; Tassone et al., 2003). While evidence so far is limited, the results from two small trials suggests a high rate of pCR among BRCA1 mutation carriers (Byrski et al., 2009) as well as in triple negative tumours in general (Silver et al., 2010) to cis-platinum treatment. Tumours arising in BRCA1 and BRCA2 mutation carriers express

a different biology, with BRCA2 mutated tumours in general being estrogen receptor positive (Palacios et al., 2008). Despite this, tumours harbouring BRCA2 mutations seems to carry a similar defect with respect to DNA double break repair as BRCA1 mutated ones (Lord and Ashworth, 2008; Martin et al., 2008) While we lack data confirming efficacy of cis-platinum in BRCA2 mutated tumours, experimental evidence suggests a beneficial effect. Interestingly, secondary deletions removing the gene fragment carrying a BRCA2 mutation have been shown to restore wt BRCA2 function and confer resistance to

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platinum compounds (Edwards et al., 2008; Sakai et al., 2008). However, more data are needed evaluating efficacy of platinum compounds in this group of patients before making therapy recommendations. A second most interesting option relates to targeted therapy with respect to tumour in BRCA1 but also BRCA2 mutation carriers as well as to basal cell-like tumours in general. The fact that BRCA2 mutated tumours, similar to those carrying a BRCA1 mutation are defective in double-strand DNA repair means both tumour types may depend on single strand repair to survive DNA damage (Farmer et al., 2005). Poly(adenosine diphosphate ribose) polymerase (PARPs) plays a critical role with respect to single-strand repair. Now, studies applying the PARP inhibitor olaparib as monotherapy have revealed anti-tumour effects in breast (Fong et al., 2009; Tutt et al., 2009) as well as in ovarian (Audeh et al., 2009) cancers occurring in BRCA1/2 mutation carriers. Furthermore, the PARP inhibitor BSI-201 has been shown to enhance antitumour effects of gemcitabine and carboplatin chemotherapy in advanced breast cancer (O’Shaugnessy et al., 2009). Notably, in that study, inclusion criteria were not based on BRCA1/2 testing; the inclusion criterion was triple negativity as evaluated by immunostaining. This raises the question whether many triple negative/basal cell-like tumours actually harbours defects in other genes apart from BRCA1/2 involved in double-strand repair? If so, identification of such defects as predictive markers could significantly improve therapy for this group of patients.

revealed by these classical parameters. A main topic is whether correlation between cellular growth rate and drug sensitivity relates to turnover rate per see or may be related to distinct alterations in particular cell growth regulating genes as the cyclins or their inhibitors. For the moment, we do not know the answer to that question; the finding however that growth rate (whether assessed by tumour grade, Ki67 or a “poor prognosis index” by a gene array profile) seems to correlate to sensitivity to multiple drug regimens suggest a more generalized effect related to cell cycling rate. Thus, a reasonable approach is to consider these gene expression profiles as a further refinement extending the predictive power of conventional growth rate parameters. So far however, these signatures have added limited knowledge to our understanding of the key genetic “drivers” that may orchestrate drug resistance. The distance from current knowledge toward a full prediction of drug sensitivity remains long. Clearly, to be able to reverse chemoresistance in all tumours, we need a complete understanding of the molecular mechanisms and alterations conferring this effect. Treatment with HER-2 antagonists, as well as PARP inhibitors reveal a way forward, confirming the importance of selective targeting pathological processes. However, such an approach should by no means be limited to “targeted” therapy alone; an understanding of the mechanisms of resistance to common cytotoxic compounds could help us developing strategies preventing these events in the future.

6.

R E F E R E N C E S

The future

The works of Greenman et al. (2007) and Sjøblom et al. (2006) introduced the terms “drivers” versus “passengers” with respect to genetic alterations directing tumour biology. Identifying multiple individual gene mutations through unbiased sequencing of multiple genes in breast and colorectal cancers, they concluded a limited number of gene mutations to occur across multiple tumours, suggestive of a key role to tumour biology (“drivers”). In contrast, mutations in most genes were seen in individual tumours only, suggesting these mutations to arise through genomic instability but with little impact on tumour phenotype (“passengers”). Such passengers however may give rise to a different genetic “make-up” as detected example by microarrays between individual tumours expressing similar outcome as well as similar response to drug therapy. Now, with high through-put sequencing becoming available, this reveals the potential of sequencing the full genome in individual tumours (Shah et al., 2009; Stephens et al., 2009; Stratton et al., 2009). While some studies (Carter et al., 2009) now have suggested the number of “driver” genes may be higher than initially anticipated, the concept remains widely accepted. Considering chemoresistance, “conventional” parameters including estrogen receptor status, tumour grade and Ki67 has been shown to moderately predict treatment efficacy. Considering gene expression profiles as the mammoprint and OncolexDL, these signatures to a large degree reflects much of the same information regarding proliferation as

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