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Study of suboptimum treatment response: lessons from breast cancer
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Study of suboptimum treatment response: lessons from breast cancer Per Eystein Lønning
Drug resistance is the main cause of therapeutic failure and death in patients with cancer. However, there have been surprisingly few studies designed specifically to investigate the mechanisms underlying poor treatment response in vivo, compared with the number of phase II and III trials investigating treatment effects. We can now analyse the expression patterns of multiple genes by use of microarrays, rapid gene sequencing, and proteomics, and so need to reassess the way we design clinical trials to take full advantage of these new opportunities. I discuss the concept of clinical studies of chemoresistance in terms of the collection of tumour samples for biological studies, the use of appropriate clinical settings, and the importance of trial design. Ideally, such studies should investigate specific biological features in relation to measurable antitumour effects of single drugs.
Tumour D Tumour A Complete response Progression
Partial response
Stable disease
Tumour B Tumour C Figure 1. Clinical outcomes for patients with solid tumours (primary or metastatic). The morphology of the tumours is depicted as cells resistant to treatment (red) and cells sensitive to treatment (blue).
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Chemoresistance is the main cause of failure of cancer treatment. With the exception of a few rare cancers such as germinal-cell tumours, some lymphoproliferative disorders, and childhood malignant disorders, contemporary chemotherapy offers little hope of a cure to patients with advanced disease. Adjuvant treatment with endocrine agents and cytotoxic drugs reduces mortality in early breast cancer;1,2 however, this reduction is only 8–37%, and most patients are not cured by their adjuvant therapy. The association between the expression of oestrogen (and progesterone) receptors and response to hormone therapy is well documented,3–5 but clinically useful predictive factors for chemotherapy in breast cancer (and most other solid tumours) are lacking. Clinical studies of the mechanisms of chemoresistance differ from other clinical studies in several ways. To define chemoresistance as the primary target may have substantial implications in the selection of a clinical setting, and also in the selection of therapeutic regimens. This review discusses the study of chemoresistance in vivo with examples from breast cancer—the most extensively studied solid tumour with respect to treatment and prognostic and predictive factors. Although this review focuses on cytotoxic agents, the topics outlined apply to other therapeutic agents, such as novel biological modifiers, antiangiogenic agents, and radiation.
Chemoresistance Drug resistance versus sensitivity
The potential causes of drug resistance are practically limitless, and the large number of novel findings reported
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annually in basic research into drug sensitivity makes it almost impossible to predict what the most useful molecular targets to study for the next 2–4 years (the time needed to complete a clinical trial) should be. Thus, a specific hypothesis on the exact cause of resistance may be of little use in the design of novel protocols. A better approach could be to consider the concept of drug resistance more generally; careful consideration of the type or category of hypothesis to be investigated may improve trial design. After exposure to a cytotoxic agent in vitro, a single cell will survive (with or without a temporary growth arrest) or die. Thus, sensitivity and resistance should be defined as mutually exclusive states. However, the in-vivo situation differs because cell populations are heterogeneous. Exposure of different tumours to a cytotoxic agent may result in variable shrinkage, no change, or continuous tumour growth, depending on the number of sensitive and resistant cells within a heterogeneous population. Although the response of tumour A (figure 1) can be interpreted as a complete response6 with no palpable (or even pathologically identifiable) tumour left, a few resistant cells with the ability to seed distant metastases can be fatal to the patient and the only clinical benefit over patients with tumours B or C may be a delay before occurance of such metastases because of a small tumour burden. There is much clinical evidence that PEL is at the Department of Medicine, Section of Oncology, Haukeland University Hospital, Bergen, Norway. Correspondence: Prof Per E Lønning, Department of Medicine, Section of Oncology, Haukeland University Hospital, N-5021, Bergen, Norway. Tel: +47 55 972027. Fax: +47 55 972046. Email: [email protected]
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patients who achieve a complete response to primary therapy for breast cancer have better survival, albeit modestly, than those who have a minor response.7 Thus, many patients with tumours like A and B in figure 1 will be classified as responders on the basis of direct tumour response but as therapeutic failures if relapse or survival is the endpoint. This situation illustrates the problem of a establishing a uniform definition of in-vivo resistance. Although a relapse at follow-up may be a more sensitive endpoint than direct measurement of the tumour, the risk of relapse is influenced by other factors, such as rate of tumour growth and metastatic potential. However, the most important argument in favour of direct tumour measurement is our inability to identify the cause of a subsequent relapse from examination of tissue obtained from tumours like tumour A, in which the number of resistant cells would be below the detection limit of current assays. Grading of sensitivity by comparison of partial and complete responses, or of patients who relapse with those who remain free of disease, may be unhelpful for identification of the discriminating biological factor in the primary tumour. The number of resistant cells in subsequent metastases can be expected to be higher; however, there is a substantial delay before the metastases appear. Furthermore, many metastatic deposits (such as those in the brain, lungs, and bone) are not easily accessible for biopsy. By contrast, in tumours that behave like tumour D in figure 1, the bulk of the cells are of the resistant phenotype, thus they provide clues to the mechanism of resistance. Our goal is to increase the rate of cure by improving the sensitivity of tumour cells, but, perhaps counterintuitively, to reach it we need to study the biological characteristics of resistant tumours. From a clinical perspective, drug resistance can be defined as a quantitative or a qualitative problem. This definition has little to do with the potential molecular mechanism involved (eg, qualitative resistance could be due to overexpression or underexpression of a certain gene such as topoisomerase II);8 it is based on whether the resistance can be overcome through increases in drug doses within clinically defined limits. Inadequate dosing can restrict response rates; the important question is whether increases in drug doses beyond normal limits can reverse drug resistance. If resistance is conceived of as a qualitative problem, such a hypothesis could challenge the design of many clinical studies, such as those assessing high-dose therapy. Four types of evidence may refute the concept that drug resistance is a quantitative problem related to drug dose or tumour burden in breast cancer: the limited effects of adjuvant therapy, the lack of effect of dose escalation above normal doses in the adjuvant setting, the lack of crossresistance to different drugs given in normal doses, and the observations that metastatic tumours inevitably develop multidrug resistance. Effects of adjuvant therapy
The discovery that many cytotoxic drugs work only in dividing cells in vitro led to the dose-density hypothesis,9–11 which suggests that more intensive treatment (higher doses
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in a given time) will eradicate the final tumour cells. An important consideration is the size of the tumour (results are better when the number of tumour cells is small), and the log of the number of cell kills10 required for tumour eradication. Although the fact that some patients with micrometastases from breast cancer are cured by adjuvant endocrine treatment1 and chemotherapy2 (despite the noncurative effects of the same treatment in metastatic disease) supports the dose-density concept, there are other explanations, such as eradication by the immune system of a small number of remaining drug-resistant cells. However, a reduction in morbidity of 8–37% with adjuvant chemotherapy emphasises the fact that most micrometastases are not eradicated, probably because of a population of drug-resistant cells. High-dose regimens
Studies on potential dose–response relations to chemotherapy in breast cancer have found that, although certain threshold doses are required for optimum response, further dose escalation, including high-dose therapy with stem-cell support, is of little benefit as an adjuvant treatment or in treatment of metastatic disease.12–16 The overexpression of ERBB2 to some extent predicts a dose–response relation to treatment with anthracyclines (which is not seen in ERBB2-negative tumours) in the adjuvant setting; this finding underlines the need for qualitative factors to be explored before potential dose–response effects are assessed.17,18 Therapies with different mechanisms of action
The limitations of the use of in-vitro models to explain the biology of human cancers were outlined by Skipper and Perry 30 years ago9 and have been strengthened by more recent biological knowledge. Immortalised cell lines and xenografts do not necessarily reflect the heterogeneity of human cancers in vivo. In addition, different immortalised cell lines are characterised by distinct genetic disturbances19–21 including mutations in genes thought to have key roles in chemotherapy-related apoptosis.22 In theory, the observation that combined therapies with, for instance, taxanes and anthracyclines, produce better response rates than single agents23 does not exclude the possibility that these drugs share a common mechanism of resistance. Combined treatment could increase overall cytotoxicity by overcoming a threshold for activating a common mechanism of apoptosis. However, the lack of cross-resistance when the same drugs are given in sequence as monotherapies24,25 clearly suggests that different mechanisms of resistance are involved. Multidrug resistance
Initially responsive metastatic tumours eventually become resistant to all types of therapeutic interventions; this change can only be explained by the outgrowth of multi-resistant cell populations. Whether the most important factor is selection or therapy-driven mutations, these cells must have biological features different from those of their parental cells that make them resistant to therapy.
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Clinical studies Translational research
A study should be designed to address a particular hypothesis. Although this concept is well known, it sometimes seems to be forgotten in discussion of strategies aimed at investigating drug resistance in clinical studies. One term that is frequently used to characterise clinical studies of the biological characteristics of tumours is translational research. In general, translational research means to take knowledge from one research area into another. Most (at least early-phase) clinical studies could be said to be in this category, since the potential mechanisms of action of the compounds under investigation have been outlined in laboratory experiments. In practice, most basic scientists and clinicians reserve the term translational research for studies specifically investigating a biological hypothesis in vivo. An important point raised by several authors is that the concept implies a two-way process;26,27 lessons from the clinic are taken back to the laboratory, creating the background for design of novel hypotheses. Although translational research does not fit into the traditional definition of phase I–III studies, most translational research can be considered as extended phase II studies.28 The reason is simple; the first mandatory step in the implementation of a new therapy has to be a dose-toxicity assessment (phase I). Since phase II investigations are aimed at showing clinical efficacy and phase III studies are large, randomised investigations comparing efficacy with the “gold standard”, translational research to show why a compound works by comparing clinical outcome with biological characteristics is a logical extension of the phase II programme. Although phase III studies are the final step in the validation of a factor that has the potential to predict respose to therapy (a “predictive factor”), they are a suboptimum setting for exploring new candidate factors. Therefore, it may be useful to define trials in which these issues can be assessed more specifically than simply calling them translational research. For the types of trials discussed in this review, chemoresistance study is a more relevant description. Such trials have strictly defined requirements and a scope that goes beyond study of general predictive factors.
Prognostic and predictive factors A prognostic factor provides information about the risk of death and relapse independent of therapy.29 Strictly defined, it can only be studied in patients who are not receiving any kind of systemic therapy. By contrast, a predictive factor provides information about the potential benefit of a certain treatment. Prognostic factors and chemoresistance
In contrast to the limited number of studies of factors predictive of response to therapy in patients with breast cancer, many studies have assessed prognostic factors. One reason for studying prognostic factors (in addition to selecting high-risk groups for treatment) is that they provide information about the biology of a tumour. However, there are various limitations to obtaining biological information in this way.
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Endpoints such as relapse-free survival and overall survival are complex and can be influenced by many biological properties including metastatic potential, rate of tumour growth, angiogenic stimuli, metastatic location, and response to therapy (if adjuvant treatment is given). Some clinical observations show that these characteristics are not necessarily related and may be linked to different gene products. In breast cancer, lymph-node metastases are associated with an increased risk of relapse and death during long-term follow-up, but do not seem to influence the response to hormones or chemotherapy in the adjuvant setting.1,2 Expression of the oestrogen receptor is a strong predictor of response to hormone therapy and is associated with short-term but probably not long-term outcome in patients who have not been given adjuvant therapy.30–32 This association is consistent with the fact that oestrogenreceptor-negative tumours have a higher rate of growth than oestrogen-receptor positive ones33 but probably not a higher risk of metastatic spread. However, both types have a propensity to spread to other organs.34,35 Survival is affected by metastatic growth and tumour location (owing to local complications) and patient-related factors such as general health. Evaluation of different prognostic factors by use of multivariate analysis has revealed strong covariance of many factors.36,37 This finding may explain some biological contradictions. For example, expression of the oestrogen receptor is associated with better short-term survival in patients who have not had adjuvant hormone therapy despite the fact that oestradiol is a potent mitogenic signal. Thus, the prognostic effect of receptor expression seems to be due not to its biological function but to its relation with high differentiation and slow tumour growth.38–40 Predictive factors
Predictive factors, as well as prognostic factors, can be studied in patients with primary cancer who are receiving adjuvant treatment. However, adjuvant therapy is a suboptimum setting for the study of predictive factors, particularly for potential causes of drug resistance. In theory, such investigations could be done in randomised studies, comparing the effects of these treatments to a control group of patients receiving no adjuvant therapy. Although this setting eliminates many confounding factors, there may be problems interpreting the results. Any difference in outcome as assessed by time to progression or survival may not necessarily reflect cell kill; an alternative explanation is a durable growth arrest in micrometastases. Although the objective response rate to chemotherapy in metastatic disease may be higher than to endocrine therapy, a larger number of patients seem to obtain durable stable disease with endocrine therapy.41–43 Thus, the finding that the death hazard ratio is more extensively reduced with adjuvant tamoxifen compared with chemotherapy in postmenopausal patients with receptor-positive tumours,1,2 suggests mechanisms other than cell kill may be important. In a study assessing predictive factors for two treatment modalities, lack of a control group receiving no systemic therapy may be a problem. Although one treatment may be better than the other, it is not possible to tell from such a
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Treatment efficacy
Treatment A Treatment B
Predictive factor
* *
Prognostic factor?
Positive for factor Negative for factor Predictive factor?
Time Figure 2. In this trial comparing two different drug regimens, the efficacy of the two compounds in subgroups of patients can be compared by measurement of a specific variable. However, if there is no non-treatment control group (green line), the predictive power of the variable cannot be calculated for patients receiving the less effective compound.
study whether a certain factor may predict better or worse outcome for a particular treatment compared with no treatment (figure 2). Furthermore, relapse and death are caused by the outgrowth of micrometastases, the biology of which may not necessarily be reflected in samples of the primary tumour; the small number of tumour cells that can be collected by bone-marrow aspiration limits their biological characterisation.44 Also, a large number of patients and durable follow-up are needed to obtain the number of observations required for statistical analysis in adjuvant studies.1,2
situation does not cause many problems in the study of chemoresistance. Despite some interesting findings in studies of adjuvant therapy with respect to predictive factors,17,18 there are several reasons why studies of chemoresistance should be done in the neoadjuvant and metastatic disease settings. There are three important advantages in studying chemoresistance in neoadjuvant therapy and metastatic disease. First, objective tumour responses can be directly measured by use of well-defined response criteria.6,47 Although direct measurement of lesion size is a crude variable, its usefulness has been well-documented.6 In addition, the response of macroscopic tumours can be assessed at the cellular level by use of variables such as the apoptotic index.48 Second, studies of patients with evaluable lesions require a much smaller cohort and a shorter followup. Third, second tumour samples can be obtained, allowing for comparison of biological features before and after therapy.45,49 Is there a scientific need to study resistance separately in metastatic and primary disease? Currently, we do not know whether the mechanisms of resistance in the two settings are similar. In particular, the proportion of resistant tumours (as defined by progression within 3 months of therapy) seems to be significantly higher in patients with metastatic disease than in primary disease. The reason for this discrepancy is unknown. Preliminary findings suggest that certain characteristics of tumour cells are predictive of particular metastatic behaviour,50 but there is limited information about potential links between the gene profile of the primary tumour and its metastases. Use of combined therapy
Potential cause of resistance
A predictive factor is a statistical, not a biological, variable. Although tumour grade is related to a patient’s response to cytotoxic drugs,45 this observation probably reflects associations with genetic changes in tumour tissue. To prove that a predictive factor is associated with the actual cause of resistance is almost impossible, as there is always the possibility that the predictive factor is a co-variant of the cause of resistance. For example, although the finding that mutations in the TP53 gene are associated with resistance to anthracycline seems logical because this gene has an important role in apoptosis,46 another possible explanation is that a mutation in TP53 is a marker of genetic instability. In general, if a particular biological disturbance were the cause of drug resistance, the restoration of its function would reverse sensitivity. Therefore, assessment of whether a predictive factor is associated with a cause of resistance requires focus on variables that may provide a biological explanation for observations. Such an approach necessitates a change in the way clinical protocols are designed.
Design of clinical trials of chemoresistance Optimum clinical settings
The observation that combined therapies may improve survival in the adjuvant treatment of breast cancer makes the use of monotherapy unethical in this setting. However, this
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Many studies assessing predictive factors have used combined treatment modalities including different cytotoxic agents or cytotoxic agents in combination with hormones or radiotherapy.51–53 Results from such studies are useful in clinical practice because they help to identify patients in whom standard combination therapy would fail so that effective alternatives can be prescribed. However, studies of predictive factors of chemoresistance in combined therapy may not provide a conceptual understanding of the underlying cause of drug resistance. Combining two noncross-resistant regimens may preclude the elucidation of mechanisms of resistance to each individual compound. This problem is exemplified by the use of anthracyclines and taxanes, which have no cross-resistance.24,25 Although specific mutations in the TP53 gene predict resistance to anthracyclines,45,47 the cytotoxic effect of taxanes seems to be independent of TP53.47,54 A study incorporating combined therapies will probably not be able to identify the role of TP53 or any other potential marker associated with resistance to one particular drug.55 Combination therapies are associated with many unpredictable pharmacological interactions. Giannakakou and colleagues reported that the transport of the p53 protein to the nucleus—a process essential for its biological function—depends on intact cellular microtubules.56 This finding suggests that treatment with taxanes could adversely
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influence the effects of anthracyclines. What is not yet known is whether such an interaction occurs in vivo and is of clinical importance. Evidence from studies of combined treatment regimens will only help to elucidate the biology of resistance if the mechanism of resistance to each of the individual drugs is known.
Paclitaxel
X
Diagnosis Randomisation staging X
Use of monotherapy
Monotherapy regimens are ideal for studying chemoresistance in vivo, but are such studies ethically justified? The use of monotherapies in the treatment of metastatic disease and the treatment of primary breast cancer may be acceptable, but there are some issues for concern. Monotherapy is an ideal treatment for metastatic disease because the clinician can assess the sensitivity of each individual tumour to therapy and modify treatment accordingly. Also, studies have shown that combined chemotherapy offers no survival benefit over the same regimens administered in sequence for metastatic breast cancer.57,58 Monotherapy with anthracyclines or taxanes is feasible.24,59 In the treatment of some malignant disorders such as high-grade lymphomas, combined therapy has such great survival benefits over monotherapy that limitation of patients to monotherapy would be unethical; however, in many other types of tumour the need for combined therapy can be challenged. Thus, monotherapies may be feasible treatment options in the advanced stages of different cancers such as low-grade lymphoma60 and melanoma.61 Is monotherapy feasible for primary treatment of breast cancer? The main distinction between primary medical treatment and adjuvant therapy is that in the primary setting the effects of each drug can be measured and an ineffective regimen substituted with an effective one, as in a current study by the Norwegian Breast Cancer Group comparing taxol with epirubicin (figure 3). There are some disadvantages of combined therapy over monotherapy, such as an increase in side-effects and the possibility of delivering suboptimum doses. However, it can be argued that it is the benefit of combined therapy that needs justification with respect to endpoints such as relapse-free and overall suvival. The issue of potential side-effects is illustrated by the theoretical example shown in figure 4. In the first example, two non-cross-resistant drugs each produce a response rate of 50% when given as a monotherapy. If the response to each drug is independent, and there are no interactions, the response rate for the combined therapy would be 75%. However, patients who respond to drug A, but not to drug B or vice versa (50% of the total population, 67% of responders) may have no benefits and more adverse effects from the combined treatment than patients who do not respond. For the 25% of patients who respond to both drugs, it must be established that combined therapy is better than sequential therapy in terms of duration of response or survival. Figure 4 also shows that the proportion of patients who respond to one drug only in a combination regimen remains high irrespective of the response rates observed. Such reasoning applies to treatment of both primary and metastatic breast cancer, but a recent study of neoadjuvant therapy is particularly informative. Smith and colleagues
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Not responding
Responding
Surgery X
Epirubicin X Not responding Figure 3. Schematic representation of the Norwegian Breast Cancer Group’s primary Epi-Tax study, which is ongoing. This study is investigating the mechanisms of resistance to sequentially administered epirubicin and paclitaxel in patients with stage III breast cancer. Patients who do not have an adequate response to first-line therapy are crossed over to the other treatment option. X marks the time of tissue collection.
compared an anthracycline-containing regimen alone or in combination with docetaxel and found that patients receiving combination therapy had a better pathological response rate.7 Particularly important is the finding that patients who did not respond to the anthracyclinecontaining regimen alone also responded poorly to subsequent docetaxel. One explanation may be a partial overlap of potential mechanisms of resistance in some cell populations; if so, the proportion of such cells could have
Drug A/ drug B
Drug A/ drug B 25%
25%
25%
25%
Drug A/ drug B
Drug A/ drug B
Drug A/ drug B Drug A/ drug B 9% 21% 21% Drug A/ drug B
49% Drug A/ drug B
Responding Non-responding
Figure 4. This diagram illustrates a theoretical scenario of outcome to treatment with two non-cross-resistant drugs, with individual response rates of 50% (a) or 70% (b) to each drug given as monotherapy. Among the responders, 67% (50/75) versus 46% (42/91) of the patients will benefit from one of the drugs and, thus, receive concomitant treatment with one ineffective drug.
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increased during therapy with the first regimen, resulting in an inferior response of the tumour to the second drug. However, the effect of the two regimens on micrometastases and subsequent survival remains unknown. Thus, although monotherapy in the primary setting should be carefully monitored, so far there is no direct evidence to dismiss its usefulness.
Biological studies Tumour samples
Prospective protocols aimed at investigating drug resistance should describe the logistics of handling tissue samples in detail. Many retrospective investigations are restricted because the only available samples are paraffin blocks. Although these preparations are suitable for most immunostaining and DNA procedures, they limit the use of novel techniques involving mRNA and proteomics. Many genes are expressed as spliced variants; thus, simple immunostaining is too limited, and complementary techniques involving mRNA need to be used.64,65 Studies of the predictive role of mutations in the TP53 gene highlight such limitations. Researchers sequencing DNA and cDNA discovered mutations in the TP53 gene that predict resistance to different regimens used in the treatment of breast cancer.45,47,49,66 By contrast, there have been more than 15 studies on breast cancer that have reported no predictive value for p53 status as assessed by immunostaining on paraffin-embedded tissue. The explanation for this disparity is that many mutations associated with resistance are not detected by p53 immunostaining.45 Thus, gene sequencing should be done in all such studies.67 Multiple variables
II,69 which is frequently coamplified with ERBB2. This example highlights the importance of studying several features, for instance by microarrays, and to combine the data with results obtained with other techniques. However, optimum application of such techniques may depend on the initial hypothesis. Currently, there is little evidence that global gene-expression analysis is useful in the understanding of drug resistance. Rather, use of arrays exploring selected genes on the basis of a functional hypothesis,70 with use of oligonucleotides for exploration of point mutations,71 may be more productive.
Do we need to tailor translation? An alternative approach to the identification of individual predictive factors or gene clusters is to assume a network hypothesis (figure 5). The results from our study of 90 patients with locally advanced breast cancer treated with doxorubicin are of interest in this setting (table 1).45 We found a significant relation between TP53 mutations affecting the L2/L3 loops (the DNA-binding domain of the p53 protein) and response to doxorubicin. However, we also observed tumours with wild-type TP53 that were resistant to therapy, and tumours with TP53 mutations affecting the L2/L3 domains that responded to treatment. One possibility is that mutations in the TP53 gene were not the cause of the resistance, but a co-variate to other factors. A second possibility is that these mutations could be the cause of resistance in some patients, but not in others. A third possibility is that resistance could be due to silencing of a particular cascade involving genes upstream and downstream of p53, but that TP53 also interacts with a redundant pathway that may compensate for loss of function. Such a hypothesis may explain the similar mutations in TP53 that have been found in patients who have responded to treatment as well as in those who have not responded.
There are few data on potential causes of chemoresistance in vivo, and the evidence can seem confusing. Comparative studies and clinical observations have suggested several different mechanisms E1 F22 E2 E3 as causes of therapy failure, such as F3 G21 overexpression of membrane pump Y1 G2 F2 G1 proteins and oncogenes, deregulation F1 G3 of enzymes, and mutations in genes Y3 Y2 involved in apoptosis. However, X1 currently there are no such predictive factors validated for use in the clinical X2 setting. Recent studies of breast cancer X4 (eg, p53) have focused on the overexpression of A1 D3 X3 the proto-oncogene ERBB2,17 defects in 8 A2 D1 the topoisomerase II enzyme, and D31 B1 D2 C3 mutations in TP53.45 Interestingly, B3 C1 although these observations imply C31 A21 B2 D11 totally different mechanisms of D21 C2 A11 C23 resistance, several of these events are B31 linked,45,68 which raises the question of B11 C12 B21 C22 C21 whether some of the events are D111 associated. For example, the association between ERBB2 overexpression Figure 5. Potential cascades involved in cellular responses to genotoxic stress. Growth-factor and the sensitivity of tumours to signalling, growth arrest, and apoptotic events are all controlled by genes in cascades. For example, anthracyclines seems to be related to inactivation of a vital gene acting upstream or downstream of p53 may have similar functional the overexpression of topoisomerase consequences to destruction of p53 itself.
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valuable as a screening tool and to give background biological information, it remains to be seen whether global gene TP53 status Number of patients (or protein) expression will be useful Responders or stable disease Progressive disease in the elucidation of drug resistance. Wild-type plus benign mutation 67 4 Alternatively, biologically customised TP53 mutation affecting L2/L3 14 5 microarrays, which include genes p=0·008. known to have a role in distinct pathways, may be particularly useful in Although mechanisms of resistance may differ between exploring a specific network cascade. Taking evidence from compounds and tumour types, the factors controlling studies of cell-cycle control and apoptosis into apoptosis and growth arrest are likely to be organised in consideration, most of the information required to produce cascades with variable degrees of redundancy. Thus, target tools specific for known events in chemoresistance is although in-vitro data suggest the existence of drug probably already available. Although novel techniques resistance in melanomas (which have a low frequency of involving mRNA amplification,82 microdissection,83 TP53 mutations), the cause may be the silencing of the proteomics,84 microarrays,55 and serial analysis of gene APAF1 gene, which has a major role in p53-dependent expression85 now provide novel opportunities for exploring apoptosis.72 Cellular pathways controlling genotoxic stress tumour biology that were unheard of 20 years ago, the major (such as apoptotic and growth-arrest pathways) or growth- challenge today is to define specific targets and design factor signalling, are controlled by cascades of appropriate models for clinical trials aimed at addressing transcriptional and non-transcriptional events such as these issues. phosphorylation. Thus, confirmation of the functional role Conflict of interest of a particular factor involved in apoptotic signalling (such None declared. as p53 or FAS) in drug resistance, requires that associated mutations or other defects in factors upstream or Acknowledgments downstream of the main target can be identified. Such I thank G Anker, R Bjerkvig, O Dahl, Ø Fluge, S Geisler, F Hoover, Lillehaug, and O Mella for their constructive criticism during the studies would confirm the functional importance of the Jpreparation of this review and my colleague Mitch Dowsett, Royal particular pathway and outline which of the many Marsden Hospital, London, for additional comments. downstream factors controls the process in question. Thus, microarray studies have shown that p53 activates or References downregulates many different genes on a transcriptional and 1 Clarke M, Collins R, Davies C, et al. Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet 1998; 351: a non-transcriptional basis.70 Use of microarray techniques 1451–67. have been validated in breast and prostate tumours, 2 Abe O, Abe R, Enomoto K, et al. Polychemotherapy for early breast cancer: an overview of the randomised trials. Lancet 1998; 352: embryonal tumours of the central nervous system, lymphomas, leukaemias, lung adenocarcinomas, and 3 930–42. McGuire WL. Steroid receptors in human breast cancer. Cancer Res melanomas, and have revealed gene clusters that correspond 1978; 38: 4289–91. to survival or malignant characteristics.73–80 However, these 4 Degenshein GA, Bloom N, Tobin E. The value of progesterone receptor assays in the management of advanced breast cancer. studies, which analysed general gene-expression data, all Cancer 1980; 46: 2789–93. described prognostic (not predictive) factors and provided 5 Elledge RM, Green S, Pugh R, et al. Estrogen receptor (ER) and progesterone receptor (PgR), by ligand-binding assay compared no information on drug resistance. Application of the term with ER, PgR and pS2, by immuno-histochemistry in predicting tailored therapy81 used in such approaches should be response to tamoxifen in metastatic breast cancer: a Southwest challenged. Oncology Group Study. Int J Cancer 2000; 89: 111–17. Irrespective of whether the model proposed in figure 5 is 6 Therasse P, Arbuck SG, Eisenhauer E, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer correct, it illustrates an important principle: that appropriate Inst 2000; 92: 205–16. use of any modern technology in the study of drug 7 Smith IC, Heys SD, Hutcheon AW, et al. Neoadjuvant chemotherapy in breast cancer: significantly enhanced response resistance—eg, microarrays, serial analysis of gene with docetaxel. J Clin Oncol 2002; 20: 1456–66. expression, and proteomics—shoud be challenged by 8 DiLeo A, Larsimont D, Gancberg D, et al. HER-2 and topobiological hypotheses. For instance if this model is an isomerase II alpha as predictive markers in a population of nodepositive breast cancer patients randomly treated with adjuvant CMF accurate depiction of the cause of drug resistance, the loss of or epirubicin plus cyclophosphamide. Ann Oncol 2001; 12: 1081–89. function of different genes in a main pathway (such as X1 or 9 Skipper H, Perry S. Kinetics of normal and leukemic leukocyte X3) may have similar effects on the pathway; however, populations and relevance to chemotherapy. Cancer Res 1970; 30: 1883–97. because each single gene can activate many downstream targets, these two events may produce different gene cluster 10 Shackney S, McCormack G, Cuchural G. Growth rate patterns of solid tumors and their relation to responsiveness to therapy. Ann profiles. Thus, although general gene profiling may be Intern Med 1978; 89: 107–21. Table 1. Doxorubicin (14 mg/m2) as primary treatment of patients with locally advanced breast cancer45
Search strategy and selection criteria Searches of Medline and Current Contents were done with the search terms “breast cancer”, “p53”, “apoptosis”, and “chemoresistance”.
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11 Norton L, Simon R. Tumor size, sensitivity to therapy, and design of treatment schedules. Cancer Treat Rep 1977; 61: 1307–17. 12 Budman DR, Berry DA, Cirrincione CT, et al. Dose and dose intensity as determinants of outcome in the adjuvant treatment of breast cancer. J Natl Cancer Inst 1998; 90: 1205–11. 13 Fisher B, Anderson S, Wickerham DL, et al. Increased
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intensification and total dose of cyclophosphamide in a doxorubicin-cyclophosphamide regimen for the treatment of primary breast cancer: findings from National Surgical Adjuvant Breast and Bowel Project B-22. J Clin Oncol 1997; 15: 1858–69. Bastit P, Chevallier B, Chevreau C, et al. Epirubicin-based chemotherapy in metastatic breast cancer patients: role of doseintensity and duration of treatment. J Clin Oncol 2000; 18: 3115–24. Bergh J, Wiklund T, Erikstein B, et al. Tailored fluorouracil, epirubicin, and cyclophosphamide compared with marrowsupported high-dose chemotherapy as adjuvant treatment for highrisk breast cancer: a randomised trial. Lancet 2000; 356: 1384–91. Stadtmauer EA, ONeill A, Goldstein LJ, et al. Conventional-dose chemotherapy compared with high-dose chemotherapy plus autologous hematopoietic stem-cell transplantation for metastatic breast cancer. N Engl J Med 2000; 342: 1069–76. Thor AD, Berry DA, Budman DR, et al.ERBB-2, p53, and efficacy of adjuvant therapy in lymph node-positive breast cancer. J Natl Cancer Inst 1998; 90: 1346–60. Paik SM, Bryant J, Park CH, et al. erbB-2 and response to doxorubicin in patients with axillary lymph node-positive, hormone receptor-negative breast cancer. J Natl Cancer Inst 1998; 90: 1361–70. Frizelle SP, Grim J, Zhou J, et al. Re-expression of p16 INK4a in mesothelioma cells results in cell cycle arrest cell death tumor suppression and tumor regression. Oncogene 1998; 16: 3087–95. Kataoka M, Wiehle S, Spitz F, et al. Down-regulation of BCL-2 is associated with p16(INK4)-mediated apoptosis in non-small cell lung cancer cells. Oncogene 2000; 19: 1589–95. Ries SJ, Brandts CH, Chung AS, et al. Loss of p14(ARF) in tumor cells facilitates replication of the adenovirus mutant dl1520 (ONYX015). Nat Med 2000; 6: 1128–33. Lowe SW, Ruley HE, Jacks T, Housman DE. p53-dependent apoptosis modulates the cytotoxicity of anticancer agents. Cell 1993; 74: 957–67. Miller KD, McCaskillStevens W, Sisk J, et al. Combination versus sequential doxorubicin and docetaxel as primary chemotherapy for breast cancer: a randomized pilot trial of the Hoosier Oncology Group. J Clin Oncol 1999; 17: 3033–37. Nabholtz JM, Senn HJ, Bezwoda WR, et al. Prospective randomised trial of docetaxel versus mitomycin plus vinblastine in patients with metastatic breast cancer progressing despite previous anthracyclinecontaining chemotherapy. J Clin Oncol 1999; 17: 1413–24. Ando M, Watanabe T, Nagata K, et al. Efficacy of docetaxel 60 mg/m(2) in patients with metastatic breast cancer according to the status of anthracycline resistance. J Clin Oncol 2001; 19: 336–42. Rustgi AK. Translational research: what is it? Gastroenterology 1999; 116: 1285. Friedman HS, Bigner SH, Bigner DD. Cyclosphosphamide therapy of medulloblastoma: from the laboratory to the clinic and back again (and again and again). J Neurooncol 1995; 24: 103–08. Bartelink H. From translational research to improved local control and survival. Int J Radiat Oncol 2001; 49: 311–18. Henderson IC, Patek AJ. The relationship between prognostic and predictive factors in the management of breast cancer. Breast Cancer Res Treat 1998; 52: 261–88. Harvey JM, Clark GM, Osborne CK, Allred DC. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 1999; 17: 1474–81. Hähnel R, Woodings T, Vivian AB. Prognostic value of estrogen receptors in primary breast cancer. Cancer 1979; 44: 671–75. Vollenweider-Zerargui L, Barrelet L, Wong Y, et al. The predictive value of estrogen and progesterone receptors’ concentrations on the clinical behavior of breast cancer in women: clinical correlation on 547 patients. Cancer 1986; 57: 1171–80. McGuire WL. Estrogen receptor versus nuclear grade as prognostic factors in axillary node negative breast cancer. J Clin Oncol 1988; 6: 1071–72. Kamby C, Rasmussen BR, Kristensen B. Oestrogen receptor status of primary breast carcinomas and their metastases: relation to pattern of spread and survival after recurrence. Br J Cancer 1989; 60: 252–57. Clark GM, Sledge GW, Osborne CK, McGuire WL. Survival from first recurrence: relative importance of prognostic factors in 1015 breast cancer patients. J Clin Oncol 1987; 5: 55–61. Battaglia F, Scambia G, Rossi S, et al. Epidermal growth factor receptor in human breast cancer: correlation with steroid hormone
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38 39 40
41
42
43
44
45
46 47 48 49 50 51
52 53
54 55
56 57
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receptors and axillary lymph node involvement. Eur J Cancer Clin Oncol 1988; 24: 1685–90. Howat JMT, Barnes DM, Harris M, Swindell R. The association of cytosol oestrogen and progesterone receptors with histological features of breast cancer and early recurrence of disease. Br J Cancer 1983; 47: 629–40. Silvestrini R, Daidone MG, DiFronzo G. Relationship between proliferative activity and estrogen receptors in breast cancer. Cancer 1979; 44: 665–70. Singh L, Wilson AJ, Baum M, et al. The relationship between histological grade, oestrogen receptor status, events and survival at 8 years in the NATO (‘Novaldex’) trial. Br J Cancer 1988; 57: 612–14. Moriki T, Takahashi T, Hiroi M, et al. Histological grade in invasive ductal carcinoma of breast correlates with the proliferative activity evaluated by BrdU: an immunohistochemical study including correlations with p53, c-erbB-2 and estrogen receptor status. Pathol Int 1996; 46: 417–25. Nabholtz JM, Mackey JR, Smylie M, et al. Phase II study of docetaxel, doxorubicin, and cyclophosphamide as first-line chemotherapy for metastatic breast cancer. J Clin Oncol 2001; 19: 314–21. Mouridsen H, Gershanovich M, Sun Y, et al. Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group. J Clin Oncol 2001; 19: 2596–606. Kaufmann M, Bajetta E, Dirix LY, et al. Exemestane is superior to megestrol acetate after tamoxifen failure in postmenopausal women with advanced breast cancer: results of a phase III randomized double-blind trial. J Clin Oncol 2000; 18: 1399–11. Gebhardt F, Zanker KS, Brandt B. Differential expression of alternatively spliced c-erbB-2 mRNA in primary tumors, lymph node metastases, and bone marrow micrometastases from breast cancer patients. Biochem Biophys Res Commun 1998; 247: 319–23. Geisler S, Lønning PE, Aas T, et al. Influence of TP53 gene alterations and c-erbB-2 expression on the response to treatment with doxorubicin in locally advanced breast cancer. Cancer Res 2001; 61: 2505–12. Bates S, Vousden KH. Mechanisms of p53-mediated apoptosis. Cell Mol Life Sci 1999; 55: 28–37. Hayward JL, Rubens RD, Carbone PP, et al. Assessment of response to therapy in advanced breast cancer. Br J Cancer 1977; 35: 292–98. Dowsett M, Detre S, Ormerod MG, et al. Analysis and sorting of apoptotic cells from fine-needle aspirates of excised human primary breast carcinomas. Cytometry 1998; 32: 291–300. Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747–52. Muller A, Homey B, Soto H, et al. Involvement of chemokine receptors in breast cancer metastasis. Nature 2001; 410: 50–56. Kandioler-Eckersberger D, Ludwig C, Rudas M, et al. TP53 mutation and p53 overexpression for prediction of response to neoadjuvant treatment in breast cancer patients. Clin Cancer Res 2000; 6: 50–56. Makris A, Powles TJ, Dowsett M, et al. Prediction of response to neoadjuvant chemoendocrine therapy in primary breast carcinomas. Clin Cancer Res 1997; 3: 593–600. Formenti SC, Dunnington G, Uzieli B, et al. Original p53 status predicts for pathological response in locally advanced breast cancer patients treated preoperatively with continuous infusion 5fluorouracil and radiation therapy. Int J Radiat Oncol Biol Phys 1997; 39: 1059–68. Wahl AF, Donaldson KL, Fairchild C, et al. Loss of normal p53 function confers sensitization to taxol by increasing G2/M arrest and apoptosis. Nat Med 1996; 2: 72–79. Steger GG, Wenzel C, Schmidiger MP, et al. HER2-status predicts complete pathological response (pCR) in primary, operable breast cancer (BC) treated with neoadjuvant epirubicin/docetaxel plus G-CSF. Breast Cancer Res Treat 2000; 64: abstr 23, 31. Giannakakou P, Sackett DL, Ward Y, et al. p53 is associated with cellular microtubules and is transported to the nucleus by dynein. Nat Cell Biol 2000; 2: 709–17. Sledge Jr GW, Neuberg D, Ingle J, et al. Phase III trial of doxorubicin (A) vs paclitaxel (T) vs doxorubicin + paclitaxel (A + T) as first-line therapy for metastatic breast cancer (MBC): an intergroup trial. Proc Am Soc Clin Oncol 1997; 16: 1a, 2 (abstr). Joensuu H, Holli K, Heikkinen M, et al. Combination
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chemotherapy versus single-agent therapy as first and second-line treatment in metastatic breast cancer: a prospective randomized trial. J Clin Oncol 1998; 16: 3720–30. Bastholt L, Dalmark M, Gjedde SB, et al. Dose-response relationship of epirubicin in the treatment of postmenopausal patients with metastatic breast cancer: a randomized study of epirubicin at four different dose levels performed by the Danish Breast Cancer Cooperative Group. J Clin Oncol 1996; 14: 1146–55. Brandt L, Kimby E, Nygren P, Glimelius B. A systematic overview of chemotherapy effects in indolent non-Hodgkins lymphoma. Acta Oncol 2001; 40: 213–23. McClay EF, McClay MET. Systemic chemotherapy for the treatment of metastatic melanoma. Semin Oncol 1996; 23: 744–53. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol 1999; 17: 460–69. Khayat D, Antoine EC, Nizri D, et al. Neoadjuvant chemotherapy in breast cancer: simple prognostic, or useful predictive factor? In: Perry MC (Ed) The education book of the 37th meeting of the American Society of Clinical Oncology (ASCO). Alexandria, VA: ASCO, 2001: 510–15. Ueda Y, Hijikata M, Takagi S, et al. Transcriptional activities of p73 splicing variants are regulated by inter-variant association. Biochem J 2001; 356: 859–66. Sigalas I, Calvert AH, Anderson JJ, et al. Alternatively spliced mdm2 transcripts with loss of p53 binding domain sequences: transforming ability and frequent detection in human cancer. Nat Med 1996; 2: 912–17. Lizard-Nacol S, Coudert B, Riedinger JM, et al. p53 gene alterations are associated with a decreased responsiveness to neoadjuvant chemotherapy in human breast cancer. Int J Oncol 1997; 10: 1203–07. Soussi T, Beroud C. Assessing TP53 status in human tumours to evaluate clinical outcome. Nat Rev Cancer 2001; 1: 233–40. Tanner M, Jarvinen P, Isola J. Amplification of HER-2/neu and topoisomerase II alpha in primary and metastatic breast cancer. Cancer Res 2001; 61: 5345–48. Järvinen TAH, Tanner M, Rantanen V, et al. Amplification and deletion of topoisomerase II alpha associate with ERBB-2 amplification and affect sensitivity to topoisomerase II inhibitor doxorubicin in breast cancer. Am J Pathol 2000; 156: 839–47. Kannan K, Amariglio N, Rechavi G, et al. DNA microarrays
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identification of primary and secondary target genes regulated by p53. Oncogene 2001; 20: 2225–34. Wen WH, Bernstein L, Lescallett J, et al. Comparison of TP53 mutations identified by oligonucleotide microarray and conventional DNA sequence analysis. Cancer Res 2000; 60: 2716–22. Soengas MS, Capodieci P, Polsky D, et al. Inactivation of the apoptosis effector apaf-1 in malignant melanoma. Nature 2001; 409: 207–11. Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98: 10869–74. van ‘t Veer L, Dal H, van de Vijver M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530–36. Dhanasekaran SM, Barrette TR, Ghosh D, et al. Delineation of prognostic biomarkers in prostate cancer. Nature 2001; 412: 822–26. Pomeroy SL, Tamayo P, Gaasenbeek M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002; 415: 436–42. Shipp MA, Ross KN, Tamayo P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002; 8: 68–74. Yeoh E, Ross M, Shurtleff S, et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002; 1: 133–43. Beer D, Kardia S, Huang C-C, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002; 8: 816–24. Bittner M, Meltzer P, Chen Y, et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000; 406: 536–40. van ‘t Veer LJ, De Jong D. The microarray way to tailored cancer treatment. Nat Med 2002; 8: 13–14. Wang E, Miller LD, Ohnmacht GA, et al. High-fidelity mRNA amplification for gene profiling. Nat Biotechnol 2000; 18: 457–59. Wittliff JL, Kunitake ST, Chu SS, Travis JC. Applications of laser capture microdissection in genomics and proteomics. J Clin Ligand Assay 2000; 23: 66–73. Voss T, Ahorn H, Haberl P, et al. Correlation of clinical data with proteomics profiles in 24 patients with B-cell chronic lymphocytic leukemia. Int J Cancer 2001; 91: 180–86. Zhang L, Zhou W, Velculescu VE, et al. Gene expression profiles in normal and cancer cells. Science 1997; 276: 1268–72.
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Study of suboptimum treatment response: lessons from breast cancer Per Eystein Lønning
Drug resistance is the main cause of therapeutic failure and death in patients with cancer. However, there have been surprisingly few studies designed specifically to investigate the mechanisms underlying poor treatment response in vivo, compared with the number of phase II and III trials investigating treatment effects. We can now analyse the expression patterns of multiple genes by use of microarrays, rapid gene sequencing, and proteomics, and so need to reassess the way we design clinical trials to take full advantage of these new opportunities. I discuss the concept of clinical studies of chemoresistance in terms of the collection of tumour samples for biological studies, the use of appropriate clinical settings, and the importance of trial design. Ideally, such studies should investigate specific biological features in relation to measurable antitumour effects of single drugs.
Tumour D Tumour A Complete response Progression
Partial response
Stable disease
Tumour B Tumour C Figure 1. Clinical outcomes for patients with solid tumours (primary or metastatic). The morphology of the tumours is depicted as cells resistant to treatment (red) and cells sensitive to treatment (blue).
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Chemoresistance is the main cause of failure of cancer treatment. With the exception of a few rare cancers such as germinal-cell tumours, some lymphoproliferative disorders, and childhood malignant disorders, contemporary chemotherapy offers little hope of a cure to patients with advanced disease. Adjuvant treatment with endocrine agents and cytotoxic drugs reduces mortality in early breast cancer;1,2 however, this reduction is only 8–37%, and most patients are not cured by their adjuvant therapy. The association between the expression of oestrogen (and progesterone) receptors and response to hormone therapy is well documented,3–5 but clinically useful predictive factors for chemotherapy in breast cancer (and most other solid tumours) are lacking. Clinical studies of the mechanisms of chemoresistance differ from other clinical studies in several ways. To define chemoresistance as the primary target may have substantial implications in the selection of a clinical setting, and also in the selection of therapeutic regimens. This review discusses the study of chemoresistance in vivo with examples from breast cancer—the most extensively studied solid tumour with respect to treatment and prognostic and predictive factors. Although this review focuses on cytotoxic agents, the topics outlined apply to other therapeutic agents, such as novel biological modifiers, antiangiogenic agents, and radiation.
Chemoresistance Drug resistance versus sensitivity
The potential causes of drug resistance are practically limitless, and the large number of novel findings reported
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annually in basic research into drug sensitivity makes it almost impossible to predict what the most useful molecular targets to study for the next 2–4 years (the time needed to complete a clinical trial) should be. Thus, a specific hypothesis on the exact cause of resistance may be of little use in the design of novel protocols. A better approach could be to consider the concept of drug resistance more generally; careful consideration of the type or category of hypothesis to be investigated may improve trial design. After exposure to a cytotoxic agent in vitro, a single cell will survive (with or without a temporary growth arrest) or die. Thus, sensitivity and resistance should be defined as mutually exclusive states. However, the in-vivo situation differs because cell populations are heterogeneous. Exposure of different tumours to a cytotoxic agent may result in variable shrinkage, no change, or continuous tumour growth, depending on the number of sensitive and resistant cells within a heterogeneous population. Although the response of tumour A (figure 1) can be interpreted as a complete response6 with no palpable (or even pathologically identifiable) tumour left, a few resistant cells with the ability to seed distant metastases can be fatal to the patient and the only clinical benefit over patients with tumours B or C may be a delay before occurance of such metastases because of a small tumour burden. There is much clinical evidence that PEL is at the Department of Medicine, Section of Oncology, Haukeland University Hospital, Bergen, Norway. Correspondence: Prof Per E Lønning, Department of Medicine, Section of Oncology, Haukeland University Hospital, N-5021, Bergen, Norway. Tel: +47 55 972027. Fax: +47 55 972046. Email: [email protected]
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patients who achieve a complete response to primary therapy for breast cancer have better survival, albeit modestly, than those who have a minor response.7 Thus, many patients with tumours like A and B in figure 1 will be classified as responders on the basis of direct tumour response but as therapeutic failures if relapse or survival is the endpoint. This situation illustrates the problem of a establishing a uniform definition of in-vivo resistance. Although a relapse at follow-up may be a more sensitive endpoint than direct measurement of the tumour, the risk of relapse is influenced by other factors, such as rate of tumour growth and metastatic potential. However, the most important argument in favour of direct tumour measurement is our inability to identify the cause of a subsequent relapse from examination of tissue obtained from tumours like tumour A, in which the number of resistant cells would be below the detection limit of current assays. Grading of sensitivity by comparison of partial and complete responses, or of patients who relapse with those who remain free of disease, may be unhelpful for identification of the discriminating biological factor in the primary tumour. The number of resistant cells in subsequent metastases can be expected to be higher; however, there is a substantial delay before the metastases appear. Furthermore, many metastatic deposits (such as those in the brain, lungs, and bone) are not easily accessible for biopsy. By contrast, in tumours that behave like tumour D in figure 1, the bulk of the cells are of the resistant phenotype, thus they provide clues to the mechanism of resistance. Our goal is to increase the rate of cure by improving the sensitivity of tumour cells, but, perhaps counterintuitively, to reach it we need to study the biological characteristics of resistant tumours. From a clinical perspective, drug resistance can be defined as a quantitative or a qualitative problem. This definition has little to do with the potential molecular mechanism involved (eg, qualitative resistance could be due to overexpression or underexpression of a certain gene such as topoisomerase II);8 it is based on whether the resistance can be overcome through increases in drug doses within clinically defined limits. Inadequate dosing can restrict response rates; the important question is whether increases in drug doses beyond normal limits can reverse drug resistance. If resistance is conceived of as a qualitative problem, such a hypothesis could challenge the design of many clinical studies, such as those assessing high-dose therapy. Four types of evidence may refute the concept that drug resistance is a quantitative problem related to drug dose or tumour burden in breast cancer: the limited effects of adjuvant therapy, the lack of effect of dose escalation above normal doses in the adjuvant setting, the lack of crossresistance to different drugs given in normal doses, and the observations that metastatic tumours inevitably develop multidrug resistance. Effects of adjuvant therapy
The discovery that many cytotoxic drugs work only in dividing cells in vitro led to the dose-density hypothesis,9–11 which suggests that more intensive treatment (higher doses
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in a given time) will eradicate the final tumour cells. An important consideration is the size of the tumour (results are better when the number of tumour cells is small), and the log of the number of cell kills10 required for tumour eradication. Although the fact that some patients with micrometastases from breast cancer are cured by adjuvant endocrine treatment1 and chemotherapy2 (despite the noncurative effects of the same treatment in metastatic disease) supports the dose-density concept, there are other explanations, such as eradication by the immune system of a small number of remaining drug-resistant cells. However, a reduction in morbidity of 8–37% with adjuvant chemotherapy emphasises the fact that most micrometastases are not eradicated, probably because of a population of drug-resistant cells. High-dose regimens
Studies on potential dose–response relations to chemotherapy in breast cancer have found that, although certain threshold doses are required for optimum response, further dose escalation, including high-dose therapy with stem-cell support, is of little benefit as an adjuvant treatment or in treatment of metastatic disease.12–16 The overexpression of ERBB2 to some extent predicts a dose–response relation to treatment with anthracyclines (which is not seen in ERBB2-negative tumours) in the adjuvant setting; this finding underlines the need for qualitative factors to be explored before potential dose–response effects are assessed.17,18 Therapies with different mechanisms of action
The limitations of the use of in-vitro models to explain the biology of human cancers were outlined by Skipper and Perry 30 years ago9 and have been strengthened by more recent biological knowledge. Immortalised cell lines and xenografts do not necessarily reflect the heterogeneity of human cancers in vivo. In addition, different immortalised cell lines are characterised by distinct genetic disturbances19–21 including mutations in genes thought to have key roles in chemotherapy-related apoptosis.22 In theory, the observation that combined therapies with, for instance, taxanes and anthracyclines, produce better response rates than single agents23 does not exclude the possibility that these drugs share a common mechanism of resistance. Combined treatment could increase overall cytotoxicity by overcoming a threshold for activating a common mechanism of apoptosis. However, the lack of cross-resistance when the same drugs are given in sequence as monotherapies24,25 clearly suggests that different mechanisms of resistance are involved. Multidrug resistance
Initially responsive metastatic tumours eventually become resistant to all types of therapeutic interventions; this change can only be explained by the outgrowth of multi-resistant cell populations. Whether the most important factor is selection or therapy-driven mutations, these cells must have biological features different from those of their parental cells that make them resistant to therapy.
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Clinical studies Translational research
A study should be designed to address a particular hypothesis. Although this concept is well known, it sometimes seems to be forgotten in discussion of strategies aimed at investigating drug resistance in clinical studies. One term that is frequently used to characterise clinical studies of the biological characteristics of tumours is translational research. In general, translational research means to take knowledge from one research area into another. Most (at least early-phase) clinical studies could be said to be in this category, since the potential mechanisms of action of the compounds under investigation have been outlined in laboratory experiments. In practice, most basic scientists and clinicians reserve the term translational research for studies specifically investigating a biological hypothesis in vivo. An important point raised by several authors is that the concept implies a two-way process;26,27 lessons from the clinic are taken back to the laboratory, creating the background for design of novel hypotheses. Although translational research does not fit into the traditional definition of phase I–III studies, most translational research can be considered as extended phase II studies.28 The reason is simple; the first mandatory step in the implementation of a new therapy has to be a dose-toxicity assessment (phase I). Since phase II investigations are aimed at showing clinical efficacy and phase III studies are large, randomised investigations comparing efficacy with the “gold standard”, translational research to show why a compound works by comparing clinical outcome with biological characteristics is a logical extension of the phase II programme. Although phase III studies are the final step in the validation of a factor that has the potential to predict respose to therapy (a “predictive factor”), they are a suboptimum setting for exploring new candidate factors. Therefore, it may be useful to define trials in which these issues can be assessed more specifically than simply calling them translational research. For the types of trials discussed in this review, chemoresistance study is a more relevant description. Such trials have strictly defined requirements and a scope that goes beyond study of general predictive factors.
Prognostic and predictive factors A prognostic factor provides information about the risk of death and relapse independent of therapy.29 Strictly defined, it can only be studied in patients who are not receiving any kind of systemic therapy. By contrast, a predictive factor provides information about the potential benefit of a certain treatment. Prognostic factors and chemoresistance
In contrast to the limited number of studies of factors predictive of response to therapy in patients with breast cancer, many studies have assessed prognostic factors. One reason for studying prognostic factors (in addition to selecting high-risk groups for treatment) is that they provide information about the biology of a tumour. However, there are various limitations to obtaining biological information in this way.
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Endpoints such as relapse-free survival and overall survival are complex and can be influenced by many biological properties including metastatic potential, rate of tumour growth, angiogenic stimuli, metastatic location, and response to therapy (if adjuvant treatment is given). Some clinical observations show that these characteristics are not necessarily related and may be linked to different gene products. In breast cancer, lymph-node metastases are associated with an increased risk of relapse and death during long-term follow-up, but do not seem to influence the response to hormones or chemotherapy in the adjuvant setting.1,2 Expression of the oestrogen receptor is a strong predictor of response to hormone therapy and is associated with short-term but probably not long-term outcome in patients who have not been given adjuvant therapy.30–32 This association is consistent with the fact that oestrogenreceptor-negative tumours have a higher rate of growth than oestrogen-receptor positive ones33 but probably not a higher risk of metastatic spread. However, both types have a propensity to spread to other organs.34,35 Survival is affected by metastatic growth and tumour location (owing to local complications) and patient-related factors such as general health. Evaluation of different prognostic factors by use of multivariate analysis has revealed strong covariance of many factors.36,37 This finding may explain some biological contradictions. For example, expression of the oestrogen receptor is associated with better short-term survival in patients who have not had adjuvant hormone therapy despite the fact that oestradiol is a potent mitogenic signal. Thus, the prognostic effect of receptor expression seems to be due not to its biological function but to its relation with high differentiation and slow tumour growth.38–40 Predictive factors
Predictive factors, as well as prognostic factors, can be studied in patients with primary cancer who are receiving adjuvant treatment. However, adjuvant therapy is a suboptimum setting for the study of predictive factors, particularly for potential causes of drug resistance. In theory, such investigations could be done in randomised studies, comparing the effects of these treatments to a control group of patients receiving no adjuvant therapy. Although this setting eliminates many confounding factors, there may be problems interpreting the results. Any difference in outcome as assessed by time to progression or survival may not necessarily reflect cell kill; an alternative explanation is a durable growth arrest in micrometastases. Although the objective response rate to chemotherapy in metastatic disease may be higher than to endocrine therapy, a larger number of patients seem to obtain durable stable disease with endocrine therapy.41–43 Thus, the finding that the death hazard ratio is more extensively reduced with adjuvant tamoxifen compared with chemotherapy in postmenopausal patients with receptor-positive tumours,1,2 suggests mechanisms other than cell kill may be important. In a study assessing predictive factors for two treatment modalities, lack of a control group receiving no systemic therapy may be a problem. Although one treatment may be better than the other, it is not possible to tell from such a
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Treatment efficacy
Treatment A Treatment B
Predictive factor
* *
Prognostic factor?
Positive for factor Negative for factor Predictive factor?
Time Figure 2. In this trial comparing two different drug regimens, the efficacy of the two compounds in subgroups of patients can be compared by measurement of a specific variable. However, if there is no non-treatment control group (green line), the predictive power of the variable cannot be calculated for patients receiving the less effective compound.
study whether a certain factor may predict better or worse outcome for a particular treatment compared with no treatment (figure 2). Furthermore, relapse and death are caused by the outgrowth of micrometastases, the biology of which may not necessarily be reflected in samples of the primary tumour; the small number of tumour cells that can be collected by bone-marrow aspiration limits their biological characterisation.44 Also, a large number of patients and durable follow-up are needed to obtain the number of observations required for statistical analysis in adjuvant studies.1,2
situation does not cause many problems in the study of chemoresistance. Despite some interesting findings in studies of adjuvant therapy with respect to predictive factors,17,18 there are several reasons why studies of chemoresistance should be done in the neoadjuvant and metastatic disease settings. There are three important advantages in studying chemoresistance in neoadjuvant therapy and metastatic disease. First, objective tumour responses can be directly measured by use of well-defined response criteria.6,47 Although direct measurement of lesion size is a crude variable, its usefulness has been well-documented.6 In addition, the response of macroscopic tumours can be assessed at the cellular level by use of variables such as the apoptotic index.48 Second, studies of patients with evaluable lesions require a much smaller cohort and a shorter followup. Third, second tumour samples can be obtained, allowing for comparison of biological features before and after therapy.45,49 Is there a scientific need to study resistance separately in metastatic and primary disease? Currently, we do not know whether the mechanisms of resistance in the two settings are similar. In particular, the proportion of resistant tumours (as defined by progression within 3 months of therapy) seems to be significantly higher in patients with metastatic disease than in primary disease. The reason for this discrepancy is unknown. Preliminary findings suggest that certain characteristics of tumour cells are predictive of particular metastatic behaviour,50 but there is limited information about potential links between the gene profile of the primary tumour and its metastases. Use of combined therapy
Potential cause of resistance
A predictive factor is a statistical, not a biological, variable. Although tumour grade is related to a patient’s response to cytotoxic drugs,45 this observation probably reflects associations with genetic changes in tumour tissue. To prove that a predictive factor is associated with the actual cause of resistance is almost impossible, as there is always the possibility that the predictive factor is a co-variant of the cause of resistance. For example, although the finding that mutations in the TP53 gene are associated with resistance to anthracycline seems logical because this gene has an important role in apoptosis,46 another possible explanation is that a mutation in TP53 is a marker of genetic instability. In general, if a particular biological disturbance were the cause of drug resistance, the restoration of its function would reverse sensitivity. Therefore, assessment of whether a predictive factor is associated with a cause of resistance requires focus on variables that may provide a biological explanation for observations. Such an approach necessitates a change in the way clinical protocols are designed.
Design of clinical trials of chemoresistance Optimum clinical settings
The observation that combined therapies may improve survival in the adjuvant treatment of breast cancer makes the use of monotherapy unethical in this setting. However, this
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Many studies assessing predictive factors have used combined treatment modalities including different cytotoxic agents or cytotoxic agents in combination with hormones or radiotherapy.51–53 Results from such studies are useful in clinical practice because they help to identify patients in whom standard combination therapy would fail so that effective alternatives can be prescribed. However, studies of predictive factors of chemoresistance in combined therapy may not provide a conceptual understanding of the underlying cause of drug resistance. Combining two noncross-resistant regimens may preclude the elucidation of mechanisms of resistance to each individual compound. This problem is exemplified by the use of anthracyclines and taxanes, which have no cross-resistance.24,25 Although specific mutations in the TP53 gene predict resistance to anthracyclines,45,47 the cytotoxic effect of taxanes seems to be independent of TP53.47,54 A study incorporating combined therapies will probably not be able to identify the role of TP53 or any other potential marker associated with resistance to one particular drug.55 Combination therapies are associated with many unpredictable pharmacological interactions. Giannakakou and colleagues reported that the transport of the p53 protein to the nucleus—a process essential for its biological function—depends on intact cellular microtubules.56 This finding suggests that treatment with taxanes could adversely
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influence the effects of anthracyclines. What is not yet known is whether such an interaction occurs in vivo and is of clinical importance. Evidence from studies of combined treatment regimens will only help to elucidate the biology of resistance if the mechanism of resistance to each of the individual drugs is known.
Paclitaxel
X
Diagnosis Randomisation staging X
Use of monotherapy
Monotherapy regimens are ideal for studying chemoresistance in vivo, but are such studies ethically justified? The use of monotherapies in the treatment of metastatic disease and the treatment of primary breast cancer may be acceptable, but there are some issues for concern. Monotherapy is an ideal treatment for metastatic disease because the clinician can assess the sensitivity of each individual tumour to therapy and modify treatment accordingly. Also, studies have shown that combined chemotherapy offers no survival benefit over the same regimens administered in sequence for metastatic breast cancer.57,58 Monotherapy with anthracyclines or taxanes is feasible.24,59 In the treatment of some malignant disorders such as high-grade lymphomas, combined therapy has such great survival benefits over monotherapy that limitation of patients to monotherapy would be unethical; however, in many other types of tumour the need for combined therapy can be challenged. Thus, monotherapies may be feasible treatment options in the advanced stages of different cancers such as low-grade lymphoma60 and melanoma.61 Is monotherapy feasible for primary treatment of breast cancer? The main distinction between primary medical treatment and adjuvant therapy is that in the primary setting the effects of each drug can be measured and an ineffective regimen substituted with an effective one, as in a current study by the Norwegian Breast Cancer Group comparing taxol with epirubicin (figure 3). There are some disadvantages of combined therapy over monotherapy, such as an increase in side-effects and the possibility of delivering suboptimum doses. However, it can be argued that it is the benefit of combined therapy that needs justification with respect to endpoints such as relapse-free and overall suvival. The issue of potential side-effects is illustrated by the theoretical example shown in figure 4. In the first example, two non-cross-resistant drugs each produce a response rate of 50% when given as a monotherapy. If the response to each drug is independent, and there are no interactions, the response rate for the combined therapy would be 75%. However, patients who respond to drug A, but not to drug B or vice versa (50% of the total population, 67% of responders) may have no benefits and more adverse effects from the combined treatment than patients who do not respond. For the 25% of patients who respond to both drugs, it must be established that combined therapy is better than sequential therapy in terms of duration of response or survival. Figure 4 also shows that the proportion of patients who respond to one drug only in a combination regimen remains high irrespective of the response rates observed. Such reasoning applies to treatment of both primary and metastatic breast cancer, but a recent study of neoadjuvant therapy is particularly informative. Smith and colleagues
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Not responding
Responding
Surgery X
Epirubicin X Not responding Figure 3. Schematic representation of the Norwegian Breast Cancer Group’s primary Epi-Tax study, which is ongoing. This study is investigating the mechanisms of resistance to sequentially administered epirubicin and paclitaxel in patients with stage III breast cancer. Patients who do not have an adequate response to first-line therapy are crossed over to the other treatment option. X marks the time of tissue collection.
compared an anthracycline-containing regimen alone or in combination with docetaxel and found that patients receiving combination therapy had a better pathological response rate.7 Particularly important is the finding that patients who did not respond to the anthracyclinecontaining regimen alone also responded poorly to subsequent docetaxel. One explanation may be a partial overlap of potential mechanisms of resistance in some cell populations; if so, the proportion of such cells could have
Drug A/ drug B
Drug A/ drug B 25%
25%
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25%
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Drug A/ drug B
Drug A/ drug B Drug A/ drug B 9% 21% 21% Drug A/ drug B
49% Drug A/ drug B
Responding Non-responding
Figure 4. This diagram illustrates a theoretical scenario of outcome to treatment with two non-cross-resistant drugs, with individual response rates of 50% (a) or 70% (b) to each drug given as monotherapy. Among the responders, 67% (50/75) versus 46% (42/91) of the patients will benefit from one of the drugs and, thus, receive concomitant treatment with one ineffective drug.
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increased during therapy with the first regimen, resulting in an inferior response of the tumour to the second drug. However, the effect of the two regimens on micrometastases and subsequent survival remains unknown. Thus, although monotherapy in the primary setting should be carefully monitored, so far there is no direct evidence to dismiss its usefulness.
Biological studies Tumour samples
Prospective protocols aimed at investigating drug resistance should describe the logistics of handling tissue samples in detail. Many retrospective investigations are restricted because the only available samples are paraffin blocks. Although these preparations are suitable for most immunostaining and DNA procedures, they limit the use of novel techniques involving mRNA and proteomics. Many genes are expressed as spliced variants; thus, simple immunostaining is too limited, and complementary techniques involving mRNA need to be used.64,65 Studies of the predictive role of mutations in the TP53 gene highlight such limitations. Researchers sequencing DNA and cDNA discovered mutations in the TP53 gene that predict resistance to different regimens used in the treatment of breast cancer.45,47,49,66 By contrast, there have been more than 15 studies on breast cancer that have reported no predictive value for p53 status as assessed by immunostaining on paraffin-embedded tissue. The explanation for this disparity is that many mutations associated with resistance are not detected by p53 immunostaining.45 Thus, gene sequencing should be done in all such studies.67 Multiple variables
II,69 which is frequently coamplified with ERBB2. This example highlights the importance of studying several features, for instance by microarrays, and to combine the data with results obtained with other techniques. However, optimum application of such techniques may depend on the initial hypothesis. Currently, there is little evidence that global gene-expression analysis is useful in the understanding of drug resistance. Rather, use of arrays exploring selected genes on the basis of a functional hypothesis,70 with use of oligonucleotides for exploration of point mutations,71 may be more productive.
Do we need to tailor translation? An alternative approach to the identification of individual predictive factors or gene clusters is to assume a network hypothesis (figure 5). The results from our study of 90 patients with locally advanced breast cancer treated with doxorubicin are of interest in this setting (table 1).45 We found a significant relation between TP53 mutations affecting the L2/L3 loops (the DNA-binding domain of the p53 protein) and response to doxorubicin. However, we also observed tumours with wild-type TP53 that were resistant to therapy, and tumours with TP53 mutations affecting the L2/L3 domains that responded to treatment. One possibility is that mutations in the TP53 gene were not the cause of the resistance, but a co-variate to other factors. A second possibility is that these mutations could be the cause of resistance in some patients, but not in others. A third possibility is that resistance could be due to silencing of a particular cascade involving genes upstream and downstream of p53, but that TP53 also interacts with a redundant pathway that may compensate for loss of function. Such a hypothesis may explain the similar mutations in TP53 that have been found in patients who have responded to treatment as well as in those who have not responded.
There are few data on potential causes of chemoresistance in vivo, and the evidence can seem confusing. Comparative studies and clinical observations have suggested several different mechanisms E1 F22 E2 E3 as causes of therapy failure, such as F3 G21 overexpression of membrane pump Y1 G2 F2 G1 proteins and oncogenes, deregulation F1 G3 of enzymes, and mutations in genes Y3 Y2 involved in apoptosis. However, X1 currently there are no such predictive factors validated for use in the clinical X2 setting. Recent studies of breast cancer X4 (eg, p53) have focused on the overexpression of A1 D3 X3 the proto-oncogene ERBB2,17 defects in 8 A2 D1 the topoisomerase II enzyme, and D31 B1 D2 C3 mutations in TP53.45 Interestingly, B3 C1 although these observations imply C31 A21 B2 D11 totally different mechanisms of D21 C2 A11 C23 resistance, several of these events are B31 linked,45,68 which raises the question of B11 C12 B21 C22 C21 whether some of the events are D111 associated. For example, the association between ERBB2 overexpression Figure 5. Potential cascades involved in cellular responses to genotoxic stress. Growth-factor and the sensitivity of tumours to signalling, growth arrest, and apoptotic events are all controlled by genes in cascades. For example, anthracyclines seems to be related to inactivation of a vital gene acting upstream or downstream of p53 may have similar functional the overexpression of topoisomerase consequences to destruction of p53 itself.
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valuable as a screening tool and to give background biological information, it remains to be seen whether global gene TP53 status Number of patients (or protein) expression will be useful Responders or stable disease Progressive disease in the elucidation of drug resistance. Wild-type plus benign mutation 67 4 Alternatively, biologically customised TP53 mutation affecting L2/L3 14 5 microarrays, which include genes p=0·008. known to have a role in distinct pathways, may be particularly useful in Although mechanisms of resistance may differ between exploring a specific network cascade. Taking evidence from compounds and tumour types, the factors controlling studies of cell-cycle control and apoptosis into apoptosis and growth arrest are likely to be organised in consideration, most of the information required to produce cascades with variable degrees of redundancy. Thus, target tools specific for known events in chemoresistance is although in-vitro data suggest the existence of drug probably already available. Although novel techniques resistance in melanomas (which have a low frequency of involving mRNA amplification,82 microdissection,83 TP53 mutations), the cause may be the silencing of the proteomics,84 microarrays,55 and serial analysis of gene APAF1 gene, which has a major role in p53-dependent expression85 now provide novel opportunities for exploring apoptosis.72 Cellular pathways controlling genotoxic stress tumour biology that were unheard of 20 years ago, the major (such as apoptotic and growth-arrest pathways) or growth- challenge today is to define specific targets and design factor signalling, are controlled by cascades of appropriate models for clinical trials aimed at addressing transcriptional and non-transcriptional events such as these issues. phosphorylation. Thus, confirmation of the functional role Conflict of interest of a particular factor involved in apoptotic signalling (such None declared. as p53 or FAS) in drug resistance, requires that associated mutations or other defects in factors upstream or Acknowledgments downstream of the main target can be identified. Such I thank G Anker, R Bjerkvig, O Dahl, Ø Fluge, S Geisler, F Hoover, Lillehaug, and O Mella for their constructive criticism during the studies would confirm the functional importance of the Jpreparation of this review and my colleague Mitch Dowsett, Royal particular pathway and outline which of the many Marsden Hospital, London, for additional comments. downstream factors controls the process in question. Thus, microarray studies have shown that p53 activates or References downregulates many different genes on a transcriptional and 1 Clarke M, Collins R, Davies C, et al. Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet 1998; 351: a non-transcriptional basis.70 Use of microarray techniques 1451–67. have been validated in breast and prostate tumours, 2 Abe O, Abe R, Enomoto K, et al. Polychemotherapy for early breast cancer: an overview of the randomised trials. Lancet 1998; 352: embryonal tumours of the central nervous system, lymphomas, leukaemias, lung adenocarcinomas, and 3 930–42. McGuire WL. Steroid receptors in human breast cancer. Cancer Res melanomas, and have revealed gene clusters that correspond 1978; 38: 4289–91. to survival or malignant characteristics.73–80 However, these 4 Degenshein GA, Bloom N, Tobin E. The value of progesterone receptor assays in the management of advanced breast cancer. studies, which analysed general gene-expression data, all Cancer 1980; 46: 2789–93. described prognostic (not predictive) factors and provided 5 Elledge RM, Green S, Pugh R, et al. Estrogen receptor (ER) and progesterone receptor (PgR), by ligand-binding assay compared no information on drug resistance. Application of the term with ER, PgR and pS2, by immuno-histochemistry in predicting tailored therapy81 used in such approaches should be response to tamoxifen in metastatic breast cancer: a Southwest challenged. Oncology Group Study. Int J Cancer 2000; 89: 111–17. Irrespective of whether the model proposed in figure 5 is 6 Therasse P, Arbuck SG, Eisenhauer E, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer correct, it illustrates an important principle: that appropriate Inst 2000; 92: 205–16. use of any modern technology in the study of drug 7 Smith IC, Heys SD, Hutcheon AW, et al. Neoadjuvant chemotherapy in breast cancer: significantly enhanced response resistance—eg, microarrays, serial analysis of gene with docetaxel. J Clin Oncol 2002; 20: 1456–66. expression, and proteomics—shoud be challenged by 8 DiLeo A, Larsimont D, Gancberg D, et al. HER-2 and topobiological hypotheses. For instance if this model is an isomerase II alpha as predictive markers in a population of nodepositive breast cancer patients randomly treated with adjuvant CMF accurate depiction of the cause of drug resistance, the loss of or epirubicin plus cyclophosphamide. Ann Oncol 2001; 12: 1081–89. function of different genes in a main pathway (such as X1 or 9 Skipper H, Perry S. Kinetics of normal and leukemic leukocyte X3) may have similar effects on the pathway; however, populations and relevance to chemotherapy. Cancer Res 1970; 30: 1883–97. because each single gene can activate many downstream targets, these two events may produce different gene cluster 10 Shackney S, McCormack G, Cuchural G. Growth rate patterns of solid tumors and their relation to responsiveness to therapy. Ann profiles. Thus, although general gene profiling may be Intern Med 1978; 89: 107–21. Table 1. Doxorubicin (14 mg/m2) as primary treatment of patients with locally advanced breast cancer45
Search strategy and selection criteria Searches of Medline and Current Contents were done with the search terms “breast cancer”, “p53”, “apoptosis”, and “chemoresistance”.
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11 Norton L, Simon R. Tumor size, sensitivity to therapy, and design of treatment schedules. Cancer Treat Rep 1977; 61: 1307–17. 12 Budman DR, Berry DA, Cirrincione CT, et al. Dose and dose intensity as determinants of outcome in the adjuvant treatment of breast cancer. J Natl Cancer Inst 1998; 90: 1205–11. 13 Fisher B, Anderson S, Wickerham DL, et al. Increased
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16
17 18
19 20 21 22 23
24
25 26 27
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31 32
33 34
35 36
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intensification and total dose of cyclophosphamide in a doxorubicin-cyclophosphamide regimen for the treatment of primary breast cancer: findings from National Surgical Adjuvant Breast and Bowel Project B-22. J Clin Oncol 1997; 15: 1858–69. Bastit P, Chevallier B, Chevreau C, et al. Epirubicin-based chemotherapy in metastatic breast cancer patients: role of doseintensity and duration of treatment. J Clin Oncol 2000; 18: 3115–24. Bergh J, Wiklund T, Erikstein B, et al. Tailored fluorouracil, epirubicin, and cyclophosphamide compared with marrowsupported high-dose chemotherapy as adjuvant treatment for highrisk breast cancer: a randomised trial. Lancet 2000; 356: 1384–91. Stadtmauer EA, ONeill A, Goldstein LJ, et al. Conventional-dose chemotherapy compared with high-dose chemotherapy plus autologous hematopoietic stem-cell transplantation for metastatic breast cancer. N Engl J Med 2000; 342: 1069–76. Thor AD, Berry DA, Budman DR, et al.ERBB-2, p53, and efficacy of adjuvant therapy in lymph node-positive breast cancer. J Natl Cancer Inst 1998; 90: 1346–60. Paik SM, Bryant J, Park CH, et al. erbB-2 and response to doxorubicin in patients with axillary lymph node-positive, hormone receptor-negative breast cancer. J Natl Cancer Inst 1998; 90: 1361–70. Frizelle SP, Grim J, Zhou J, et al. Re-expression of p16 INK4a in mesothelioma cells results in cell cycle arrest cell death tumor suppression and tumor regression. Oncogene 1998; 16: 3087–95. Kataoka M, Wiehle S, Spitz F, et al. Down-regulation of BCL-2 is associated with p16(INK4)-mediated apoptosis in non-small cell lung cancer cells. Oncogene 2000; 19: 1589–95. Ries SJ, Brandts CH, Chung AS, et al. Loss of p14(ARF) in tumor cells facilitates replication of the adenovirus mutant dl1520 (ONYX015). Nat Med 2000; 6: 1128–33. Lowe SW, Ruley HE, Jacks T, Housman DE. p53-dependent apoptosis modulates the cytotoxicity of anticancer agents. Cell 1993; 74: 957–67. Miller KD, McCaskillStevens W, Sisk J, et al. Combination versus sequential doxorubicin and docetaxel as primary chemotherapy for breast cancer: a randomized pilot trial of the Hoosier Oncology Group. J Clin Oncol 1999; 17: 3033–37. Nabholtz JM, Senn HJ, Bezwoda WR, et al. Prospective randomised trial of docetaxel versus mitomycin plus vinblastine in patients with metastatic breast cancer progressing despite previous anthracyclinecontaining chemotherapy. J Clin Oncol 1999; 17: 1413–24. Ando M, Watanabe T, Nagata K, et al. Efficacy of docetaxel 60 mg/m(2) in patients with metastatic breast cancer according to the status of anthracycline resistance. J Clin Oncol 2001; 19: 336–42. Rustgi AK. Translational research: what is it? Gastroenterology 1999; 116: 1285. Friedman HS, Bigner SH, Bigner DD. Cyclosphosphamide therapy of medulloblastoma: from the laboratory to the clinic and back again (and again and again). J Neurooncol 1995; 24: 103–08. Bartelink H. From translational research to improved local control and survival. Int J Radiat Oncol 2001; 49: 311–18. Henderson IC, Patek AJ. The relationship between prognostic and predictive factors in the management of breast cancer. Breast Cancer Res Treat 1998; 52: 261–88. Harvey JM, Clark GM, Osborne CK, Allred DC. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 1999; 17: 1474–81. Hähnel R, Woodings T, Vivian AB. Prognostic value of estrogen receptors in primary breast cancer. Cancer 1979; 44: 671–75. Vollenweider-Zerargui L, Barrelet L, Wong Y, et al. The predictive value of estrogen and progesterone receptors’ concentrations on the clinical behavior of breast cancer in women: clinical correlation on 547 patients. Cancer 1986; 57: 1171–80. McGuire WL. Estrogen receptor versus nuclear grade as prognostic factors in axillary node negative breast cancer. J Clin Oncol 1988; 6: 1071–72. Kamby C, Rasmussen BR, Kristensen B. Oestrogen receptor status of primary breast carcinomas and their metastases: relation to pattern of spread and survival after recurrence. Br J Cancer 1989; 60: 252–57. Clark GM, Sledge GW, Osborne CK, McGuire WL. Survival from first recurrence: relative importance of prognostic factors in 1015 breast cancer patients. J Clin Oncol 1987; 5: 55–61. Battaglia F, Scambia G, Rossi S, et al. Epidermal growth factor receptor in human breast cancer: correlation with steroid hormone
184
37
38 39 40
41
42
43
44
45
46 47 48 49 50 51
52 53
54 55
56 57
58
receptors and axillary lymph node involvement. Eur J Cancer Clin Oncol 1988; 24: 1685–90. Howat JMT, Barnes DM, Harris M, Swindell R. The association of cytosol oestrogen and progesterone receptors with histological features of breast cancer and early recurrence of disease. Br J Cancer 1983; 47: 629–40. Silvestrini R, Daidone MG, DiFronzo G. Relationship between proliferative activity and estrogen receptors in breast cancer. Cancer 1979; 44: 665–70. Singh L, Wilson AJ, Baum M, et al. The relationship between histological grade, oestrogen receptor status, events and survival at 8 years in the NATO (‘Novaldex’) trial. Br J Cancer 1988; 57: 612–14. Moriki T, Takahashi T, Hiroi M, et al. Histological grade in invasive ductal carcinoma of breast correlates with the proliferative activity evaluated by BrdU: an immunohistochemical study including correlations with p53, c-erbB-2 and estrogen receptor status. Pathol Int 1996; 46: 417–25. Nabholtz JM, Mackey JR, Smylie M, et al. Phase II study of docetaxel, doxorubicin, and cyclophosphamide as first-line chemotherapy for metastatic breast cancer. J Clin Oncol 2001; 19: 314–21. Mouridsen H, Gershanovich M, Sun Y, et al. Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group. J Clin Oncol 2001; 19: 2596–606. Kaufmann M, Bajetta E, Dirix LY, et al. Exemestane is superior to megestrol acetate after tamoxifen failure in postmenopausal women with advanced breast cancer: results of a phase III randomized double-blind trial. J Clin Oncol 2000; 18: 1399–11. Gebhardt F, Zanker KS, Brandt B. Differential expression of alternatively spliced c-erbB-2 mRNA in primary tumors, lymph node metastases, and bone marrow micrometastases from breast cancer patients. Biochem Biophys Res Commun 1998; 247: 319–23. Geisler S, Lønning PE, Aas T, et al. Influence of TP53 gene alterations and c-erbB-2 expression on the response to treatment with doxorubicin in locally advanced breast cancer. Cancer Res 2001; 61: 2505–12. Bates S, Vousden KH. Mechanisms of p53-mediated apoptosis. Cell Mol Life Sci 1999; 55: 28–37. Hayward JL, Rubens RD, Carbone PP, et al. Assessment of response to therapy in advanced breast cancer. Br J Cancer 1977; 35: 292–98. Dowsett M, Detre S, Ormerod MG, et al. Analysis and sorting of apoptotic cells from fine-needle aspirates of excised human primary breast carcinomas. Cytometry 1998; 32: 291–300. Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747–52. Muller A, Homey B, Soto H, et al. Involvement of chemokine receptors in breast cancer metastasis. Nature 2001; 410: 50–56. Kandioler-Eckersberger D, Ludwig C, Rudas M, et al. TP53 mutation and p53 overexpression for prediction of response to neoadjuvant treatment in breast cancer patients. Clin Cancer Res 2000; 6: 50–56. Makris A, Powles TJ, Dowsett M, et al. Prediction of response to neoadjuvant chemoendocrine therapy in primary breast carcinomas. Clin Cancer Res 1997; 3: 593–600. Formenti SC, Dunnington G, Uzieli B, et al. Original p53 status predicts for pathological response in locally advanced breast cancer patients treated preoperatively with continuous infusion 5fluorouracil and radiation therapy. Int J Radiat Oncol Biol Phys 1997; 39: 1059–68. Wahl AF, Donaldson KL, Fairchild C, et al. Loss of normal p53 function confers sensitization to taxol by increasing G2/M arrest and apoptosis. Nat Med 1996; 2: 72–79. Steger GG, Wenzel C, Schmidiger MP, et al. HER2-status predicts complete pathological response (pCR) in primary, operable breast cancer (BC) treated with neoadjuvant epirubicin/docetaxel plus G-CSF. Breast Cancer Res Treat 2000; 64: abstr 23, 31. Giannakakou P, Sackett DL, Ward Y, et al. p53 is associated with cellular microtubules and is transported to the nucleus by dynein. Nat Cell Biol 2000; 2: 709–17. Sledge Jr GW, Neuberg D, Ingle J, et al. Phase III trial of doxorubicin (A) vs paclitaxel (T) vs doxorubicin + paclitaxel (A + T) as first-line therapy for metastatic breast cancer (MBC): an intergroup trial. Proc Am Soc Clin Oncol 1997; 16: 1a, 2 (abstr). Joensuu H, Holli K, Heikkinen M, et al. Combination
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Study of treatment response
59
60 61 62
63
64 65
66
67 68 69
70
chemotherapy versus single-agent therapy as first and second-line treatment in metastatic breast cancer: a prospective randomized trial. J Clin Oncol 1998; 16: 3720–30. Bastholt L, Dalmark M, Gjedde SB, et al. Dose-response relationship of epirubicin in the treatment of postmenopausal patients with metastatic breast cancer: a randomized study of epirubicin at four different dose levels performed by the Danish Breast Cancer Cooperative Group. J Clin Oncol 1996; 14: 1146–55. Brandt L, Kimby E, Nygren P, Glimelius B. A systematic overview of chemotherapy effects in indolent non-Hodgkins lymphoma. Acta Oncol 2001; 40: 213–23. McClay EF, McClay MET. Systemic chemotherapy for the treatment of metastatic melanoma. Semin Oncol 1996; 23: 744–53. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol 1999; 17: 460–69. Khayat D, Antoine EC, Nizri D, et al. Neoadjuvant chemotherapy in breast cancer: simple prognostic, or useful predictive factor? In: Perry MC (Ed) The education book of the 37th meeting of the American Society of Clinical Oncology (ASCO). Alexandria, VA: ASCO, 2001: 510–15. Ueda Y, Hijikata M, Takagi S, et al. Transcriptional activities of p73 splicing variants are regulated by inter-variant association. Biochem J 2001; 356: 859–66. Sigalas I, Calvert AH, Anderson JJ, et al. Alternatively spliced mdm2 transcripts with loss of p53 binding domain sequences: transforming ability and frequent detection in human cancer. Nat Med 1996; 2: 912–17. Lizard-Nacol S, Coudert B, Riedinger JM, et al. p53 gene alterations are associated with a decreased responsiveness to neoadjuvant chemotherapy in human breast cancer. Int J Oncol 1997; 10: 1203–07. Soussi T, Beroud C. Assessing TP53 status in human tumours to evaluate clinical outcome. Nat Rev Cancer 2001; 1: 233–40. Tanner M, Jarvinen P, Isola J. Amplification of HER-2/neu and topoisomerase II alpha in primary and metastatic breast cancer. Cancer Res 2001; 61: 5345–48. Järvinen TAH, Tanner M, Rantanen V, et al. Amplification and deletion of topoisomerase II alpha associate with ERBB-2 amplification and affect sensitivity to topoisomerase II inhibitor doxorubicin in breast cancer. Am J Pathol 2000; 156: 839–47. Kannan K, Amariglio N, Rechavi G, et al. DNA microarrays
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identification of primary and secondary target genes regulated by p53. Oncogene 2001; 20: 2225–34. Wen WH, Bernstein L, Lescallett J, et al. Comparison of TP53 mutations identified by oligonucleotide microarray and conventional DNA sequence analysis. Cancer Res 2000; 60: 2716–22. Soengas MS, Capodieci P, Polsky D, et al. Inactivation of the apoptosis effector apaf-1 in malignant melanoma. Nature 2001; 409: 207–11. Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98: 10869–74. van ‘t Veer L, Dal H, van de Vijver M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530–36. Dhanasekaran SM, Barrette TR, Ghosh D, et al. Delineation of prognostic biomarkers in prostate cancer. Nature 2001; 412: 822–26. Pomeroy SL, Tamayo P, Gaasenbeek M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002; 415: 436–42. Shipp MA, Ross KN, Tamayo P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002; 8: 68–74. Yeoh E, Ross M, Shurtleff S, et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002; 1: 133–43. Beer D, Kardia S, Huang C-C, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002; 8: 816–24. Bittner M, Meltzer P, Chen Y, et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000; 406: 536–40. van ‘t Veer LJ, De Jong D. The microarray way to tailored cancer treatment. Nat Med 2002; 8: 13–14. Wang E, Miller LD, Ohnmacht GA, et al. High-fidelity mRNA amplification for gene profiling. Nat Biotechnol 2000; 18: 457–59. Wittliff JL, Kunitake ST, Chu SS, Travis JC. Applications of laser capture microdissection in genomics and proteomics. J Clin Ligand Assay 2000; 23: 66–73. Voss T, Ahorn H, Haberl P, et al. Correlation of clinical data with proteomics profiles in 24 patients with B-cell chronic lymphocytic leukemia. Int J Cancer 2001; 91: 180–86. Zhang L, Zhou W, Velculescu VE, et al. Gene expression profiles in normal and cancer cells. Science 1997; 276: 1268–72.
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