Oncogene (2014), 1–10 © 2014 Macmillan Publishers Limited All rights reserved 0950-9232/14 www.nature.com/onc

REVIEW

Mechanism-based cancer therapy: resistance to therapy, therapy for resistance P Ramos1,2 and M Bentires-Alj1 The introduction of targeted therapy promised personalized and efficacious cancer treatments. However, although some targeted therapies have undoubtedly improved prognosis and outcome for specific cancer patients, the recurrent problem of therapeutic resistance subdues present revolutionary claims in this field. The plasticity of tumor cells leads to the development of drug resistance by distinct mechanisms: (1) mutations in the target, (2) reactivation of the targeted pathway, (3) hyperactivation of alternative pathways and (4) cross-talk with the microenvironment. Moreover, the intra-tumor heterogeneity of most tumors can also limit therapeutic response. Interestingly, the early identification of some mechanisms of resistance led to the use of alternative agents that improved clinical benefit, demonstrating that an understanding of the molecular mechanisms driving resistance to specific therapies is of paramount importance. Here we review the most generalized mechanisms of resistance to targeted therapies, together with some experimental strategies employed to identify such mechanisms. Therapeutic failure is not an option and we need to understand the dynamics of tumor adaptation in order to adequately adjust therapies; in essence ‘to fight fire with fire’. Oncogene advance online publication, 29 September 2014; doi:10.1038/onc.2014.314

INTRODUCTION In recent years, understanding of the molecular, cellular and systemic processes driving cancer initiation, progression, heterogeneity and metastatic spread has evolved tremendously. Sequencing technology and data analysis have allowed the identification of numerous genomic alterations within tumors;1–5 yet the number of genes identified to date as cancer-promoting genes is only about 125, of which 55 are classified as oncogenes and 70 as tumor-suppressor genes.6 Although an average of three to six mutations in genes known to promote tumorigenesis are found in common adult tumors, the total number of nonsynonymous mutations predicted to alter gene function ranges from 40 to 100 in most solid tumors, but can go as high as several hundred in some tumors like lung cancer or colorectal cancer with microsatellite instability.6 Thus, the genomic landscape of a given tumor is quite complex, with a small number of cancer-promoting mutations in a sea of ‘passenger’ mutations accumulating during tumor progression and branched clonal evolution. Irrespective of this genomic complexity, the majority of the known oncogenes or tumor-suppressor genes can be classified into 12 pathways.6 Furthermore, tumor-promoting mutations seem to be involved in three major biological processes: (1) cell survival, sensitive to mutations in EGFR, HER2, PIK3CA, BRAF, PTEN, MYC and others; (2) cell fate, influenced by mutations in APC, NOTCH, AR, GATA2, KLF4 and (3) genomic stability, altered by mutations in TP53, ATM, BRCA1, BRCA2 and others.6 The characterization of tumor genomic landscapes brings the exciting promise of tailored mechanismbased targeted therapies aimed at inhibiting specific oncogenic pathways. Indeed, several inhibitors of the most common oncogenic pathways have been tested in pre-clinical models of cancer and are already included in the care of cancer patients (for example, anti-human epidermal growth factor receptor 2 (HER2), 1

anti-breakpoint cluster region-Abl proto-oncogene 1 (BCR-ABL) and anti-epidermal growth factor receptor (EGFR) inhibitors), or are now being evaluated in human clinical trials.7,8 Despite the specificity of many of these approaches, the clinical translation of most single therapies has been sub-optimal; they were either ineffective or transiently effective with subsequent emergence of resistance. Therefore, the mechanisms giving rise to limited therapeutic responses must be carefully assessed in order to better tailor the therapy to each cancer patient. The genomic profiling of tumors has not only identified new putative ‘cancer genes’ but also, more importantly, has highlighted the elevated degree of intra-tumor heterogeneity.3,9,10 In other words, within a given tumor we are confronted with distinct mutational profiles and, thus, the therapeutic response to any given anti-cancer agent might not be uniform within the full tumor mass. This increases the likelihood that a subpopulation of cancer cells will not be affected by a single therapy. Moreover, the therapeutic response of any given cell will be determined not only by the presence of the target but also by the presence or subsequent occurrence of mutations/genomic alterations that may skew therapeutic response. In fact, the biology of tumors is far more complex than originally thought. As Hanahan and Weinberg11 make clear in their thorough and comprehensive review, the ‘hallmarks of cancer’ go far beyond oncogenic alterations that lead to an uncontrolled hyper-proliferative state. Indeed, tumor growth and propagation to distant sites are influenced by a multitude of cell-intrinsic features as well as interactions with the surrounding microenvironment. Thus, when considering the use of targeted therapy in the context of cancer, it is necessary to understand and integrate the multiple mechanisms of tumor cell adaptation to a given therapy in order to design a combination of therapies with optimal anti-tumor effects. Here, we review some of the most

Mechanisms of Cancer, Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland and 2Oncology, Novartis Institutes for Biomedical Research, Basel, Switzerland. Correspondence: Dr M Bentires-Alj, Mechanisms of Cancer, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland. E-mail: [email protected] Received 24 May 2014; revised 19 August 2014; accepted 19 August 2014

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2 important and generalized mechanisms of developed resistance to targeted therapies, as well as the issues to be tackled when predicting therapeutic response and designing combination therapies. Given the teleological connotation of the word ‘acquired’, we refrain from using this word here when referring to resistance of cancer cells. Indeed ‘acquired resistance’ implies a deliberate action on the part of the cells. Therefore, we prefer to use ‘developed resistance’ when referring to this phenomenon. MOST COMMONLY USED TARGETED THERAPIES Long before the era of broad tumor genomic characterization and the generalized interest in mechanism-based targeted therapies for cancer, tamoxifen was proposed as a groundbreaking therapy for hormone receptor-positive breast cancer.12–14 This drug was first proposed as an anticancer agent in the 1970s following pioneering work led by Dr VC Jordan and Dr R Nicholson (reviewed in reference Jordan12). Promising in vivo results of tamoxifen as an anti-breast cancer agent quickly led to its widespread application as a standard of care for estrogen receptor (ER)-positive breast tumor. In a large overview of 10- and 15-year follow-ups on a series of randomized breast cancer trials, it was concluded that 5 years of adjuvant tamoxifen therapy significantly improved survival of ERpositive breast cancer patients.15 This successful use of tamoxifen as a targeted therapy coupled with the identification of common oncogenic pathways and increased understanding of mechanisms driving tumorigenesis has dramatically influenced the way the scientific community approaches anti-cancer therapy. Indeed, since the 1990s there has been an increase in mechanism-based targeted therapies used effectively in different cancers. Some classical successful examples include: (a) imatinib (Gleevec) for the treatment of chronic myeloid leukemia;16–19 (b) the anti-HER2 antibody trastuzumab and the HER2 small-molecule inhibitor lapatinib for HER2-amplified breast cancer patients20–23 or (c) anti-EGFR therapies (including the small-molecule inhibitors gefitinib and erlotinib or the anti-EGFR monoclonal antibodies cetuximab or panitumumab) for the treatment of several cancers with alterations in EGFR, such as non-small-cell lung cancer, colorectal cancer, head and neck squamous cell carcinoma and pancreatic cancer.24–27 More recently, additional agents have been approved by the Food and Drug Administration based on impressive clinical benefit. These include but are not limited to vemurafenib for the treatment of B-rapidly accelerated fibrosarcoma (BRAF) mutant melanoma28,29 and crizotinib for patients with anaplastic lymphoma receptor tyrosine kinase (ALK)-positive nonsmall-cell lung cancer.29,30 In addition, the pipelines of the major pharmaceutical companies include novel anti-cancer drugs, reflecting the booming interest in targeted therapies that promise to revolutionize cancer treatment. A remarkable example is the Phosphatidylinositol 3-Kinase (PI3K) pathway, which presents itself as a broad-range targeting strategy because of the high incidence of alterations in the pathway observed in several cancer types.31–33 Not surprisingly, a number of PI3K, mammalian target of rapamycin (mTOR) or v-akt murine thymoma viral oncogene homolog 1 (AKT) inhibitors have either been approved for some indications or are under advanced clinical evaluation and may extend the list of anticancer agents approved by the regulatory agencies in the next few years.32,34–37 However, despite the great promise that these targeted-therapeutic approaches hold, there is growing evidence that any given agent is likely to fail because of the development of resistance. TUMOR CELL-INTRINSIC MECHANISMS OF RESISTANCE Resistance mediated by alterations in the target One of the most prominent and common mechanism of resistance to targeted therapy is associated with alterations (for Oncogene (2014), 1 – 10

example, mutation) in the target, leading to decreased competence of the inhibitor (Figure 1a). A classic example is imatinib (see Karvela et al.38 for a review). Even though imatinib shows significant clinical benefit in BCR-ABL-positive chronic myeloid leukemia patients,16–19 the responses in some patients are shortlived, especially those treated with imatinib after progressing to the accelerated phase or blast crisis of the disease. In many cases, this is due to mutations in BCR-ABL that disrupt the interaction of imatinib with the kinase pocket and lead to resistance.38,39 Multiple point or frameshift mutations in BCR-ABL have been associated with imatinib resistance and account for at least 50% of cases of resistance to this compound.38–40 The discovery of these resistance mechanisms has allowed the development of secondgeneration (dasatinib or nilotinib) and third-generation (ponatibib, bosutinib or INNO-406) BCR-ABL inhibitors that can bind most of the mutated forms of BCR-ABL and thus promise therapeutic benefits to patients with imatinib-resistant BCR-ABL mutations.38,41 This example highlights the impact that early identification of the mechanism of tumor adaptation to therapy can have designing approaches to surpass resistance to mechanism-based therapy. Resistance to EGFR, fms-related tyrosine kinase 3 (FLT3), fibroblast growth factor receptor (FGFR), Sarcoma proto-oncogene (SRC) or mitogen-activated protein kinase kinase 1 (MEK1) inhibitors42–46 are further examples of resistance because of the mutation of the target. Amplification of the targeted oncogene upon inhibitor treatment can also lead to resistance, even though this mechanism seems to be less common than mutations in the target. For instance, amplification of mutant EGFRT790M conferred resistantce to the irreversible EGFR inhibitor PF0029980 in an in vitro study.47 Temporal analysis of non-small-cell lung cancer tumor biopsies revealed EGFRT790M amplification in ~ 8% of relapsed patients, whereas T790M mutation in EGFR without amplification was present in approximately 40% of resistant patients.48 Resistance mediated by reactivation of the targeted pathway (upstream or downstream of the target) In addition to mutations in the drug target that circumvent the inhibitory effect of a given anti-cancer agent, resistance can also arise through reactivation of the targeted oncogenic pathway either upstream or downstream of the target (Figure 1b). This leads to increased pathway output and thus further dependence on that signaling axis. For example, upregulation of BRAF was found to confer resistance to MEK inhibitors,49 whereas mutations in MEK1 can confer resistance to BRAF inhibition.46 A further well-characterized example of such a mechanism is the resistance of breast cancer cells to anti-HER2 therapies. The first-line treatment for HER2-amplified breast cancer has been trastuzumab or lapatinib in combination with chemotherapy in the adjuvant or neo-adjuvant setting.20,22,50 Even though the response rates to this combination are far higher than chemotherapy alone (up to 50% increase in response rate and 30% increase in overall survival, depending on the trial), the effects are usually transitory,51,52 suggesting a high incidence of either primary or developed resistance. Research on the mechanisms of resistance to HER2 inhibitors in ERBB2-amplified breast cancer has shown that they are often based on further activation of the HER2 signaling pathway. It has been proposed that activation of the PI3K pathway, which is critical for signaling downstream of HER2, is central to the de novo and developed resistance to anti-HER2 agents like trastuzumab or lapatinib. The most common PI3K pathway alterations that confer resistance to these drugs are either Phosphatase and Tensin Homolog (PTEN) deficiency or activating mutations in PIK3CA, encoding for the alpha-catalytic subunit of PI3K. PTEN deficiency was found to be predictive of trastuzumab resistance in breast cancer patients as early as 2004,53 an observation that was confirmed in subsequent © 2014 Macmillan Publishers Limited

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Figure 1. Schematic illustration of tumor cell-intrinsic mechanisms of resistance. Characterization of the molecular alterations leading to tumor progression has led to the identification of a suitable target (T) for therapy. Unfortunately, resistance to such therapies often develops via distinct mechanisms. (a) Mutation (upper panel) or amplification (lower panel) of the therapeutic target (T) may disrupt efficacy of the targeted-therapy and allow for continuous tumor progression. (b) Activation of the targeted pathway may also lead to the development of resistance. This is usually the consequence of the amplification of a member of the pathway upstream of the target (P1), which results in increased pathway input limiting inhibitory effects of the drug (upper panel), or of the alteration of a member of the pathway downstream of the target (P2), rendering tumor cells insensitive to the inhibitory effects of the drug. (c) Rewiring in tumor cells that shifts their oncogenic dependency to a pathway parallel to the drug target may also result in the therapeutic resistance.

studies.54,55 Consistently, PTEN was found to be the only gene whose knockdown conferred resistance to trastuzumab in a largescale small interfering RNA screen,56 further highlighting the importance of activation of the PI3K pathway in the development of resistance to trastuzumab. In addition to PTEN deficiency, resistance to this therapy can arise by oncogenic PIK3CA mutants, either when expressed in HER2-amplified breast cancer cell lines56,57 or when present or developed during trastuzumab treatment in breast cancer patients.54–56,58 Similarly, PTEN loss or PIK3CA mutation was found to lower the clinical benefit of lapatinib in HER2-amplified metastatic breast cancer and to be responsible for lapatinib resistance in breast cancer cell lines.57,59 Altogether, these reports pinpoint the critical involvement of the PI3K pathway in the development of resistance to anti-HER2 agents, supporting the rational use of PI3K inhibitors in HER2amplified breast cancer patients, which progressed on trastuzumab or lapatinib treatments. This is further reinforced by preclinical data showing that the combination of trastuzumab or lapatinib with PI3K inhibitors generates anti-tumor activity in models resistant to anti-HER2 agents.57,60,61 Moreover, combination of the mTOR inhibitor everolimus with chemotherapy and © 2014 Macmillan Publishers Limited

trastuzumab in patients with ErbB2-amplified tumors that progressed on trastuzumab therapy was shown to have a clinical benefit of 74%, with 7% of patients showing complete response and 37% a partial response.62 Interestingly, the development of a new generation of anti-HER2 agents like trastuzumab-DM1 (TDM1), which combines the target specificity and effect of trastuzumab with the cytotoxic effects of the maytansine 1 derivative DM1, makes for renewed optimism. The dual anti-tumor effect of T-DM1 effectively inhibits growth of trastuzumab- or lapatinib-resistant breast cancer cell lines because of PTEN deletion or PIK3CA-activating mutations.63,64 Indeed, the clinical efficacy of T-DM1 was shown to be superior to other agents, even when tested in trastuzumab- or lapatinib-refractory patients.65,66 Resistance mediated by drug-dependent amplification of the oncogenic pathway As discussed above, activation of the targeted pathway often constitutes a rewiring mechanism in tumor cells that circumvents inhibitory effects of a targeted therapy. In pioneering work using patient-tumor xenograft (PTX) models of BRAFV600E-mutant Oncogene (2014), 1 – 10

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4 melanoma, it was shown that tumors treated with the BRAF inhibitor vemurafenib become dependent on the drug for survival.67 In fact, tumors resistant to vemurafinib showed an elevation of mitogen-activated protein kinase (MAPK) activity to levels that became cytotoxic upon cessation of drug treatment. Thus, tumor growth was sustained during drug treatment but was markedly suppressed after vemurafinib cessation. Based on these observations, they demonstrated that a schedule of intermittent drug administration prevents tumor growth and increases overall patient survival in these PTX models. When considering cancer drug response and administration regimens, it is usually accepted that the maximal duration of target inhibition will ultimately produce the best results. However, the above results demonstrate that not only drug dosage but also administration regimens can have a critical role in therapeutic outcome. In conclusion, understanding the different adaptive responses should allow the design of better therapeutic approaches, integrating an optimal drug dose with an adequate administration schedule. Resistance mediated by activation of parallel pathways Tumor cell survival often depends on a given oncogenic pathway and the maintenance of that signaling network. Not surprisingly, a number of resistance mechanisms to targeted-therapy arise from the maintenance of the signaling pathway on which tumor cells depend for survival. As described above, mutations that impede drug-target interaction or that maintain oncogenic signaling output through reactivation of that signaling cascade are common mechanisms of resistance to targeted anti-cancer drugs. In addition, tumor cells may also become drug resistant when a signaling pathway parallel to the original oncogenic signaling axis becomes activated (Figure 1c). This often involves the activation of well-known canonical signaling pathways like the PI3K or MAPK signaling cascades. These cascades are involved in the transmission of signaling downstream of several growth factor receptors, regulating gene expression in response to multiple stimuli, and ultimately controlling several cellular processes, including cell survival and apoptosis.68,69 Moreover, the crucial nature of these pathways in cancer is further highlighted by the fact that oncogenic alterations frequently occur in members of these signaling cascades (RAS, RAF, PIK3CA, PTEN) or upstream receptors (EGFR, ERBB2, KIT). Thus, it is not surprising that the emergence of inhibitors targeting these pathways raised a high level of expectation for future cancer therapy.70 The observation that therapeutic resistance often occurs via activation of the MAPK and PI3K cascades, suggests that their targeting could represent an effective strategy to overcome resistance.68,70–73 Interestingly, increasing evidence points to close cross-talk between the PI3K and MAPK signaling pathways,74 and suggests that this interaction may be the reason why tumor cells are resistant to inhibition of either pathway alone. Indeed, two studies reported that activation of the MAPK pathway upon blockade of mTORC1 by rapamycin or rapalogs (in a S6K1- and PI3K-dependent manner) accounted for resistance to these agents in in vitro and in vivo tumor models, as well as in patient samples.75,76 In a separate study, Serra and colleagues demonstrated that the application of a dual PI3K-mTOR inhibitor in an ERBB2-amplified model could also lead to MAPK activation through a mechanism involving compensatory stimulation of HER receptors.77 Moreover, MAPK activation was identified as a determinant of susceptibility to PI3K inhibitors in cell lines and patients, correlating positively with resistant to these compounds.78,79 Furthermore, a recent study has shown that transient inhibition of RAS-ERK signaling was necessary for the induction of apoptosis by PI3K inhibitors.80 Conversely, activation of the PI3K pathway was identified as one mechanism of resistance to MEK inhibitors both in vivo and in vitro.81,82 These observations promoted the inhibition of both pathways as an Oncogene (2014), 1 – 10

effective anti-tumor therapy. Indeed, combined inhibition of the MAPK and PI3K pathways does inhibit growth of melanoma, colon,83 lung84,85 and breast tumor models80 more effectively than either compound alone. Given these data, several clinical trials are underway to evaluate the safety and efficacy of this combination strategy as a new anti-cancer therapy for advanced metastatic cancer (see McCubrey et al.70 for a review). Remarkably, resistance to inhibitors of the PI3K pathway frequently involves activation of alternative signaling pathways, reflecting the complexity of the PI3K signaling network and regulatory feedback loops.34 Indeed, resistance to PI3K inhibition is associated with several different mechanisms in pre-clinical models, including activation of MAPK (as discussed above), increased expression and phosphorylation of multiple receptor tyrosine kinases (RTKs),86 activation of the Janus kinase/ signal transducers and activators of transcription pathway,87,88 amplification of MYC and activation of NOTCH or Wnt-βcatenin.89–91 Even though most of these mechanisms have not been confirmed in patient tumor samples, the data suggest that inhibitors of the PI3K pathway need to be combined with inhibitors of the pathways endowing resistance. This era of widespread genomics should lead to the identification of factors modulating sensitivity to these compounds, allowing for better stratification of patients who are highly responsive to PI3K inhibitors and the rational design of clinical trials exploring the benefits of combination therapies, challenges not yet overcome (see Rodon et al.34 for a review). MICROENVIRONMENT-MEDIATED RESISTANCE The contribution of the tumor microenvironment to tumor progression and metastatic spread has been studied extensively over the past decade. It is now uncontested that close interaction with the surrounding microenvironment provides various layers of support to tumor growth and metastasis.11 Indeed, a number of targeted therapies focus on disturbing the relationship between these two components, including agents targeting tumor vasculature such as anti-vascular endothelial growth factor, tumor-associated inflammation-like anti-chemokine (C-C motif) ligand 2 (CCL2) and anti-Interleukin 6 (IL6), or aimed at the interaction with the microenvironment, such as integrin inhibitors (see Fang and Declerck92 for a review). Given this close cross-talk, the tumor microenvironment probably contributes to the development of drug resistance in some contexts (reviewed in Junttila and de Sauvage93 and Figure 2). Even though most approaches to mechanisms of resistance to targeted therapy have focused on the intrinsic potential within tumor cells of resistance to a given therapy, a number of studies have suggested that the tumor microenvironment is a niche that protects some tumor cells from targeted therapies. One such mechanism, which has been described extensively in multiple myeloma but is also seen in other tumor models, arises from cell-adhesive interactions within the tumor niche.94–100 This resistance concept stipulates that celladhesive interactions stimulate alternative survival pathways that result in tumor cell resistance to the cytotoxic effects of anticancer therapy. Thus, disturbance of these survival-promoting celladhesive interactions could increase therapeutic efficacy. In support of this concept, the cytotoxic effects of chemotherapeutic drugs for multiple myeloma have been enhanced in vivo by their combination with anti-α4 integrin antibody.95 Moreover, celladhesion-mediated resistance has been shown to occur in response to many therapeutic strategies, including chemotherapy, radiotherapy and some targeted-therapies.96–100 For example, β1integrin was shown to promote survival of breast cancer cells resistant to lapatinib through activation of focal adhesion kinase and SRC, a phenotype that could be reverted by administration of an anti-β1 integrin blocking antibody.98 A further study demonstrated that inhibition of PI3K/mTOR in three-dimensional cultures © 2014 Macmillan Publishers Limited

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Figure 2. Schematic illustration of microenvironment-mediated resistance. Tumor cells grow in a specialized microenvironment composed of extracellular matrix and distinct cell types, including bone marrow-derived cells, mesenchymal stem cells, fibroblasts and so on. The cross-talk between tumor cells and the microenvironment influences all steps of tumor development and progression, including response to therapy. Resistance to targeted-therapy mediated by the microenvironment has been associated with at least two different mechanisms: (1) cell adhesive interactions between tumor cells and the microenvironment (upper panel); (2) signals transmitted by soluble molecules, including growth factors (GFs) and cytokines, between tumor cells and the surrounding microenvironment. Study of these interactions and their influence on therapeutic response is critical to the development of effective cancer treatments.

led to a dichotomous response, where matrix-attached outer cells were resistant to apoptosis and matrix-deprived inner cells were sensitive to the compound.99 These demonstrations that cell–cell or cell–extracellular matrix interactions within the tumor microenvironment can decisively modulate therapeutic response should be carefully evaluated when considering therapeutic strategies. In addition to cell adhesion-mediated resistance, the tumor drug response could also be blunted by the secretion of soluble growth factors or cytokines. Indeed, two recent studies using a panel of kinase-addicted cell lines showed that drug resistance invariably developed after exposure of tumor cells to one or more soluble growth factors.101,102 Straussman et al. when assessing the effects of co-culturing stromal and cancer cell lines on the responses to 35 anti-cancer drugs103 found the rate of stromainduced resistance to be surprisingly high, particularly in response to targeted therapies. For example, the stromal-derived hepatocyte growth factor caused resistance to rapidly accelerated fibrosarcoma (RAF) inhibitors through activation of MAPK and PI3K downstream of its receptor c-MET. Altogether these studies further support the concept that disruption of the cross-talk between tumor cells and their microenvironment may be crucial for therapeutic success. TACKLING THERAPEUTIC EFFICACY: THE NEEDLE IN A HAYSTACK Intra-tumor heterogeneity and drug sensitivity Tumors are entities with a complex genomic landscape, bearing as many as several hundred mutations.6 Even though the great majority of those mutations are not relevant to tumor progression, the large number of genomic alterations confers a high degree of heterogeneity within each tumor. Studies of multi-region or single-cell sequencing have produced two striking observations.9,10,104–106 On the one hand, the majority of somatic mutations or chromosomal rearrangements are not found across all samples from a given tumor or metastatic lesions. On the other hand, the intra-tumor heterogeneity seems to stem from branched evolution during tumor progression, which may possibly allow reconstruction of a phylogenetic tree of genomic alterations during clonal evolution. This means that heterogeneity within a tumor develops in time and space, usually leading to a complex © 2014 Macmillan Publishers Limited

mix of clones evolving separately. Interestingly, some studies have suggested that clonal evolution although separated is not always divergent.9,104,107 Indeed, convergent clonal evolution leading to the accumulation of different parallel mutations in a given gene has been identified in various studies. The study of such evolutionary clonal selection processes highlights the importance of a given pathway for the progression of a specific tumor, providing clinically relevant targets for therapeutic intervention.9,104,107 One obvious consequence of intra-tumor heterogeneity is that therapeutic response within a given tumor is heterogeneous. The clinical decision to use a specific targeted therapy is often based on the presence of the target in the primary lesion as assessed by pathological or molecular analysis. However, this approach poses clear challenges given the elevated intra-tumor heterogeneity and clonal evolution within each tumor. First, the target alteration might not be present in all tumor cells within the primary site or the metastases. Indeed, a recent study of multi-region biopsies from primary tumors and matched metastases in renal carcinoma patients found that only 31–37% of all mutations were found in all tumor regions.9 This high level of clonal heterogeneity might reduce the toxic effects of the compound on the tumor as a whole and lead to a poor response (Figure 3a). Second, intra-tumor heterogeneity and clonal diversity are thought to be the key to the evolution of cancer to a metastatic disease through competitive selection.108 This hypothesis proposes that metastatic disease arises from the adaptation of tumor cells better fitted to the new microenvironment at the metastatic site. This would lead to enrichment of some tumor subclones at the metastatic site and to some degree of genomic and, more importantly, phenotypic discrepancy between the primary tumor and metastases. Several studies focusing on classical clinical tumor biomarkers have confirmed such a phenotypic discrepancy between the primary tumor and metastases in different tumor types (reviewed in Vignot et al.109). In contrast, other studies have reported the genomic landscape of paired primary tumors and metastases to be very similar, with the majority of copy number alterations or mutations in the primary tumor being maintained at the metastatic site.110,111 Nevertheless, some genetic alterations seem to arise de novo in the metastatic lesion,111 suggesting that tumor cells at the metastatic sites evolve and become adapted to the new environment. Given such phenotypic and microenvironmental divergence between primary tumors and metastatic lesions, Oncogene (2014), 1 – 10

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Figure 3. Schematic illustration of intra-tumor heterogeneity and resistance. (a) Heterogeneity of tumor cells may result in a diverse therapeutic response to specific targeted-therapies. As exemplified here, following administration of a given targeted therapy, it is likely that a small proportion of tumor clones survive the therapy because of insensitivity. The growth of such clones will lead to disease relapse and tumor progression. Understanding the contribution of tumor heterogeneity to therapeutic response is critical for the success of cancer treatments. Thus, modulation of this phenomenon in vivo could lead to significant advances in the management of cancer treatments. Intratumor heterogeneity can be recapitulated by distinct approaches. (b) The first approach includes implantation of a patient tumor into immunodeficient mice. These patient-tumor xenograft (PTX) models seem to maintain the heterogeneity of the tumor from which they originate and thus might be suitable for preclinical studies of resistance to targeted therapies. (c) A second approach relies on the use of genomic tools to increase heterogeneity from an otherwise less heterogeneous or homogeneous cancer model. This can be achieved by transposon mutagenesis or pooled small hairpin RNA. Employment of targeted-therapies in such models would ultimately result in resistance linked to specific genomic features. Characterization of the mechanisms driving resistance in the models described in b and c, as well as the identification of clinical biomarkers of resistance, could return promising targets for clinical application.

the therapeutic response of the primary tumor and its metastases might be different, and conceivably also that of metastases at different sites. Indeed, some studies have shown different levels of therapeutic response between primary tumors and corresponding metastases in xenograft models.112 As the eradication of metastatic disease is essential for successful cancer therapeutics, a better understanding of the mechanisms limiting therapeutic response or driving resistance at metastatic sites is crucial. Although it is thought that similar mechanisms drive therapeutic resistance at the primary and metastatic sites, the intrinsic microenvironmental differences and divergent evolution may present distinct therapeutic opportunities, highlighting the need for predictive biomarkers of metastatic disease. The understanding of tumor progression and heterogeneity demands a change in clinical practice or research approaches in Oncogene (2014), 1 – 10

order to identify the appropriate biomarkers that can help personalize cancer treatment. The implementation of new clinical practices, involving multiple regional and temporal sampling along with histological and molecular profiling from a single patient, would be a step in the right direction. Even though some trials already apply this approach, the systematic introduction of such clinical practices might be impractical in the future for logistical, ethical or financial reasons.113,114 Although the costs of next-generation sequencing are rapidly decreasing, the routine use of such platforms seems at the moment to be out of reach for most cancer centers. More so, the collection of biopsies from advanced metastatic patients, growing these as xenografts, and testing their response to therapy may be useful for designing optimal therapeutic strategies and anticipating resistance.115,116 However, this is still not possible in most centers and often raises © 2014 Macmillan Publishers Limited

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7 ethical concerns. Alternatives would be techniques such as characterization of circulating tumor cells or cell-free DNA.113,114 These would have several advantages: they are noninvasive, these samples likely yield tumor genomes from multiple sites, and are more cost effective. One study has shown that sequential exomesequencing of cell-free DNA could be used to identify the emergence of mutant alleles associated with therapy resistance,117 suggesting that it can be predictive of the most prevalent genetic alterations emerging upon therapeutic intervention across multiple sites. However, these technologies are relatively new and their routine clinical implementation will take some time. Experimental models of intra-tumor heterogeneity Considering the challenges that intra-tumor heterogeneity poses for the clinical success of targeted therapies, experimental models mimicking intra-tumor heterogeneity, such as PTX or unbiased high-throughput genome-wide mutagenesis,118–121 could improve prediction of clinical responses and the emergence of resistance. PTX models seem to maintain histological and genomic properties similar to those of the original tumors, including heterogeneity.122,123 Therefore, these models could also be used to evaluate the efficacy of rational combination therapies in specific contexts (Figure 3b). In addition, other genomic approaches such as transposon-mediated mutagenesis or pooled small hairpin RNA screens can also be applied to model intratumor heterogeneity. Interestingly, several mouse models carrying transposon systems have been generated and validated as tools for cancer gene discovery.118,119,121 These systems lead to heterogeneous activation/inactivation of genes with full genomic coverage and, thus, may enhance our understanding of alterations that promote tumor growth, progression, metastatic dissemination and therapeutic resistance (Figure 3c).118–121,124 Even though such approaches would not directly help in clinical stratification of patients, they may aid the identification of clinical biomarkers that predict therapeutic responses and report the development of resistance to targeted-therapy in specific models. Fighting fire with fire: combination therapy and the adjustment of treatment regimens to overcome resistance The development of resistance in tumors to the toxic effects of targeted therapies reveals the remarkable metabolic flexibility of tumor cells. The understanding of this property might fuel the development of effective tools to counter resistance. In response to specific targeted therapies, the pathway dependencies of tumor cells can change by distinct mechanisms, for example, the mechanisms of resistance to PI3K inhibitors described above, including activation of MAPK, Janus kinase/signal transducers and activators of transcription or Myc.75,77,87,89,90 It has been demonstrated that co-targeting the PI3K pathway and the activated pathway that confers resistance can result in a significant therapeutic benefit in preclinical models; this provides a rationale for testing these combinations in the clinic. Understanding how the sensitivity of tumor cells to selected therapies changes will help counter tumor progression by anticipating the mechanisms driving the growth of resistant clones.125 Rational design of effective combination therapies and/or treatment regimens will be possible.74,126 Digging deeper into the distinct mechanisms of resistance to a given agent will significantly improve clinical decisions. Intra-tumor heterogeneity makes it likely that therapeutic responses to targeted therapies will be heterogeneous. Clonal evolution of a tumor is governed by the specific selective pressure applied and leads to the development of new dependencies. However, tumor cells do not ‘predict’ and ‘adapt’ a priori to a therapy that is still to come and this could be used to the patient’s advantage in the clinic. By anticipating the new oncogenic dependencies that will develop upon specific cancer treatments, © 2014 Macmillan Publishers Limited

one should be able to design effective combined or sequential treatments. PTX models or genome-wide transposon mutagenesis21,22,31,100 could be used to recapitulate tumor heterogeneity seen in cancer patients and, when combined with the appropriate clinical studies, should allow the definition of those alterations that render tumor cells resistant to the applied therapy. Finally, adaptation of tumor cells often involves cross-talk with the surrounding microenvironment. Most studies investigating therapeutic resistance rely on simple in vitro systems or immunocompromised animals. Although these efforts can be informative, complementary approaches, in which tumor–microenvironment interactions and the contribution of the immune system are evaluated, are crucial for the reliable prediction of tumor adaptation. Importantly, the efficacy of combination therapies in experimental models is usually accessed by simultaneous application of both inhibitors.75,77,87,89,90 In contrast, clinical application of such combinations would require anticipation of the resistance mechanism for each patient in order to provide the optimal combination therapy. This might entail both identification of reliable biomarkers for each mechanism of resistance associated with a specific compound and the implementation of a sequential treatment regimen in which the first agent is administered, the biomarker identified and the combination with the second agent is then applied. The testing and systematic use of such sequential treatment regimens in experimental models is still lacking in most cases. Of interest, it was shown in in vitro models of melanoma that sequential treatment with PI3K, MEK or Janus kinase inhibitors and then with SRC inhibitors produced greater synergistic anti-proliferative effects than concomitant administration of both inhibitors.127 Thus, the rewiring of tumor cells may depend on the regimen followed, which is an issue that should not be ignored. As discussed above, choice of treatment schedules could also allow control of tumor growth, as shown for BRAF inhibition in melanoma.67 Altogether, these results provide evidence that our understanding of the mechanisms driving therapeutic resistance is increasing, but there is still a long road ahead to curative cancer therapy. Commitment to the design of clinically relevant experimental approaches to testing targeted therapies, the emergence of resistance and effective combination therapies must increase. FUTURE PERSPECTIVES Cancer targeted therapies hold great promise, as they target specific alterations driving tumor growth and progression while minimizing side effects on normal tissues. Indeed, drugs such as tamoxifen, trastuzumab, lapatinib or imatinib have shifted the paradigm of chemo- or radiotherapy-based clinical practice into one involving the combination of targeted agents with surgery or conventional therapy. Nevertheless, resistance to targeted therapies remains a major challenge in the clinic. The accurate identification of resistance mechanisms remains difficult for several reasons. First, cancer cell sensitivity to a targeted agent can change via several different mechanisms, including mutations in the target/targeted pathway or changes in signaling pathway dependency. Second, the surrounding microenvironment and its cross-talk with tumor cells can halt therapeutic responses. Third, tumor heterogeneity and clonal evolution in response to the selective pressure of a toxic agent usually prevents long-term efficacy of any monotherapy. To win the clinical battle against cancer, we need to better understand the mechanisms driving resistance and to identify reliable biomarkers that predict the prevalent mechanism of resistance to a given targeted therapy. In this way, effective, personalized combination therapy can be designed and administered. For this, a greater effort in resistant studies using more physiologically relevant models is needed. Although cancer cell lines cultured in monolayers in vitro have aided in the Oncogene (2014), 1 – 10

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8 identification of some mechanisms of resistance, sole reliance on such models is limiting. Comprehensive co-culture or threedimensional culture systems would be more reliable models for in vitro studies and high-throughput screening.128,129 In addition, in vivo models still remain the gold standard for prediction of tumor response. Xenograft, transgenic tumor models, PTX or transposon-mutagenesis mice present an array of tools to study therapeutic resistance arising from cell intrinsic, environmental and/or evolutionary mechanisms. The prediction of such adaptive mechanisms can be achieved and the identification of reliable biomarkers would help decipher the path to efficient therapy. The perspective of personalized cancer therapeutics is tangible if the research community as a whole continues to guide efforts in the right direction. For that, basic research and clinical practice need to work hand-in-hand to design suitable protocols for targeted therapy testing; the conclusion of such efforts would have a place in clinical history. CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS Research in the laboratory of MB-A is supported by the Novartis Research Foundation, the European Research Council (ERC starting grant 243211-PTPsBDC), the Swiss Cancer League, the Swiss national science foundation and the Krebsliga Beider Basel. PR recevied a ‘Novartis presidential postdoc fellowship’.

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