Although great progress has been made in developing new anti-cancer drugs, a obvious problem lies in the fact that the majority of drugs that show preclinical efficacy fail to predict clinical response. A major stumbling block that exists in getting these cancer drugs into the clinic is the lack of appropriate preclinical experimental models that mimic the complexity of different human cancer diseases. To date, numerous models have been developed to study human cancer, examples of these preclinical experimental models include hollow fibre assays, xenografts, and more recently genetically engineered mouse models (GEMs). Whilst each model has its benefits and limitations, the vast majority of preclinical tests are still carried out in xenografts, partly due to their high degree of reliability, reproducibility and rapidity of tumor formation. There are however concerns with the use of xenografts in preclinical testing, as a number of studies have shown that the ability of xenografts to accurately predict drug efficacy in human cancer patients has been disappointing. There is an urgent need for alternative preclinical experimental models to test these promising anti-cancer drugs. A potential emerging and exciting alternative are genetically engineered mouse models (GEMs). The aim of this critical appraisal was therefore to determine whether GEMs are indeed attractive alternatives and thus the future for preclinical evaluation of anti-cancer drugs. GEMs generally are histologically and genetically accurate models of human cancer, they however have disadvantages of heterogeneity with regard to frequency, latency, and growth. Although a number of studies undertaken in GEMs have shown promising results, however, they have not been validated against known clinically effective cancer drugs. Furthermore, the use of GEMs is still relatively in its infancy and a lot more studies are needed to determine whether they are promising future models for preclinical testing of anti-cancer drugs.
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There has been a considerable advance in molecular biology recently, which has facilitated our understanding of the biology of cancer. However, these discoveries have not yet been fully translated into improved treatments for patients with cancer (Becher and Holland, 2006). Although great progress has been made in developing new anti-cancer drugs, an obvious problem lies in the fact that the majority of drugs that show preclinical efficacy fail to predict clinical response (Becher and Holland, 2006). In fact, even amongst those cancer drugs that pass phase 1 testing, only 1 in 10 are actually approved. A few of the reasons why preclinical studies fail to correlate with clinical efficacy is partly due to drug efficacy studies in mice not addressing differences in drug metabolism, pharmacokinetics and pharmacodynamics and perhaps molecularly targeted drugs failing to reach the appropriate target therapies (Peterson and Houghton, 2004). In addition, the use of immunocompromised mice for preclinical testing makes it difficult to predict the role of the host in response to therapies (Robles and Varticovski, 2008). Moreover, one of the factors limiting the translation of knowledge from preclinical studies to the clinic has been the limitations of in vivo cancer models. The obvious next challenge has now become the efficient screening of promising therapies, either alone or in combination, in biologically relevant systems (Suggitt and Bibby, 2005).
Preclinical animal models are important tools used for the selection and development of anticancer agents. Before a suitable mouse model to test drugs can be established, it has to be first determined as to what constitutes the ideal mouse model (Olive and Tuveson, 2006). The following features should be considered in assessing the usefulness of a mouse model for preclinical studies. Firstly, genetic manipulations should accurately reflect the genetics of the human disease. Secondly, the histology of the model should closely reflect that of human tumors. Nearly all of the early models of cancer involving viral oncoprotein overexpression produced histologies distinct from that of common human tumors. The third step of model validation is to explore the tumor phenotype at a molecular level. This should include an assessment of gene expression, with particular emphasis placed on known tumor markers from human studies, as well as an analysis of genetic and genomic alterations frequently seen during tumor progression in humans. Finally, administering to the mice those drugs that have previously been tested in human patients. If a model responds differently to the drug compared to human patients, then its predictive utility for that agent is poor (Olive and Tuveson, 2006). The ideal mouse model for drug development would also posses a short tumor latency and high penetrance and be relatively simple to generate and easy to use. (Olive and Tuveson, 2006)
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Numerous in vivo models for testing anti-cancer drugs are currently in use. These include xenograft models as well as hollow fibre assays (Gutmann et al., 2006). Xenografts are generated by directly implanting cancer cell lines established from human tumors into mice. They have been widely used for drug discovery (Frijhoff et al., 2004). Unfortunately, xenografts are not particularly successful in predicting drug responses in humans. The major limitations of these xenograft models are the requirement for an immunocompromised host and the inability of these models to fully recapitulate the complex relationship between the tumor and its microenvironment (Oost et al., 2004). In contrast, hollow fibre assays, which are a combination of in vivo and in vitro models, provide a better correlation with human disease (Decker et al., 2004). However, as with xenografts, they lack the complex interactions that occur when tumor cells are growing and interacting with the host tissue (Decker et al., 2004). An exciting emerging alternative are genetically engineered mouse models (GEMs). It has been proposed that GEM models of cancer would improve anti-cancer drug development (Robles and Varticovski, 2008).
The aim of this critical appraisal is therefore to determine whether genetically engineered mouse models are the future for preclinical evaluation of anti-cancer drugs. This will be achieved by critically analysing the current literature for preclinical studies that have already been undertaken in genetically engineered mouse models. Furthermore, the advantages and disadvantages of genetically engineered models (GEM) for the preclinical evaluation of potential cancer therapeutics shall be discussed. Additionally, a critical analysis of other currently used models such as the hollow fibre assay and more significantly of the xenografts shall be carried out. This will help determine whether GEMs are attractive alternatives and the future for preclinical evaluation of anti-cancer drugs.
2.2 The hollow fibre assay (HFA)
The HFA was developed by the National Cancer Institute (NCI) as a low cost, high throughput, preliminary in vivo screening assay for the evaluation of anti-cancer agents (Suggitt and Bibby 2005). It was anticipated that it would help bridge the gap between the in vitro cell based assays and human xenograft models in immuno-deficient mice (Hall et al., 2000). The goal was to develop an intermediate assay that could better predict which compounds found active in the 60-cell line panel that would be active in subsequent xenograft models. This was necessary due to the high cost of the traditional xenograft assay in terms of number of animals required and the time required for assay completion (Damia and D'Incalci, 2009). The current NCI HFA protocol involves the short term in vitro culture (24-48 hours) of a panel of 12 cell lines followed by in vivo implantation at both intraperitoneal (i.p.) and subcutaneous (s.c.) sites of nude mice (Suggitt and Bibby, 2005). The assay has the potential to simultaneously evaluate compound efficacy against a maximum of six cell lines (Hollingshead et al., 1995).
The hollow fibre model offers an amenable, rapid, cost effective and ethically acceptable model in which to investigate unlimited drug-target interaction/target inhibition pre-clinically (Suggit et al., 2006). An experiment carried out by Suggit et al 2006 showed that a modified hollow fibre assay is an effective ethical assay for the evaluation of anti cancer agents. This modified hollow fibre assay whilst implementing the 3Rs, it also provides drug/target interaction data that can facilitate the selection of lead compounds for further evaluation in more sophisticated solid tumour models such as xenografts (Suggit et al., 2006). The HFA has been more recently used to investigate the pharmacodynamics of anticancer agents in vivo. (Suggit et al., 2006;Temmink et al., 2007). Pharmacodynamic end points investigated include protein/gene/mRNA expression, tubulin/DNA damage, and cell cycle disruption (Suggitt et al., 2006; Leong et al., 2004; Suggitt et al., 2004).
Although hollow fibre assays have numerous benefits mentioned above such as its relatively rapid and cost effective demonstration of in vivo activity compared with xenograft models, it is however, more commonly employed as a routine preliminary in vivo screening assay prior to testing in xenografts (Shnyder et al., 2006). Moreover, it does not model the complex interactions and phenomena that occur when tumor cells are growing in and interacting with the host tissues, therefore it is not entirely representative of human tumors. The continuous use of the hollow fibre assays may be limited in defining specific pharmacodynamic end points at an early stage in a drug's evaluation, and possibly also in helping eliminate those agents not shown to interact with their putative targets in vivo at an early preclinical stage (Damia and D'Incalci, 2009).
2.3 Human xenografts for preclinical testing
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To date, the vast majority of preclinical efficacy studies of various therapeutic agents have been carried out in xenograft models (Richmond and Su, 2008). Xenografts are human cells or human cell lines grown in an immunodeficient mouse. There are two main sites used for tumor xenografts: ectopic (s.c.) and orthotopic, a term which refers to the native site of the tumor (Sausville and Burger, 2006). There are several advantages to s.c. xenografts. The progression of a large number of synchronized, easily observable tumors can be followed, such that initiation of treatment can begin when the tumors are of an optimal size. Furthermore, xenografts have a high degree of predictability and rapidity of tumor formation, which makes them easy to use. Lastly, only a few mice are needed for drug efficacy studies (Becher and Holland, 2006). The primary shortcoming of xenografts is that cell lines be passaged for many generations in culture and therefore due to selection pressures under these conditions, are not representative of original tumor in its native state. Cells in culture lack the architectural and cellular complexity of in vivo, which include inflammatory cells, vasculature, and other stromal components (Becher and Holland, 2006). Consequently, the results obtained from a number of xenograft studies (Boehm et al., 1997; Sarraf et al., 1998) have not translated well into the clinic (Twombly, 2002; Kulke et al., 2002). As a result, there has been considerable debate regarding the value of the xenograft model (Gopinathan and Tuveson, 2008).
However, several groups have detailed studies supporting the value of the s.c. xenograft model for predicting clinical activity. Fiebig and colleagues, established a large panel of xenografts derived from patient biopsies, and activity in xenografts was compared with clinical response. A correct prediction of clinical outcome was observed for both tumor resistance (97%) and tumor sensitivity (90%). These results also revealed that the xenograft model was highly predictive of clinical activity (Fiebig et al., 2004; Scholz et al., 1990). Additionally, there are a number of other important successes. For example, xenografts of multiple myeloma cell lines into syngeneic mice responded to the proteasome inhibitor, bortezomib, which has shown significant promise for the treatment of multiple myeloma (LeBlanc et al., 2002; Moreau et al., 2008; Oyajobi and Mundy, 2003). Another example is that of Herceptin, which was shown to enhance the anti-tumor activity of paclitaxel and doxorubicin against HER2/neu-overexpressing human breast cancer xenografts, and this led to subsequent successful clinical trials (Baselga et al., 1998; Sporn and Bilgrami, 1999). It is anticipated that the s.c. xenograft model will still be of value in this modern era of target-driven anticancer drug discovery if used appropriately. (Marie Suggitt and Michael C. Bibby 2005). Nevertheless, animal models with better predictive capability will facilitate anti-cancer drug development, thus GEMs were developed.
2.4 Genetically engineered mouse models (GEMMS)
Genetically engineered mouse models of human cancer refer to mouse strains in which the genome has been manipulated to achieve gain or loss of oncogene or tumor suppressor gene function, respectively, the consequences of which are manifested in tumor phenotypes(Van Dyke and Jacks, 2002). GEM models provide an opportunity to investigate carcinogenesis in the context of the whole organism. The main aim of GEM models is to recapitulate the genetic and molecular changes in human cancer and use these to test novel anticancer therapeutics in an attempt to accurately predict clinical response (Abate-Shen et al., 2008).
The first GEM models of human cancer were the transgenic models, where by cellular/viral oncogenes were introduced to the mouse germ line. One of the first transgenic cancer models involved the constitutive expression of the c-myc oncogene under the control of the mouse mammary tumor virus promoter leading to the development of mammary tumors (Stewart et al., 1984). Many transgenic experiments have followed and clearly shown that the manipulation of the mouse germ line could predispose the mice to cancer (Stewart et al., 1984). These oncomice provided some of the earliest GEM models for pancreatic, breast, prostate, and brain cancer, and their analyses over the past two decades have provided the foundation for many investigations of cancer mechanisms, and have been used in pre-clinical studies for both prevention and experimental therapeutics (Abate-Shen et al., 2008). The advantages of transgenic models include their relative simplicity and the fact their provide a straightforward means of assessing the consequences of gain-of-function of particular genes for tumorigenesis. However, the evolution of the disease in transgenic mice may not be similar to that of most human cancers, since it is initiated by expression of an exogenous gene and typically not stoichastically (Abate-Shen et al., 2008)
With the discovery that loss of tumour suppressor gene (TSG) has a causative role in the development of tumours, transgenic mouse models were developed introducing a mutant TSG in the mouse germ line both by targeted gene knockout or through the expression of a dominant-negative form of the TSG (Knudson et al 1993). One of the first TSG mutants was the Rb ''knockout'' mouse and the targeted deletion of p53 (Jacks et al., 1992). Notably, their tumor spectrum was found to be dissimilar to the consequences of mutations of these tumor suppressor genes in human cancer, highlighting the lack of relevance of these GEM mouse models for human cancer (Abate-Shen et al., 2008). However, subsequent studies have shown that gain of function mutations of Tp53, in contrast to null mutations, lead to more similar tumor phenotypes to human cancer (Lang et al., 2004; Olive et al., 2004). Interestingly, although these and other germ-line mutant mouse alleles have been extensively utilized to investigate cancer mechanisms, to date they have not been widely used for cancer prevention studies (Green and Hudson, 2005).
Improved technologies for manipulating the mouse genome have led to more sophisticated gene targeting approaches, where selected genes of interest are conditionally inactivated/activated in spatially and temporally restricted domains. These conditional models include those based on loss-of-function of tumor suppressor genes, such as Trp53 and Pten, as well as gain-of-function of oncogenes, such as Kras (Van Dyke and Jacks, 2002). Conditional gene targeting offers many advantages over traditional gene targeting in the germ-line, such as overcoming the problem of embryonic lethality and in addition, selective gene targeting often yields GEM models with a more restricted spectrum of tumor phenotypes, which are more suitable for pre-clinical studies (Abate-Shen et al., 2008) .
The Cre-Lox system is the most widely used for both transient conditional knockout (Le and Sauer, 2000) and oncogene expression (Lakso et al., 1992). As an alternative to gene targeting approaches, new technologies for the stable "knock-down" of gene expression in vivo by delivery of RNAi moieties are proving to be effective for developing mouse models of cancer (Dickins et al., 2007). Finally, major technological advances in small animal imaging approaches, which now enable the effective visualization of tumors in vivo has made a huge impact on the effective utilization of GEM models and will surely be an asset for their application for cancer prevention (Weissleder, 2002). The different types of GEM models and their applications are summarised below (table 1).
Table 1: Applications of the different types of GEM models, their advantages and disadvantages. (Abate-Shen et al., 2008)
It is evident that different GEM models have their advantages as well as disadvantages associated with there use (table1). Unfortunately, relatively few studies of cancer prevention have been done using this generation of more sophisticated GEM models of human cancer. Thus, potential promise of GEM models for cancer prevention research remains largely unexplored.
2.4 Advantages of genetically engineered mouse models
Advantages of the many genetically engineered mouse cancer models are that the initiating genetic lesion is known, the mice are immunocompetent, and the tumors develop spontaneously in situ in the appropriate tissue compartment. Complex processes, such as tumor angiogenesis, can be modeled in these in vivo systems (Richmond and Su, 2008).
GEM cancer models are becoming increasingly sophisticated in their ability to accurately mimic the histology and biological behaviour of human cancers. Numerous tissue-specific GEM models have been developed that exhibit many biologic hallmarks of human cancer, including angiogenesis and stromal interactions, as well as similar histopathologic and genetic abnormalities (Gutmann et al., 2006). Moreover, GEM cancer models, which allow assessment of therapeutic efficacy on a uniform genetic background, are particularly useful for performing preclinical studies of rare cancers and for assessing synergy between therapeutic agents. They can also potentially provide the tools needed to learn more about the histologic and biochemical effects of specific agents prior to human testing. While GEM models offer many advantages, the cancers typically arise from genetic events that are expressed simultaneously in many cells throughout an animal or in an entire tissue. By contrast, most human tumors are believed to arise from single cells or from a small population of mutant cells. To overcome this limitation, strategies have been developed that allow mutant alleles to be expressed in small populations of cells in vivo(Gutmann et al., 2006).
2.5 Preclinical Trials in GEMs
There are only a few drugs thus far for which GE preclinical models accord with clinical success. Rego et al reported that retinoic acid and arsenic work well as anti-cancer agents in a genetically accurate acute promyelocytic leukemia murine model in accordance with clinical experience (Rego et al., 2000). In addition, ST1571, a potential BCR-ABL inhibitor that is active against chronic myelogenous leukemia, has been shown to limit the development of BCR-ABL mutations in P190 BCR-ABL GEM mice (Brain et al., 2002). In this study, pre-leukemic P190 (Bcr-Abl) and the control mice were injected with the c-Abl specific kinase inhibitor STI571 for 10 consecutive days. A decrease in the Bcr-Abl induced mutation frequencies in spleen and kidney tissue from mice treated with STI571 was observed. These results confirmed that Bcr-Abl can directly and reversibly induce an increase in point mutation frequencies that could contribute to the genomic instability observed in Bcr-Abl positive leukemias (Brain et al., 2002).
In another study, it was shown that the GEM models; RIP-Tag (pancreatic) and TRAMP (prostate) were effective in testing the efficacy of angiogenesis/matrix metalloproteinase inhibitors (Bergers et al 1999; 2000).
Additionally, Small molecule inhibitors have been used to target farnesyl transferase, epidermal growth factor receptor, and FLT3 using GEM models. They have been predominantly shown to block tumor development or regress established malignancy (Kohl et al., 1995; Lenferink et al., 2000; Levis et al., 2002). However, farnesyltransferase inhibitors were developed as inhibitors of Ras processing (James et al., 1993), and although farnesyltransferase inhibitors showed exceptional potency in causing regression of mammary gland tumors in transgenic mice ectopically expressing the HRASG12V oncogene in the mammary epithelium ((Kohl et al., 1995), these results did not predict the overall clinical failure of farnesyltransferase inhibitors in patients suffering from neoplasms that harbored RAS mutations. Interestingly, farnesyltransferase inhibitors did not show preclinical efficacy in GEMs that activated the Ras pathway due to deficiencies in NF1 (Mahgoub et al., 1999). Olive and Tuveson 2006 therefore suggest that GEMs, based on a physiologic genetic context, may be more suitable for certain preclinical therapeutic investigations (Olive and Tuveson, 2006).
To date, GEM models of breast, brain and lung cancer have been used in preclinical evaluations of therapeutic agents (Gopinathan and Tuveson, 2008). Similar to clinical experience, lung adenocarcinomas arising as a result of mutant epidermal growth factor receptor (EGFR)-expression regressed on treatment with erlotinib and cetuximab (Ji et al., 2006; Politi et al., 2006). In another study, the response of mammary tumors in p53- and Brca1-deficient mice to the chemotherapeutic agents doxorubicin, docetaxel and cisplatin was evaluated (Rottenberg et al., 2007). Breast tumors in this model demonstrated sensitivity to the chemotherapeutic agents and acquired resistance in a manner that mimicked clinical experience. Therefore, these early results obtained with GEM models suggest they can provide similar therapeutic responses to those observed in clinical practice (Gopinathan and Tuveson, 2008). Although numerous therapeutic studies have been performed in xenograft models, and a few in GEM models, a direct comparison of the two has yet to be reported for
any given tumor type (Gopinathan and Tuveson, 2008). Some examples of the use of GEM to test the activity of anticancer agents are presented below (table 2).
Table 2: Examples of the use of GEM models to test the activity of anticancer agents (Damia and D'Incalci, 2009).
2.6 The challenge and Limitations of using GEM
There are several drawbacks and limitations associated with the use of GEMs, compared to the traditional xenograft model, GEM models are expensive and time consuming and their use is often restricted by intellectual property rights and patents (Suggit et al 2005; (Weiss and Shannon, 2003). It is not usually a primary tumor which kills a patient, but metastatic disease, and unfortunately this advanced stage is not represented by most GEM. In addition, species specific differences also exist in the role of different genes in different cell types, which can lead to different mutant phenotypes in both man and mouse (Jacks, 1996). For instance, transgenic Rb mice heterozygous for a null Rb allele developed pituitary adenocarcinomas, medullary thyroid carcinoma, and/or phaeochromocytomas, whereas this same mutation in man causes retinoblastoma (Suggit et al; 2005). The complexity of the human tumor cannot be reliably mimicked and, second, mouse tumors are not human tumors and do not often predict what will happen in the human tumor with regard to therapeutic response. We can cure many mouse tumors, but there is not a direct correlation between response in the mouse and response in the clinic (Richmond and Yingjun 2008).
2.7 Are GEMs ethically acceptable models?
The growing use of genetically engineered mice (GEM) in scientific research has raised many concerns about the animal welfare of such mice. It is vitally important that any ethical issues associated with preclinical testing are taken into consideration before any form of experiment is undertaken. The Animals Scientific Act of 1987 included a number of points in order to reduce the number of testing carried out on animals. William Russell and Rex Burch in 1959 introduced the principle of replacement, refinement and reduction (3Rs) which influenced new legislation aimed at controlling the use of animals (Balls, 2009). Similarly, the types of welfare concerns associated with GEMs may differ within the three stages that comprise the establishment of GEM animal models, their development, production, and research use. It should be noted that not all genetic manipulations are performed to model disease and not all genetically modified animals express clinical disease. In many cases, no clinical signs or disease may be expressed, but changes exist in metabolic pathways or physiological processes that may have no visible or substantial effect on animals. Therefore it is necessary to have a thorough knowledge of the animal model in this case, the phenotypic expression of the GEM animal (Brown and Murray, 2006).
However, some have suggested that the use of GEM may not actually increase welfare concerns. They have questioned whether there is a morally relevant difference between conventional mice given viruses or chemicals to induce cancer and transgenic mice expressing a tumour gene. In fact, targeting a disease or specific aspects of a disease using genetically modified animals often permits the use of fewer animals in the experimental process, thus leading to one of Russell and Burch's (1959) three principles (3Rs) of humane experimental technique reduction. For example, the ability to harvest tissue that expresses a specific gene may allow for studies to be conducted in vitro on those tissues rather than using large number of animals for in vivo studies (Brown and Murray, 2006). Nevertheless it is recommended to perform a case by case ethical analysis of genetic manipulation and investigators are urged not to neglect potential ethical issues (Loew., 1994).
Despite these challenges, the future of GEM models for cancer prevention is very promising. The time is now ideal for exploiting a new generation of highly sophisticated mouse models to address critical issues in cancer prevention. However, for this to be fully realized, careful consideration needs to be paid not only to the design of the models but also to the design of the experimental paradigms in which they are used. Moreover, there is a need to recognize and overcome the limitations of the model systems, as well as practical limitations that have hindered their effective use for cancer prevention research (Suggit et al., 2005).
After a thorough review of the literature on GEMs, it is evident that GEMs hold various advantages over xenografts and other currently available in vivo models, such as being the closest model that replicates human tumors in terms of modelling angiogenesis and providing an insight into the role of the immune system and tumour microenvironment in tumor initiation and metastasis. However, they are not without limitations, they are expensive and time consuming and their use is often restricted due to intellectual rights and patents. Moreover, their relevance for human cancer has not been established (Rangarajan and Weinberg, 2003). On the other hand, it may be that the problem is not that the models aren't relevant, but that the experimental parameters have not been designed in such as way as to effectively translate studies from mice to human cancer (Frese and Tuveson, 2007; Sharpless and Depinho, 2006). Indeed, a number of factors such as choice of model, design of the experiment and other logistical issues play an important role in determining the applicability of prevention studies carried out in GEM models to human cancers. Ultimately, for studies in GEM models to be applicable to humans, the models need to be appropriately chosen such that their biological and pathological properties are relevant for the experimental question being asked and, conversely, the experimental design of the study should be analogous to design of prevention research in humans (Abate-Shen et al., 2008).
It should also be noted that there are no ideal models that can be used for any kind of drug, but depending on the drug the most suitable experimental models should be selected. For example, for compounds with unknown modes of action, it seems sensible to use a variety of highly characterised human xenografts that will possibly provide indications on their therapeutic index, mode of action and the determinants of their anti tumour effects. Genetic models are particularly relevant for the validation of the potential therapeutic value of new targets. These models, which although in principle are the ideal ones for investigating novel target therapies, are in most cases of limited value for a quantitative statistical evaluation of the antitumor effects of a new drug because of their very variable growth in mice (Abate-Shen et al., 2008). In support of this, a broad analysis of in vitro models and tumor xenografts done at the National Cancer Institute found poor correlations with activity in phase II clinical trials and generally concluded that only compounds that are successful in a large number of different models are likely to be effective in the clinic.
In conclusion, GEMs hold a number of benefits as well as limitations, unfortunately few studies have tested known clinically effective agents using GEM models. The limited studies that have been undertaken in GEMs provide optimism that GEM models may indeed be of value in predicting clinical response (Suggit et al; 2005). Despite such promise, the value of GEM mouse models in anticancer drug discovery is yet to be determined. Furthermore, many obstacles associated with use of GEMs such as cost, patents etc must be overcome. Moreover, questions such as whether genetically manipulating the genes of mice for drug testing is ethically acceptable still remained to be answered. Nevertheless, it is known that further studies are needed to elucidate their role against known clinically effective agents. Only after this has been achieved, can we really answer the question of whether GEMs are the future for preclinical evaluation of anti cancer drugs.