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 Table of Contents  
REVIEW
Year : 2016  |  Volume : 2  |  Issue : 6  |  Page : 197-202

Patient-derived xenografts as models for personalized medicine research in cancer


Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla, Seville, Spain

Date of Submission14-Nov-2016
Date of Acceptance29-Nov-2016
Date of Web Publication28-Dec-2016

Correspondence Address:
Dr. Amancio Carnero
Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla, C/Manuel Siurot, s/n, 41013 Seville
Spain
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2395-3977.196913

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  Abstract 

Basic research and clinical trials are essential components of the process of discovery and development of new drugs. The use of preclinical models is a key component in every aspect of drug development in cancer. Unfortunately, preclinical models often fail to capture the diverse heterogeneity of human malignancies, and the correlation between the antitumor activity of cytotoxic agents observed in these animal models and that observed in humans is poor. In recent years, there has been an increasing interest in the application of preclinical cancer models which can actually recapitulate the clinical disease, including patient-derived xenografts (PDXs). PDX models maintain the phenotypic, genetic, and molecular characteristics of the original tumor and reflect tumor pathology. This review discusses the limitation of the conventional strategy of developing new drugs in oncology and proposes the PDX models as a powerful technology for the biological study of tumors and to evaluate the antitumoral effect of new compounds.

Keywords: Animal models, drug development, personalized medicine, tumor graft, xenograft


How to cite this article:
Perez M, Navas L, Carnero A. Patient-derived xenografts as models for personalized medicine research in cancer. Cancer Transl Med 2016;2:197-202

How to cite this URL:
Perez M, Navas L, Carnero A. Patient-derived xenografts as models for personalized medicine research in cancer. Cancer Transl Med [serial online] 2016 [cited 2019 Nov 19];2:197-202. Available from: http://www.cancertm.com/text.asp?2016/2/6/197/196913


  Introduction Top


The field of oncology has evolved in recent decades, witnessing a rapid change in its therapeutic approach.[1] It has been generalized to a more personalized option, trying to molecularly characterize each tumor, and bringing it to a more selective approach from a therapeutic point of view.[2],[3],[4],[5] An example is the therapy of colorectal cancer (CRC), small-cell carcinoma (nonsmall-cell lung cancer) or breast cancer that has undergone radical changes in its management in the last 20 years.[5],[6],[7],[8],[9],[10] However, the development of treatments directed at a specific target has not meant an improvement in overall patient survival in all cases. There remain, however, neoplastic entities, for which biomarkers of efficacy have not been identified and therefore are not eligible to receive a personalized therapy. We often lack a thorough understanding of the molecular basis of response to targeted treatments, leading sometimes erroneously to clinical trials with wrong therapies. Current evidence highlights significant differences in sensitivity to such therapies among different patients, which suggests a greater potential in customizing treatments.

For these reasons, it is necessary to identify more and better biomarkers that are actually predictive of response, with the ultimate aim of improving the personalization of cancer treatment, which also translates into better toxicity profile and control of the disease.


  Development of Antineoplastic Drugs Top


Most advanced solid tumors in adults are not curable with the currently available conventional therapeutic armamentarium.[11] The introduction of targeted therapies, such as inhibitors of epidermal growth factor receptor (EGFR) or antiangiogenic, among others, has quietly improved the survival of some malignancies.[12],[13],[14],[15] However, pharmacological treatments aimed at specific molecular targets are effective if the target is really inhibited by the drug or is relevant for tumor cell survival (oncogene addiction).[16] The reason for the high level of failures in clinical trials of new drugs is, among others, the failure of one of these two basic requirements. The conventional strategy of developing new drugs in oncology, based on a selection of maximum tolerated dose toxicity and an empirical evaluation of the effectiveness, has not been very productive. The development of EGFR inhibitors in lung cancer is an illustrative example. The activity of these drugs was very limited in the unselected population, in which the trial was conducted initially. More recent studies have discovered molecular factors involved in the susceptibility or resistance to these agents, allowing more appropriate selection of patients, and substantially improving the therapeutic benefit.[17],[18],[19],[20],[21],[22] These molecular factors, or biomarkers, were discovered after thousands of patients were treated with these drugs, in many cases without any clinical benefit, with high toxicity and a high economic cost. It is, therefore, possible that many agents have failed in the clinic because they were tested in an unselected population and its potential therapeutic effect was diluted by the inclusion of patients who did not benefit from treatment. Furthermore, failure of any of these agents, for example, gefitinib (EGFR inhibitor), was caused by insufficient patient selection to exert pharmacodynamic effect in patients with nonmutated native receptor. This suggests that the development of appropriate tests for pharmacodynamic monitoring and a selection of appropriate doses are critical in the development of new drugs.

There is usually limited information available on the factors that determine the effectiveness of a specific drug at the time when human clinical trials begin. This is one of the critical reasons that condition the high failure rate experienced by antineoplastic drugs. Preclinical trials are performed on commercial cell lines that have undergone multiple passages in culture that are then used to generate xenografts [Figure 1]. Further, the correlation between the antitumor activity of cytotoxic agents observed in these animal models and that observed in humans is poor.[23],[24] While these tumor models are human in origin, they have been maintained in an artificial environment for a long time and probably do not reproduce the neoplastic disease as first observed in the clinic. In fact, the adaptation of the tumor to laboratory conditions with multiple passages in culture for long periods of time causes genetic alterations that result in different parental tumor cells.[23]
Figure 1: Xenografts derived from cell lines. A diagram of the procedure for subcutaneous injection of commercial tumor cell lines in mice is shown

Click here to view


Both basic research and clinical trials are essential components of the process of discovery and development of new drugs. The active compounds in preclinical studies are selected for further development and initiate early clinical trials (Phase I–II). This selection is made assuming that the observed activity for a particular drug in a preclinical model translates at least some clinical efficacy of that compound, i.e. assuming that laboratory tumor models are predictive of efficacy. There are two potential approaches:

  • Compound oriented assuming that a particular drug can be potentially active in all cancers if preclinical activity exists against a specific type of neoplasia
  • Disease oriented assuming that an active drug in preclinical studies against a particular tumor predicts the activity of this compound against the same tumor type in the clinic.


Neither of these approaches has been clearly demonstrated by the studies conducted to date. In any case, any study intended to answer this question suffers from a bias inherent in the fact that the compounds that are not active in preclinical models are never evaluated in humans.

The use of preclinical cancer models for the selection of drugs with potential anticancer activity was initiated in the US in the 1950s led by the National Cancer Institute (NCI). Screening strategies used until 1990 were essentially compound oriented and involved a small number of mainly murine tumor allografts. Several studies, however, showed that these models had a very poor predictive value [23],[25],[26] and were biased to select compounds active against human leukemias and lymphomas.[27],[28] Therefore, in 1990, the NCI introduced the 60 cell line screening panel oriented to neoplastic disease including 60 cell lines of the most common human tumors.[29],[30],[31],[32],[33 This screening platform was designed so that each tumor type was represented by a panel of cell lines including different histological subtypes and drug-resistance profiles. Recently, the NCI has examined the correlation between the activity of drugs in preclinical models of xenografts and that observed in Phase II clinical trials.[34] The main findings of this analysis are with the exception of nonsmall-cell lung carcinoma, there is a poor correlation between the drug activity observed in human tumor xenografts and its activity in Phase II clinical trials; with the exception of colon and breast cancers, the xenografts of human tumor cell lines did not predict the clinical efficacy of the compound in other tumor types; compounds that were not active in at least one-third of all evaluated xenografts had high probability to be inactive in the clinic.

Other preclinical screenings have used models of human cancer-oriented tumors, obtaining both positive (the pattern was predictive) and negative (the model did not predict the clinical activity of the compound) in different types of human malignancies.[23],[34],[35] In any case, they all have based their conclusions in the observation rather than in rigorous statistical analyzed trends. On the other hand, these studies have used dichotomous definitions of antineoplastic activity in both preclinical and clinical data, based on arbitrary, nonvalidated cutoffs (i.e., 20% response rates in Phase II clinical trials, 42% in xenografts cell lines of human and murine grafts). Therefore, the processes of discovery of new anticancer agents require a platform that can be actually predictive of the expected activity in humans of tested compounds. Preclinical models currently available are not well suited to achieve these objectives. Cell line xenografts do not reconstitute the architecture or the microenvironment of human cancer and carry adaptive mutations not present in the original tumor.


  Patient-Derived Xenograft Models of Cancer Top


The use of preclinical models is a key component in every aspect of translational research in cancer. In recent years, there has been an increasing interest in the application of preclinical cancer models which can actually recapitulate the clinical disease, including patient-derived xenografts (PDXs). The PDX models are not new, and in the 1980s, some studies showed a high correlation between clinical response to cytotoxic drugs in adults and the response observed in PDX models.[36],[37] The process of generating PDX models from fresh primary or metastatic tumors is a technique widely described in the literature.[38],[39],[40],[41] It is based on transferring the primary tumor from the patient directly to the immunosuppressed mouse. Briefly, the tumor (primary or metastatic tissue) is obtained from fresh surgical resection or biopsy. It will be mechanically or chemically cut into small pieces, carefully discarding the necrotic areas, and implanted into an immunocompromised mouse (e.g., nude or NOD-SCID) [Figure 2]. Tumor pieces can be implanted alone or enriched with matrigel or human fibroblasts.
Figure 2: Schematic representation of patient-derived tumor xenograft generation: engraftment, expansion, and treatment phases are shown

Click here to view


Matrigel implantation provides nutrients to the newly implanted tissue, while human fibroblasts enrichment serves to compensate the recruitment of newly-specific cells from host extracellular matrix. When tumors have grown, another cohort is reimplanted to expand the sample [Figure 2]. The time required for the successful implantation is variable, usually from 1 to 12 months. In the expansion phase, samples are reimplanted generating cohorts of the same tumors in immunodeficient mice to begin biological studies, clinical efficacy of drug studies, or search for response biomarkers to new therapies. These latter studies usually develop in early generations, called Phase 0 trials.[40],[42] It has been observed that the mouse-mouse tumor spread does not make substantial changes in the characteristics of the tumor or the response to treatment. In fact, some studies have compared the response to treatment in several steps in PDX models and have shown a stable response between generations, supporting the phenotypic stability of these models.[35],[43] Different groups have developed specific implementation techniques, being the most common site for implantation the dorsal region (subcutaneous implantation). The orthotopic implantation, in the same organ from which the tumor originated, may be a better alternative when possible.[44],[45],[46],[47],[48] In some cases, regardless of the origin, the primary tumor has been implanted in the renal capsule to increase the efficiency of tumor growth.[49],[50],[51],[52],[53] Among the various techniques, orthotopic implantation is considered the one that more accurately reproduces the original tumor, both histologically and within its transcriptional profile.[54],[55] This is mainly due to the effect of the microenvironment since the interplay between tumor and stroma is tissue specific.[56] Although some studies show some variation in gene expression associated to the human stroma compartment, because the tumor recruits stroma from the host,[57],[58] the single nucleotide polymorphisms, and copy number variations are reproducible.[54],[55]

In short, PDX models maintain at the phenotypic, histological, and molecular level, the characteristics of the primary tumor and faithfully reproduce the disease as observed in the clinic.[59],[60],[61],[62],[63],[64] Therefore, they provide an excellent tool for the biological study of tumors and the evaluation of the antitumoral effect of new compounds. However, with increasing reimplantations, the human tumor is invaded by mouse stromal cells, and a mixture of subpopulations has to be considered.[65],[66]


  Personalized Medicine in Patient-Derived Xenograft Models Top


A large number of studies have been conducted with PDX models of CRC, sarcomas, lung and renal cancer, glioblastoma, head and neck squamous cell carcinoma and other types of cancer to search for new biomarkers of sensitivity/resistance to drugs and to establish a personalized medicine. PDX model trials have been conducted to test the response rate to drugs used as standard of care in medical oncology 43, 49, 61, 67-69 or to search for new therapeutic options in cancer.45,70-75

In a study with PDX models of CRC, HER2 amplification has been identified not only as a biomarker of resistance to EGFR but also as a positive predictor of response amplified metastatic CRCs.[70] We have recently shown that one of the mediators of resistance to oxaliplatin in liver metastasis of colon tumors is the active tyrosine kinase, Src. Furthermore, p-Src (phosphorylated at Tyr419) is a good predictive marker of dasatinib efficacy in restoring oxaliplatin sensitivity in vivo. In this study, we have shown that dasatinib, a dual oral inhibitor of Src/V-abl, effectively sensitizes CRC liver metastasis to oxaliplatin in orthotopically-grown PDXs. Dasatinib is only effective in tumors with high Src phosphorylation at Tyr419.[71]

We have also explored the efficacy of CDK4 inhibition using palbociclib (PD0332991) in a panel of sarcoma PDXs. We have observed that palbociclib is active in vivo against sarcomas with high levels of CDK4 but not against sarcomas displaying low levels of CDK4 and high levels of p16Ink4a.[72] Therefore, our data reinforce previously published data showing the suitability of CDK4 inhibition in cell cycle-dependent tumors and support the potential of new molecularly directed clinical trials for several types of sarcoma.[76]

Using again, the PDX sarcoma platform, we have also observed that tumors in vivo with high levels of MAP17, a small nonglycosylated membrane protein commonly overexpressed in carcinomas, respond to the proteasome inhibitor bortezomib (Velcade, PS-341), a molecule approved by the Food and Drug Administration for the treatment of mantle cell lymphoma and multiple myeloma. This work provides the first evidence that this therapy could be newly applied to second- or third-line sarcoma patients, currently without any other therapeutic option.[73]

The value/benefit ratio of PDX models as platforms for clinical trials has also been shown in a study of pancreatic ductal adenocarcinoma. This work showed that the combination of nab-paclitaxel and gemcitabine is effective in reducing tumor size, a finding that correlated with the clinical efficacy of this combination.[77]

Other authors have also shown relevant results in vivo with a large panel of glioblastoma multiforme PDX. They tested the combination efficacy of veliparib with temozolomide (TMZ), a critical component of therapy for patients with glioblastoma. Intriguingly, veliparib enhances the efficacy of TMZ in tumors with promoter methylation in the DNA repair gene O6-methylguanine-DNA methyltransferase (MGMT).[78] Based on these data, MGMT promoter hypermethylation is being used as an eligibility criterion for A071102 (NCT02152982), a Phase II/III clinical trial evaluating TMZ/veliparib combination in patients with glioblastoma.


  Conclusion Top


PDX maintains the phenotypic and genetic characteristics of the original tumor and reflects tumor pathology, growth, metastasis, and disease outcome. Its high potential as clinically relevant is becoming an integral part of the drug development process and must be considered an essential tool for personalized cancer medicine strategies.

Financial support and sponsorship

The laboratory was supported by grants from the Spanish Ministry of Economy and Competitiveness, Plan Estatal de I+D+I 2013–2016, ISCIII (FIS: PI15/00045), co-funded by FEDER from the Regional Development European Funds (European Union), Consejeria de Ciencia e Innovacion (CTS-1848), and Consejeria de Salud of the Junta de Andalucia (PI-0306-2012, PI-0096-2014 and ECAIF2-0176-2013). This work has also been made possible thanks to the Fundacion BBVA.

Conflicts of interest

There are no conflicts of interest.

 
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