PERSPECTIVES VIEWPOINT

Examining the utility of patientderived xenograft mouse models Samuel Aparicio, Manuel Hidalgo and Andrew L. Kung

Abstract | Patient-derived xenograft (PDX) models are now being widely used in cancer research and have the potential to greatly inform our understanding of cancer biology. However, many questions remain, especially regarding the ability of PDX models to affect clinical decision making. With these points in mind, we asked three scientists to give their opinions on the generation and uses of PDX models and the future of this field. How well do patient-derived xenograft mice actually resemble patient tumours (for example, in terms of morphology and histology, clonality, and genomics and epigenomics)? Samuel Aparicio. The question is impre‑ cise, because it really depends on what aspect of the patient’s tumour one is trying to model. The first thing to say about all models is that they are just that: all models are approximations of the real situation, not the real situation itself. This is often forgotten when the results are extrapolated to more general conclusions with a lack of intellectual rigor, often leading to expensive failures downstream. If one considers tumour histomorphology and imaging characteristics, the appear‑ ance of the malignant epithelial cells and even global gene expression profiles in immunodeficient mice seems to be quite similar in many cases. This has been recently re‑documented in a series of papers emphasizing the similarities between the originating tumours and xenografts at this level1–5. Immunodeficient mice of course lack an immune system, so the inflam‑ matory immune cell component of breast cancers is lacking in the models most fre‑ quently reported. Although the tumours excite a stromal response, with the growth of endothelial cells and fibroblasts into the stroma of the tumour, these stromal cells originate from mice. Nevertheless, xeno­ engrafted breast tumours can recapitulate the drug-sensitivity patterns seen in the patients from which they derive (for exam‑ ple, see REF. 2), which makes them important tools for drug discovery. The situation becomes more complex when one considers the clonal architec‑ ture of patient-derived xenografts (PDXs)

derived from patients with breast cancer. First, it is worth noting that our under‑ standing of the clonal dynamics of breast tumours in patients is still in its infancy, so the comparisons are hard to make and inter‑ pret. However, it is now clear, and perhaps obvious, that the engraftment of tumour cells into a foreign host exerts a selection pressure, which changes the clonal composi‑ tion of the engrafting tumour in every case examined. The selection pressures are poorly understood, but the lack of an immune sys‑ tem and site-specific microenvironmental differences are likely to have a substantial role. For example, site-specific engraft‑ ment tendency has been documented in individual tumours during breast xenograft studies, showing that tissue tropism can be a barrier. Recently, the clonal dynamics of breast xenoengraftment have been examined to single-cell resolution by our group6, using recently developed methods for genomic clonal analysis in solid tissues7,8. It is evident that the spectrum of initial clonal selection pressure is quite variable, from mild reshap‑ ing of clonal prevalence to extreme counter selection where only a minor clone repre‑ senting a small percentage of the originating cells contributes to the xenograft. The logical extensions of the initial selec‑ tion questions relate to the ongoing stability of the xenografts and the reproducibility of the clonal dynamics. Here it seems that the stronger the initial selection, the more stable the clonal populations are in subsequent passages. This is likely to be an indication that most of the changes in clonal prevalence reflect selection acting on pre-existing clones (dynamics), rather than the generation of new clones (evolution). Interestingly, in experiments in which the repeated xeno­ engraftment of the same tumour cell popu‑ lations has been undertaken, a fair degree

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of reproducibility has been observed. In other words, the same clones, as defined by genomic aberrations, tend to grow or decline. This suggests that the changes in clonal prevalence seen over time are not necessarily stochastic, but can be determin‑ istically linked to the pattern of somatic mutations in the genome. This also provides an experimental approach for distinguish‑ ing non-deterministic from deterministic clonal dynamics. Thus, the question of how well the xeno‑ grafts resemble the human situation is a complex one. There is clearly no exact clini‑ cal analogue of the xenograft — tumours are never deliberately re‑transplanted from one human to another (very rarely accidental engraftment of humans with another human tumour can occur during organ transplanta‑ tion). It is speculated that xenoengraftment may most closely mirror the metastatic pro‑ cess in humans, and there is some evidence from small-scale early genomic studies that xenoengrafted breast cancers may look more like metastatic cancers in the same patients9. It has been noted that xenoengraftment is a negative prognostic factor in breast can‑ cer; that is, if a breast tumour engrafts in a mouse, then the patient is more likely to experience a recurrence or an incomplete pathological response during the course of treatment. Drug development has tended to shy away from using PDX models because they are perceived to be slow and highly variable. But in fact these perceived disadvantages may be a better reflection of the real-world behaviour of human tumours, and so inter‑ est is growing in the use and adaptation of these models to drug discovery. Certainly polyclonality is mirrored in breast xeno‑ grafts, even if the exact clonal composition differs. Key to using these models will be to monitor them not simply by measuring tumour sizes, but by monitoring clonal selection and dynamics, as well as functional imaging responses. Taken together, these would make breast xenografts useful adjunct tools to understand some aspects of the responses of candidate drug molecules in a polyclonal tumour situation. Arguably, this may be a more realistic situation than merely testing drugs in cell line panels, which have mostly been derived from multiplypretreated cancers and so do not reflect the capacity of breast cancers to evolve in response to partial treatment. Therefore, the message from the com‑ bined studies suggests that breast xeno‑ grafts can be useful models, providing that the clonal dynamics are considered and VOLUME 15 | MAY 2015 | 311

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PERSPECTIVES measured as a factor in the experiments being undertaken, alongside the limitations of the lack of a native antitumour immune response. Manuel Hidalgo. One of the key prem‑ ises of developing PDX models for cancer research is the assumption that these models faithfully resemble the original tumour from which they were developed and that this similarity is maintained across passages. In general terms this appears to be correct. Serially passaged PDXs show biological consistency with the tumour of origin, are phenotypically stable across multiple transplant generations at the histological, transcriptomic, proteomic and genomic levels, and show comparable treatment responses to those observed clinically10. Studies using basic morphological assess‑ ments do indeed show that PDX models share the same histological structure as the original tumour, including fine tissue archi‑ tecture such as gland formation and keratin deposition2. Likewise, clustering studies using, for example, gene expression profiles show that PDX models cluster with their counterpart tumours11. It should be noted, however, that there is a selection process in the generation of a PDX model in that it is likely that only the less differentiated cells of the tumour grow in the mice. In addi‑ tion, other studies have shown that PDX models indeed have biological features that are reminiscent of metastatic cells, further suggesting that selection does occur 12. Along this line, very recent studies using singlecell sequencing approaches suggest that in some cases only a limited number of clones propagate in mice, indicating a selection process6. One important issue, that is indeed a limitation of PDX models, is the replace‑ ment of human stromal components — such as cancer-associated fibroblasts, endothelial cells, and immune and inflammatory cells — by murine elements, as well as the lack of a functional immune system13. In summary, while some studies suggest that there is good concordance, clonal selection and replace‑ ment of critical elements by murine tissues are limitations. Andrew L. Kung. A primary impetus for the advancement of PDX models is the well-­recognized limitations of cell linederived xenograft models for predicting the efficacy of therapeutics in the clinic14,15. Although many characteristics of PDX models maintain fidelity when compared to the originating tumours, this does not

mean per se that PDX models will better predict clinical outcome. A variety of stud‑ ies have demonstrated that the genetics, gene expression patterns and histology of PDX tumours are generally stable with serial propagation through mice16–20. The major limitations of PDX models are the necessity for immunodeficient hosts, the rapid loss of non-transformed stromal ele‑ ments and the selective outgrowth of more malignant cells1,12,20. However, when viewed through a comparative lens, many of these same limitations are frequently used to criticize cell line-based models. In other words, a cell line-based xenograft tumour, if compared with the originating tumour, would also likely have genetic, gene expres‑ sion and morphological features that are reminiscent of the originating tumour sam‑ ple, but would be limited by the outgrowth of a subpopulation of cells. Although dif‑ ferent from the selective pressure imposed by the creation of a cell line, it is clear that development of a PDX model exerts selec‑ tive pressures that result in engraftment and growth that favour tumours and clones with more aggressive cell-autonomous phenotypes1,12,20. On the point of predictive value, three major shortcomings are not addressed by PDX models. First, many drugs have pharma­cokinetic properties that are vastly different across species21. Evaluations of drugs that mimic clinically achievable

exposures, not just weight-adjusted clini‑ cal dose, are likely to better predict clinical efficacy. Second, preclinical measures of success are vastly different from clinical criteria for success. A 70% reduction in tumour growth may be highly statistically significant in a preclinical experiment, but in the clinic 30% tumour growth during therapy is defined as progressive disease and a treatment failure. Finally, the use of severely immuno­compromised hosts precludes the evaluation of immuno­ modulatory or stroma-directed therapeutic approaches. Therefore, although we have misgivings about cell line-based models, just because PDX models are different does not mean that they are more clinically relevant. In the era of targeted therapies, rather than just viewing these models simply as anatomical or histological class-specific disease models, it is imperative that the biological and molecular characteristics under investigation (whether genetic, epigenetic or signalling) be recapitulated in the PDX model. Moreover, it is crucial that we not only ensure that the model resembles patient tumours, but also that the treatment regimen resembles what can be achieved in patients and that the defi‑ nition of success is aligned with what we hope to achieve in patients (that is, stable disease or regression) and not reduced tumour growth.

The contributors Samuel Aparicio is the Nan and Lorraine Robertson Chair in Breast Cancer Research. He is also Head of the British Columbia Cancer Agency’s Department of Breast and Molecular Oncology, and a Professor in the Department of Pathology and Laboratory Medicine at the University of British Columbia, Canada. His research programme encompasses the fields of cancer genomics, clonal evolution, single-cell genomics, drug discovery and translational breast cancer research. His work on the genomic molecular taxonomy of primary breast cancer has redefined breast cancer subtypes. His recent work has established methods for studying clonal evolution quantitatively in cancer in solid tissues and single cells, including in patient-derived xenografts. Manuel Hidalgo is currently the Director of the Clinical Research Program and the Head of the Gastrointestinal Cancer Clinical Research Unit of the Spanish National Cancer Research Centre (CNIO), Vice Director for Translational Research and Director of the Centro Integral Oncológico Clara Campal. He trained in Medical Oncology and Drug Development at the Hospital 12 de Octubre in Madrid, Spain, and at the University of Texas Health Science Center in San Antonio, USA. He also served as Director of the Gastrointestinal Oncology Program at Johns Hopkins University, USA. His main focus of research has been new drug development in pancreatic cancer. His group popularized the use of Avatar mouse models for cancer research and recently contributed to the development of Nab-paclitaxel for pancreatic cancer treatment. Andrew L. Kung is the Robert and Ellen Kapito Professor of Pediatrics at Columbia University Medical Center, USA, and is Chief of the Division of Pediatric Hematology, Oncology and Stem Cell Transplantation. His research is focused on the identification of new cancer targets, cancer genomics and the development of new treatments for cancer. He has led the development of the Precision in Pediatric Sequencing (PIPseq) program, which is a comprehensive precision medicine programme for children with cancer. All patients undergo full genomic characterization of both the tumour and the germ line, and in parallel patient-derived xenograft models are created to evaluate genomically-informed therapeutic strategies.

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PERSPECTIVES Do you foresee these models being useful in clinical decisions, or are they better suited to research? S.A. The current role for PDX models is in the research domain, where they clearly have an important part to play, but I do not believe that they will be generally useful in clinical decision making in the foreseeable future. Breast tumour tissue xenoengraft‑ ment is unpredictable and expensive, as well as hard to align to the timescales of clinical decision making and treatment. The precise role for PDX models as real-time predictive tools for therapeutic intervention has not yet been defined and it seems likely that for the foreseeable future the effort required will be to establish whether a posteriori the responses of xenografts match those in patients. It is conceivable that in some indi‑ vidual instances capturing patient tumours as xenografts will allow for detailed molecu‑ lar understanding of the unique mutational events driving tumorigenesis. It is also pos‑ sible that tumour organoids may offer an easier route to drug-sensitivity testing. An underappreciated and under-investigated area is the link between functional imag‑ ing responses in xenografts and genomic aberrations. In the future, joint functional imaging and genomic stratification of PDX models could lead to a better link between responses in the xenografts and predict‑ ability responses seen in patients. A further development of this system would be the incorporation of an immune system in the xenograft environment. This is pos‑ sible, in principle, by mobilizing peripheral blood stem cells from patients with breast cancer who have donated tumours for xeno­engraftment and by performing an engraftment of the patient’s immune system into the host mouse. This is still technically challenging, as not all immune lineages are easily recovered from stem cell transplants, although transgenic mice engineered to sup‑ port the relevant growth factors may prove effective. These approaches would allow for more direct studies of tumour immunity in a captive model system. M.H. At the present time PDX models are research tools. The idea that the so‑called Avatar models are predictive of clinical outcome is certainly intuitive and some early studies indeed support this concept 22. Studies have shown that there is an excellent correlation between responses of an agent against a PDX model and the clinical response to that agent in the patient 23. In the more recent data, the positive predictive

value of this approach is in excess of 80%, which is quite remarkable (M.H., unpublished observations). Perhaps the major impact of this technology for clinical decisions is not as a stand-alone method but rather integrated with other approaches such as sequencing studies. In this sense, preliminary studies have shown promising results24. The ultimate goal, however, should be the utilization of Avatar models to guide clinical decisions. To this end, it would be necessary to conduct prospective, prop‑ erly powered, Phase III clinical trials in which the implementation of this technol‑ ogy, either alone or integrated with other approaches such as genomic analysis, results in improved survival. The first generations of these trials, spearheaded by academic groups and biotechnology companies, are ongoing. For example, at the Spanish National Cancer Research Centre (CNIO) and Comprehensive Cancer Centre Clara Campal (CIOCC), Spain, we are conduct‑ ing the Avatar trial in which patients with pancreatic cancer are randomized to either a standard-of-care arm or a personalized treat‑ ment arm that includes the assessment of mutations in a targeted panel of 409 cancerrelevant genes and the generation of an Avatar model. The idea is to select the most promising agents based on the genomic analysis and bench test their efficacy in the Avatar model to determine patient treat‑ ment. Similar studies are ongoing in ovarian cancer (Mayo Clinic, USA) and soft tissue sarcomas (Champions Oncology, USA). However, several technical issues still limit the conduction of these trials. The abil‑ ity of patient-derived tumour cells to grow in mice is not universal, and in 30% of patients an Avatar model cannot be generated. In other patients, the time needed to develop the model is too long for clinical decision making, particularly in diseases such as pancreatic ductal adenocarcinoma (PDA) that have an aggressive behaviour. With cur‑ rent technology the process is still labour intensive and expensive, thus limiting the number of drugs and combinations that can be tested. Certainly, incorporating methods to perform a primary selection of candidate agents by, for example, genomic analysis or by the generation of cell cultures suitable for high-throughput screening would help to optimize the process25. Finally, there can be limited access to the drugs that are identified by the models for patients. Thus, while the approach is very promising, there are still some hurdles that will need to be overcome to conduct efficacy-oriented trials that eventually lead to clinical acceptance.

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A.L.K. There are substantial limitations to the use of Avatar PDX models for clinical decision making for individual patients. First, PDX models are in many cases more replicative of the pace of tumour growth in humans, and specifically grow to terminal size over the course of several months, not weeks. The timeframe to obtain preclinical study results may thus be too slow com‑ pared with the clinical decision timeframe for the patient. Second, as discussed above, the predictive value of preclinical studies is generally unclear. With PDX models, there are examples of both positive and negative correlations with clinical results, and differences in drug pharmacokinetics remain a concern16,21,22,26,27. Third, most standard-of-care therapies are multi-agent treatment regimens, which are poorly repli‑ cated in preclinical models. Therefore, on a comparative basis, the efficacy of combina‑ torial standard-of-care regimens is likely to be underestimated if agents are tested indi‑ vidually. Together, these limitations make it premature to utilize empiric testing in Avatar models to make clinical decisions for individual patients. The clinical impact of PDX models on ‘precision medicine’, however, may be quite substantial, by elucidating biomarkers that predict sensitivity or resistance to a particu‑ lar therapy. In this case, a diverse portfolio of PDX models representing the molecular diversity of a given disease could be used to assess the therapeutic efficacy of a tar‑ geted therapy or a combination (described below). This approach would help to iden‑ tify therapeutic options for future patients based on more rapidly assessed biomarker end points (for example, genetic, imaging and gene expression). Thus, a PDX model would probably not be useful for identify‑ ing a therapeutic recommendation for the patient from which it was derived, but could help to identify precision medicine strate‑ gies for future patients who have molecular characteristics that are similar to those of the originating patient. What do you think is the most promising technology or technique for creating PDX models, and what are the major technological issues? S.A. The factors leading to effective engraft‑ ment are still incompletely understood, but work on the survival and engraftment of non-malignant mammary epithelium suggests that the rapid transfer of tumour cells with minimum manipulation (for example, using paddle blender disruption VOLUME 15 | MAY 2015 | 313

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PERSPECTIVES with minimal enzymatic treatment) and some support matrix, either fibroblasts or a laminin and collagen matrix such as Matrigel, helps to improve the survival of malignant cells. This may be a function of providing survival signals through inte‑ grin receptors and related proteins. Our experience has been that overall success in engraftment is no different with subcutane‑ ous versus orthotopic or renal capsule trans‑ plants, although some tumours do show clear site tropism. Many groups, including ours, have reported that primary, not previ‑ ously treated, low mitotic rate oestrogen receptor-positive (ER+) breast cancers are the hardest to engraft 1,2,5,6. Our experience is that in some cases tumours take up to 1 year after transplant to re‑emerge and that these are most often of the primary ER+ subtype. This is a length of time that most experimentalists will find unwieldy, although it may simply be reflective of the low rate of cell division in this subtype. Thus, the finding recapitulated by many groups is that the biology of indo‑ lent ER+ cancers is not well represented by xenotransplantation (although ER signalling characteristics and responses are retained in ER+ cancers that do engraft), whereas HER2+ (also known as ERBB2+) and triplenegative breast cancer (TNBC) subtypes engraft much more readily. Disruption of tumours to form organoids28–31 may prove to be a useful intermediate step to engraft‑ ment (and vice versa), extending the range of tumours that can be tested for sensitivity to drugs; however, almost nothing is known at present about the clonal representation and clonal dynamics of organoid-style explants as model systems in breast cancer. M.H. The majority of studies conducted thus far have used surgically obtained or biopsy materials from primary or metastatic lesions that are implanted subcutaneously in immune-deficient mice10. This process needs to be optimized on all fronts. One area of great interest is the genera‑ tion of models from tissues obtained from minimally invasive procedures such as fineneedle aspirates and even circulating tumour cells (CTCs). A recent study in breast cancer does indeed show that PDX models can be generated from CTCs32. Although this is still an early result it is certainly promising. As mentioned above, one of the major prob‑ lems with PDX models is that they do not reproduce the human cancer stroma with its associated elements. There is interest in determining whether PDX models would be improved by combining tumour tis‑ sues with other supporting human-derived

tissues such as cancer-associated fibroblasts or mesenchymal stem cells. In breast cancer, co‑engraftment of primary human mesen‑ chymal stem cells maintains the phenotypic stability of the tumour grafts and increases tumour growth by promoting angiogenesis1. While most of the studies have focused on subcutaneous implantation, others have explored the value of implanting the tumours in orthotopic locations or in better-vascularized tissues such as the renal capsule33. Arguably, these more physiological implantation sites maintain a better tumour integrity and recapitulate some phenotypic features such as the development of metas‑ tasis. However, these aspects have not been compared in properly designed studies. To avoid allograft rejection, PDX models are developed in mouse strains with vary‑ ing degrees of immune deficiency ranging from nude mice to NOD/SCID/IL2Rγ-null (NSG) mice. It appears that more immune-­ compromised hosts are more permissive, resulting in higher engraftment rates, and are therefore preferred for initial implantation. However, how the differences in the immune system affect drug response and predictabil‑ ity for therapeutic efficacy in human tumours is not known. One area of great interest is the reconstitution of a human immune system in the mouse. These models, called immuno­ avatar, are of interest for the study of immune therapeutics. Finally, there is interest in creating arti‑ ficial PDX models by implanting malignant tissues in three-dimensional culture systems and bioreactors28. While these techniques are still in their infancy, these systems may have important advantages such as the pos‑ sibility of controlling the conditions, higher throughput studies, shorter time to achieve results, and lower cost and animal utilization. A.L.K. The technical approaches for creating PDX models are well established for both solid and haematological malignancies10,34. Importantly, the trend towards the estab‑ lishment of orthotopic tumours is likely to improve the fidelity of PDX models by virtue of replicating some aspects of the micro­ environment of the originating tumour, as the subcutaneous compartment is an ectopic environment for most tumours. Consistent with clinical oncology, orthotopic models generally necessitate non-invasive imaging end points to follow disease progression and response to therapy. The incorporation of clinically relevant measures of success (for example, stable disease or disease regression) is likely to further improve the predictive value of these models.

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PDX models have largely been developed within individual institutions. There is a growing recognition of the need to develop collaborative groups that will allow models to be aggregated and shared, as replicating the molecular diversity of cancer types is unlikely to be achieved through single insti‑ tution efforts. Given the importance of full annotation, standardized databases are being developed to facilitate data dissemination. Beyond standard clinical information, full molecular characterization is critical and increasingly this includes whole-genome or whole-exome sequencing data, the dissemination of which remains a challenge. Moreover, beyond a cataloguing exercise, there is a need for collaborative groups to be able to distribute PDX models to requesting investigators. Beyond the need for stand‑ ardized passaging methods, nomenclature, banking, and so on, there is the pragmatic issue of how to assuage biosafety concerns regarding the movement of biomaterial between animal facilities. Given the increasing importance of immuno­modulatory approaches for cancer therapy, the development of PDX models in hosts with humanized immune systems is an emerging approach that requires further inno‑ vation. While there are existing approaches to engraft human immune cells into immuno­ deficient hosts, current approaches are labour intensive and difficult to scale to higher throughput drug treatment studies35. What is the most interesting thing that we have discovered using these models in cancer research (or the most interesting thing that they have been used for to date)? S.A. In my view PDX models or related mod‑ els using mixtures of human cell lines have yielded the most interesting insights about cancer biology when the clonal dynamics have been exploited; that is, the polyclonal‑ ity and ability of clonal fitness to become expressed through defined manipulations has been central to the observations. There are several recent examples, encompassing the interplay between genomically determin‑ istic and non-deterministic clonal expansion in colorectal cancers (CRCs) exposed to broad-spectrum chemotherapy 36, the inter‑ play between clones revealing how secreted factors might result in clonal dependency, as well as competition in an artificially constructed system of clonality 37, and the demonstration that clonal dynamics can be deterministic, as determined by the observa‑ tion of the same clones re‑emerging during passaging and/or engraftment dynamics6. www.nature.com/reviews/cancer

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PERSPECTIVES M.H. PDX models have been used in cancer research for biomarker development, under‑ standing of mechanisms of drug resistance, drug screening and personalized medicine applications. Studies in CRC PDX models have clearly shown that KRAS-mutant tumours do not respond to the epidermal growth factor receptor (EGFR) inhibitor cetuximab16. This observation is totally consistent with clinical data that were generated after the analysis of large randomized clinical trials that showed lack of benefit of cetuximab in patients with KRAS-mutant CRC. Indeed, if these data had been known and taken into account early on it would have saved years and millions of US dollars in clinical research. Likewise, detailed analyses of mechanisms of resistance to EGFR inhibitors in CRC PDX models identified targeted alterations such as HER2 and MET amplification that have led to novel clinical trials16,38. Similar data have been recently shown in lung cancer where PDX models developed from patients with targeted therapy-resistant lung cancer have been used to identify mechanisms of drug resistance and effective treatment combinations in patients25. With regard to drug screening, the most extensive analysis has been conducted in PDA. These studies led to the identification of Nab-paclitaxel as an active agent in this can‑ cer type and pointed to an effect in PDA stroma as a possible mechanism of an anti‑ tumour effect 39,40. These observations were subsequently shown in clinical trials that led to the approval of this drug to treat metastatic PDA worldwide41. With regards to a system for personal‑ ized medicine, the data are still preliminary and clinical trials are ongoing, but the high positive and negative predictive values are encouraging. However, the technical and logistical issues of incorporating PDX models in clinical research and clinical practice are not trivial and it is likely that until these are optimized the full application of PDX models for clinical decision-making processes is limited. A.L.K. PDX models and cell line-based xenograft models have been utilized for over 40 years, and like all other model systems they are imperfect in replicating human disease. One problem with conventional cell line-based xenograft models is the lack of representation of the broad molecular diversity of disease. As we have moved from an anatomical or histological classification of cancer to understanding the molecular bases of disease, it has become apparent that

eight to ten cell lines do not adequately reca‑ pitulate the molecular diversity of any cancer type. Recent methodological advances and the surge in interest in PDX models have increased the penetrance of these models across academia and industry, and large numbers of well-annotated PDX models have now emerged. Therefore, the quantum advancement is not the know-how and abil‑ ity to create PDX models per se, but rather that the widespread adoption of PDX meth‑ odologies has engendered a massive expan‑ sion in the models now available. Coupled with dramatic advancements in molecular characterization, we have in a short time‑ frame shifted from a limited repertoire of anatomically or histologically defined cell line-based models to a large number of PDX models across the molecular diversity of many diseases. Importantly, these large diverse collec‑ tions will allow us to more precisely mimic how we test drugs in patients. Rather than testing a drug in 40 mice implanted with one tumour model, we can envision testing 40 different models. Rather than design‑ ing studies to be able to see small changes in tumour growth in single models, we can take advantage of the diversity of response across a large number of models to identify molecular characteristics (for example, genetic alterations) that stratify with objec‑ tive response. In recent years, these types of PDX studies have been performed to retrospectively assess certain existing thera‑ peutics. These studies successfully ‘redis‑ covered’ known predictors of sensitivity and resistance to cetuximab in a variety of cancers16,20,42 and provide support for the use of PDX models to identify clinically relevant predictors of response. The most interest‑ ing experiments using PDX models are yet to be done — to use them to prospectively identify clinical translation hypotheses and to affect the clinical development of new drugs by identifying enriched responder populations. Samuel Aparicio is at the Department of Pathology and Laboratory Medicine, University of British Columbia, and BC Cancer Agency, 675 West 10th Avenue, Vancouver V5Z 1L3, British Columbia, Canada. e-mail: [email protected] Manuel Hidalgo is at the Gastrointestinal Cancer Clinical Research Unit, Clinical Research Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain. e-mail: [email protected] Andrew L. Kung is at the Department of Pediatrics, Columbia University Medical Center, New York, New York 10032, USA. e-mail: [email protected] doi:10.1038/nrc3944

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Competing interests statement

The authors declare no competing interests.

www.nature.com/reviews/cancer © 2015 Macmillan Publishers Limited. All rights reserved

Examining the utility of patient-derived xenograft mouse models.

Patient-derived xenograft (PDX) models are now being widely used in cancer research and have the potential to greatly inform our understanding of canc...
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