Ignyta Inc., San Diego, CA 92121, USA.
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The success of targeted therapies for cancer patients rests on three major components: the right target(s), the right drug and drug combination, and the right patient population. Although much progress has been made in understanding the mechanism of disease and in reﬁ ning pharmaceutical properties of therapeutic agents, the attrition rates between target discovery and drug marketing approval have been high, especially in oncology. One of the main reasons underlying this undesirable statistics is believed to be the lack of predictive power of the model systems used in the preclinical setting. Several strategies have been employed with the aim of improving the predictive value of the preclinical studies, such as incorporating genomic proﬁ ling and molecular segmentation into model selection, and enhancing the development and application of patient-derived xenograft models even during early stage of drug discovery. This brief review will summarize some of the recent concept and practice in incorporating patient-derived models into all stages of drug discovery process, from target to clinical development.
Animal models, drug discovery, oncology, patient-derived xenograft, translational research
The past decades have witnessed an explosive growth of scientific understanding of human diseases especially those of highly unmet medical needs. In the field of oncology, the significant progress in basic research coupled with technology advancement in drug discovery has resulted in a significant number of breakthrough therapies with improved efficacy and manageable toxicity. However, the overall track record of oncology drug research and development remains one of the worst in all therapeutic areas, with high attrition rate and prohibitive cost.[1,2] Recent survey indicated that in oncology drug development, close to 95% of drugs tested in Phase I trials failed to reach marketing authorization stage. Significant efforts have been invested in scrutinizing every aspect of the drug discovery and development process and looking for ways to improve the success rate and efficiency. Among all, three pivotal areas have received much attention. First, it is commonly accepted that more refined, clinically relevant preclinical models are critical for accurately predicting patient response in clinical trials. Second, as we have fully embraced the concept and practice of personalized medicine and targeted therapy, tumor profiling and patient segmentation based on predictive biomarkers need to be an integral part of preclinical and clinical research and drug development. Finally, there is a need for bi-directional flow of information between preclinical and clinical investigators, and for increased collaboration between industry, academia and regulatory agencies to ensure optimal alignment of interests and resources. This short review will only focus on patient-derived models as a promising approach for improving the successful rate of oncology programs.
In the past 4 decades, significant progress has been made in the understanding of cancer biology and emergency of new classes of targeted therapies that have significantly changed the landscape of cancer treatment and management. The key to these successes has been the identification and validation of cancer targets that distinguish cancer cells and tissues from normal ones, as elegantly summarized in the landmark articles by Hanahan and Weinberg.[4,5] Although a dauntingly complex disease, cancer can be viewed as evolved around a number of rational commonalities, or hallmarks, necessary for tumor initiation, progression, metastasis, evasion of immune surveillance and resistance to therapeutic intervention. These processes involve not only genetic and epigenetic changes in the cancer cells themselves, but also recruitment and alterations in the tumor-associated stroma and micro-environmental factors. Therefore, it is conceivable that therapeutic approaches involving targeting multiple hallmark functions will continue to be the cornerstone for targeted cancer therapy and management.
Cancer target identification traditionally involves the search for differential expression and function between cancer and normal cells and tissues at the DNA, RNA, protein and microRNA levels. Multiple approaches of various through-put have been developed to identify differentially expressed genes and proteins.[7,8] Recent advances in transcriptomics, proteomics, genomics, functional genomics, epigenomics and metabolomics have significantly expanded the scope and depth of novel targets as well as utility of existing targets.[6,9-11] Although cell lines have been traditionally used due to their availability and accessibility, most recent efforts have been focused on patient samples, tumor biopsies and resections, for example, for their clinical relevance and heterogeneity. Once a potential candidate target is identified, the next key step is to functionally validate the target in the context of relevant patient population. The routinely employed approaches include tool compound, blocking antibody, dominant negative and RNA interference/short hairpin RNA. In addition, it is imperative to investigate whether the target identified in a small set of cells and tissues are reflected in a larger population ideally identifiable with selective biomarkers. To this end, a collection of large number of clinically collected tumor samples and patient-derived tumor models are critical to ensure translatability from target to drug and from laboratory to clinic.
Although cancer cell lines are the most widely used starting material as they are readily available and propagated to provide sufficient material for in vitro manipulation and in vivo tumor growth, most of them have been established long time ago and have been selected and cultured under nonphysiological conditions. In contrast, the least manipulated samples are those directly obtained from patients through surgical procedures or needle biopsies. However, one of the major challenges of using primary patient tumors is their limited “shelf-life” and very low quantity in most cases. Compared with cell line models and patient tissues, patient-derived xenografts (PDXs) provide a practical solution by both preserving the fidelity of clinical characteristics and providing tumor supply sufficient for most target identification and validation strategies.[12,13] Another significant benefit of using PDX for target identification and validation is that the process from target identification to validation and then to efficacy screening can be streamlined around the same models, therefore, offering a complete circle from patient to mouse and then back to patient.
Typically, when patient samples are obtained for establishing PDX models, basic patient information (such as age, sex, ethnicity, clinical diagnosis) with the exception of patient identity will be provided. Once the tumors are established in immune-compromised mice, comprehensive characterization at DNA, RNA and protein levels will be carried out to gain detailed understanding of the histological, biochemical, molecular and genomic characteristics of the models.[14-16] As many of the technologies have become more efficient and affordable, whole-genome or transcriptome sequencing is increasingly being used to replace traditional microarray-based gene expression profiling and copy number variation studies. Next generation sequencing (NGS) approaches such as exome sequencing or whole genome sequencing also provide information on mutations and chromosomal aberrations such as duplication, deletion and translocation, many of which identify tumor suppressors or oncogenic drivers and potentially predict drugs likely to be efficacious in particular patient subgroups.
A number of studies were carried out to study the impact of successive passages on the gene expression, chromosomal stability and copy number variation. Although not definitive and most likely model-dependent, the general consensus in the field is that PDX models should be used at early passages. At relatively low passage, the histological features, gene expression profile, copy numbers and chromosomal stability remains very similar to the matching tumor directly harvested from patient.[20-23] On the other hand, with each passage to a new mouse host, subsequent genetic changes may occur at different tendencies intrinsic to individual tumors, although the extent and impact of these alterations remain unclear.
In reality, each cancer patient’s tumor is heterogeneous and unique. And within each of the tumor indications mainly defined by anatomic locations of tumor incident (e.g. lung cancer, breast cancer), many subtypes can be identified by histopathology and immunohistochemistry (IHC) of an abbreviated panel of markers. Although these approaches have been widely used to describe and categorize tumors, they have largely failed to capture the variation of disease within indications. Recently, gene expression profiling and NGS have helped further refine the models via molecular subtyping within individual cancer indications.[25-29] Such molecular subtyping can be particularly helpful in delineating subtypes that can be challenging to distinguish with routine histopathology or IHC. For example, traditionally, breast cancer subtyping is mainly based on histology findings of IHC staining of selected markers. Recent molecular profiling has identified six distinct subtypes (luminal A, luminal B, human epidermal growth factor receptor 2, basal-like, claudin-low, and a normal-like) with clinically significant differences in risk factors, incidence, prognosis, and treatment response.[30-33] A similar approach has also been used in lung cancer to define clinically relevant subtypes to which targeted therapy can be applied to achieve optimal efficacy. In lung cancer, especially in non-small cell lung cancer (NSCLC), recurrent oncogenic drivers such as epidermal growth factor receptor, KRAS, anaplastic lymphoma kinase, as well as their related pathways can be successfully employed to select responsive patients and predict response and resistance.[34-36]
Accumulating evidence has indicated PDX models are superior to traditional cell line xenograft models because they maintain more similarities to the tumors found in actual patients. For example, a detailed cytogenetic analysis of PDX models revealed strong preservation of the chromosomal architecture observed in patients. Furthermore, other studies have shown strong fidelity in histology,[37,38] transcriptome, polymorphism and copy number variations. In some cases, certain oncogenic gene amplification can be found in cell lines at levels that are several-multitude higher than in patient rumors, a cell culture-derived artifact that may lead to over-predict drug response in the clinic (unpublished data). On the other hand, emerging data started to show that PDX models may be more accurately reflect clinical response when treated with therapeutic agents at clinically relevant doses (CRDs).
Despite the continuously growing arsenal of new and improved anti-cancer drugs, for most cancer patients with advanced diseases, treatment failure remains an inevitable outcome. To a given treatment, only a fraction of the patients would respond the regimen favorably (responders), which stresses the importance of selecting patients with the appropriate molecular and pathological characteristics for maximal therapeutic benefit. On the other hand, even when a particular treatment is initially efficacious in selected patients, drug resistance will develop overtime. Therefore, drug resistance is a fundamental cause of therapeutic failure in cancer therapy. Numerous studies have attempted to unravel the mechanisms of drug resistance to traditional chemotherapeutic agents and to recently developed targeted, small molecule and antibody based drugs. Briefly, the mechanisms of resistance can be roughly mapped to four categories: (1) Multi-drug resistance (MDR). MDR is caused by expression and/or induction of efflux proteins, which are members of the ABC transporter superfamily involved in the transport of both hydrophobic and hydrophilic compounds. This mechanism is relatively more common for cytotoxic drugs and payload of antibody-drug conjugates than targeted agents; (2) Tumor initiating cells/cancer stem cells (TICs/CSCs). As discussed earlier, these cells have the capability of self-renewal and differentiation, remain relatively quiescent, and can tolerate higher level of DNA damaging agents and oxidative stress. These characteristics are important for TICs to survive chemotherapy and radiation and ignite tumor re-growth when the condition permits;[43-46] (3) Tumor genetic and epigenetic alterations. These alterations can take place at multiple points during tumor initiation, progression and treatment, and they can be preexisting mutations, acquired mutations, or changes in downstream genes and pathways. For example, resistance to EGFR tyrosine kinase inhibitors can be attributed to multiple mechanisms, such as gatekeeper mutation (T790M),[47-49] c-Met amplification, activation of alternative pathways such as insulin-like growth factor receptor and AXL,[48,51] trans-differentiation to mesenchymal cells or small cell features; and (4) Tumor microenvironment. Emerging data has indicated tumor microenvironment as a key mediator of drug resistance. For example, several potential mechanisms of resistance to anti-angiogenic drugs are microenvironment-derived, including up regulation of alternative pro-angiogenic signals,[55,56] recruitment of bone marrow progenitors, and increased pericyte coverage. Another example can be found in pancreatic ductal adenocarcinoma, in which gemcitabine resistance has been attributed to inefficient drug delivery due to poorly perfused tumors.
There are obvious advantages of using PDX models to study drug resistance mechanism and to characterize therapeutic agents for efficacy. As discussed earlier, PDX models are heterogeneous in nature, and more closely reflective of tumors in actual patients, and a more appropriate system for understanding acquired and de novo drug resistance through enrichment of preexisting changes in subsets of cells.[61,62] A large collection of PDX models can best represent a broad patient population with various preexisting mutations and susceptibility to generate additional mutations, which cannot be achieved by other models including cell line xenografts. In addition, PDX models contain TICs/CSCs, and proper tumor stroma (albeit controversial) that can potentially contribute to resistance as well. Furthermore, it has become possible to establish PDX models with tumors that had already been treated and later became refractory. This is an important point because in clinic, most patients entering clinical trials have been treated with standard of cares previously and have relapsed with refractory disease. Compared to cell line xenografts, PDX models should better recapitulate patients with refractory and metastatic cancer.
A number of studies have taken the advantages of PDX models to study drug resistance. Krumbach et al. investigated response to cetuximab in 79 PDX models generated from colon, gastric, head and neck, lung and mammary cancer. After an in-depth analysis of different molecular characteristics of the tumors, they identified c-MET activation as a key mechanism for drug resistance, especially in NSCLC adenocarcinomas. In another study: using PDX models of NSCLC, Dong et al. identified foci of resistance cells after cisplatin treatment as a single agent or in combination with vinorelbine, docetaxel, or gemcitabine. The authors suggested that these drug-resistant cells were TICs-like and could be responsible for tumor recurrence.
Traditionally, pharmacology, biomarker and pharmacokinetics/ pharmacodynamics studies for oncology programs almost exclusively relied on tumor xenograft and to a much lesser degree, syngeneic models. With the significant increase in the availability and affordability of PDX models offered by both academic institutions and contract research organizations, PDX models have seen increasingly their utility in routine research activities. A quick survey of oncology discovery programs published in the past 3 years shows that increasing number of programs use PDX models at some point during the preclinical discovery and translational research stages.[14,65-67] In addition, there is an industry-wide trend to include PDX model readout as a key component of the required data package for both internal use as well as regulatory submission. The history of using incorporating PDX models in drug discovery can be traced back to several decades ago. For example, one of the earliest reports involving cancer drugs and PDX models by Fiebig et al. studied a number of chemotherapy drugs at their respective maximal tolerated doses (MTDs) in PDX models derived from 34 patients, and demonstrated 92% accuracy in predicting efficacy and 97% in predicting no-response. Similar predictive value was seen in a later study by the same group. However, additional studies suggest that the predictive value can fluctuate due to factors such as tumor histology and location, stage of disease from which the models are derived, the quality of PDX models, sample size and dosing regimen.[64,70,71] In addition to selecting models that are histologically, molecularly and genetically relevant to the patients in clinical, another important factor for improving translatability of preclinical findings is the drug exposure. Not surprisingly, preclinical model species, in most cases immunocompromised mice, can exhibit different tolerability and adsorption, distribution, metabolism and excretion property than those in human. It is commonly seen that drug exposure levels at MTD dose in mice are higher than clinically achievable levels in human. Therefore, a compound given at mouse MTD to xenograft, allograft or syngeneic models may generate exaggerated efficacy that over-predicts human response in the clinic. This phenomenon has been seen for both chemotherapy agents[12,73,74] as well as targeted agents such as vascular endothelial growth factor receptor inhibitors and PI3K inhibitors. A key concept and practice to avoid the pitfalls of using mouse MTD dose and exposure as the sole basis for efficacy prediction is to use CRD or clinically relevant exposure (CRE) whenever a CRD or CRE can be determined.
An evolving concept and practice, PDX mouse clinical trial, has started to yield positive results that had real-life impact on selected patients. In this setting, PDX models established from the very same patients on trial are being treated ahead of patient therapy or concurrently, and results from the mouse trial is provided in real-time to help guide clinical management of the patient's tumor. Further powered by the molecular characterization of the tumors, this highly personalized approach has the potential to revolutionize the drug development and patient care. For example, a recent study by Stebbing et al. reported 22 sarcoma PDX models were successfully established from 29 patients (76% take rate) and screened for drug sensitivity to a panel of therapeutic agents. The entire process typically took 3-6 months depending on individual tumor growth characteristics and treatment regimen. Of the 22 patients, 6 died before data became available. Of the 16 remaining patients, 13 (81%) demonstrated a correlation between the results from their PDX mouse trial and clinical outcome. Similar approach has also been reported in advanced adenoid cystic carcinoma, ovarian, and other cancer types. The current data, although limited, appears to support the use of PDX models to prioritize therapeutic agents against individual tumors. However, some key challenges remain before this strategy can be broadly implemented in clinical practice. For example, establishment of PDX models is still a technically challenging and time-consuming process, even after much progress has been made to improve the take rate and optimize the expansion scheme. In addition, the algorithm for the selection of agents to be tested needs to be further developed and refined. Lastly, to effectively demonstrate the feasibility and clinical benefit of the PDX-guided treatment prioritization in the patient care setting, properly controlled clinical trials are needed.
Although PDX models present an exciting opportunity for improving predictive value of preclinical and translational studies, and offer a number of advantages over conventional cell line xenograft models, just like any other preclinical model platforms, there are several limitations that one needs to be aware of. First, the utilization of severely immune-compromised host mouse strains, particularly the nonobese diabetic severe combined immune deficiency gamma mice, while allowing higher take rate and more consistent growth of xenografted human tumors, is inherently inadequate in modeling immune responses. Although human stroma components including immune cells originally present in the tumor biopsy can be grafted together with the tumor tissue, they normally cannot survive beyond the first passage, and will be completely lost in the subsequent expansion. The other stroma components including fibroblasts and vasculature are quickly replaced by murine counterparts. The lack of functional immune system limits the utility of these models in studies where immune responses are required. For example, immunotherapy cannot be readily studied in the PDX models established in immune-compromised mice. It is well documented and accepted that immune system is an important part of tumor stroma and significantly contributes to tumor initiation, progression, metastasis and therapeutic response.[84,85] The introduction of mice with partially or completely humanized immune systems can potentially ameliorate this issue, but significant technical challenges still exist.[86,87]
Second, although technical advances have gradually improved the tumor take, different tumor types, and different subtypes within the same tumor type, have varying rates of success. This has led to imbalanced representation of tumor types/subtypes that is more determined by take rate rather than clinical incidence rate. Although PDX models can avoid artificial selection in extended culture on plastic, the in vivo selection process exists as soon as the tumors are implanted. For example, high-grade, fast proliferating tumors tend to be easier to establish as PDX models than low-grade, slowly growing but progressive tumors.[88,89]
Additionally, compared to cell lines, PDX models are difficult to manipulate genetically. Most PDX models are established from and passaged as tumor fragments, and conventional transfection or transduction are not efficient to genetically modify the tumors or introduce detection markers (such as luciferase or fluorescent proteins). Therefore, PDX tumors are rarely established as orthotopic models, unless there is a surrogate biomarker that be readily used to measure tumor burden noninvasively.
Although hardly a new concept, PDX models have gained much attention and premium status in the past few years as they are becoming increasingly available and affordable, and are believed to offer a superior predictive value over conventional cell line xenograft models. Ample data indicated that PDX models maintain heterogeneity and tumor initiation ability, as well as molecular and genetic characteristics reflective of human tumors. Emerging data indicated an improved predictive value of the PDX models; however, it is still early to conclude whether the advantage in translatability is applicable to large sample size and to various therapeutic mechanisms and modalities. The mouse clinical trial has the potential to accelerate and de-risk human clinical trials and hopefully reduce clinical attrition rates for novel compounds, and to prioritize therapies by allowing parallel testing of multiple treatment schemes for an individual patient. However, there are still much to be done to address technical challenges to make this approach feasible and affordable and to convince the medical and insurance community of the value this approach can offer. At the same time, one cannot overlook the limitations of PDX models and should take into consideration of their shortcomings when design and interpret studies. Collectively, these new developments emphasize the importance of employing PDX models in key areas of oncology drug discovery and development.
There are no conﬂicts of interest.
1. DiMasi JA, Reichert JM, Feldman L, Malins A. Clinical approval success rates for investigational cancer drugs. Clin Pharmacol Ther 2013;94:329-35.DOIPubMed
2. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3:711-5.
3. Rosfjord E, Lucas J, Li G, Gerber HP. Advances in patient-derived tumor xenografts: From target identification to predicting clinical response rates in oncology. Biochem Pharmacol 2014;91:135-43.DOIPubMed
4. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell 2011;144:646-74.DOIPubMed
5. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57-70.DOI
6. Hoelder S, Clarke PA, Workman P. Discovery of small molecule cancer drugs: Successes, challenges and opportunities. Mol Oncol 2012;6:155-76.DOIPubMedPMC
7. Gibbs JB. Mechanism-based target identification and drug discovery in cancer research. Science 2000;287:1969-73.DOIPubMed
8. Carter P, Smith L, Ryan M. Identification and validation of cell surface antigens for antibody targeting in oncology. Endocr Relat Cancer 2004;11:659-87.DOIPubMed
9. Wang IM, Stone DJ, Nickle D, Loboda A, Puig O, Roberts C. Systems biology approach for new target and biomarker identification. Curr Top Microbiol Immunol 2013;363:169-99.
10. Rius M, Lyko F. Epigenetic cancer therapy: Rationales, targets and drugs. Oncogene 2012;31:4257-65.DOIPubMed
11. Jerby L, Ruppin E. Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 2012;18:5572-84.DOIPubMed
12. Boven E, Winograd B, Berger DP, Dumont MP, Braakhuis BJ, Fodstad O, Langdon S, Fiebig HH. Phase II preclinical drug screening in human tumor xenografts: A first European multicenter collaborative study. Cancer Res 1992;52:5940-7.
13. Voskoglou-Nomikos T, Pater JL, Seymour L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin Cancer Res 2003;9:4227-39.
14. Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM, Arcaroli JJ, Messersmith WA, Eckhardt SG. Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 2012;9:338-50.DOIPubMedPMC
15. Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, Davies H, Teague J, Butler A, Stevens C, Edkins S, O'Meara S, Vastrik I, Schmidt EE, Avis T, Barthorpe S, Bhamra G, Buck G, Choudhury B, Clements J, Cole J, Dicks E, Forbes S, Gray K, Halliday K, Harrison R, Hills K, Hinton J, Jenkinson A, Jones D, Menzies A, Mironenko T, Perry J, Raine K, Richardson D, Shepherd R, Small A, Tofts C, Varian J, Webb T, West S, Widaa S, Yates A, Cahill DP, Louis DN, Goldstraw P, Nicholson AG, Brasseur F, Looijenga L, Weber BL, Chiew YE, DeFazio A, Greaves MF, Green AR, Campbell P, Birney E, Easton DF, Chenevix-Trench G, Tan MH, Khoo SK, Teh BT, Yuen ST, Leung SY, Wooster R, Futreal PA, Stratton MR. Patterns of somatic mutation in human cancer genomes. Nature 2007;446:153-8.DOIPubMedPMC
16. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature 2009;458:719-24.DOIPubMedPMC
17. Chin L, Andersen JN, Futreal PA. Cancer genomics: From discovery science to personalized medicine. Nat Med 2011;17:297-303.DOIPubMed
18. Berman DM, Bosenberg MW, Orwant RL, Thurberg BL, Draetta GF, Fletcher CD, Loda M. Investigative pathology: Leading the post-genomic revolution. Lab Invest 2012;92:4-8.DOIPubMed
19. Rubio-Viqueira B, Jimeno A, Cusatis G, Zhang X, Iacobuzio-Donahue C, Karikari C, Shi C, Danenberg K, Danenberg PV, Kuramochi H, Tanaka K, Singh S, Salimi-Moosavi H, Bouraoud N, Amador ML, Altiok S, Kulesza P, Yeo C, Messersmith W, Eshleman J, Hruban RH, Maitra A, Hidalgo M. An in vivo platform for translational drug development in pancreatic cancer. Clin Cancer Res 2006;12:4652-61.
20. Julien S, Merino-Trigo A, Lacroix L, Pocard M, Goéré D, Mariani P, Landron S, Bigot L, Nemati F, Dartigues P, Weiswald LB, Lantuas D, Morgand L, Pham E, Gonin P, Dangles-Marie V, Job B, Dessen P, Bruno A, Pierre A, De The H, Soliman H, Nunes M, Lardier G, Calvet L, Demers B, Prevost G, Vrignaud P, Roman-Roman S, Duchamp O, Berthet C. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer. Clin Cancer Res 2012;18:5314-28.DOIPubMed
21. Bertotti A, Migliardi G, Galimi F, Sassi F, Torti D, Isella C, Cora D, Di Nicolantonio F, Buscarino M, Petti C, Ribero D, Russolillo N, Muratore A, Massucco P, Pisacane A, Molinaro L, Valtorta E, Sartore-Bianchi A, Risio M, Capussotti L, Gambacorta M, Siena S, Medico E, Sapino A, Marsoni S, Comoglio PM, Bardelli A, Trusolino L. A molecularly annotated platform of patient-derived xenografts ("xenopatients") identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov 2011;1:508-23.DOIPubMed
22. Petrillo LA, Wolf DM, Kapoun AM, Wang NJ, Barczak A, Xiao Y, Korkaya H, Baehner F, Lewicki J, Wicha M, Park JW, Spellman PT, Gray JW, Van't Veer L, Esserman LJ. Xenografts faithfully recapitulate breast cancer-specific gene expression patterns of parent primary breast tumors. Breast Cancer Res Treat 2012;135:913-22.DOIPubMedPMC
23. Reyal F, Guyader C, Decraene C, Lucchesi C, Auger N, Assayag F, De Plater L, Gentien D, Poupon MF, Cottu P, de Cremoux P, Gestraud P, Vincent-Salomon A, Fontaine JJ, Roman-Roman S, Delattre O, Decaudin D, Marangoni E. Molecular profiling of patient-derived breast cancer xenografts. Breast Cancer Res 2012;14:R11.
24. Siolas D, Hannon GJ. Patient-derived tumor xenografts: Transforming clinical samples into mouse models. Cancer Res 2013;73:5315-9.DOIPubMedPMC
25. Mardis ER. Genome sequencing and cancer. Curr Opin Genet Dev 2012;22:245-50.DOIPubMedPMC
26. Baron JA. Screening for cancer with molecular markers: Progress comes with potential problems. Nat Rev Cancer 2012;12:368-71.DOIPubMedPMC
27. Sleijfer S, Bogaerts J, Siu LL. Designing transformative clinical trials in the cancer genome era. J Clin Oncol 2013;31:1834-41.DOIPubMed
28. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.DOIPubMed
29. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark EJ, Lander ES, Wong W, Johnson BE, Golub TR, Sugarbaker DJ, Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001;98:13790-5.DOIPubMedPMC
30. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature 2000;406:747-52.DOIPubMed
31. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009;27:1160-7.DOIPubMedPMC
32. van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999-2009.DOIPubMed
33. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 2011;121:2750-67.DOIPubMedPMC
34. Zhou JX, Yang H, Deng Q, Gu X, He P, Lin Y, Zhao M, Jiang J, Chen H, Lin Y, Yin W, Mo L, He J. Oncogenic driver mutations in patients with non-small-cell lung cancer at various clinical stages. Ann Oncol 2013;24:1319-25.DOIPubMed
35. Chen Z, Feng J, Saldivar JS, Gu D, Bockholt A, Sommer SS. EGFR somatic doublets in lung cancer are frequent and generally arise from a pair of driver mutations uncommonly seen as singlet mutations: One-third of doublets occur at five pairs of amino acids. Oncogene 2008;27:4336-43.DOIPubMed
36. Peifer M, Fernández-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T, Menon R, Koker M, Dahmen I, Muller C, Di Cerbo V, Schildhaus HU, Altmuller J, Baessmann I, Becker C, de Wilde B, Vandesompele J, Bohm D, Ansen S, Gabler F, Wilkening I, Heynck S, Heuckmann JM, Lu X, Carter SL, Cibulskis K, Banerji S, Getz G, Park KS, Rauh D, Grutter C, Fischer M, Pasqualucci L, Wright G, Wainer Z, Russell P, Petersen I, Chen Y, Stoelben E, Ludwig C, Schnabel P, Hoffmann H, Muley T, Brockmann M, Engel-Riedel W, Muscarella LA, Fazio VM, Groen H, Timens W, Sietsma H, Thunnissen E, Smit E, Heideman DA, Snijders PJ, Cappuzzo F, Ligorio C, Damiani S, Field J, Solberg S, Brustugun OT, Lund-Iversen M, Sanger J, Clement JH, Soltermann A, Moch H, Weder W, Solomon B, Soria JC, Validire P, Besse B, Brambilla E, Brambilla C, Lantuejoul S, Lorimier P, Schneider PM, Hallek M, Pao W, Meyerson M, Sage J, Shendure J, Schneider R, Buttner R, Wolf J, Nurnberg P, Perner S, Heukamp LC, Brindle PK, Haas S, Thomas RK. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 2012;44:1104-10.DOIPubMedPMC
37. Loukopoulos P, Kanetaka K, Takamura M, Shibata T, Sakamoto M, Hirohashi S. Orthotopic transplantation models of pancreatic adenocarcinoma derived from cell lines and primary tumors and displaying varying metastatic activity. Pancreas 2004;29:193-203.DOIPubMed
38. DeRose YS, Wang G, Lin YC, Bernard PS, Buys SS, Ebbert MT, Factor R, Matsen C, Milash BA, Nelson E, Neumayer L, Randall RL, Stijleman IJ, Welm BE, Welm AL. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med 2011;17:1514-20.DOIPubMedPMC
39. Zhao X, Liu Z, Yu L, Zhang Y, Baxter P, Voicu H, Gurusiddappa S, Luan J, Su JM, Leung HC, Li XN. Global gene expression profiling confirms the molecular fidelity of primary tumor-based orthotopic xenograft mouse models of medulloblastoma. Neuro Oncol 2012;14:574-83.DOIPubMedPMC
40. McEvoy J, Ulyanov A, Brennan R, Wu G, Pounds S, Zhang J, Dyer MA. Analysis of MDM2 and MDM4 single nucleotide polymorphisms, mRNA splicing and protein expression in retinoblastoma. PLoS One 2012;7:e42739.
41. Morton CL, Houghton PJ. Establishment of human tumor xenografts in immunodeficient mice. Nat Protoc 2007;2:247-50.DOIPubMed
42. Goda K, Bacsó Z, Szabó G. Multidrug resistance through the spectacle of P-glycoprotein. Curr Cancer Drug Targets 2009;9:281-97.DOIPubMed
43. Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature 2001;414:105-11.DOIPubMed
44. Diehn M, Cho RW, Lobo NA, Kalisky T, Dorie MJ, Kulp AN, Qian D, Lam JS, Ailles LE, Wong M, Joshua B, Kaplan MJ, Wapnir I, Dirbas FM, Somlo G, Garberoglio C, Paz B, Shen J, Lau SK, Quake SR, Brown JM, Weissman IL, Clarke MF. Association of reactive oxygen species levels and radioresistance in cancer stem cells. Nature 2009;458:780-3.DOIPubMedPMC
45. Zhou J, Zhang Y. Cancer stem cells: Models, mechanisms and implications for improved treatment. Cell Cycle 2008;7:1360-70.DOIPubMed
46. Morrison R, Schleicher SM, Sun Y, Niermann KJ, Kim S, Spratt DE, Chung CH, Lu B. Targeting the mechanisms of resistance to chemotherapy and radiotherapy with the cancer stem cell hypothesis. J Oncol 2011;2011:941876.DOIPubMedPMC
47. Hammerman PS, Jänne PA, Johnson BE. Resistance to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer. Clin Cancer Res 2009;15:7502-509.DOIPubMed
48. Lee JC, Jang SH, Lee KY, Kim YC. Treatment of non-small cell lung carcinoma after failure of epidermal growth factor receptor tyrosine kinase inhibitor. Cancer Res Treat 2013;45:79-85.DOIPubMedPMC
49. Ogino A, Kitao H, Hirano S, Uchida A, Ishiai M, Kozuki T, Takigawa N, Takata M, Kiura K, Tanimoto M. Emergence of epidermal growth factor receptor T790M mutation during chronic exposure to gefitinib in a non small cell lung cancer cell line. Cancer Res 2007;67:7807-14.DOIPubMed
50. Sadiq AA, Salgia R. MET as a possible target for non-small-cell lung cancer. J Clin Oncol 2013;31:1089-96.DOIPubMedPMC
51. Zhang Z, Lee JC, Lin L, Olivas V, Au V, LaFramboise T, Abdel-Rahman M, Wang X, Levine AD, Rho JK, Choi YJ, Choi CM, Kim SW, Jang SJ, Park YS, Kim WS, Lee DH, Lee JS, Miller VA, Arcila M, Ladanyi M, Moonsamy P, Sawyers C, Boggon TJ, Ma PC, Costa C, Taron M, Rosell R, Halmos B, Bivona TG. Activation of the AXL kinase causes resistance to EGFR-targeted therapy in lung cancer. Nat Genet 2012;44:852-60.
52. Nurwidya F, Takahashi F, Murakami A, Takahashi K. Epithelial mesenchymal transition in drug resistance and metastasis of lung cancer. Cancer Res Treat 2012;44:151-6.DOIPubMedPMC
53. Sequist LV, Waltman BA, Dias-Santagata D, Digumarthy S, Turke AB, Fidias P, Bergethon K, Shaw AT, Gettinger S, Cosper AK, Akhavanfard S, Heist RS, Temel J, Christensen JG, Wain JC, Lynch TJ, Vernovsky K, Mark EJ, Lanuti M, Iafrate AJ, Mino-Kenudson M, Engelman JA. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci Transl Med 2011;3:75ra26.
54. Meads MB, Gatenby RA, Dalton WS. Environment-mediated drug resistance: A major contributor to minimal residual disease. Nat Rev Cancer 2009;9:665-74.DOIPubMed
55. Huang D, Ding Y, Zhou M, Rini BI, Petillo D, Qian CN, Kahnoski R, Futreal PA, Furge KA, Teh BT. Interleukin-8 mediates resistance to antiangiogenic agent sunitinib in renal cell carcinoma. Cancer Res 2010;70:1063-71.DOIPubMedPMC
56. Shojaei F, Lee JH, Simmons BH, Wong A, Esparza CO, Plumlee PA, Feng J, Stewart AE, Hu-Lowe DD, Christensen JG. HGF/c-Met acts as an alternative angiogenic pathway in sunitinib-resistant tumors. Cancer Res 2010;70:10090-100.DOIPubMed
57. Shaked Y, Henke E, Roodhart JM, Mancuso P, Langenberg MH, Colleoni M, Daenen LG, Man S, Xu P, Emmenegger U, Tang T, Zhu Z, Witte L, Strieter RM, Bertolini F, Voest EE, Benezra R, Kerbel RS. Rapid chemotherapy-induced acute endothelial progenitor cell mobilization: Implications for antiangiogenic drugs as chemosensitizing agents. Cancer Cell 2008;14:263-73.DOIPubMedPMC
58. Bergers G, Hanahan D. Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 2008;8:592-603.DOIPubMedPMC
59. Olive KP, Jacobetz MA, Davidson CJ, Gopinathan A, McIntyre D, Honess D, Madhu B, Goldgraben MA, Caldwell ME, Allard D, Frese KK, Denicola G, Feig C, Combs C, Winter SP, Ireland-Zecchini H, Reichelt S, Howat WJ, Chang A, Dhara M, Wang L, Ruckert F, Grutzmann R, Pilarsky C, Izeradjene K, Hingorani SR, Huang P, Davies SE, Plunkett W, Egorin M, Hruban RH, Whitebread N, McGovern K, Adams J, Iacobuzio-Donahue C, Griffiths J, Tuveson DA. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 2009;324:1457-61.DOIPubMedPMC
60. Krumbach R, Schüler J, Hofmann M, Giesemann T, Fiebig HH, Beckers T. Primary resistance to cetuximab in a panel of patient-derived tumour xenograft models: Activation of MET as one mechanism for drug resistance. Eur J Cancer 2011;47:1231-43.DOIPubMed
61. Diaz LA Jr, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, Allen B, Bozic I, Reiter JG, Nowak MA, Kinzler KW, Oliner KS, Vogelstein B. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 2012;486:537-40.DOIPubMedPMC
62. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G, Bashashati A, Prentice LM, Khattra J, Burleigh A, Yap D, Bernard V, McPherson A, Shumansky K, Crisan A, Giuliany R, Heravi-Moussavi A, Rosner J, Lai D, Birol I, Varhol R, Tam A, Dhalla N, Zeng T, Ma K, Chan SK, Griffith M, Moradian A, Cheng SW, Morin GB, Watson P, Gelmon K, Chia S, Chin SF, Curtis C, Rueda OM, Pharoah PD, Damaraju S, Mackey J, Hoon K, Harkins T, Tadigotla V, Sigaroudinia M, Gascard P, Tlsty T, Costello JF, Meyer IM, Eaves CJ, Wasserman WW, Jones S, Huntsman D, Hirst M, Caldas C, Marra MA, Aparicio S. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012;486:395-9.DOI
63. Kim MP, Truty MJ, Choi W, Kang Y, Chopin-Lally X, Gallick GE, Wang H, McConkey DJ, Hwang R, Logsdon C, Abbruzzesse J, Fleming JB. Molecular profiling of direct xenograft tumors established from human pancreatic adenocarcinoma after neoadjuvant therapy. Ann Surg Oncol 2012;19 Suppl 3:S395-403.
64. Dong X, Guan J, English JC, Flint J, Yee J, Evans K, Murray N, Macaulay C, Ng RT, Gout PW, Lam WL, Laskin J, Ling V, Lam S, Wang Y. Patient-derived first generation xenografts of non-small cell lung cancers: Promising tools for predicting drug responses for personalized chemotherapy. Clin Cancer Res 2010;16:1442-51.DOIPubMed
65. Kortmann U, McAlpine JN, Xue H, Guan J, Ha G, Tully S, Shafait S, Lau A, Cranston AN, O'Connor MJ, Huntsman DG, Wang Y, Gilks CB. Tumor growth inhibition by olaparib in BRCA2 germline-mutated patient-derived ovarian cancer tissue xenografts. Clin Cancer Res 2011;17:783-91.DOIPubMed
66. Scott CL, Becker MA, Haluska P, Samimi G. Patient-derived xenograft models to improve targeted therapy in epithelial ovarian cancer treatment. Front Oncol 2013;3:295.DOIPubMedPMC
67. Gelmon KA, Tischkowitz M, Mackay H, Swenerton K, Robidoux A, Tonkin K, Hirte H, Huntsman D, Clemons M, Gilks B, Yerushalmi R, Macpherson E, Carmichael J, Oza A. Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: A phase 2, multicentre, open-label, non-randomised study. Lancet Oncol 2011;12:852-61.DOI
68. Fiebig HH, Schuchhardt C, Henss H, Fiedler L, Löhr GW. Comparison of tumor response in nude mice and in the patients. Behring Inst Mitt 1984;74:343-52.
69. Fiebig HH, Dengler W, Hendriks HR. No evidence of tumor growth stimulation in human tumors in vitro following treatment with recombinant human growth hormone. Anticancer Drugs 2000;11:659-64.DOIPubMed
70. Mattern J, Bak M, Hahn EW, Volm M. Human tumor xenografts as model for drug testing. Cancer Metastasis Rev 1988;7:263-84.DOIPubMed
71. Némati F, Sastre-Garau X, Laurent C, Couturier J, Mariani P, Desjardins L, Piperno-Neumann S, Lantz O, Asselain B, Plancher C, Robert D, Peguillet I, Donnadieu MH, Dahmani A, Bessard MA, Gentien D, Reyes C, Saule S, Barillot E, Roman-Roman S, Decaudin D. Establishment and characterization of a panel of human uveal melanoma xenografts derived from primary and/or metastatic tumors. Clin Cancer Res 2010;16:2352-62.
72. Peterson JK, Houghton PJ. Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer 2004;40:837-44.
73. Leggas M, Stewart CF, Woo MH, Fouladi M, Cheshire PJ, Peterson JK, Friedman HS, Billups C, Houghton PJ. Relation between Irofulven (MGI-114) systemic exposure and tumor response in human solid tumor xenografts. Clin Cancer Res 2002;8:3000-7.
74. Hammer S, Sommer A, Fichtner I, Becker M, Rolff J, Merk J, Klar U, Hoffmann J. Comparative profiling of the novel epothilone, sagopilone, in xenografts derived from primary non-small cell lung cancer. Clin Cancer Res 2010;16:1452-65.DOIPubMed
75. Wong H, Choo EF, Alicke B, Ding X, La H, McNamara E, Theil FP, Tibbitts J, Friedman LS, Hop CE, Gould SE. Antitumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response. Clin Cancer Res 2012;18:3846-55.DOIPubMed
76. Malaney P, Nicosia SV, Davé V. One mouse, one patient paradigm: New avatars of personalized cancer therapy. Cancer Lett 2014;344:1-12.DOIPubMedPMC
77. Nardella C, Lunardi A, Patnaik A, Cantley LC, Pandolfi PP. The APL paradigm and the "co-clinical trial" project. Cancer Discov 2011;1:108-16.DOIPubMedPMC
78. Stebbing J, Paz K, Schwartz GK, Wexler LH, Maki R, Pollock RE, Morris R, Cohen R, Shankar A, Blackman G, Harding V, Vasquez D, Krell J, Ciznadija D, Katz A, Sidransky D. Patient-derived xenografts for individualized care in advanced sarcoma. Cancer 2014;120:2006-15.DOIPubMedPMC
79. Morelli MP, Calvo E, Ordo-ez E, Wick MJ, Viqueira BR, Lopez-Casas PP, Bruckheimer E, Calles-Blanco A, Sidransky D, Hidalgo M. Prioritizing phase I treatment options through preclinical testing on personalized tumorgraft. J Clin Oncol 2012;30:e45-8.
80. Weroha SJ, Becker MA, Enderica-Gonzalez S, Harrington SC, Oberg AL, Maurer MJ, Perkins SE, AlHilli M, Butler KA, McKinstry S, Fink S, Jenkins RB, Hou X, Kalli KR, Goodman KM, Sarkaria JN, Karlan BY, Kumar A, Kaufmann SH, Hartmann LC, Haluska P. Tumorgrafts as in vivo surrogates for women with ovarian cancer. Clin Cancer Res 2014;20:1288-97.DOIPubMedPMC
81. Hidalgo M, Bruckheimer E, Rajeshkumar NV, Garrido-Laguna I, De Oliveira E, Rubio-Viqueira B, Strawn S, Wick MJ, Martell J, Sidransky D. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther 2011;10:1311-6.DOIPubMedPMC
82. Bankert RB, Egilmez NK, Hess SD. Human-SCID mouse chimeric models for the evaluation of anti-cancer therapies. Trends Immunol 2001;22:386-93.DOI
83. Hylander BL, Punt N, Tang H, Hillman J, Vaughan M, Bshara W, Pitoniak R, Repasky EA. Origin of the vasculature supporting growth of primary patient tumor xenografts. J Transl Med 2013;11:110.DOIPubMedPMC
84. Klemm F, Joyce JA. Microenvironmental regulation of therapeutic response in cancer. Trends Cell Biol 2014 ; doi: 10.1016/j.tcb.2014.11.006.DOI
85. Pickup MW, Mouw JK, Weaver VM. The extracellular matrix modulates the hallmarks of cancer. EMBO Rep 2014;15:1243-53.DOIPubMedPMC
86. Bankert RB, Balu-Iyer SV, Odunsi K, Shultz LD, Kelleher RJ Jr, Barnas JL, Simpson-Abelson M, Parsons R, Yokota SJ. Humanized mouse model of ovarian cancer recapitulates patient solid tumor progression, ascites formation, and metastasis. PLoS One 2011;6:e24420.
87. Rongvaux A, Willinger T, Martinek J, Strowig T, Gearty SV, Teichmann LL, Saito Y, Marches F, Halene S, Palucka AK, Manz MG, Flavell RA. Development and function of human innate immune cells in a humanized mouse model. Nat Biotechnol 2014;32:364-72.DOIPubMedPMC
88. Ilie M, Nunes M, Blot L, Hofman V, Long-Mira E, Butori C, Selva E, Merino-Trigo A, Venissac N, Mouroux J, Vrignaud P, Hofman P. Setting up a wide panel of patient-derived tumor xenografts of non-small cell lung cancer by improving the preanalytical steps. Cancer Med 2015;4:201-11.DOIPubMedPMC
89. Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Maelandsmo GM, Roman-Roman S, Seoane J, Trusolino L, Villanueva A. Patient-derived xenograft models: An emerging platform for translational cancer research. Cancer Discov 2014;4:998-1013.
90. Xin H, Wang K, Hu G, Xie F, Ouyang K, Tang X, Wang M, Wen D, Zhu Y, Qin X. Establishment and characterization of 7 novel hepatocellular carcinoma cell lines from patient-derived tumor xenografts. PLoS One 2014;9:e85308.
Li G. Patient-derived xenograft models for oncology drug discovery. J Cancer Metastasis Treat 2015;1:8-15. http://dx.doi.org/10.4103/2394-4722.152769
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