Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges

Significant advances have been made in developing novel therapeutics for cancer treatment, and targeted therapies have revolutionized the treatment of some cancers. Despite the promise, only about five percent of new cancer drugs are approved, and most fail due to lack of efficacy. The indication is that current preclinical methods are limited in predicting successful outcomes. Such failure exacts enormous cost, both financial and in the quality of human life. This Primer explores the current status, promise, and challenges of preclinical evaluation in advanced mouse cancer models and briefly addresses emerging models for early-stage preclinical development. Significant advances have been made in developing novel therapeutics for cancer treatment, and targeted therapies have revolutionized the treatment of some cancers. Despite the promise, only about five percent of new cancer drugs are approved, and most fail due to lack of efficacy. The indication is that current preclinical methods are limited in predicting successful outcomes. Such failure exacts enormous cost, both financial and in the quality of human life. This Primer explores the current status, promise, and challenges of preclinical evaluation in advanced mouse cancer models and briefly addresses emerging models for early-stage preclinical development. Ever-increasing knowledge of cancer biology has yielded countless possibilities for diagnostic and therapeutic strategies (Figure 1), while at the same time revealing enormous disease complexities that challenge clinical success. Such challenges include tumor microenvironment complexities, intra- and inter-tumor molecular and biological heterogeneity, systemic and tumoral immune and metabolic response heterogeneity, and the ability of drug-resistant stem-like cancer-initiating cells to repopulate treated cancers (Pattabiraman and Weinberg, 2014Pattabiraman D.R. Weinberg R.A. Tackling the cancer stem cells - what challenges do they pose?.Nat. Rev. Drug Discov. 2014; 13: 497-512Crossref PubMed Scopus (269) Google Scholar). Too often, experimental targeted therapies designed to assimilate known disease complexity have proven ineffective, only to highlight the limitations in our understanding. In contrast to most experimental targeted therapies, encouraging advancements have been made using a number of cell-based and targeted immunotherapies, which have produced sustained responses in patients (Page et al., 2014Page D.B. Postow M.A. Callahan M.K. Allison J.P. Wolchok J.D. Immune modulation in cancer with antibodies.Annu. Rev. Med. 2014; 65: 185-202Crossref PubMed Scopus (173) Google Scholar). However, only a fraction of patients respond to these therapies. Over the last decade, cancer classification has shifted from relying solely on histiopathologic properties to including key molecular attributes that can predict therapeutic outcomes. That certain molecular aberrations are targets for effective therapy first led to clinical practice in 1995 after a leukemia (APL) bearing the PML-RARα translocation was shown to be sensitive to retinoic acid (tretinoin) (Quignon et al., 1997Quignon F. Chen Z. de Thé H. Retinoic acid and arsenic: towards oncogene-targeted treatments of acute promyelocytic leukaemia.Biochim. Biophys. Acta. 1997; 1333: M53-M61PubMed Google Scholar), which targets the RARα component to effect leukemic cell differentiation. Soon thereafter, Herceptin (a Her2 inhibitor) was approved for treating Her2+ breast cancer (1998), and Gleevec (a BCR-ABL inhibitor) was approved for CML treatment (2001). These highly effective drugs rapidly became the standard of care. Although these successes establish the promise of targeted therapies, most attempts to attain similar results targeting known molecular drivers have failed, and the reasons are often elusive because of human research limitations. Some general principles have been recognized that emphasize the need for preclinical platforms approximating human cancers. For example, in each of the noted successes, single potent cancer drivers present in a significant fraction of patient malignancies were targeted; however, when a minor fraction of patients are responsive, all-comer clinical trial data may mask the responders. This was first demonstrated in non-small-cell lung cancer (NSCLC) patient trials that initially failed to show significant responsiveness to EGFR-targeted tyrosine kinase inhibitors; however, the ∼10% of patients whose tumors actually harbored activating EGFR mutations were uniquely sensitive (Lynch et al., 2004Lynch T.J. Bell D.W. Sordella R. Gurubhagavatula S. Okimoto R.A. Brannigan B.W. Harris P.L. Haserlat S.M. Supko J.G. Haluska F.G. et al.Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib.N. Engl. J. Med. 2004; 350: 2129-2139Crossref PubMed Scopus (7983) Google Scholar, Paez et al., 2004Paez J.G. Jänne P.A. Lee J.C. Tracy S. Greulich H. Gabriel S. Herman P. Kaye F.J. Lindeman N. Boggon T.J. et al.EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy.Science. 2004; 304: 1497-1500Crossref PubMed Scopus (6758) Google Scholar). Now, screening of lung cancers for such mutations prior to therapy is routine practice. Lung cancer is the most prevalent US cancer; if limited to clinical trials, accurate identification of therapies effective in a fraction of less-common cancer types may not be possible. Nonetheless, when a specific target was known, stratification of patients has identified additional effective therapies, such as inhibitors for BRAF mutant melanomas and ALK translocation-positive NSCLCs (Pagliarini et al., 2015Pagliarini R. Shao W. Sellers W.R. Oncogene addiction: pathways of therapeutic response, resistance, and road maps toward a cure.EMBO Rep. 2015; 16: 280-296Crossref PubMed Scopus (56) Google Scholar). Unfortunately, patients treated with single targeted therapies inevitably relapse with cancers that are resistant to the original drug. Another challenge in targeting single drivers is the feedback response upon molecular network disruption that prevents efficacy or causes increased severity. Understanding such molecular responses can aid in the discovery of more effective combination therapies. In addition, unbiased molecular queries are showing promise in identifying signatures that correspond to prognosis and/or therapeutic outcomes. For example, in some cases, unique transcriptome signatures stratify cancers into distinct therapeutic and/or prognostic categories and thus improve patient management (e.g., Garraway, 2013Garraway L.A. Genomics-driven oncology: framework for an emerging paradigm.J. Clin. Oncol. 2013; 31: 1806-1814Crossref PubMed Scopus (144) Google Scholar). Thus far, this approach has been used primarily for determining which patients require aggressive chemotherapy treatment, hence reducing the frequency of over-treatment. Oncotype DX and FDA-approved MammaPrint tests, both based on distinguishing transcriptome signatures, are now utilized in the clinic to identify the low risk breast cancer patients to be excluded from aggressive treatment. Yet, accuracy is not optimal, and numerous challenges currently prevent broad implementation of molecular signature diagnostics (van't Veer and Bernards, 2008van't Veer L.J. Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns.Nature. 2008; 452: 564-570Crossref PubMed Scopus (323) Google Scholar). Additionally, the hope is that molecular signatures can be identified via unbiased compound or molecular screens that will dictate specific effective treatments even when the targets are unknown. Thus, although clearly impactful, the use of cancer molecular constitution to guide clinical practice is in its infancy, and research to identify parameters that hone specificity and improve accuracy is ongoing. If confined to human research, achieving maximum effectiveness is likely impossible due to low frequencies of each molecular subtype within most cancers and limitations associated with clinical trials. More challenging is understanding the impact of complex and varied inherited genetic constitution on clinical outcomes with subsequent conversion to clinical practice (Hood and Friend, 2011Hood L. Friend S.H. Predictive, personalized, preventive, participatory (P4) cancer medicine.Nat. Rev. Clin. Oncol. 2011; 8: 184-187Crossref PubMed Scopus (247) Google Scholar). In this regard, the sophistication of complex trait evaluation in mice using the collaborative and diversity crosses may offer a path to discovery (Churchill et al., 2004Churchill G.A. Airey D.C. Allayee H. Angel J.M. Attie A.D. Beatty J. Beavis W.D. Belknap J.K. Bennett B. Berrettini W. et al.Complex Trait ConsortiumThe Collaborative Cross, a community resource for the genetic analysis of complex traits.Nat. Genet. 2004; 36: 1133-1137Crossref PubMed Scopus (549) Google Scholar, Svenson et al., 2012Svenson K.L. Gatti D.M. Valdar W. Welsh C.E. Cheng R. Chesler E.J. Palmer A.A. McMillan L. Churchill G.A. High-resolution genetic mapping using the Mouse Diversity outbred population.Genetics. 2012; 190: 437-447Crossref PubMed Scopus (237) Google Scholar). The above summary provides only a cross-section of the therapeutic and diagnostic possibilities currently under investigation, and the reader is referred to current review articles for more comprehensive information (Chin et al., 2011Chin L. Andersen J.N. Futreal P.A. Cancer genomics: from discovery science to personalized medicine.Nat. Med. 2011; 17: 297-303Crossref PubMed Scopus (275) Google Scholar, Hood and Friend, 2011Hood L. Friend S.H. Predictive, personalized, preventive, participatory (P4) cancer medicine.Nat. Rev. Clin. Oncol. 2011; 8: 184-187Crossref PubMed Scopus (247) Google Scholar, Yap et al., 2013Yap T.A. Omlin A. de Bono J.S. Development of therapeutic combinations targeting major cancer signaling pathways.J. Clin. Oncol. 2013; 31: 1592-1605Crossref PubMed Scopus (83) Google Scholar). Ultimately, the current limitation to improving cancer patient care within reasonable timeframes may not be the availability of potentially efficacious therapies; rather, a major blockade is the lack of a fully developed and integrated set of reliable preclinical technologies that can navigate complex variables in therapeutic responses and diagnostic accuracy. To optimally develop efficacious therapies, preclinical research must utilize a diversity of models that collectively incorporate the biology and genetics dictating therapeutic outcomes for specific cancers, and yet achieve sufficient throughput. Here we summarize the value and constraints of mouse cancer models, highlight recent progress indicating promise, summarize non-mammalian and ex vivo preclinical models, and explore the needs for, and challenges to, developing robust multi-faceted preclinical platforms for routine use. Murine cancer models designed to capture the complexities of human cancers currently offer the most advanced preclinical opportunity for navigating diverse mechanisms that provide rationale for therapeutic development (Van Dyke and Jacks, 2002Van Dyke T. Jacks T. Cancer modeling in the modern era: progress and challenges.Cell. 2002; 108: 135-144Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar). One approach is to probe pathobiology mechanisms to design effective treatments by perturbation with molecularly targeted therapies (Olive and Tuveson, 2006Olive K.P. Tuveson D.A. The use of targeted mouse models for preclinical testing of novel cancer therapeutics.Clin. Cancer Res. 2006; 12: 5277-5287Crossref PubMed Scopus (145) Google Scholar). Additionally, the models are being used/developed as preclinical efficacy determination platforms to guide clinical trial designs (Singh et al., 2012Singh M. Murriel C.L. Johnson L. Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes.Cancer Res. 2012; 72: 2695-2700Crossref PubMed Scopus (56) Google Scholar). However, the application of complex cancer models to clinical research directives is an emerging science, currently executed in individual settings and with limited resources. Significant research, ideally in a team-directed, multi-institutional effort, is required to hone existing technologies into integrated preclinical workflows to optimally accelerate positive clinical outcomes. A variety of approaches to mouse cancer modeling are now available (Figure 2), and each has strengths and weaknesses (Table 1). Here, we address the limitations of standard Cell line-Derived Xenograft (CDX) models, describe genetically and biologically engineered mouse cancer models [Genetically Engineered Mouse (GEM), GEM-Derived Allograft (GDA), Patient-Derived Xenograft (PDX) models], review values and constraints, and highlight recent progress. Thus far, results indicate promise in understanding cancer pathobiology and in the enhancement of clinical efficacy prediction, but also underscore the need for further development to achieve consistent reliability.Table 1Comparison of Clinical and Preclinical Model PropertiesImmune Status of the HostMicro-environment ContextHuman RelevanceTissue AvailabilityDisease ComplexityExperimental RobustnessInitiation/ ProgressionFeasibility in Pathway EngineeringCostClinical TrialsFunctionalNaturalStandardHighly LimitedHighLowIntactIrrelevantVery HighCancer Cell Line-Derived Xenografts (CDX)DeficientXenogeneicSituational∗CDX models have been shown to be limited as predictors of clinical outcome. However, they continue to be valuable for evaluating resistance mechanisms, for identifying non-targeted cytotoxic agents, for assessing drug toxicity, and as a platform to triage potentially effective targeted therapies. In general, the longer cells are in culture, the further they drift from normal tumor evolution, and the less relevant they become.(passage number dependent)ExpandableLowHighBypassedYesLowPatient cancer-Derived Xenografts (PDX)DeficientXenogeneicHighExpandable/LimitedModerateHighBypassedNoHighGenetically Engineered Mice (GEM)FunctionalNaturalHigh/ Variable (model dependent)LimitedHighModerate/VariableIntactYesHighGEM-Derived Allografts (GDA)FunctionalAllogeneicLow/ VariableExpandableModerateHighBypassedYesModerate∗ CDX models have been shown to be limited as predictors of clinical outcome. However, they continue to be valuable for evaluating resistance mechanisms, for identifying non-targeted cytotoxic agents, for assessing drug toxicity, and as a platform to triage potentially effective targeted therapies. In general, the longer cells are in culture, the further they drift from normal tumor evolution, and the less relevant they become. Open table in a new tab Historically, preclinical mouse models have co-evolved with cancer therapy development (Figure 3). The earliest models were built through transplantation of murine tumors into immunocompetent host mice (DeVita and Chu, 2008DeVita Jr., V.T. Chu E. A history of cancer chemotherapy.Cancer Res. 2008; 68: 8643-8653Crossref PubMed Scopus (390) Google Scholar, Talmadge et al., 2007Talmadge J.E. Singh R.K. Fidler I.J. Raz A. Murine models to evaluate novel and conventional therapeutic strategies for cancer.Am. J. Pathol. 2007; 170: 793-804Abstract Full Text Full Text PDF PubMed Scopus (245) Google Scholar). These early mouse-in-mouse isograft models served as workhorses for drug screening during the 1960s and 1970s, and were successful in identifying a number of effective cytotoxic drugs such as vincristine and procarbazine (DeVita and Chu, 2008DeVita Jr., V.T. Chu E. A history of cancer chemotherapy.Cancer Res. 2008; 68: 8643-8653Crossref PubMed Scopus (390) Google Scholar). During the 1980s, researchers explored mechanisms of metastasis using selected murine and human tumor cell lines. A series of investigations by Fidler and colleagues demonstrated that metastasis is not random but site-selective (Fidler and Hart, 1982Fidler I.J. Hart I.R. Biological diversity in metastatic neoplasms: origins and implications.Science. 1982; 217: 998-1003Crossref PubMed Google Scholar), and that metastatic patterns are injection site-dependent, supporting the establishment of "orthotopic" models (Talmadge et al., 2007Talmadge J.E. Singh R.K. Fidler I.J. Raz A. Murine models to evaluate novel and conventional therapeutic strategies for cancer.Am. J. Pathol. 2007; 170: 793-804Abstract Full Text Full Text PDF PubMed Scopus (245) Google Scholar). Since then, cancer therapeutic development has relied upon the more tractable CDX transplantation models, in which tumors develop after subcutaneous injection of in vitro-established human cancer cells into immunocompromised mice (Figure 2). The cell lines have been selected over many passages for rapid 2D growth on plastic in serum-containing media. The NCI60 cell line panel (DeVita and Chu, 2008DeVita Jr., V.T. Chu E. A history of cancer chemotherapy.Cancer Res. 2008; 68: 8643-8653Crossref PubMed Scopus (390) Google Scholar, Talmadge et al., 2007Talmadge J.E. Singh R.K. Fidler I.J. Raz A. Murine models to evaluate novel and conventional therapeutic strategies for cancer.Am. J. Pathol. 2007; 170: 793-804Abstract Full Text Full Text PDF PubMed Scopus (245) Google Scholar) provided a valuable resource from which most CDXs were generated, and recent efforts have greatly expanded the repertoire (Reinhold et al., 2015Reinhold W.C. Varma S. Rajapakse V.N. Luna A. Sousa F.G. Kohn K.W. Pommier Y.G. Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer.Hum. Genet. 2015; 134: 3-11Crossref PubMed Scopus (10) Google Scholar). These models are easily established in a wide variety of laboratory settings and have been successfully used to identify an abundance of cytotoxic drugs leading to chemotherapy treatments that still dominate clinical cancer management (Figure 3). Unfortunately, CDXs have failed to predict human efficacy for most therapies targeted to cancer-driving proteins (Johnson et al., 2001Johnson J.I. Decker S. Zaharevitz D. Rubinstein L.V. Venditti J.M. Schepartz S. Kalyandrug S. Christian M. Arbuck S. Hollingshead M. Sausville E.A. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials.Br. J. Cancer. 2001; 84: 1424-1431Crossref PubMed Scopus (481) Google Scholar), as evidenced by the low FDA approval rate of 5%–7% for targeted therapeutics (Sharpless and Depinho, 2006Sharpless N.E. Depinho R.A. The mighty mouse: genetically engineered mouse models in cancer drug development.Nat. Rev. Drug Discov. 2006; 5: 741-754Crossref PubMed Scopus (356) Google Scholar). With an average time from discovery to clinical practice of 12 years, at an average estimated cost of $0.5–$2.0 billion (Adams and Brantner, 2006Adams C.P. Brantner V.V. Estimating the cost of new drug development: is it really 802 million dollars?.Health Aff. (Millwood). 2006; 25: 420-428Crossref PubMed Scopus (471) Google Scholar) and an immeasurable human price, this low yield forestalls even a goal to chronically manage, rather than cure, cancers. The observation that most cancer therapeutics fail in clinical phase II and III efficacy assessment indicates that current standard preclinical practice inadequately addresses complex challenges to successful treatment, such as host immune responses, cancer heterogeneity, and drug resistance. Consequently, the system cannot be used to optimize a multitude of variables known to influence therapeutic outcomes, such as combinatorial therapies, dosing schedules, and drug delivery methods (Al-Lazikani et al., 2012Al-Lazikani B. Banerji U. Workman P. Combinatorial drug therapy for cancer in the post-genomic era.Nat. Biotechnol. 2012; 30: 679-692Crossref PubMed Scopus (321) Google Scholar). CDXs continue to be valuable in identifying non-targeted cytotoxic agents and in primary assessment of drug toxicity (Teicher, 2006Teicher B.A. Tumor models for efficacy determination.Mol. Cancer Ther. 2006; 5: 2435-2443Crossref PubMed Scopus (122) Google Scholar), for analyzing resistance mechanisms (Garraway and Jänne, 2012Garraway L.A. Jänne P.A. Circumventing cancer drug resistance in the era of personalized medicine.Cancer Discov. 2012; 2: 214-226Crossref PubMed Scopus (188) Google Scholar) and in triaging potentially effective targeted therapies for evaluation in more representative models. Mice and humans are believed to have diverged from each other ∼87 million years ago (Bailey et al., 2013Bailey M. Christoforidou Z. Lewis M.C. The evolutionary basis for differences between the immune systems of man, mouse, pig and ruminants.Vet. Immunol. Immunopathol. 2013; 152: 13-19Crossref PubMed Scopus (10) Google Scholar), so naturally there are numerous significant similarities between the two species, and also many marked disparities, including differences in immune systems and drug metabolism. Based on the premise that many cancers have been cured in mice and not in people, many argue that mice are inappropriate for use in therapeutic development (Leaf, 2004Leaf C. Why we're losing the war on cancer (and how to win it).Fortune. 2004; 149 (84–86, 88 passim): 76-82PubMed Google Scholar). However, it is critical to understand that "cures" have been attained only in CDX models, thus dismissal of all mouse cancer models as irrelevant is unwarranted. Human cancers are enormously complex, and their evolutionary etiology generates vast diversity among and within them, thus challenging the attainment of successful treatments. However, as knowledge of cancer complexities has increased, so has the ability to design mouse models that better represent cancer patients. PDX and GEM models develop tumors with the greatest similarity to human diseases yet achieved, and the past 5 years have seen an increase in their employment in preclinical research. As with all models, each approach has its strengths and limitations (Table 1). Early studies suggest promise for improved guidance in the development of successful clinical treatments (Table 2), and yet also emphasize the need for further scrutiny and refinement. The following provides a balanced consideration of model advantages and limitations, their ramifications in obtaining optimally accurate preclinical data, and the logistical requirements for achieving efficiency, accuracy, and reproducibility.Table 2Representative Clinically Relevant Mouse TrialsTrial DesignCancer TypeModel TypeEngineered DriversDrugs/ TreatmentSignificanceRelevant PublicationsPreclinicalHematopoietic(APL)GEMPML-RARα fusionPLZF-RARα fusionRetinoic acidDemonstrated the efficacy of retinoic acid plus As2O3 in specific APL subtypes, validated in clinic(Ablain and de Thé, 2014Ablain J. de Thé H. Retinoic acid signaling in cancer: The parable of acute promyelocytic leukemia.Int. J. Cancer. 2014; 135: 2262-2272Crossref PubMed Scopus (23) Google Scholar, Pandolfi, 2001Pandolfi P.P. Oncogenes and tumor suppressors in the molecular pathogenesis of acute promyelocytic leukemia.Hum. Mol. Genet. 2001; 10: 769-775Crossref PubMed Google Scholar)PreclinicalPancreas(Neuro-endocrine)GEMRIP1-Tag2SunitinibDemonstrated the efficacy of Sunitinib plus Imatinib, validated in clinic. FDA approved for pancreatic cancer treatment in 2011.(Pietras and Hanahan, 2005Pietras K. Hanahan D. A multitargeted, metronomic, and maximum-tolerated dose "chemo-switch" regimen is antiangiogenic, producing objective responses and survival benefit in a mouse model of cancer.J. Clin. Oncol. 2005; 23: 939-952Crossref PubMed Scopus (372) Google Scholar, Raymond et al., 2011Raymond E. Dahan L. Raoul J.-L. Bang Y.-J. Borbath I. Lombard-Bohas C. Valle J. Metrakos P. Smith D. Vinik A. et al.Sunitinib malate for the treatment of pancreatic neuroendocrine tumors.N. Engl. J. Med. 2011; 364: 501-513Crossref PubMed Scopus (1191) Google Scholar)PreclinicalMedulla-blastomaGEMPtc1+/−P53−/−GDC-0449(SMO inhibitor)Demonstrated the efficacy of an Shh pathway small molecule inhibitor, validated in clinic(Romer et al., 2004Romer J.T. Kimura H. Magdaleno S. Sasai K. Fuller C. Baines H. Connelly M. Stewart C.F. Gould S. Rubin L.L. Curran T. Suppression of the Shh pathway using a small molecule inhibitor eliminates medulloblastoma in Ptc1(+/-)p53(-/-) mice.Cancer Cell. 2004; 6: 229-240Abstract Full Text Full Text PDF PubMed Scopus (394) Google Scholar, Rudin et al., 2009Rudin C.M. Hann C.L. Laterra J. Yauch R.L. Callahan C.A. Fu L. Holcomb T. Stinson J. Gould S.E. Coleman B. et al.Treatment of medulloblastoma with hedgehog pathway inhibitor GDC-0449.N. Engl. J. Med. 2009; 361: 1173-1178Crossref PubMed Scopus (682) Google Scholar)PreclinicalPancreas(Neuro-endocrine)GEMRIP1-Tag2ErlotinibRapamycinDemonstrated efficacy of combining drugs targeting EGFR and mTOR(Chiu et al., 2010Chiu C.W. Nozawa H. Hanahan D. Survival benefit with proapoptotic molecular and pathologic responses from dual targeting of mammalian target of rapamycin and epidermal growth factor receptor in a preclinical model of pancreatic neuroendocrine carcinogenesis.J. Clin. Oncol. 2010; 28: 4425-4433Crossref PubMed Scopus (62) Google Scholar)Co-clinicalPancreas(PDA)GEMLSL-KrasG12DLSL-Trp53R172HPdx-1-CreGemcitabineNab-PaclitaxelProvided mechanistic insight into clinical cooperation between Gemcitabine and Nab-Paclitaxel(Frese et al., 2012Frese K.K. Neesse A. Cook N. Bapiro T.E. Lolkema M.P. Jodrell D.I. Tuveson D.A. nab-Paclitaxel potentiates gemcitabine activity by reducing cytidine deaminase levels in a mouse model of pancreatic cancer.Cancer Discov. 2012; 2: 260-269Crossref PubMed Scopus (180) Google Scholar, Goldstein et al., 2015Goldstein D. El-Maraghi R.H. Hammel P. Heinemann V. Kunzmann V. Sastre J. Scheithauer W. Siena S. Tabernero J. Teixeira L. et al.nab-Paclitaxel plus gemcitabine for metastatic pancreatic cancer: long-term survival from a phase III trial.J. Natl. Cancer Inst. 2015; 107Crossref Scopus (0) Google Scholar)Co-clinicalPancreas (PDA)GEMLSL-KrasG12DLSL-Trp53R172HPdx-1-CreCD40 monoclonal antibody GemcitabineDemonstrated that targeting stroma was effective in treatment of metastatic PDA(Beatty et al., 2013Beatty G.L. Torigian D.A. Chiorean E.G. Saboury B. Brothers A. Alavi A. Troxel A.B. Sun W. Teitelbaum U.R. Vonderheide R.H. O'Dwyer P.J. A phase I study of an agonist CD40 monoclonal antibody (CP-870,893) in combination with gemcitabine in patients with advanced pancreatic ductal adenocarcinoma.Clin. Cancer Res. 2013; 19: 6286-6295Crossref PubMed Scopus (142) Google Scholar)Co-clinicalLung(NSCLC)GEMKRASG12Dp53fl/flLkb1fl/flSelumetinibDocetaxelValidation of improved response of adding Selumetinib to Docetaxel treatment(Chen et al., 2012Chen Z. Cheng K. Walton Z. Wang Y. Ebi H. Shimamura T. Liu Y. Tupper T. Ouyang J. Li J. et al.A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response.Nature. 2012; 483: 613-617Crossref PubMed Scopus (239) Google Scholar, Jänne et al., 2013Jänne P.A. Shaw A.T. Pereira J.R. Jeannin G. Vansteenkiste J. Barrios C. Franke F.A. Grinsted L. Zazulina V. Smith P. et al.Selumetinib plus docetaxel for KRAS-mutant advanced non-small-cell lung cancer: a randomised, multicentre, placebo-controlled, phase 2 study.Lancet Oncol. 2013; 14: 38-47Abstract Full Text Full Text PDF PubMed Scopus (374) Google Scholar)Co-clinicalLung(NSCLC)GEMEML4-ALK fusionCrizotinibDocetaxelPemetrexedGEM model predicted clinical outcome of drug combinations(Chen et al., 2014Chen Z. Akbay E. Mikse O. Tupper T. Cheng K. Wang Y. Tan X. Altabef A. Woo S.-A. Chen L. et al.Co-clinical trials demonstrate superiority of crizotinib to chemotherapy in ALK-rearranged non-small cell lung cancer and predict strategies to overcome resistance.Clin. Cancer Res. 2014; 20: 1204-1211Crossref PubMed Scopus (29) Google Scholar, Lunardi and Pandolfi, 2015Lunardi A. Pandolfi P.P. A co-clinical platform to accelerate cancer treatment optimization.Trends Mol. Med. 2015; 21: 1-5Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar)Co-clinicalVarious SarcomasPDXN/AVarious chemotherapiesPDX testing predicted clinical outcome of drug combinations(Stebbing et al., 2014Stebbing J. Paz K. Schwartz G.K. Wexler L.H. Maki R. Pollock R.E. Morris R. Cohen R. Shankar A. Blackman G. et al.Patient-derived xenografts for individualized care in advanced sarcoma.Cancer. 2014; 120: 2006-2015Crossref PubMed Scopus (50) Google Scholar)PostclinicalOvarian(SEOC)GDA;PDXRB/p53-deficientBRCA1/2-deficientOlaparibCisplatinValidation of treatment efficacy in BRCA mutant tumors in both GDA and PDX models(Kortmann et al., 2011Kortmann U. McAlpine J.N. Xue H. Guan J. Ha G. Tully S. Shafait S. Lau A. Cranston A.N. O'Connor M.J. et al.Tumor growth inhibition by olaparib in BRCA2 germline-mutated patient-derived ovarian cancer tissue xenografts.Clin. Cancer Res. 2011; 17: 783-791Crossref PubMed Scopus (48) Google Scholar, Szabova et al., 2014Szabova L. Bupp S. Kamal M. Householder D.B. Hernandez L. Schlomer J.J. Baran M.L. Yi M. Stephens R.M. Annunziata C.M. et al.Pathway-specific engineered mouse allograft models functionally recapitulate human serous epithelial ovarian cancer.PLoS ONE. 2014; 9: e95649Crossref PubMed Scopus (3) Google Scholar)PostclinicalPancreas(Neuro-endocrine)GDARIP1-Tag2Anti-VEGFR1 and anti-VEGFR2 antibodiesIdentification of mechanisms of resistance to anti-angiogenic therapies(Casanovas et al., 2005Casanovas O. Hicklin D.J. Bergers G. Hanahan D. Drug resistance by evasion of antiangiogenic targeting of VEGF signaling in late-stage pancreatic islet tumors.Cancer Cell. 2005; 8: 299-309Abstract Full Text Full Text PDF PubMed Scopus (1121) Google Scholar)BiomarkerLung(NSCLC)GEM;Carcinogen-inducedVarious ModelsN/AUsed in-depth quantitative MS-based proteomics to profile plasma pr