Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models. Significance: PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field. Cancer Discov; 4(9); 998–1013. ©2014 AACR .

https://cancerdiscovery.aacrjournals.org/content/candisc/4/9/998.full.pdf

Patient-derived xenograft models: an emerging platform for translational cancer research.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response

The MYC oncogene codes for a transcription factor that is overexpressed in many human cancers. Here we show that MYC regulates the expression of two immune checkpoint proteins on the tumor cell surface: the innate immune regulator CD47 (cluster of differentiation 47) and the adaptive immune checkpoint PD-L1 (programmed death-ligand 1). Suppression of MYC in mouse tumors and human tumor cells caused a reduction in the levels of CD47 and PD-L1 messenger RNA and protein. MYC was found to bind directly to the promoters of the Cd47 and Pd-l1 genes. MYC inactivation in mouse tumors down-regulated CD47 and PD-L1 expression and enhanced the antitumor immune response. In contrast, when MYC was inactivated in tumors with enforced expression of CD47 or PD-L1, the immune response was suppressed, and tumors continued to grow. Thus, MYC appears to initiate and maintain tumorigenesis, in part, through the modulation of immune regulatory molecules.

http://science.sciencemag.org/content/sci/early/2016/03/09/science.aac9935.full.pdf

MYC regulates the antitumor immune response through CD47 and PD-L1

Colon cancer is a clinically diverse disease. This heterogeneity makes it difficult to determine which patients will benefit most from adjuvant therapy and impedes the development of new targeted agents. More insight into the biological diversity of colon cancers, especially in relation to clinical features, is therefore needed. We demonstrate, using an unsupervised classification strategy involving over 1,100 individuals with colon cancer, that three main molecularly distinct subtypes can be recognized. Two subtypes have been previously identified and are well characterized (chromosomal-instable and microsatellite-instable cancers). The third subtype is largely microsatellite stable and contains relatively more CpG island methylator phenotype-positive carcinomas but cannot be identified on the basis of characteristic mutations. We provide evidence that this subtype relates to sessile-serrated adenomas, which show highly similar gene expression profiles, including upregulation of genes involved in matrix remodeling and epithelial-mesenchymal transition. The identification of this subtype is crucial, as it has a very unfavorable prognosis and, moreover, is refractory to epidermal growth factor receptor-targeted therapy.

Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions

Tumor graft models (also known as patient-derived xenografts or PDX) are based on the transfer of primary tumors directly from the patient into an immunodeficient mouse. Because PDX mice are derived from human tumors, they offer a tool for developing anticancer therapies and personalized medicine for patients with cancer. In addition, these models can be used to study metastasis and tumor genetic evolution. This review examines the development, challenges, and broad use of these attractive preclinical models.

https://cancerres.aacrjournals.org/content/canres/73/17/5315.full.pdf

Patient-derived tumor xenografts: transforming clinical samples into mouse models.

Purpose: Patient-derived xenograft models are considered to represent the heterogeneity of human cancers and advanced preclinical models. Our consortium joins efforts to extensively develop and characterize a new collection of patient-derived colorectal cancer (CRC) models. Experimental Design: From the 85 unsupervised surgical colorectal samples collection, 54 tumors were successfully xenografted in immunodeficient mice and rats, representing 35 primary tumors, 5 peritoneal carcinoses and 14 metastases. Histologic and molecular characterization of patient tumors, first and late passages on mice includes the sequence of key genes involved in CRC (i.e., APC , KRAS , TP53 ), aCGH, and transcriptomic analysis. Results: This comprehensive characterization shows that our collection recapitulates the clinical situation about the histopathology and molecular diversity of CRC. Moreover, patient tumors and corresponding models are clustering together allowing comparison studies between clinical and preclinical data. Hence, we conducted pharmacologic monotherapy studies with standard of care for CRC (5-fluorouracil, oxaliplatin, irinotecan, and cetuximab). Through this extensive in vivo analysis, we have shown the loss of human stroma cells after engraftment, observed a metastatic phenotype in some models, and finally compared the molecular profile with the drug sensitivity of each tumor model. Through an experimental cetuximab phase II trial, we confirmed the key role of KRAS mutation in cetuximab resistance. Conclusions: This new collection could bring benefit to evaluate novel targeted therapeutic strategies and to better understand the basis for sensitivity or resistance of tumors from individual patients. Clin Cancer Res; 18(19); 5314–28. ©2012 AACR .

https://clincancerres.aacrjournals.org/content/clincanres/18/19/5314.full.pdf

Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer.

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

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Emmanuel Pham

Papers

Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?