Patient-derived xenograft (PDX) models of a growing spectrum of cancers are rapidly supplanting long-established traditional cell lines as preferred models for conducting basic and translational pre-clinical research. In breast cancer, to complement the now curated collection of approximately 45 long-established human breast cancer cell lines, a newly formed consortium of academic laboratories, currently from Europe, Australia, and North America, herein summarizes data on over 500 stably transplantable PDX models representing all three clinical subtypes of breast cancer (ER+, HER2+, and “Triple-negative” (TNBC)). Many of these models are well-characterized with respect to genomic, transcriptomic, and proteomic features, metastatic behavior, and treatment response to a variety of standard-of-care and experimental therapeutics. These stably transplantable PDX lines are generally available for dissemination to laboratories conducting translational research, and contact information for each collection is provided. This review summarizes current experiences related to PDX generation across participating groups, efforts to develop data standards for annotation and dissemination of patient clinical information that does not compromise patient privacy, efforts to develop complementary data standards for annotation of PDX characteristics and biology, and progress toward “credentialing” of PDX models as surrogates to represent individual patients for use in pre-clinical and co-clinical translational research. In addition, this review highlights important unresolved questions, as well as current limitations, that have hampered more efficient generation of PDX lines and more rapid adoption of PDX use in translational breast cancer research.

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Background: PDX have become critical elements of preclinical drug development as they better reflect the heterogeneity, molecular and histopathologic signatures of the original tumor than cell lines or genetically engineered mouse models, and their drug response profiles correlate with clinical response. While PDX models have become a powerful tool in drug discovery and development, limitations include low throughput for broad drug screening, lack of dose-response curves, high cost and progressive loss of human-derived stromal elements over serial passages, restricting utility for certain therapeutic classes. A potential mechanism to overcome the low throughput and high cost of PDX models is the incorporation of ex vivo 3D (EV3D) DRP on cells isolated from early passage PDX models. Thus, we correlated DRP results using PDX with genetic mutations and drug response of PDX tested in vivo. Materials & Methods: Cells were isolated from low-passage triple negative breast, invasive bladder, and non-small cell lung PDX tumors propagated in NSG mice and cultured as 3D spheroids. 3D spheroid cultures were exposed to 15 clinically-relevant chemotherapy and targeted agents and assayed for cell viability over a range of concentrations. Non-linear regression curves were generated and relative IC50s estimated. In vivo response with limited numbers of agents at clinically relevant concentrations (3 including controls) was assessed. Results: 3D cultures and testing were successfully established across all PDX and IC50s were successfully generated in 98% of drugs tested. EV3D DRP of PDX tumors differentiated activity of cytotoxic and targeted agents across tumors of similar histologic site of origin. Gemcitabine (IC50 = .007 versus 27 uM) and docetaxel (0.2 versus 40uM) activity was highly correlated with in vivo response in bladder and breast cancers, respectively, whereas cisplatin was equally active across all tumor types (IC50 = 3-8uM). hENT1 mRNA expression was not predictive of gemcitabine activity. EV3D DRP data correlated with PDX and clinical outcome. It identified Erlotinib as being relatively inactive (3 uM) against lung cancer PDX with an EGFR e19del, T790M mutation which correlated with the outcome seen in the PDX mouse and the clinical patient outcome in which the patient became nonresponsive to erlotinib. Trametinib was highly active against lung cancer PDX with a KRAS G12C mutation (IC50 6.7 × 10-6 versus 1.1 × 10-3) and will be used to perform efficacy studies in the KRAS mutant lung PDX model Conclusions: EV3D DRP predicts in vivo response and correlates with pathway activating mutations. EV3D DRP using PDX may represent a novel high throughput and predictive drug response platform that enables compound ranking for preclinical and clinical applications. Citation Format: Tessa M. DesRochers, Christina Mattingly, Stephen Shuford, Matthew Gevaert, David Orr, Carol Bult, Susie Airhart, Mingshan Cheng, Minan Wang, James Keck, Howland Crosswell. Enhancing drug discovery and development throughput without sacrificing predictivity: ex vivo 3D drug response profiling (DRP) using patient-derived xenografts (PDX). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 318. doi:10.1158/1538-7445.AM2015-318

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of highrisk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

The Jackson Laboratory has established more than 400 unique patient-derived xenograft (PDX) cancer models from patient tumors in the immunocompromised NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (aka, NSGTM) mouse strain, spanning across more than 30 tumor types. At low passages, these engrafted models are known to retain similar molecular characteristics and heterogeneity to the originating human tumor. As such, PDX models offer an excellent preclinical platform to test drug responses of novel cancer therapeutics and a powerful resource for conducting preclinical cancer pharmacogenomic studies. To aid the selection of suitable PDX models for preclinical studies and for the research purpose to understand tumor biology and response or resistance to a given treatment, we have characterized the PDX models for their transcriptomic, mutational and copy number profiles using sequencing and array approaches. We have established a compendium of PDX-tailored computational pipelines as the analysis of genomic data from PDX models could be challenging due to a) the contamination of PDX sample with mouse stroma, which complicates downstream bioinformatics analyses as mouse genome is almost 90% homologous to the human genome, and b) the lack of matched normal material to call somatic events. Our pipelines incorporate various filters to identify tumor specific single nucleotide variants, indels, copy number changes and expression profile in the PDX model. For the purpose of validating the accuracy of our analysis pipelines and demonstrating that the JAX PDX models are indeed representative of patient tumors, we compared JAX’s PDX cohort with patient cohorts in TCGA for mutations, copy number aberrations and RNA expression concordance. Using gene sets representative of each tumor type, we found that the overall genomic profile of each PDX tumor type is more correlated to the same tumor type in TCGA than other tumor types. In addition, an integrative analysis across all data types reveals that there are more common affected pathways between the same tumor type in PDX and TCGA. This comprehensive analysis revealed that the PDX and patient cohorts exhibit similar molecular characteristics, hence establishing the suitability of JAX PDX models as in vivo models to study fundamental tumor biology as well as to carry out preclinical studies of cancer drugs, including identification of biomarkers of response or resistance. Citation Format: Xing Yi Woo, Vinod Yadav, Al Simons, Anuj Srivastava, Guruprasad Ananda, Vishal Kumar Sarsani, Roger Liu, Grace Stafford, Joel Graber, Krishna Karuturi, Susie Airhart, Joshy George, Carol Bult. Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3842. doi:10.1158/1538-7445.AM2017-3842