Repositive organised a networking event, running in parallel with the American Association for Cancer Research (AACR) annual meeting, 17th April, Marriott Marquis Chicago, USA.
This event marks the first in a series of events for the Cancer Models Community initiative, based around our Cancer Models Platform. This initiative is aimed at promoting scientific discussion between a wide range of researchers from the biotechnology and pharmaceutical industries as well as from contract research organisations (CROs).
The meeting sparked an interesting discussion between attendees from CROs and research companies on their experiences of developing and promoting, and discovering and using cancer models and services. Highlights of the evening include a scientific debate around the following topics:
- Use of CDX and PDX for non-IO or targeted therapy drug discovery
- Modification of PDX models
- Challenges of working with CDX/PDX models and steps to minimise risk
- What makes a good PDX model and key parameters for identifying one
- Applications of PDX for drug discovery projects
- Standardisation of model generation and characterisation
- In vivo models for IO drug discovery projects
- Ex vivo and in vitro models for IO drug discovery (3D cultures)
We want to share the highlights of the discussion in a series of blog posts. You can find the first one here:
Use of CDX and PDX for non-IO or targeted therapy drug discovery
The discussion started with a review of the current most popular model formats used for non-immuno-oncology or targeted therapy drug discovery: Cell-Derived Xenograft (CDX) and Patient-Derived Xenograft (PDX) models. These model types have distinct advantages and disadvantages:
CDX models are typically applied to the initial screening stages of drug discovery. Typically they are used in high-throughput screening approaches for investigating the efficacy, pharmacokinetics and pharmacodynamics of compounds. They are relatively easy to grow, standardise, manipulate and generate an isogenic model, providing a reproducible platform for non-IO drug discovery work.
PDX models are typically applied later in the drug discovery pipeline as the compound gets closer to the clinic. The intra-tumoural heterogeneity and preservation of the tumour microenvironment makes the PDX model a closer representation of a real patient. With this added complexity comes a reduction in reproducibility of the model, making PDX models less suited for early screening work. They are more applicable when a compound is being translated from a concept into a clinical product and a model which better represents a real patient is needed.
A point highlighted during the discussion was the advantages of orthotopic engraftment. The main difference is that the engraftment site is matched to the original tumour site, compared to subcutaneous engraftment where the tumour is engrafted under the skin on the flank of the mouse. This method makes it possible to observe metastases in the mouse and studies suggest that PDX model metastases are significantly correlated to the appearance of the original patient’s metastases. Examples of research evaluating orthotopic implantation and cancer development include studies on small cell lung cancer (Taromi et al. 2016), metastatic uveal melanoma (Kageyama, et al. 2017), neuroblastoma (Van Doord, et al. 2017), among others. One limitation of this approach is that it can be labour intensive and a technical challenge to develop such models.
It is important to remember that both CDX and PDX models are experimental systems. The community agrees that no single model is perfect, having access to a good panel of models provides a pharmacologist with multiple points of validation and greater confidence in a compound before progressing to the next step in the pipeline.
Our subsequent blog posts will include a summary of new technologies to modify PDX models, challenges of working with CDX/PDX models and steps to minimise risk. We will also discuss the key parameters used to identify a good PDX model.
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Kageyama, et al. (2017) Establishment of an orthotopic patient-derived xenograft mouse model using uveal melanoma hepatic metastasis. J Transl Med. 15(1):145. https://doi.org/10.1186/s12967-017-1247-z
Taromi, et al. (2016) An orthotopic mouse model of small cell lung cancer reflects the clinical course in patients Clin Exp Metastasis. 33(7):651. https://doi.org/10.1007/s10585-016-9808-8
Van Doord, et al. (2017) Tissue-directed implantation using ultrasound visualization for development of biologically relevant metastatic tumor enografts. In Vivo. 31(5): 779. https://dx.doi.org/10.21873%2Finvivo.11131