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Stefano Cairo, PhD, from Champions Oncology, answers key questions from his recent webinar.
1) Is there a similarity between the tumor microenvironment in PDX and matched patient material of these models?
During PDX development, a patient's tumor microenvironment is replaced by cells and tissues of murine origin. Although it is possible to study the impact of human tumors on mouse tumor microenvironment such as tumor vascularization, fibroblast polarization, and, depending on the degree of immunodeficiency of the host, infiltration of innate immune system components such as neutrophils and NK cells, a proper comparison between the patient and mouse microenvironment is challenging to make.
To fill this gap in the preclinical landscape, we are now collecting, whenever possible, matched normal tissue at the time of patient tumor sampling. In addition to our standard PDX development program, we are now also developing patient-derived and PDX-derived 3D cultures and performing ex vivo experiments by co-culturing patient or PDX tumor cells with patient-matched stromal and immune cell components.
2) How many PDX in vivo models would be needed per cancer indication to get reliable results of drug response?
There is no predefined number, the experimental design is tailored to the scientific question that is addressed. All our PDXs are very well characterized at the molecular level, including RNAseq, WES, and whole proteome analysis, which integrate detailed clinical information and PDX growth parameters, and we have developed a web-based tool to facilitate the selection of the models of interest. It may happen that, given the variety and extension of our PDX panel, the number of PDXs selected is too wide and should be reduced for reasons of time and or budget, in these cases it is possible to run an ex vivo prescreen of the selected models to prioritize the PDXs that will be tested in vivo.
3) Can you please share to which extent 3D culture improves the translatability of ex vivo results to in vivo results?
In our experience, the drug response correlation for a given tumor grown in 3D culture or in vivo is strongly dependent on the experimental settings. A drug that affects a tumor cell's intrinsic oncogenic pathway can be tested in a 3D monoculture, and the mechanism of action of the drug and an extrapolation of the maximum tolerated dose in vivo should guide the experimental setup. If the evaluation of drug activity requires or depends on the tumor microenvironment, tumor cells should be co-cultured with TME components, ideally patient-matched. Our ex vivo program is conceived to manage all these scenarios.
4) What specific factors would you use in selecting PDX models when targeting solid tumors with ADCs?
The most important factors that need to be considered to select the appropriate PDXs for meaningful evaluation of ADC efficacy are the expression of the target, the receptor density, and the payload internalization. All these features can be evaluated in our models: ADC target expression can be evaluated in our RNAseq and proteome database, validated by IHC, and we can measure receptor density, occupancy, and internalization by flow cytometry.
5) How translational are ex vivo studies with ADCs?
Ex vivo studies can provide a correlation between ADC efficacy and the parameters just mentioned. As we can propose ex vivo testing for all our PDXs, it could be a time and cost-effective solution to shortlist the PDXs that should be tested in vivo.
6) It is great that you are able to classify responders and non-responders based on a multi-omic approach. In the clinical routine, it is, however, not possible to test multi-omics for patients. (e.g combining gene expression and protein or gene expression and mutations) How do you see this translate to clinical practice for patient enrollment based on the identified biomarkers/signature?
The bottleneck of many molecular classifiers is their application in the clinical routine. The most straightforward approach for a multi-omics-based classifier would be to focus on the molecular parameters identified by the classifier that can be measured in the pathology laboratories, evaluate the classifier's robustness in the training and test set, and retrospectively validate it in clinical cohorts.
