Q&A Report: What’s Next in Preclinical Cancer Immunotherapy Research?
Questions in this Q&A Report are answered by:
Gera Goverse, PhD
Director of Immuno-Oncology
Rajendra Kumari, PhD
Executive Director of Integrated Solutions
Can you comment on the recent FDA modernization act that removes the requirement for all drugs to be tested in animal models before progressing to human trials?
By easing regulatory requirements for animal testing for every new drug, the use of alternative technologies in drug development can reduce the time it takes to reach approvals of novel therapeutics for human trials, and offer a more cost-effective drug development process as well as supporting the 3Rs (replace, reduce, and refine animal use). In vitro organoids and ex vivo patient tissue that use human cells or tissues are more clinically relevant than traditional 2D and 3D cell lines and their corresponding cell-derived xenograft systems. These platforms can be applied to functional/phenotypic screening, proof of concept, lead optimization, and combination analysis, which can be used for candidate drug prediction. High-content imaging (HCI) capability also adds significant value to drug discovery, as well as our large biobank of models across many different cancer types.
Can you discuss the challenges associated with identifying and validating biomarkers for immunotherapy response, and how can these challenges be addressed?
The tumor microenvironment (TME) is a complex interplay between tumor and surrounding cells and the tumor itself is heterogenous which changes and evolves under treatment pressure and over time, which in turn further manipulates its environment. Sampling patient tumors at any given time gives a snapshot in time of that site. In addition, patient diversity and interpatient variation adds further complexity for any single non-invasive biomarker to be sufficiently predictive. Identification of biomarkers informing on response that can be detected early in the course of treatment, for example immune activation or toxicity, would enable adjustments to treatments. Thus, harnessing models that recapitulate the complexity of cancer can provide deeper understanding of the response to treatment, especially when incorporating patient tissue to represent patient diversity and heterogeneity as well as HCI to capture the changes within these complex model systems.
When is it most important to use organoids instead of traditional in vivo models or 2D cell cultures?
The use of PDX-derived or patient-derived organoids within in vitro 3D testing is of high value as they more closely resemble patient tissue compared to more simple in vitro assays performed in 2D with cell lines. Moreover, these organoids can still be used for preclinical drug screening in a high throughput format testing multiple compounds, doses, and combinations at once, while this type of screening will be more challenging and costly with the use of in vivo models. In addition, multiple organoids from one indication or multiple indications can be screened and thereby identify a patient group of interest for the drug. These assays will thereby be able to identify lead compounds or combinations analyzed in relevant material that could be followed up with in vivo testing in selected mice models for a complete understanding of a drug and its mechanism of action.
How do organoids compare to traditional 2D cellular models in terms of their ability to recapitulate the complexity and heterogeneity of human tumors?
With the organoid technology, which is based on stem cells which grow and differentiate into epithelial cells, these multicellular structures are heterogenous in their form. To further recapitulate the complexity of the tumor microenvironment as found within the patient, additional cellular players can be added to these organoid cultures in 3D, such as different immune cells or fibroblasts. Thereby, these assays are very flexible in their design which allows the testing of preclinical drugs in a complex but controlled setting that captures the tumor microenvironment as seen within the patients.
Can you discuss the potential role of microbiome modulation in enhancing cancer immunotherapy response, and how this can be studied?
The role of the microbiome in cancer has become more evident over the last decade, with the gut microbiota shaping the systemic immune response and influencing the efficacy of immunotherapeutics such as immune checkpoint inhibitors like anti-PD-1 therapies. The gut microbiota itself can be impacted by anti-cancer therapies like chemotherapy resulting in dysbiosis. Germ-free mice housed in germ-free isolators provide a controlled environment for the study of host-microbiome interactions within the context of a specific disease (e.g. cancer or IBD) combined with therapeutic treatment (e.g. immune checkpoint inhibitors). The diversity and abundance of microbes can be evaluated by shotgun metagenomic and metatranscriptomic sequencing and immune cell responses characterized by FACs or mouse I/O RNA-seq panel screening. While the additional complexity of the microbiome is challenging to study within in vitro testing, intestinal organoid co-cultures with microbes have also been reported.
What are some of the challenges associated with scaling up organoid cultures for high-throughput screening or other applications?
While organoid cultures are more complex compared to cell line cultures and therefore need highly trained and skilled scientists, some high throughput systems with 384- or 1536-well plates only require a small amount of material. Therefore, by making use of the right systems that have been well validated, organoid technology can very well be used in high throughput systems.
How can high-content imaging be used to study the interactions between T cells and cancer cells, and to monitor the effects of T cell-based therapies?
Image-based analysis platforms allow the examination of the relative positioning of T cells, or any other immune cells, or cellular therapy compared to the tumor material. Therby high-content imaging technology can be used to visualize and analyze the interactions between the immune cells and the tumor. Measurements of immune cell positioning or clustering around the tumor can be identified as well as infiltration into the tumor. Additional analysis with higher resolution with confocal microscopy could even visualize the formation of immune synapses between the immune cells and tumor. Thereby the effects of cellular therapy could be measured via high-content imaging, showing enhanced migration towards the tumor, enhanced infiltration into tumor which in the end leads to killing of the tumor measured by reduced volume of the tumor.