The evolution of assays for immuno-oncology research

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By Dr Shailendra Singh, founder and CEO of Cellomatics Biosciences.

Cancer is a complex heterogenous multistep disease characterised by uncontrolled cell proliferation combined with a dysregulated immune response. The number of new cases recorded between 2016 and 2018 was 375,400, with 167,142 deaths caused by cancer between 2017 and 2019. Half of people diagnosed with cancer in England and Wales survive their disease for 10 years or more (2010-11)1. Existing therapies have been focused on eliminating the cancer cells and/or on preventing its progression by using a combination of chemotherapy, radiotherapy, and surgical approaches. This continues to be the standard of care. However, over the last few decades, it has been widely accepted that interaction between the tumour and host immune response plays a critical role in regulating tumour biology and hence its progression. 

A healthy immune system is naturally primed to eliminate malignant and abnormal cells through a synchronised and dynamic interplay between adaptive and innate immunity. However, cancer cells continually evolve to evade the host immune defences by mechanisms that downregulate the expression of surface antigens, recognised by critical immune cells including T lymphocytes and natural killer (NK) cells. This complexity is further amplified by a tumour microenvironment (TME) created by the malignant cells2. TME comprises a combination of tumour, stromal, and inflammatory cells together with the extracellular matrix components that act as an effective defence against the immune system. Evolution of such defence mechanisms can lead to multi-drug resistance, further disease progression, and cancer metastasis. Initiation and progression of cancer can therefore be attributed to suppressed or poor immune-mediated defence mechanisms.

History of immuno-oncology

The concept of tumour immunotherapy or the principles of immuno-oncology (IO) have been around for over 100 years. IO harnesses the capabilities of an activated immune system to target cancer cell killing. The first such research dates back to 1891, when William Coley attempted to inject heat-inactivated bacteria to treat osteosarcoma. Another revolutionary discovery was the elucidation of the role of IL2 in T cell growth and activation in the 1970s, that later paved the way for the treatment of patients with metastatic melanoma and renal cancer. Genetically modified T cells stimulated with IL2 are now administered as cell therapy for multiple cancer cell types with beneficial results3,4.

Further discovery of PD-1 and CTLA4 as negative immune regulators led to the development of a new class of cancer therapeutics, namely the immune checkpoint inhibitors5,6. These have been the most promising form of antibodies being developed, with Ipilimumab (CTLA4 blocking) and Nivolumab (PD1 blocking) now approved by the US Food and Drug Administration (FDA) for more than nine cancer types. 

Between 2017 and 2020, the number of IO therapies being developed has increased significantly from 2030 (for 265 targets) to 4,720 (for 504 targets). Of recent note has been the advances made in the generation of effector T cells expressing chimeric antigen receptors (CAR T), as therapeutics for several haematological cancers. Further studies elucidating the role of tumour microenvironment and its immunosuppressive elements including tumour associated macrophages have shown encouraging results in improving the efficacy of IO therapies.  

Rising demand

The global immuno-oncology assay market is expected to grow at a compound annual growth rate (CAGR) of 11.87% from 2021 to 2030 to reach 11.74bn USD by 20307. This is an exciting field of cancer research which involves harnessing the body’s own immune system to target cancerous cells. Over 250 clinical trials are currently in progress, and novel immuno-oncological treatments are already proving to be successful. Combination therapy involving Nivolumab and Ipilimumab8, which work together to allow immune cells to attack tumours, was approved by NICE in June 2021. This has been fuelled by a steep increase in the number of IO programmes being developed for various cancers and targeting multiple antigens. 

Sophisticated and continually evolving preclinical IO models (in vivo and in vitro) are particularly helpful in defining translational therapeutic strategies and understanding the efficacy of combination immunotherapies and refractory cancers, and in the identification of novel prognostic biomarkers. To date, several preclinical IO models have been developed, with each type having their respective strengths and weaknesses. 

In vivo models

Cell-line derived xenografts and syngeneic mouse tumour models have been quite popular in IO research over several decades. The syngeneic mouse tumour model involves transplantation of a tumour cell line that is established from a mouse of a specific strain to the recipient mouse of the same strain. These are widely available for several solid and haematological tumours. As the different elements of this model are derived from the same species strain, they offer several advantages including a fully established and functional immune system, their suitability to study the effect on cancer metastasis, and their flexibility to be customised or modified to express luciferase gene and its subsequent application for small animal in vivo imaging. These are also excellent models to study pharmacodynamics and for use in mechanistic studies, but the downside is that translation of the clinical outcomes in human is poor.

Another widely available commercial model is the cell-line derived xenograft (CDX)9 that involves inoculation of fast-growing tumour cell lines into immunodeficient mice for various applications including pharmacokinetics, efficacy of cell killing, and studying tumour metastasis by in vivo imaging. Poor predictive potential and difficulty in the evaluation of immune-mediated responses are some of the drawbacks of this model. 

Genetically engineered mouse models (GEMM) recapitulate the stromal biology of different human cancers including the tumour microenvironment that regulates the disease process.9 These models are therefore suitable to understand disease progression or tumorigenesis, but are difficult to establish, and limit the formation of broad neo-antigens. 

Patient-derived xenograft (PDX) models9 are widely used for oncology and immuno-oncology research. These are based on patient-derived tumour tissues transplanted in immunodeficient mice. PDX models maintain the molecular and structural heterogeneity of the tumours and are therefore considered to be better predictors of clinical outcome. Challenging to establish and with a slower growth rate, these models can be costly. Furthermore, as the model is established in immunodeficient mice, it is not suitable to investigate the role of the immune system in tumour development. Therefore, it may not be a suitable model for all IO therapy applications10.

Mice with a humanised immune system produced by engraftment of human immune cells are promising models to study T cell mediate response to tumours9. However, major histocompatibility complex (MHC) mismatch between mouse and human T lymphocytes results in the development of graft-versus-host disease, thereby limiting its application. 

In vitro models

All the above approaches involve the use of animal models. However, there is now a greater emphasis on transitioning away from the use of in vivo animal models towards creating sophisticated and translational human in vitro models. This approach supports the 3R principles of replacement, reduction, and refinement of animals for research. With this view, novel in vitro cell based approaches using either spheroids or organoids are in development. 

Tumour spheroid cultures have generated traction within the global research community for their ability to accurately replicate solid tumour biology and architecture. These can be unicellular/multicellular with their inherent pathophysiological gradients including oxygen, nutrients, carbon dioxide with proliferating live cells on the outer layer, quiescent cells on the inner layer, and necrotic cells in the spheroid’s core that eventually undergo apoptosis/necrosis. Another advantage is their ability to be cultured in microwells allowing for high throughput screening. Benefits over 2D monolayer culture include the possibility of investigating drug penetration and drug resistance offered by the tumour cells. Several challenges exist in the development of these tumour spheroids. Uniformity and reproducibility of the spheroid formation to limit inter-well and intra-well variability is the most common. This approach therefore requires several stages of development with each cancer cell line, to identify optimal culture conditions, seeding density, and the most suitable day (post-spheroid formation) for suitability and use in high throughput screening.  

Recently, organoids11 have been identified as a more robust model, challenging the status quo of existing in vitro models that are known to replicate disease biology. 3D organoid models provide more valuable information as they recapitulate both the structural and functional characteristics of a tumour, including glucose consumption, oxidative stress and lactic acid production. Although there has been some success in establishing patient-derived 3D organoid models for prostate cancer, colorectal cancer, lung cancer and pancreatic cancer, this is a time, resource, and cost-intensive process. Collectively, this makes it a less viable option for application across different forms of cancer or for high throughput studies.

Due to low cost and ease of process, 2D monolayer cultures are still recognised as the primary research model, particularly for high throughput screens. Advanced in vitro models including spheroids/organoids are applied to further validate ‘hits’ from 2D screens. However, the significant progress made with 3D spheroids can capture the complexity of the tumour environment, and therefore   offer high throughput   screening-analysis of novel anticancer drugs.

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A, B: Different forms of spheroids generated from cancer cell lines. C: By using our JuliStage imaging platform, we can capture images for quantitative analysis-therefore, by plotting spheroid area against time we can analyse growth rates. This demonstrates that spheroid growth is determined by seeding density. D: The 3D CellTiter Glo viability assay demonstrates that spheroids are comprised of viable cells. Exposure to toxic compounds such as Doxorubicin reduces spheroid area along with cell viability.

Bespoke and customised approach

The last decade has seen a significant shift in the paradigm for the therapeutic management of cancers. This has been possible with the advent of multiple IO-based therapies including anti-tumour antibodies, immunostimulatory cytokines, co-inhibitory antagonists, co-stimulatory agonists, immunological cell death inducers, and therapeutic cancer vaccines. With advances made in technology and the availability of genetic information from different types of tumours, patient groups segmented based on their genetic and biomarker profiles are being used in IO clinical trials to improve outcomes and shorten development timelines9. The long-term objective is to offer bespoke and personalised therapy for individuals based  on their genetic profile and tumour signature.

Traditionally, in vitro cell culture models used in the laboratory have been based on 2D monolayers, where cells adhere to and grow on coated plastic surfaces in various items of culture-ware. But where in the body do cells grow on coated plastic surfaces, and how realistic a model for testing does this provide?

Whilst current monolayer models perform valiantly for the study of the basic biology of mechanisms in isolation, how well do they replicate conditions within the body, where cells are bounded on all sides by other cells or biological matrices? In recent years, this idea has been built upon to develop a range of 3D culture models such as spheroids formed of a single cell type and organoids formed of multiple cell types which self-organise into organotypic structures (mimicking the tumour microenvironment). By receiving developmental and phenotypic cues from neighbouring cells rather than an artificial surface, they form a more realistic model for cell biologists to use to assess how genes and chemicals can modify the cellular phenotype. The mode of growth of cells in culture and the interaction with their growth substrate may influence cellular phenotypes. 

Multiple methods exist for generating 3D culture models. Cancer cell lines can be cultured as a traditional 2D monolayer but when single-cell suspensions are plated out in an ultra-low-adhesion multi-well plate, they coalesce to form a single spheroid in each well. Lack of adhesion to the plate surface ‘encourages’ them to adhere to one another, and the nascent cell aggregate rounds up and forms a rough ball of dividing and interacting cells. Cells can be embedded in matrices, for instance agarose or Matrigel, whereby cells divide to form encapsulated balls of cells. 

A more ambitious approach to generate 3D aggregates of cells involves levitation in an ultrasound standing wave, which demonstrates that the cytoskeleton of cells grown in 3D more closely mimics that seen in tissue samples than cells grown on a plastic surface. These models show differing biochemistry to cells grown on traditional culture-ware, from differing drug responses to more rapidly acquiring specific phenotypic functions. More recently, 3D printing techniques have been applied to generate organoids en masse with a higher degree of reproducibility using cells (both cell lines and patient-derived cells) and self-gelling matrix components which show upregulated genes involved in hypoxia, adhesion and EGFR signalling, downregulated cell cycle genes, and enhanced resistance to commonly-used chemotherapeutic agents compared to 2D cultures.

Developing spheroid models for certain tumours including breast, ovarian, colon, and lung, can offer new perspectives on research projects. 

Besides spheroids, another area of interest is antibody-directed cell cytotoxicity (ADCC). Therapeutic antibodies bind to target cells, and subsequently direct ‘effector’ cells from the immune system (expressing the FcγR receptor, and frequently NK cells) to attack and kill the cancer cells. Reagents that can augment and enhance ADCC are of interest in addition to the antibodies themselves. Trastuzumab targets breast cancer cells over-expressing Her2, subsequently binding to the receptor and slowing down cell proliferation but may function additionally by inducing ADCC. Developing high throughput assays to assess ADCC will offer companies bespoke and highly customisable models. Routine cell culture coupled with high content screening is already in place, and this provides a solid foundation for developing such an assay.

Having the ambition to drive the evolution of these models by enabling experiments to be performed in a 3D environment coupled with high-throughput screening will enhance the assessment of client-specified combinations of target and effector cells along with potential therapeutic antibodies and promoter reagents. As a result, the data generated will more accurately reflect the way that these cells interact in the body.

Future for immuno-oncology assays

The development of preclinical models for immuno-oncology applications continues to evolve. Great strides have been made in this journey during the last decade. These models have greatly contributed to the clinical development of several tumour immunotherapies. However, further optimisation of preclinical in vitro models that are flexible and reflect the in vivo tumour biology and architecture more accurately is required. There may not be one model that is fit-for-purpose for all cancer types. However, it is important for the research community to collaborate and develop appropriate cancer-specific models to reduce the attrition rate in clinical trials. 

Continually evolving immuno-oncology assays should aim to push the boundaries of IO-based therapies. Although considerable advances have been made, we now face the issue of resistance to immune therapies that is being identified as a key area of unmet need. Further research to understand the biology is critical in addressing this issue. In this era of precision/personalised medicine, identifying predictive or prognostic biomarkers and devising single or combination therapies that combine safety and efficacy in population groups with refractory cancer is not beyond reach, but we must develop robust models to test them, and the CRO environment is the ideal setting to achieve this.  

DDW Volume 24 – Issue 1, Winter 2022/2023

References

  1. Cancer Research UK. Cancer Statistics for the UK. https://www.cancerresearchuk.org/health-professional/cancer-statistics-for-the-uk/ (2015)
  2. Zhou, C.; Liu, Q.; Xiang, Y.; Gou, X.; Li,W. Role of the tumor immune microenvironment in tumor immunotherapy (Review).
  3. Oncol. Lett. 2021, 23, 53. Decker WK, da Silva RF, Sanabria MH, Angelo LS, Guimarães F, Burt BM, et al. Cancer immunotherapy: historical perspective of a clinical revolution and emerging preclinical animal models. Front Immunol. (2017) 8:829. doi: 10.3389/fimmu.2017.00829
  4. Lombard M, Pastoret PP, Moulin AM. A brief history of vaccines and vaccination. Rev Sci Tech. (2007) 26:29–48. doi: 10.20506/rst.26. 1.1724
  5. Leach, D. R., Krummel, M. F. & Allison, J. P. Enhancement of antitumor immunity by Ctla-4 blockade. Science 271, 1734–1736 (1996).
  6. Korman, A. J., Peggs, K. S. & Allison, J. P. Checkpoint blockade in cancer immunotherapy. Adv. Immunol. 90, 297–339 (2006).
  7. Strategic Market Research Report; Immuno-oncology Assays Market Size, Trend Analysis 2030. https://www.strategicmarketresearch.com/market-report/immuno-oncology-assays-market (2022)
  8. Somasundaram R, Herlyn M. Nivolumab in combination with ipilimumab for the treatment of melanoma. Expert Rev Anticancer Ther. 2015;15(10):1135-41. doi: 10.1586/14737140.2015.1093418. PMID: 26402246; PMCID: PMC4669949.
  9. Franklin MR, Platero S, Saini KS, et al. Immuno-oncology trends: preclinical models, biomarkers, and clinical developmentJournal for ImmunoTherapy of Cancer 2022;10:e003231. doi: 10.1136/jitc-2021-003231
  10. Kersten K, de Visser KE, van Miltenburg MH, et al. Genetically engineered mouse models in oncology research and cancer medicine. EMBO Mol Med 2017;9:137–53.doi:10.15252/emmm.201606857pmid:http://www.ncbi.nlm.nih.gov/pubmed/28028012 
  11. Rauth S, Karmakar S, Batra SK, Ponnusamy MP. Recent advances in organoid development and applications in disease modeling. Biochim Biophys Acta Rev Cancer. 2021 Apr;1875(2):188527. doi: 10.1016/j.bbcan.2021.188527. Epub 2021 Feb 26. PMID: 33640383; PMCID: PMC8068668.


About the author

Dr Shailendra SinghDr Shailendra Singh is founder and CEO of laboratory-based contract research organisation (CRO), Cellomatics Biosciences. An experienced life science research scientist, Singh has over 18 years’ academic and commercial experience across diagnostics, pharmaceutical, and the biotech/biomedical sectors. Originally qualifying as a medical doctor specialising in immunology, he went on to train as a pathologist and immunologist, before completing a PhD in molecular medicine. 

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