Can organoids replace animals in preclinical cancer drug testing?

by | Dec 14, 2023 | Cancer, CRISPR, NGS, Organoids, Single Cell, Stem Cell

Image 1: T-lymphocytes and cancer cells.



Cancer therapies have several clinical challenges associated with them, including treatment toxicity, treatment resistance and relapse. Factors like patient profiles or the tumour microenvironment (TME), there are hurdles to get through in advancing effective treatments that have low toxicity, can lessen emergence of resistance and occurrence of relapse [1]. Cancer development has the highest drug erosion rates with only 1 in 10,000 preclinical candidates reaching the market.

To relieve this high erosion rate, more mimetic and sustainable preclinical models which can also acquire the disease biology as in the patient are needed. Organoids and next generation 3D tissue engineering is an emerging area that aims to approach this issue [1].

In a recent review published by Biochemical Pharmacology titled ‘Next generation organoid engineering to replace animals in cancer drug testing’, Hockney al et. highlight some of the traditional preclinical models used in cancer drug testing and debate how the next generation organoids are being used to replace animal models and some of the more elementary in vitro approaches.

Dolly from Lab-A-Porter extracted some of the highlights from the review article while we featured some of the organoid research reagents and tools that may support your next organoid research projects.





  • 1.73 million experimental procedures were carried out on live animals in the UK in 2021 – 12% were used for oncology [1].
  • Although there is a general trend in decreasing use of animal following the 2014 EU directive to ensure animal used for scientific purposes are more protected, they are still heavily used in medical research [1].
  • A movement away from animal models is promoted now in preclinical models [1].
  • Use of animal products in ex vivo cultures is now understood to result in variable results due to batch variance [1].
  • In vivo experimentation is not a sustainable approach for testing novel therapeutics [1].
      • It leads to long model development and experimental times [1].
      • Finances surrounding the housing and upkeep animal is high due to specialist living conditions, food and care [1].

Image 2: Embryo of zebrafish


Cell lines

  • It is considered as one of the least complex and least mimetic of the available in vitro preclinical models [1].
  • They remain a popular choice due to their ease of culture and availability [1].
  • They provide standard model cells to study cancer pathophysiology without the inherent variance associated with patient-derived samples [1].
  • Cancer cell lines maintain the cytogenetic abnormalities of the disease whilst providing a more established and consistent method of cell culture [1].
  • Limitations associated with using 2D cell line culture:
      • When cultured in vitro, they do not reply on cell signalling from neighbouring other cell types, so their growth is not controlled by the environment as it is in vivo [1].
      • The lack of cell-cell interactions would mean that tissue architecture is not accurately present therefore does not represent spatial organization [1].
      • Comparisons of cell lines and matched tumour cells from patients has highlighted that there is considerable variation in gene expression and enrichment of microenvironment modification-related genes [1].

3D tissue slices

  • 3D models, such as tissue slices in cancer studies provides patient derived-cells in native tissue so allows consideration of more environmental and cell-cell characteristics.
  • Pros: Features such as the tumour-stroma interactions, immune landscape and epigenetic features can be assessed using their approaches
  • Pros: Short generation time, high success rates.
  • Cons: Patient-derived material can be difficult to source and tissue slices may not be applicable for call cancer types such as liquid cancers

iPSCs in organoid modelling

  • The emergence of iPSC technology has meant that samples of somatic cells can now be taken and reprogrammed to an embryonic stem cell-like state [1].
  • The pluripotent nature of iPSC then allows differentiation into any cell type belonging to the 3 germ layers, the ectoderm, mesoderm, and endoderm [1].
  • Furthermore, as these cells are derived from somatic cells and do not require destruction of embryos, they are well regarded from an ethical point of view [1].
  • iPSC-derived lineage provides an optimal cell source for organoid modelling – a powerful technology specially in disease modelling, drug screening and precision medicine [1].
  • IPSC have been used to generate several organoids representative of several tissues. Models of brain, kidneys, and heart, for example, have been generated using cells derived from iPSC lines [1].
  • The IPSC generated contain specific cancer causing genetic aberrations and when differentiated produce cells of the desired tissue and with the relevant cancerous phenotype [1].


Patient-derived models

  • Typical 2D culture: As with cell lines, these cells offer a relatively straightforward method for having available cells with the correct genetic abnormalities [1].
  • Co-cultures with feeder cells from the typical microenvironment: It can extend the life plan of patient-derived cells in the lab [1].
  • Cons for 2D culture: limited growth potential, lack of cell-environment interactions and difficulty in obtaining samples [1].
  • Cons for co-cultures with feeder cells: Co-culture comes with the added issue of sourcing feeder cells which can be difficult and may have source-to-source variance leading to varied culture success [1].
  • Cons for both 2D culture and co-culture with feeder cells: Human body is 3D where cell-cell homotypic and heterotypic interactions, as well as cell signalling is influenced by cell polarity, which in turn cannot be faithfully captured in 2D mono and co-cultures [1].


  • They provide a much closer physiological relevance for studying disease [1].
  • These models tend to be on the smaller scale when compared to organoid models with size being 100-500 μm in diameter [1].
  • Pros: These models have typically been applied for therapeutic testing due to their ease of creation and low cost compared to in vivo approaches [1].



  • Organoids are complex 3D structures derived from multi-lineage cell types and can be self-organising [1].
  • The development of organoid technology has opened many opportunities for disease modelling without the need for animals [1].
  • In cancer, organoid models for a host of organs have been generated including bone marrow, breast, lung and rectal cancers among others [1].
  • Pros: Organoid can be used for a wide range of applications in translational medical research by enabling study of disease pathobiology. They offer a more complex modelling system to spheroids in that they can include multiple cell types from the organ of interest alongside ECM [1].
  • Pros: ECM serves as a scaffold surrounding cells in a tissue providing a stable microenvironment that allows for cell migration, proliferation and differentiation [1].
  • Cons: Whilst organoid models using cells of patient-origin are vastly important to modelling disease, they present a problem in lack of sample availability and therefore can mean a limited cell resource [1].

Image 3: Kidney Organoids. A plate with testing chambers containing kidney organoids that were generated by robots from human stem cells. The different colours mark distinct segments of the kidney. This image (Credit: University of Washington Photo/Freedman Lab) is used under Public Domain Mark 1.0:



    3D bio-printing

    • 3D bio-printing is a fabrication technology that has been applied to preclinical models in order to increase the reliability, reproducibility and scalability [1].
    • In bio printing, small units of cells and polymers or ECM components are precisely dispensed to generate tissue-like structures [1].
    • The methodologies for dispensing cells must therefore be compatible with deposition of live cells whilst maintaining high accuracy and high-resolution [1].
    • 3D bio-printing utilise bio-gels have all the benefits of 3D cultures [1].

    Image 4: Researcher getting 3D bioprinter ready to 3D print cells on an electrode

    Image 4: Organ-on-a-chip



    • One of the limitations with organoid models is that they do not represent the tissue-tissue interfaces, organ-level structures, fluid flows and mechanical cues that are present within the body [1].
    • Techniques have been developed using microfluidic cell culture technology.
    • These in vitro approaches known as organs-on-chips are being utilised for modelling cancer microenvironments [1].
    • This technology relies on manipulating fluids at a microscale level [1].
    • These systems are beneficial as many parameters can be tightly controlled and therefore more accurately assessed in relation to cancer cell biology [1].
    • Pros: Where these models differ from other 3D in invitro cultures is that controllable shear stress, shear flow and nutrient concentration gradients create mechanical forces on the cultures that are critical for tissue development and organ morphogenesis [1].
    • Limitations: Utilizing these models in a high throughput manner is still not an option due to the difficulty in scaling up model production [1].
    • These model are complicated to handle reducing the widespread application outside specialised laboratories and groups [1].



    Brain and retinol organoids

    • The brain is the most complicated organ in a vertebrate and its development is controlled in different ways by different parts of the body [1].
    • Some of the most difficult continuing tasks in neurobiology include understanding the development of the human nervous system and understanding the pathways that lead to neuronal disorders [1].
    • Access to healthy and diseased human brain and retina tissue for functional molecular and cellular investigations is a substantial barrier [1].
    • Brain and retinal organoids offer a solution as well as access to an infinite supply of human tissue [1].
    • One of the examples: Retinoblastoma organoids have been developed from patient-derived iPSC cell sources with germline RB1 mutations and have been used to induce retinoblastoma in immunocompromised mice [1].

    Muscle organoid

    • Cancer is the most studied cause of sarcopenia, a condition described by the decline in muscle mass, strength and physical function [1].
    • In recent years in intro muscle models have been developed to study muscle physiology and diseases [1].
    • The model will be used to investigate novel pathophysiological mechanism in sarcopenia and to test new drug treatment [1].


    • There has been limited success with available treatment of colorectal cancer which are mainly based on chemotherapy and/or radiation therapy, most of which have been around for decades [1].
    • There is an unmet need for representative preclinical models of colorectal cancer in which more targeted therapies using small molecular inhibitors and monoclonal antibodies can be validated [1].
    • While animal models can be very helpful at the initial stages of drug testing, they often fail to mimic the conditions in human tissue [1].
    • For example, underlying cellular and molecular mechanisms can be significantly different, and therefore that small molecular inhibitors and monoclonal antibodies will work differently, or not at all. Thus, many treatment fail when transferred into the clinical setting [1].
    • One challenges in this investigation is due to the widespread negligence of the physiological oxygen gradient present in the healthy colon wall, which is not recapitulated in the cell cultures [1].
    • Although oxygen gradient can be recapitulated in spheroid models, these lack the complexity of including various cell types that are allowed to differentiate within the model as they would in vivo. Primary cell cultures are also short-lived due o most normal colon tissue cells being already terminally differentiated. Colonoids therefore could provide a promising alternative to animal models that recapitulate the human in in vivo environment for long-term studies [1].
    • The applications of colonoids within biomedical research are vast. They provide a method of assessing anticancer drug toxity that is specific to the human condition and can be monitored in long-term experiments [1].




    Highthrough applications of organoids


    • Advancements in 3D imaging techniques have allowed visualization of these complex models, allowing studies of cell-cell and cell-ECM to take place [1].
    • High throughput and high content technique can be applied to organoid models. Targeted organoid sequencing uses targeted RNA-seq to highlight expression of gene signatures allowing for phenotypic evaluation of cells within the organoid [1].
    • In response to treatment, targeted organoid sequencing can elucidate information as to mode of action of drugs [1].
    • A major application of this technology in colorectal cancer research has included identification of 56 drugs that could induce intestinal epithelial differentiation – a key driver in cancer progression with stem cell-like cells playing a role in tumour maintenance and metasis [1].
    • High content imaging can provide quantitative analysis on organoids to highlight variable morphologies within 3D cultures. Light-sheet microscopy has been used to capture these cellular features whilst reducing the photobleaching or phototoxic effects that confocal imaging would have [1].
    • For example, a new technology described by Beghon et al. captured features such as mitosis, apoptosis, organoid shape and ceullular organisation via an automated microscopy technique, which conventionally is manually assessed by trained biologists [1].

    Spatial transcriptomics as a research tools to characterise multicellular organoid models


    • Traditionally, analysis of genes expressed in tissue or organs has been carried out via bulk RNA seq and more recently single cell, or single nuclei seq [1].
    • These methods, while very informative, are limited to profiling quantitative gene expression levels present in a particular piece of tissue or cell [1].
    • The location of where these genes are specially expressed within the tissue, or the position of the cell within the tissue they were isolated from remain unknown [1].
    • Spatial transcriptomics is a powerful technology, capable of visualising gene expression of all mRNAs in a section of tissue, within a positional context. Each capture region contains 5000 spots, each spot consisting of a high-density group of spatially barcoded probes that contain a sequenc adaptor, a spatial barcode which is unique to that spot, a 12 base UMI and a poly-T region designed to capture poly-adenylated mRNA [1].
    • Spatial Transcirptomics is a particulcularly beneficial technique when studying human disease in both adult and developing tissue, this can be carried out in a single tissue section of consecutive sections throughout a tissue sample or organoid to gain this information in 3D context [1].
    • By highlighting changes in the transcriptome between normal and diseased tissue we can implicate the underlying mechanism behind diseases, increasing the potential of new targeted treatments to be developed as a result, in addition to gaining a better understanding of how this disease initially develop and evolve [1].
    • It is imperative to validate organoid tissue alongside normal and diseased human tissue to further increase the reliability and accuracy of human disease modelling in organoids and spheroids, further reducing the need for animal models of diseases. Spatial transcriptomics is an informative and reliable method of validation for this purpose [1].



    Although the trend of extensive publications and patents emerging in the field of 3D in vitro cancer models, further model development is mandated to facilitate their clinical and commercial translatability [1].

    2D traditional cultures remain core in vitro preclinical models for testing cancer therapeutics, largely owing to the inherent simplicity and accessibility of such model systems [1].

    A key barrier to the wide endorsement of complex multicellular 3D models as the go-to preclinical approach is the technical complexity associated with developing these to the extent where they are [1]:


    • Scalable
    • efficiently biomimetic
    • widely transferrable
    • show superior clinical and commercial translatability

    The urgent need for improved preclinical models to successfully lessen drug erosion in cancer drug development has revolutionised the field of in vitro culture, specifically with the emergence of next generation ex vivo 3D preclinical models [1].

    Organoid models in the correct culture setting can aim to faithfully recapitulate the cancer as it would arise, propagate, and respond to treatment in the patient [1].

    Ultimately, optimal next generation 3D preclinical platforms will aim to successfully meet 4 key objectives [1]:


    • Optimal clinical translatability
    • Transferability of experimental set up between different laboratories
    • Reproducibility of outputs
    • Scalability to ensure endorsement by both academic and industrial sectors


    1. Hockney, Sean et al. “Next generation organoid engineering to replace animals in cancer drug testing.” Biochemical pharmacology vol. 213 (2023): 115586. doi:10.1016/j.bcp.2023.115586


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