It is undeniable that cancer has been one of the most formidable disease throughout these past centuries. Up to this day, it is unfortunately common to see our close friends or family develop cancer as they come to age. Despite of how prevalent this disease has become, we still lack the knowledge and understanding of the tumor cell and an effective treatment for cancer has yet to be discovered.
Cancer cells are extremely hard to study and understand, mainly due to their heterogeneity. Cancer arises from a single cell that proliferates to form a clonal population (Kuo and Curtis, 2018). Heterogeneity then arises due to the genetic and non-genetic differences between the cells in that population. Genetic differences emerge from specific mutation within each cell, whereas non-genetic differences are generated from epigenetic alterations, such as methylation and other histone modifications (Seoane, 2017). This ultimately causes cell diversification and, as tumor cells proliferate, they undergo rapid evolution. Accordingly, to develop an effective cancer treatment, it is important to establish a high fidelity preclinical cancer model that expands our understanding of cancer-related molecular evolution patterns (Fan, Demirci and Chen, 2019).
There have been a variety of preclinical cancer models established throughout time; these include the traditional genetically engineered mouse models (GEMMs), cell lines and patient derived xenografts (PDXs) (Tuveson and Clevers, 2019). However, these models have been proven to not be entirely effective, since the model either fails to retain heterogeneity or consumes too much time to produce results. Recently, a new preclinical cancer model, cancer organoid technology (COT), has been introduced to the biomedical field. With its ability to retain heterogeneity, low cost and ease of use, it is a promising methodology to examine cancer evolution patterns.
Recently, Roerink and his team were able to demonstrate the effectiveness of COT by producing a 3-dimensional organoid structure from human colorectal cancers. This experimental process was achieved by extracting single cells from 4 to 6 different sites of colorectal cancers, which were then individually grown in vitro to form 3-dimensional cultures called organoids. Healthy cells nearby were also extracted to compare the genetic material between the healthy and cancerous cells. Later, Roerink and his team performed many analyses, including DNA sequencing and mutation analysis. Through examining the different mutations in each individual cell, they were able to construct a phylogenetic tree, which demonstrated lineage relationships between the organoids’ genomes (Kuo and Curtis, 2018). Accordingly, scientists were able to better analyze the evolution of cancer cells, including the process of carcinogenesis and metastasis. For example, Matano and his team demonstrated that inducing mutations in APC, SMAD4, TP53, KRAS, and PIK3CA in intestinal organoids could model the genesis of adenoma (Fan, Demirci and Chen, 2019).
As a result, cancer organoids have provided scientists and doctors an abundance of new information regarding cancer cells. Organoids have revolutionized personalized anti-cancer therapy and opened up new pathways in the area of cancer therapy. Patient derived organoids can identify the specific genetic alterations, like gene expression and histopathology in each individual patient. This means that personalized drug screening is now much more effective (Fan, Demirci and Chen, 2019) as a variety of drugs can be tested on the organoids to identify the best personal cancer treatment. Furthermore, with more data, a common drug treatment for a specific cancer type can be developed.
COT has become a valuable tool in the biomedical world. It has pushed scientists and doctors to the next step in further understanding the evolutionary patterns of cancer cells. However, there are still many improvements to be made, like perfectly mimicking the environment of the cancer cells. Nonetheless, as we slowly expand our knowledge and evolve the preclinical models, COT could revolutionize cancer treatment!
Fan, H., Demirci, U. and Chen, P. (2019) ‘Emerging organoid models: leaping forward in cancer research’. doi: 10.1186/s13045-019-0832-4.
Kuo, C. J. and Curtis, C. (2018) ‘Organoids reveal cancer dynamics’, 556, pp. 441–442. Available at: https://www.mendeley.com/viewer/?fileId=4f8d326e-58b8-92c9-59ff-02a27aa2de11&documentId=082f373f-390e-3963-b409-142645cad3f4.
Seoane, J. (2017) ‘Division hierarchy leads to cell heterogeneity’, 549, pp. 164–166. Available at: chrome-extension://dagcmkpagjlhakfdhnbomgmjdpkdklff/enhanced-reader.html?pdf=https%3A%2F%2Fbrxt.mendeley.com%2Fcatalog%2Fcontent%2Fe7bca1b1-1e17-3bc0-b73c-8656aa2722d4.
Tuveson, D. and Clevers, H. (2019) Cancer modeling meets human organoid technology. Available at: http://science.sciencemag.org/.