Microscopy & Microtechniques
3 Types of Cancer Modelling
Nov 21 2021
Computational modelling is an increasingly popular technique when it comes to disease research. Given that cancer is one of the most widespread and deadly diseases on the planet, it’s only logical that the scientific community has been pursuing this avenue to further their understanding of how cancerous cells behave in the human body.
By allowing researchers to undertake experiments in silico, they can learn more about the likely outcome of any medical procedures prior to performing them in vivo. This helps greatly with predicting the growth and nature of the tumour, the creation of a personalised treatment plan for individual sufferers of the disease and the reaction of their body to the treatment as it occurs. With that in mind, here’s a brief introduction to the various forms of cancer modelling and the benefits they hold.
Tumour growth modelling
Depending on the type of cancer, the environment it is hosted in and the treatments it is subjected to, cancerous cells can behave in a myriad number of ways. This heterogeneity has made it highly challenging for researchers to come up with consistently successful methods of treating the disease, which is why modelling the growth of tumours has been such a popular area of investigation. By monitoring how a tumour develops in patients experiencing chemotherapy (as well as those who are not), it can give more information about which treatments are likely to be effective.
Patient-derived xenograft (PDX) modelling is an emerging technique which relies on the extraction of cancerous tissue or cells from a patient, then direct injection into an immunodeficient mouse. This differs from traditional methods of xenograft modelling in that it dispenses with the in vitro passaging of the tissues, which can alter the genetic makeup of the cancer and make the results of the analysis less reliable when transplanted to an in vivo situation. As such, PDX modelling is viewed as being far more accurate in terms of the environment in which the cancer is hosted and therefore more reliable when predicting outcomes.
Cancer on a chip
Whereas the two aforementioned methods of cancer modelling rely on in vivo analysis, where live tissue is extracted and examined from either animals or humans, cancer on a chip is an in silico alternative. In essence, it involves replicating the physiology, functions and structures of human organs on a microchip, then implanting the cancerous tissue from a patient into the chip. This is not only less invasive (or less cruel, for animals) and more cost-effective, but allows for continuous analysis instead of a single snapshot of the tumour’s activity. As such, it has the potential to be far more effective in predicting how it will behave when subjected to various treatment types.
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