Cancer is inherently difficult to detect and treat. To add to this complication, some patients develop drug resistance to treatment, leading to tumour recurrence or metastasis. Much research is further needed to characterise and understand how tumours respond to drugs and therapy until the tumour is successfully eradicated completely.

To begin, it is important to have a better understanding of oncology drug resistance, how and why it occurs, and then how to avoid resistance. Oncology drug resistance is where the tumour initially responded to a treatment, but over time, the body develops mechanisms to bypass the drug’s effects. In some cases, the body did not respond to treatment from the outset. There are many mechanisms where resistance to cancer therapy is developed. Genetic mutations within the cancer cells can render treatment ineffective. Also, changes in the tumour microenvironment (TME) can have a role in drug resistance. Immune evasion is another situation where drug resistance can occur. In this case, there may be upregulation of immune checkpoints, downregulation of tumour antigens, or increased production of immunosuppressive cytokines. By understanding mechanisms used for resistance, better treatment and therapies can be developed for the patient. 

Oncology Drug Resistance Models 
Resistance is a multifaceted challenge that requires different approaches to find the right answer. As such, there’s a variety of resistance models available that can provide different insights into the resistance mechanism of interest. On one hand, we have pre-existing models with intrinsic or acquired resistance. Within the acquired resistant models, pretreated models are a great tool to provide a more clinically relevant system. These are models (e.g., pretreated PDX models) that have been generated after the patient has received a certain treatment(s) and has relapsed. They can be used to investigate novel compounds or potential combination therapies to overcome the resistance being observed in the clinic. If a model of interest is not readily available, there are different approaches that can be used to generate resistance models. When investigating target resistance, engineering techniques such as CRISPR-Cas9 can be used to knock out the gene of interest and study the sensitivity of the compound compared to the wild-type version. On the other hand, if the interest lies in off-target resistance, it is possible to generate an in vitro or in vivo model by drug challenge, that is continuous dosing with the relevant SoC until the model acquires resistance. This approach can elucidate previously unknown mechanisms of resistance. Last, but not least, metastatic modeling is another category of resistant models, when the combination of in vivo models with imaging techniques can help track the progress of the disease in real time. Overall, a strategy needs to be developed to gain a full understanding of the potential resistance development and how to overcome it.