Response to anticancer treatment is dictated not only by availability of molecular targets within the tumor, but also by the highly heterogenous, patient-specific composition of cells within tumors and their complex tumor microenvironment (TME). Consequently, some patients demonstrate remarkable responses to anticancer therapies, whereas others experience negligible effects. Thus, there is an urgent need for more effective strategies to preselect patients for therapies, mitigating the risk of administering ineffective and potentially harmful drugs and speeding up the process for patients to receive the most effective treatment.
The most promising solution to tackle heterogenous responses to treatment is the creation of patient-derived models (‘patient avatars’) that accurately represent an individual’s tumor. Tailoring therapies for cancer patients based on their avatars’ responses to anticancer drugs could change the entire approach to treating patients with solid tumor diseases. Although introducing these human in vitro systems into clinical settings has been challenging, recent evidence has demonstrated the readiness of these models for clinical translation.
