Improving therapeutic approaches for the treatment of Non-small Cell Lung Cancer using an ex-vivo explant culture system

Lung Cancer is the leading cause of cancer death worldwide in both males and females. Non-small cell lung cancer (NSCLC) accounts for ~80-85% of lung cancers. NSCLC is a very heterogeneous disease, both genetically and histologically, and there is an increasing list of mutations and copy number alterations in cancer associated genes. Several drugs that could potentially improve lung cancer outcomes are in development and some have entered clinical trials. However, the current established preclinical models, particularly animal xenografts, are not always predictive of patient outcome and there has been a large attrition of clinical candidate drugs at the Phase III stage. The aim of this project was to establish a primary NSCLC explant culture system with the view to developing a better platform to test the efficacy of existing drugs as well as novel drug combinations. The tissue architecture and tumour heterogeneity of individual NSCLC patients can be examined in an ex-vivo NSCLC explant culture system which maintains viability and proliferation in a short period of 24 hours + recovery (16-20 hours). Even though there is a moderate effect of cultivation, the ex-vivo NSCLC explant culture system can be used for assessing in situ drug responses over short periods. Responses of explants were assessed after treatment with cisplatin, MEK and PI3K inhibitors singly and in combination and TRAIL and ABT-737 singly and in combination in the presence or absence of cisplatin. This model points towards being more predictive of patient outcome in clinical studies than in vitro studies or animal models. The data show that the explant system has the potential to improve on current preclinical models for lung cancer or other solid cancers and help the drug development process achieve greater successes in the clinic. The model could provide a platform for personalising treatment to each patient and for identifying effective biomarkers for drug responses.