Harnessing pre-clinical lung cancer patient models for precision immuno-oncology (360G-Wellcome-209199_Z_17_Z)

Only 16% of non-small cell lung cancer (NSCLC) patients survive for 5 years. Improvement in survival has been slow as the histological and genomic features of the disease are heterogeneous. Tumour heterogeneity poses a challenge for therapy development and suggests the importance of a personalised medicine approach. One approach is to expand the subset of tumour-infiltrating lymphocytes (TILs) that target neoantigens generated by tumour mutations; however, the lack of model systems that recapitulate the complexity of human disease is a significant barrier to research in this area. Patient-derived xenografts (PDXs) have considerable advantages over murine cancer models and cell lines so I will generate PDX models from patients enrolled in the TRACERx clinical study, which aims to delineate the evolutionary trajectories of NSCLC through multi-region genetic analyses of primary, recurrence and metastatic tumours. My first goal is to establish the extent to which PDX tumours represent patient intratumour heterogeneity using the extensive TRACERx dataset. I will further use these models - along with patient-matched TILs - in in vitro and in vivo assays to investigate the key determinants of the ability of TILs to destroy cancer cells and to investigate the effect of dual checkpoint inhibitor and TIL therapy.