Generating patient-derived models of soft-tissue sarcoma for the evaluation of therapy response and resistance

Abstract

For the majority of patients, advanced soft-tissue sarcoma (STS) is fatal, with an overall survival of 14-19 months. Systemic chemotherapy has remained the cornerstone of advanced STS control for more than 30 years and prognosis has seen little change in the past decade, highlighting the urgent need for novel treatment modalities. In order to improve clinical efficacy of targeted therapies, identification of response and resistance mechanisms and candidate biomarkers of response are vital. However, a major obstacle in the study of STS drug response mechanisms is the lack of models that closely represent patient tumours. This obstacle restricts our ability to develop effective treatment strategies for advanced sarcoma that can be translated into clinical benefit. This project seeks to address this issue by first establishing a patient-derived model pipeline, starting with STS patient tumour biopsies, and using this to develop a panel of patient-derived xenografts (PDXs) as well as 2D and 3D in vitro PDX-derived cell cultures, with a particular focus on the leiomyosarcoma (LMS) subtype. These models have been characterised via proteomic profiling, by measuring growth kinetics and, in the case of in vitro cultures, tumourigenicity by in vivo injection. These models were used in cell-based and in vivo assays to assess the degree of sensitivity to standard pf care chemotherapies such as doxorubicin, gemcitabine or docetaxel as well as a panel of small molecule inhibitors targeting key oncogenic signalling pathways. This analysis was followed by functional assessment of candidate drug response mechanisms. Through these means, I reported the mechanisms of response to standard of care therapeutics and targeted therapies such as PI3K/mTOR and PARP inhibitors while highlighting predictive biomarkers of response including phosphatase and tensin homolog (PTEN) deletion. Additionally, the impact of chemo-resistance on subsequent response to targeted therapies was investigated to inform future drug treatment regimens.