Mapping the dynamic immune landscape associated with therapeutic response in soft tissue sarcoma
Thesis or Dissertation
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Soft tissue sarcomas (STS) are a group of rare and heterogenous cancers of unmet need. In the advanced setting, and following failure of first-line anthracycline therapy, further line therapeutic options offer limited efficacy. Although the tyrosine kinase inhibitor initially showed promising results in early stage clinical trials, a lack of statistical improvement in overall survival in the PALETTE phase III trial led to withdrawal of funding via the NHS cancer drug fund. However, subsequent post-hoc analysis identified a subset of patients who were deemed long-term responders and survivors following pazopanib therapy, but we currently have no way of identifying these patients in the clinic. Although immune evasion is a key hallmark of cancer development, the immune tumour microenvironment (TME) in STS is incompletely characterised. This thesis will focus on mapping the immune TME with the aim of identifying potentially clinically relevant biomarkers for pazopanib response in STS. The thesis will lay out the composition and generation of three distinct cohorts to address this aim; a discovery cohort of patients treated with pazopanib to explore immune-based characteristics and their association with pazopanib response, a validation cohort of patients treated with pazopanib designed to validate any findings identified in the discovery cohort, and a comparator cohort of patients not treated with pazopanib to identify if any significant findings are predictive of response to pazopanib or generally prognostic in STS. Tumour tissue will be interrogated via immunohistochemistry assessment of tumour infiltrating lymphocytes, and these levels will be associated with overall and progression-free survival following pazopanib administration. RNA extracted from tumour samples will provide the input for targeted gene expression analysis of 770 immune related genes via the Nanostring platform. This data generated will be analysed using the R platform, with methods including principle component analysis, significance analysis of microarrays, consensus clustering and single sample gene set enrichment analysis. Through this, an immune-based signature will be generated from the discovery cohort and then be tested against the validation and comparator cohorts with the ultimate aim of identifying potentially clinically relevant biomarkers associated with pazopanib response in STS. In addition, matched temporal samples from patients will be interrogated in the same manner, to map dynamic changes in the immune TME. Temporal samples, including a subset of patients with paired pre and post-pazopanib samples are available, will shed light on how the tumour immune contexture as a function of time changes, and when associated with outcome may offer insight into how the immune landscape influences the behaviour of STS.
Mol and Systems Oncology
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Institute of Cancer Research (University Of London)