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Title: Deconvolution of the immune landscape of cancer transcriptomics data, its relationship to patient survival and tumour subtypes
Author: Nirmal, Ajit Johnson
ISNI:       0000 0004 7429 6385
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2018
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The immune response to a given cancer can profoundly influence a tumour's trajectory and response to treatment, but the ability to analyse this component of the microenvironment is still limited. To this end, a number of immune marker gene signatures have been reported which were designed to enable the profiling of the immune system from transcriptomics data from tissue and blood samples. Our initial analyses of these resources suggested that these existing signatures had a number of serious deficiencies. In this study, a co-expression based approach led to the development of a new set of immune cell marker gene signatures (ImSig). ImSig supports the quantitative and qualitative assessment of eight immune cell types in expression data generated from either blood or tissue. The utility of ImSig was validated across a wide variety of clinical datasets and compared to published signatures. Evidence is provided for the superiority of ImSig and the utility of network analysis for data deconvolution, demonstrating the ability to monitor changes in immune cell abundance and activation state. ImSig was also used to examine immune infiltration in the context of cancer classification and treatment. Patient-matched ER+ breast cancer samples before and after treatment with letrozole were analysed. Significant elevation of infiltration of macrophages and T cells on treatment was observed in responders but not in non-responders, potentially revealing a biomarker for response. ImSig was also used to study the immune infiltrate in 12 cancer types. By computing the relative abundance of immune cells in these samples, their relationship to survival was investigated. It was interesting to observe that half of the cancers showed trends towards poor prognosis with increased infiltration of immune cells. ImSig alongside the network-based framework can also be used for a more explorative analysis such as to identify biomarkers and activation or differentiation states of immune cells. Melanoma is a highly immunogenic cancer and has shown tremendous success with immune checkpoint inhibitors in a subset of patients. In chapter-6, the molecular subgrouping of melanoma was explored using a network-based approach. Despite the plethora of evidence suggesting various aspects of the immune system to contribute towards the response to immunotherapy in melanoma, there has been little to no effort to consider this heterogeneity while developing molecular subgroups. The use of ImSig was therefore explored for the stratification of melanoma patients into immuno-subgroups. The subgrouping methodology divided the tumours into four groups with different immune profiles. Interestingly, these groupings showed prognostic significance, reiterating the need to consider the heterogeneity of immune cells in future studies. On identifying the most dominant phenotypes that contribute towards prognosis of these patients and in comparison to the published subgroupings of melanoma, we argue that the subgroup of samples enriched in keratin genes are not clinically meaningful. ImSig and the associated analysis framework described in this work, support the retrospective analysis of tissue derived transcriptomics data enabling better characterisation of immune infiltrate associated with disease, and in so doing, provide a resource useful for prognosis and potentially in guiding treatment.
Supervisor: Freeman, Thomas ; Sims, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: transcriptomics ; bioinformatics ; cancer ; immunology ; immune cells