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
