Characterisation of the Tumour Microenvironment in Ovarian Cancer
University of Cambridge
Cancer Research UK Cambridge Institute
Doctor of Philosophy (PhD)
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Jiménez Sánchez, A. (2019). Characterisation of the Tumour Microenvironment in Ovarian Cancer (Doctoral thesis). https://doi.org/10.17863/CAM.35250
The tumour microenvironment comprises the non-cancerous cells present in the tumour mass (fibroblasts, endothelial, and immune cells), as well as signalling molecules and extracellular matrix. Tumour growth, invasion, metastasis, and response to therapy are influenced by the tumour microenvironment. Therefore, characterising the cellular and molecular components of the tumour microenvironment, and understanding how they influence tumour progression, represent a crucial aim for the success of cancer therapies. High-grade serous ovarian cancer provides an excellent opportunity to systematically study the tumour microenvironment due to its clinical presentation of advanced disseminated disease and debulking surgery being standard of care. This thesis first presents a case report of a long-term survivor (>10 years) of metastatic high-grade serous ovarian cancer who exhibited concomitant regression/progression of the metastatic lesions (5 samples). We found that progressing metastases were characterized by immune cell exclusion, whereas regressing metastases were infiltrated by CD8+ and CD4+ T cells. Through a T cell - neoepitope challenge assay we demonstrated that pre- dicted neoepitopes were recognised by the CD8+ T cells obtained from blood drawn from the patient, suggesting that regressing tumours were subjected to immune attack. Immune excluded tumours presented a higher expression of immunosuppressive Wnt signalling, while infiltrated tumours showed a higher expression of the T cell chemoattractant CXCL9 and evidence of immunoediting. These findings suggest that multiple distinct tumour immune microenvironments can co-exist within a single individual and may explain in part the hetero- geneous fates of metastatic lesions often observed in the clinic post-therapy. Second, this thesis explores the prevalence of intra-patient tumour microenvironment het- erogeneity in high-grade serous ovarian cancer at diagnosis (38 samples from 8 patients), as well as the effect of chemotherapy on the tumour microenvironment (80 paired samples from 40 patients). Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumours revealed highly prevalent variability in immune signalling and distinct immune microenvironments co-existing within the same individuals at diagnosis. ConsensusTME, a method that generates consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods that predict immune cell populations using bulk RNA data was developed. ConsensusTME improved accuracy and sensitivity of T cell and leukocyte deconvolutions in ovarian cancer samples. As previously observed in the case report, Wnt signalling expression positively correlated with immune cell exclusion. To evaluate the effect of chemotherapy on the tumour microenvironment, we compared site-matched and site-unmatched tumours before and after neoadjuvant chemotherapy. Site- matched samples showed increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, unlike site-unmatched samples where heterogeneity could not be accounted for. In addition, low levels of immune activation pre-chemotherapy were found to be correlated with immune activation upon chemotherapy treatment. These results cor- roborate that the tumour-immune interface in advanced high-grade serous ovarian cancer is intrinsically heterogeneous, and that chemotherapy induces an immunogenic effect mediated by cytotoxic cells. Finally, the different deconvolution methods were benchmarked along with ConsensusTME in a pan-cancer setting by comparing deconvolution scores to DNA-based purity scores, leukocyte methylation data, and tumour infiltrating lymphocyte counts from image analysis. In so far as it has been benchmarked, unlike the other methods, ConsensusTME performs consistently among the top three methods across cancer-related benchmarks. Additionally, ConsensusTME provides a dynamic and evolvable framework that can integrate newer de- convolution tools and benchmark their performance against itself, thus generating an ever updated version. Overall, this thesis presents a systematic characterisation of the tumour microenvironment of high grade serous ovarian cancer in treatment-naive and chemotherapy treated samples, and puts forward the development of an integrative computational method for the systematic analysis of the tumour microenvironment of different tumour types using bulk RNA data.
Tumour microenvironment, High grade serous ovarian cancer, Bioinformatics, Genomics, Systems biology, Computational biology, Cancer, Cancer immunology, Chemotherapy, Immunotherapy, Tumour heterogeneity, Personalised medicine, Computational method development
Cancer Research UK Cambridge Institute provided funding for my studentship. The Mexican National Council of Science and Technology provided funding for my studentship.
This record's DOI: https://doi.org/10.17863/CAM.35250
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