Integrated Transcriptomic and Proteomic Analysis of Red Blood Cells from Rainbow Trout Challenged with VHSV Point Towards Novel Immunomodulant Targets

Integrated Transcriptomic and Proteomic Analysis of Red Blood Cells from Rainbow Trout Challenged with VHSV Point Towards Novel Immunomodulant TargetsThis work was supported by the European Research Council (ERC Starting Grant GA639249).The proteomic analysis was performed in the Proteomics Facility of the Spanish National Center for Biotechnology (CNB-CSIC) belonging to ProteoRed, PRB3-ISCIII, supported by grant PT17/001

Neue Ansätze in der Tumorimmunologie: Implikationen aus der massenspektrometrischen Analyse von HLA-Liganden

Molecularly defined approaches in tumour immunotherapy require the identifi- cation of appropriate tumour (associated) antigens. Therefore, a quantitative mass spectrometric approach based on the differential N-terminal isotope coding (dNIC) strategy was chosen for analysis of various HLA-bound peptide pools. In a first step, we could show that the systemic variations in the dNIC approach are low enough that biological differences in two samples can be detected reliably. In a TAP-deficient cell line we have identified several over-presented peptides, which share some common features: Most of these peptides had a reduced binding affinity to MHC-I molecules and were derived from signal sequences. However, several of the TAP-independently presented peptides were presumably generated by the proteasome and transported into the ER via alternative pathways. Using primary tumour tissue and autologous normal tissue, we were able to show that the correlation of over-presented peptides to their corresponding mRNA levels was very weak. This result emphasized the importance of quantitative MHC ligand analysis for a fast and reliable identification of tumour (associated) antigens. Finally, we took an HLA ligand which was identified as being majorly overpresented in our tumour vs. normal tissue comparison and demonstrated that this VEGF-derived ligand can be used as a CTL epitope derived from the tumour associated antigen VEGF. Concluding, the dNIC strategy is a powerful MS-based approach for the identi- fication of differences in the HLA ligandome of two samples. This can be utilised for a broad spectrum of applications ranging from quantitative HLA ligandome comparisons to the identification of tumour associated CTL epitopes.Molekular definierte Ansätze in der Tumorimmuntherapie setzen die Identifikation geeigneter Tumor (assoziierter) Antigene voraus. Deshalb wurde ein massenspektrometrischer Ansatz, basierend auf der differentiellen N-terminalen Isotopencodierungs (dNIC) Strategie, gewählt um diverse Vergleiche von HLA-gebundenen Peptidpools durchzuführen. In einem ersten Schritt konnten wir zeigen, dass die systemischen Variationen der dNIC-Methode klein genug sind, um biologische Unterschiede zwischen zwei Proben verlässlich zu identifizieren. In einer TAP-defizienten Zelllinie konnten wir mehrere überpräsentierte Peptide identifizieren, die einige Gemeinsamkeiten teilten: Die meisten dieser Peptide hatten eine verminderte Bindungsaffinität zu MHC-I Molekülen und kamen aus Signalsequenzen. Einige der TAP-unabhängig präsentierten Peptide wurden jedoch wahrscheinlich durch das Proteasom erzeugt und gelangten durch einen alternativen Weg in das ER. Bei einem Vergleich von Tumor- und autologem Normalgewebe konnten wir zeigen, dass die Korrelation von überpräsentierten Peptiden mit ihren korrespondierenden mRNA-Verhältnissen nur sehr gering war. Dieses Ergebnis betont die Bedeutung der quantitativen MHC-Ligandenanalyse für eine schnelle und verlässliche Identifizierung von Tumor (assoziierten) Antigenen. Zuletzt verwendeten wir einen HLA-Liganden, der in unserem Tumor versus Normalgewebsvergleich als besonders überpräsentiert identifiziert wurde. Für diesen Liganden aus VEGF konnten wir zeigen, dass er als CTL-Epitop des Tumor-assoziierten Antigens VEGF verwendet werden kann. Schlussfolgernd betrachtet ist die dNIC-Strategie ein MS-basierter Ansatz mit großem Potential für die Identifizierung von Unterschieden im HLA-Ligandom von zwei Proben. Dies kann für ein breites Anwendungsspektrum eingesetzt werden, welches von den quantitativen HLA-Ligandomanalysen bis hin zur Identifizierung von Tumor-assoziierten Antigenen reicht

Identification of HLA-A2-Restricted Mycobacterial Lipoprotein Z Peptides Recognized by T CellsFrom Patients With ActiveTuberculosis Infection

Identification of HLA-restricted peptides derived from mycobacterial antigens that are endowed with high affinity and strong antigenicity is not only of interest in tuberculosis (TB) diagnostics and treatment efficacy evaluation, but might also provide potential candidates for the development of therapeutic vaccines against drug-resistant TB. Our previous work demonstrated that lipoprotein Z (LppZ) displayed high immunogenicity and antigenicity in active TB patients. In the present study, ten HLA-A2-restricted LppZ peptides (LppZp1-10) were predicted by bioinformatics, among which LppZp7 and LppZp10 were verified to possess high affinity to HLA-A2 molecules using T2 cell-based affinity binding assay. Moreover, results from ELISpot assay showed that both LppZp7 and LppZp10 peptides were able to induce more IFN-γ producing cells upon ex vivo stimulation of PBMC from HLA-A2+ active TB (ATB) patients as compared to those from healthy controls (HCs). Also, the numbers of LppZp7 and LppZp10-specific IFN-γ producing cells exhibited positive correlations with those of ESAT-6 peptide (E6p) or CFP-10 peptide (C10p) in ATB. Interestingly, stimulation with LppZp7/p10 mixture was able to induce higher intracellular expression of IFN-γ and IL-2 cytokines in CD8+ and CD4+ T cells from ATB as compared to HC, associated with lower expression of TNF-α in both CD8+ and CD4+ T cells. Taken together, HLA-A2-restricted LppZp7 and LppZp10 peptides display high immunoreactivity in HLA-matched ATB patients demonstrated by high responsiveness in both CD8+ and CD4+ T cells. With the ability to induce strong antigen-specific cellular responses, LppZp7 and LppZp10 are of potential value for the future applications in the prevention and control of TB

HLA class I supertype and supermotif definition by chemometric approaches.

Activation of cytotoxic T cells in human requires specific binding of antigenic peptides to human leukocyte antigen (HLA) molecules. HLA is the most polymorphic protein in the human body, currently 1814 different alleles collected in the HLA sequence database at the European Bioinformatics Institute. Most of the HLA molecules recognise different peptides. Also, some peptides can be recognised by several of HLA molecules. In the present project, all available class I HLA alleles are classified into supertypes. Super - binding motifs for peptides binding to some supertypes are defined where binding data are available. A variety of chemometric techniques are used in the project, including 2D and 3D QSAR techniques and different variable selection methods like SIMCA, GOLPE and genetic algorithm. Principal component analysis combined with molecular interaction fields calculation by the program GRID is used in the class I HLA classification. This thesis defines an HLA-A3 supermotif using two QSAR methods: the 3D-QSAR method CoMSIA, and a recently developed 2D-QSAR method, which is named the additive method. Four alleles with high phenotype frequency were included in the study: HLA-A*0301, HLA-A*1101, HLA-A*3101 and HLA- A*6801. An A*020T binding motif is also defined using amino acid descriptors and variable selection methods. Novel peptides have been designed according to the motifs and the binding affinity is tested experimentally. The results of the additive method are used in the online server, MHCPred, to predict binding affinity of unknown peptides. In HLA classification, the HLA-A, B and C molecules are classified into supertypes separately. A total of eight supertypes are observed for class I HLA, including A2, A3, A24, B7, B27, B44, CI and C4 supertype. Using the HLA classification, any newly discovered class I HLA molecule can be grouped into a supertype easily, thus simplifying the experimental function characterisation process

Defining the human endothelial transcriptome

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 91-100).Advances in microarray technology facilitate the study of biological systems at a genome-wide level. Meaningful analysis of these transcriptional profiling studies, however, demands the concomitant development of novel computational techniques that take into account the size and complexity of the data. We have devised statistical algorithms that use replicate microarrays to define a genome-wide expression profile of a given cell type and to determine a list of genes that are significantly differentially expressed between experimental conditions. Applying these algorithms to the study of cultured human umbilical vein endothelial cells (HUVEC), we have found approximately 54% of all genes to be expressed at a detectable level in HUVEC under basal conditions. The set of highest expressed genes is enriched in nucleic acid binding proteins, cytoskeletal proteins and isomerases as well as certain known markers of endothelium, and the complete list of genes can be found at ... We have also studied the effect of a 4-hour exposure of HUVEC to 10 U/mL of IL-1, and detected 491 upregulated and 259 downregulated statistically significant genes, including several chemokines and cytokines, as well as members of the TNFAIP3 family, the KLFfamily and the Notch pathway. Applying these rigorous statistical techniques to genome-wide expression datasets underscores known patterns of endothelial inflammatory gene regulation and unveils new pathways as well.(cont.) Finally, we performed a direct comparison of direct-labeled microarrays with amplified RNA microarrays for an initial assessment of the effect of the additional noise of amplification on the outputs of the statistical algorithms. These techniques can be applied to additional genome-wide profiling studies of endothelium and other cell types to refine our understanding of transcriptomes and the gene regulatory network governing cellular function and pathophysiology.by Sripriya Natarajan.S.M

Discrete arginine topologies guide escape of miniature proteins from early endosomes to the cytoplasm

Polypeptides and peptide mimetics sample a wide chemical space with broad potential to modulate cellular function, but their application to cytoplasmic targets is limited because when added to cells their cytosolic concentration remains low. This limitation is due to a diffusion barrier (the plasma membrane) and absence of dedicated import machinery. Highly cationic peptides and proteins sometimes gain cytosolic access, but how they do so is not well understood. Using a small library of cationic miniature proteins, I probe the influence of positive charge number and orientation on the ability the miniature protein to access the cytoplasm. Using a novel assay, I identify a cationic miniature protein, which we called 5·3, that carries a discrete arginine motif and efficiently reaches the cytoplasm. Database searches find that the precise motif identified (an arginine present in positions i, i + 4, i + 7, i + 10, and i + 11 of an alpha-helix) is not present in nature, but that similar motifs are present in natural proteins that interact with cellular membranes. Finally, I examine the cellular pathway by which 5·3 reaches the cytoplasm. I find that this miniature protein enters the cell via a dynamin and cholesterol dependent endocytic mechanism and is delivered to Rab5+ early endosomes. In contrast to the shiga-like toxins, and many non-enveloped viruses (which escape to the cytoplasm from the endoplasmic reticulum) as well as other peptides previously identified as \u27cell penetrating\u27, only 5·3 escapes from early endosomes. These findings should enable the future dissection of the precise molecular events underlying cytoplasmic access of peptides and proteins, and may illuminate principles for the engineering of peptides and peptidomimetics that access cytoplasmic targets

INTEGRATED MOLECULAR PROFILING FOR ANALYZING AND PREDICTING THERAPEUTIC MECHANISM, RESPONSE, BIOMARKER AND TARGET

Ph.DDOCTOR OF PHILOSOPH

An Integrated Immunopeptidomics and Proteogenomics Framework to Discover Non-Canonical Targets for Cancer Immunotherapy

Un élément essentiel de l’immunothérapie appliquée au cancer est l’identification de peptides liant les antigènes des leucocytes humains (HLA) et capables d’induire une puissante réponse T anti-tumorale. La spectrométrie de masse (MS) constitue actuellement la seule méthode non-biaisée permettant une analyse détaillée du panel d’antigènes susceptibles d’être présentés aux lymphocytes T in vivo. L’utilisation de cette méthode en clinique requiert toutefois des améliorations significatives de la méthodologie utilisée lors de l’identification des peptides HLA. Un consortium multidisciplinaire de chercheurs a récemment mis en lumière les problèmes actuellement liés à l’utilisation de la MS en immunopeptidomique, soulignant le besoin de développer de nouvelles méthodes et mettant en évidence le défi que représente la standardisation de l’immuno-purification des molécules HLA. La première partie de cette thèse vise à optimiser les méthodes expérimentales permettant l’extraction des peptides apprêtés aux HLA. L’optimisation de la méthodologie de base a permis des améliorations notables en terme de débit, de reproductibilité, de sensibilité et a permis une purification séquentielle des molécules de HLA de classe I de classe II ainsi que de leurs peptides, à partir de lignées cellulaires ou de tissus. En comparaison avec les méthodes existantes, ce protocole comprend moins d’étapes et permet de limiter la manipulation des échantillons ainsi que le temps de purification. Cette méthode, pour les peptides HLA extraits, a permis d’obtenir des taux de reproductibilité et de sensibilité sans précédents (corrélations de Pearson jusqu'à 0,98 et 0,97 pour les HLA de classe I et de classe II, respectivement). De plus, la faisabilité d’études comparatives robustes a été démontrée à partir d’une lignée cellulaire de cancer de l’ovaire, traitée à l'interféron gamma. En effet, cette nouvelle méthode a mis en évidence des changements quantitatifs et qualitatifs du catalogue de peptides présentés aux HLA. Les résultats obtenus ont mis en avant une augmentation de la présentation de longs ligands chymotryptiques de classe I. Ce phénomène est probablement lié à la modulation de la machinerie de traitement et de présentation des antigènes. Dans cette première partie de thèse, nous avons développé une méthodologie robuste et rationalisée, facilitant la purification des HLA et pouvant être appliquée en recherche fondamentale et translationnelle. Bien que les néoantigènes représentent une cible attractive, des études récentes ont mis en évidence l’existence des antigènes non canoniques. Ces antigènes tumoraux, bien que non mutés, sont aussi spécifiques aux cellules cancéreuses et semblent jouer un rôle important dans l’immunité anti-tumorale. La seconde partie de cette thèse a pour objectif le développement d’une méthodologie d’analyse permettant l’identification ainsi que la validation de ces antigènes particuliers. Les antigènes non canoniques sont d'origine présumée non codante et ne sont, par conséquent, que rarement inclus dans les bases de données des séquences de protéines de référence. De ce fait, ils ne sont généralement pas pris en compte lors des recherches de MS utilisant de telles bases de données. Afin de palier ce problème et de permettre leur identification par MS, le séquençage de l'exome entier, le séquençage de l'ARN sur une population de cellules et sur des cellules uniques, ainsi que le profilage des ribosomes ont été intégrés aux données d’immunopeptidomique. Ainsi, NewAnce, un programme informatique permettant de combiner les données de deux outils de recherche MS en tandem, a été développé afin de calculer le taux d’antigènes non canoniques identifiés comme faux positifs. L’utilisation de NewAnce sur des lignées cellulaires provenant de patients atteints de mélanomes ainsi que sur des biopsies de cancer du poumon a permis l’identification précise de centaines de peptides HLA non classiques, spécifiques aux cellules tumorales et communs à plusieurs patients. Le niveau de confirmation des peptides non canoniques a ensuite été testé à l’aide d’une approche de MS ciblée. Les peptides résultant de ces analyses ont été minutieusement validés pour un des échantillons de mélanome disponibles. De plus, le profilage des ribosomes a révélé que les nouveaux cadres de lecture ouverts, desquels résultent certains de ces peptides non classiques, sont activement traduits. L’évaluation de l’immunogenicité de ces peptides a été évaluée avec des cellules immunitaires autologues et a révélé un épitope immunogène non canonique, provenant d'un cadre de lecture ouvert alternatif du gène ABCB5, un marqueur des cellules souches du mélanome. De manière globale, les résultats obtenus au cours de cette thèse soulignent la possibilité d’inclure ce type d’analyse de proteogénomique dans un protocole d’identification de néoantigènes existant. Cela permettrait d’inclure et prioriser des antigènes tumoraux non classiques et de proposer aux patients en impasse thérapeutique des immunothérapies anti-tumorales personnalisées. -- A central factor to the development of cancer immunotherapy is the identification of clinically relevant human leukocyte antigen (HLA)-bound peptides that elicit potent anti-tumor T cell responses. Mass spectrometry (MS) is the only unbiased technique that captures the in vivo presented HLA repertoire. However, significant improvements in MS-based HLA peptide discovery methodologies are necessary to enable the smooth transition to the clinic. Recently, a consortium of multidisciplinary researchers presented current issues in clinical MS-based immunopeptidomics, highlighting method development and standardization challenges in HLA immunoaffinity purification. The first part of this thesis addresses improvements to the experimental method for HLA peptide extraction. The approach was optimized with several new developments, facilitating high-throughput, reproducible, scalable, and sensitive sequential immunoaffinity purification of HLA class I and class II peptides from cell lines and tissue samples. The method showed increased speed, and reduced sample handling when compared to previous methods. Unprecedented depth and high reproducibility were achieved for the obtained HLA peptides (Pearson correlations up to 0.98 and 0.97 for HLA class I and HLA class II, respectively). Additionally, the feasibility of performing robust comparative studies was demonstrated on an ovarian cancer cell line treated with interferon gamma. Both quantitative and qualitative changes were detected in the cancer HLA repertoire upon treatment. Specifically, a yet unreported and interesting phenomenon was the upregulated presentation of longer and chymotryptic-like HLA class I ligands, likely related to the modulation of the antigen processing and presentation machinery. Taken together, a robust and streamlined framework was built that facilitates peptide purification and its application in basic and translational research. Furthermore, recent studies have shed light that, along with the highly attractive mutated neoantigens, other non-mutated, yet tumor-specific, non-canonical antigens may also play an important role in anti-tumor immunity. Non-canonical antigens are of presumed non-coding origin and not commonly included in protein reference databases, and are therefore typically disregarded in database-dependent MS searches. The second part of this thesis develops an analytical workflow enabling the confident identification and validation of non- canonical tumor antigens. For this purpose, whole exome sequencing, bulk and single-cell RNA sequencing and ribosome profiling were integrated with MS-based immunopeptidomics for personalized non-canonical HLA peptide discovery. A computational module called NewAnce was designed, which combines the results of two tandem MS search tools and implements group-specific false discovery rate calculations to control the error specifically for the non-canonical peptide group. When applied to patient-derived melanoma cell lines and paired lung cancer and normal tissues, NewAnce resulted in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides. Next, the level of non-canonical peptide confirmation was tested in a targeted MS-based approach, and selected non-canonical peptides were extensively validated for one melanoma sample. Furthermore, the novel open reading frames that generate a selection of these non- canonical peptides were found to be actively translated by ribosome profiling. Importantly, these peptides were assessed with autologous immune cells and a non-canonical immunogenic epitope was discovered from an alternative open reading frame of melanoma stem cell marker gene ABCB5. This thesis concludes by highlighting the possibility of incorporating the proteogenomics pipeline into existing neoantigen discovery engines in order to prioritize tumor-specific non-canonical peptides for cancer immunotherapy. -- Maladie très hétérogène et multifactorielle, le cancer représente à ce jour la seconde cause de décès dans le monde. Bien que le système immunitaire soit capable de reconnaître puis d’éliminer les cellules cancéreuses, ces dernières peuvent à leur tour s’adapter et accumuler des mutations leur permettant d’échapper à cette reconnaissance. L’immunothérapie anti-tumorale démontre le rôle clé de l’immunité dans l’éradication des tumeurs. Cependant, ces thérapies prometteuses ne sont efficaces que chez une petite proportion des patients traités. Une étape majeure dans l’établissement d’une réponse immunitaire anti-tumorale est la reconnaissance d’antigènes associés aux tumeurs. Des études récentes ont montré que les antigènes tumoraux issus de régions non-codantes du génome (antigènes non-canoniques) peuvent jouer un rôle clé dans l’induction de réponses immunitaires. Ainsi, l’identification de ces antigènes tumoraux particuliers permettrait de guider le développement d’immunothérapies anti-cancéreuses personnalisées telles que la vaccination ou encore le transfert adoptif de lymphocytes T reconnaissant ces cibles. La spectrométrie de masse (MS) est une technique non biaisée permettant l’identification et l’analyse du répertoire des antigènes présentés in vivo. Cependant, cette technique nécessite d’être optimisée et standardisée afin d’être utilisée en clinique. Ainsi, la première partie de ces travaux de thèse a été dédiée à l’optimisation expérimentale de cette méthode à partir d’échantillons de tissus et de lignées cellulaires. En comparaison avec les protocoles standards, cette technique permet une couverture plus complète, rapide et reproductible du répertoire de peptides apprêtés aux HLA. La seconde partie de cette thèse a été consacrée au développement d’une méthode permettant l’identification d’antigènes tumoraux non-canoniques via le séquençage d’ARN cellulaire, ribosomique et l’utilisation de notre méthode d’immunopeptidomique optimisée. Afin de contrôler l’identification de faux positifs, nous avons élaboré un nouveau module computationnel. Ce module a permis l’identification de plusieurs centaines de peptides-HLA non-canoniques, partagés et spécifiques au mélanome et au cancer du poumon. Le séquençage des ARN ribosomiques a mis en évidence la traduction de nouveaux cadre ouverts de lecture desquels sont traduits de nouveaux peptides non-canoniques. Cette technique nous a permis de mettre en évidence un épitope immunogène issu du gène ABCB5, un marqueur de cellules souches cancéreuses préalablement identifié dans le mélanome. De manière globale, ces travaux de thèse, alliant immunopeptidomique et protéogénomique, ont permis la mise au point d’une méthode expérimentale permettant une meilleure identification d’antigènes tumoraux. Nous espérons que ces résultats amélioreront l’identification et la priorisation de cibles pertinentes pour l’immunothérapie anti-cancéreuse en clinique

Characterisation of the Tumour Microenvironment in Ovarian Cancer

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.Cancer Research UK Cambridge Institute provided funding for my studentship. The Mexican National Council of Science and Technology provided funding for my studentship