During the past few decades, the landscape of available therapeutic interventions for cancer treatment has widely expanded, boosted mainly by immunotherapy progress and the precision oncology paradigm. The extensive use of pre-clinical models in cancer research has led to the discovery of new effective treatment options for patients. Despite the notable advancements, some cancer types have found minor benefits from the use of precision-oncology interventions. Characterized by a heterogeneous molecular landscape, bladder cancer is one of the most frequent cancer types in which standard-of- care treatments involve surgical operations accompanied by broad-spectrum chemotherapy. My research stems from the need for precision oncology interventions in bladder cancer and specifically focuses on the development of computational frameworks to guide the discovery of new therapeutic opportunities. This work first introduces the exploration of possible therapeutic interventions in 9p21.3 depleted bladder tumors through the analysis of an in-house large High-Content Drug Screening that tested 2,349 compounds. By combining cell count changes and morphological quantitative features extracted from fluorescence images, we nominate cytarabine as a putative candidate eliciting specific cytotoxic effects in an engineered 9p21.3 depleted bladder cancer model compared to an isogenic wild-type clone. Focusing on the development of computational methodologies to nominate robust context-specific vulnerabilities, I further describe PRODE (PROtein interactions informed Differential Essentiality), an analytical workflow that integrates protein-protein interaction data and Loss of Function screening data. I extensively tested PRODE against the most commonly used and alternative methodologies and demonstrated its superior performance when classifying reference essential and context-essential genes collected from experimental and literature sources. Furthermore, we applied PRODE to a real case scenario, seeking essential genes selectively in the context of HER2+ Breast Cancer tumors. Finally, I report the computational analyses performed on Patient-Derived Organoids (PDOs) established from a bladder cancer cohort. PDOs are demonstrated as informative models when assessing the therapeutic sensitivity of patients to drugs. Overall, this research highlights novel precision-oncology applications by ad-hoc computational analyses that address key open technical and biological challenges in the field of bladder cancer and beyond.

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.

Breast cancer is the most common cancer in women. It is a highly heterogeneous disease in terms of histology, therapeutic response and patient outcomes. Early and accurate detection of breast cancer is crucial as the patient prognosis varies greatly depending on the diagnosis of the disease. Nonetheless current breast cancer classification methods fail to precisely sub-classify the disease, resulting in potential inadequate therapeutic management of patients and subsequent poor clinical outcomes. Substantial effort is therefore put in cancer research to develop methods and find new biomarkers efficiently identifying and characterizing breast tumor cells. Moreover it is now well-recognized that the intensive cross-talk between cancer cells and their microenvironment (including non-tumor cells) highly influences cancer progression. Recently, a growing body of clinical evidence reported the prognostic and predictive value associated with the presence of tumor infiltrating lymphocytes (TILs) in the microenvironment of breast tumors. Although the evaluation of TILs would be of great value for the management of patients and the development of new immunotherapies, it is currently not assessed in routine practice. Furthermore Fourier transform infrared (FTIR) imaging has shown its usefulness to study a panel of human cancers. Infrared (IR) spectroscopy coupled to microscopy provides images composed of multiple spectra reflecting the biochemical composition and subtle modifications within biological samples. IR imaging therefore provides useful information to improve breast cancer identification and characterization. The ultimate aim of this thesis is to improve breast cancer diagnosis using FTIR imaging to better identify and characterize cancer cells and the tumor microenvironment of breast cancers. In a first step we carried out a feasibility study aiming at evaluating the impact of the sample fixation process on IR spectra. While spectra were undeniably influenced by this biochemical alteration, our results indicated that closely-related cell types were influenced similarly and could still be discriminated on the basis of their spectral features. We then demonstrated the capability of IR imaging to discriminate a tumor from a normal tissue environment based on the spectral features of tumor cells and the surrounding extracellular matrix. A particular focus was placed on the identification of lymphocyte spectral signatures of cells isolated from blood or present within secondary lymphoid organs such as tonsils. Our results revealed that IR imaging was sensitive enough to discriminate lymphocyte subpopulations and to identify a particular spectral signature that we assigned to lymphocyte activation. Finally we highlighted the potential value of IR imaging as complementary tool to identify and characterize TILs in breast tumor samples. Altogether, our results suggest that IR imaging provides interesting and reliable information to improve breast cancer characterization and to assess the immune microenvironment of breast tumors.<p>/<p>Le cancer du sein est le carcinome le plus fréquent chez la femme. C’est une maladie très hétérogène du point de vue histologique, de la réponse thérapeutique et de l’évolution clinique. Une détection rapide et précise de la maladie est cruciale, un diagnostic du cancer du sein dès les premiers stades de la maladie permet une meilleure prise en charge du patient et est directement associé à un meilleur pronostic. Néanmoins la classification actuelle des cancers du sein ne permet souvent pas de caractériser la maladie de manière précise, ce qui donne lieu à la mise en place de traitements moins ciblés et une évolution clinique peu favorable. Pour remédier à cela, des efforts conséquents sont réalisés en recherche, dans le but de mettre au point des méthodes capables d’identifier et de caractériser les cellules tumorales. De plus il est actuellement reconnu que le micro-environnement tumoral (composé des cellules non-tumorales) influence fortement la progression du cancer. Récemment de nombreuses études ont montré que la présence de lymphocytes au niveau des tumeurs mammaires (TILs) était corrélée à un meilleur facteur pronostic et prédictif. Bien que l’évaluation des TILs soit de grande importance dans le cadre des immunothérapies, cet élément n’est actuellement pas pris en compte dans les analyses de routine. Par ailleurs, l’imagerie infrarouge par transformée de Fourier (FTIR) a démontré son utilité dans l’étude de plusieurs cancers humains. La spectroscopie infrarouge (IR) couplée à la microscopie fourni des images composées de multiples spectres qui reflètent la composition biochimique et les modifications dans les échantillons biologiques. De ce fait l’imagerie infrarouge procure des informations utiles pour améliorer l’identification et la caractérisation du cancer du sein. L’objectif général de cette thèse est d’améliorer le diagnostic du cancer du sein par imagerie FTIR pour mieux identifier et caractériser les cellules cancéreuses et le micro-environnement tumoral des tumeurs mammaires. Dans un premier temps nous avons effectué une étude de faisabilité afin d’évaluer l’impact du protocole de fixation des tissus sur les spectres IR. Bien que les spectres soient indéniablement influencés par cette altération biochimique, nos résultats indiquent que des types cellulaires proches sont influencés de manière similaire et peuvent donc être discriminés sur base de leurs caractéristiques spectrales. Nous avons ensuite démontré la capacité de l’imagerie IR de distinguer un environnement tumoral d’un environnement normal sur base des particularités spectrales des cellules tumorales et de la matrice extracellulaire. Une attention particulière a ensuite été portée afin d’identifier des signatures spectrales de cellules immunitaires du sang et au sein d’organes lymphoïdes secondaires, tels que les amygdales. Nos résultats ont révélé que l’imagerie IR permet d'identifier une signature spectrale particulière, que nous avons associée à une stimulation lymphocytaire. Finalement nous avons mis en évidence l’utilité de l’imagerie IR en tant qu’outil complémentaire pour identifier et caractériser les TILs dans les échantillons tumoraux mammaires. De manière générale, nos résultats suggèrent que l’imagerie IR fournit des informations intéressantes et fiables pour améliorer la caractérisation et l’évaluation du micro-environnement immunitaire dans les tumeurs mammaires. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

In this recent study,1 the authors analyzed the metabolic gene essentiality scores from genome-wide loss of function CRISPR screens in 769 human cancer cell lines and noticed that TCA cycle-associated genes clustered in two separate groups: one forming the traditional TCA cycle and another related to a non-canonical TCA cycle module. They monitored both modules with elegant tracing studies using [U-13C]glucose which generates citrate labeled with two 13C atoms (M+2).