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.

Dans ce travail nous avons élaboré une nouvelle méthode de l'analyse de réseau à définir des membres possibles des voies moléculaires qui sont important pour ce phénotype en utilisant la « hit-liste » des expériences « omics » qui travaille dans le réseau intégré (le réseau comprend des interactions protéine-protéine, de transcription, l’acide ribonucléique micro-l’acide ribonucléique messager et celles métaboliques). La méthode tire des sous-réseaux qui sont construit des voies de quatre types les plus courtes (qui ne se composent des interactions protéine-protéine, ayant au minimum une interaction de transcription, ayant au minimum une interaction l’acide ribonucléique micro-l’acide ribonucléique messager, ayant au minimum une interaction métabolique) entre des hit –gènes et des soi-disant « exécuteurs terminaux » - les composants biologiques qui participent à la réalisation du phénotype finale (s’ils sont connus) ou entre les hit-gènes (si « des exécuteurs terminaux » sont inconnus). La méthode calcule la valeur de la centralité de chaque point culminant et de chaque voie dans le sous-réseau comme la quantité des voies les plus courtes trouvées sur la route précédente et passant à travers le point culminant et la voie. L'importance statistique des valeurs de la centralité est estimée en comparaison avec des valeurs de la centralité dans les sous-réseaux construit des voies les plus courtes pour les hit-listes choisi occasionnellement. Il est supposé que les points culminant et les voies avec les valeurs de la centralité statistiquement signifiantes peuvent être examinés comme les membres possibles des voies moléculaires menant à ce phénotype. S’il y a des valeurs expérimentales et la P-valeur pour un grand nombre des points culminant dans le réseau, la méthode fait possible de calculer les valeurs expérimentales pour les voies (comme le moyen des valeurs expérimentales des points culminant sur la route) et les P-valeurs expérimentales (en utilisant la méthode de Fischer et des transpositions multiples).A l'aide de la méthode masterPATH on a analysé les données de la perte de fonction criblage de l’acide ribonucléique micro et l'analyse de transcription de la différenciation terminal musculaire et les données de la perte de fonction criblage du procès de la réparation de l'ADN. On peut trouver le code initial de la méthode si l’on suit le lien https://github.com/daggoo/masterPATH / In this work we developed a new exploratory network analysis method, that works on an integrated network (the network consists of protein-protein, transcriptional, miRNA-mRNA, metabolic interactions) and aims at uncovering potential members of molecular pathways important for a given phenotype using hit list dataset from “omics” experiments. The method extracts subnetwork built from the shortest paths of 4 different types (with only protein-protein interactions, with at least one transcription interaction, with at least one miRNA-mRNA interaction, with at least one metabolic interaction) between hit genes and so called “final implementers” – biological components that are involved in molecular events responsible for final phenotypical realization (if known) or between hit genes (if “final implementers” are not known). The method calculates centrality score for each node and each path in the subnetwork as a number of the shortest paths found in the previous step that pass through the node and the path. Then, the statistical significance of each centrality score is assessed by comparing it with centrality scores in subnetworks built from the shortest paths for randomly sampled hit lists. It is hypothesized that the nodes and the paths with statistically significant centrality score can be considered as putative members of molecular pathways leading to the studied phenotype. In case experimental scores and p-values are available for a large number of nodes in the network, the method can also calculate paths’ experiment-based scores (as an average of the experimental scores of the nodes in the path) and experiment-based p-values (by aggregating p-values of the nodes in the path using Fisher’s combined probability test and permutation approach). The method is illustrated by analyzing the results of miRNA loss-of-function screening and transcriptomic profiling of terminal muscle differentiation and of ‘druggable’ loss-of-function screening of the DNA repair process. The Java source code is available on GitHub page https://github.com/daggoo/masterPATH