Urothelial cancer (UC) is a malignancy originating in the bladder with a challenging prognosis, even in its early disease stages. Currently, the standard of care to cure the disease involves surgical removal of the tumor through radical cystectomy (RC), which is often complemented by pre-operative chemotherapy (NAC, neoadjuvant chemotherapy). In the past two decades, the disease treatment landscape has remained unchanged.

Immune checkpoint inhibitors (ICIs) are a novel treatment type that blocks specific proteins, known as immune checkpoints, that downregulate an immune response. Upon immune checkpoint blockade, immune cells can effectively recognize cancer cells and induce an immune response against cancer cells. In the UC context, early-phase clinical trials have demonstrated both the feasibility and efficacy of pre-operative ICIs and have shown long-lasting clinical responses. Therefore, ICIs hold promise for changing UC clinical management in the future. However, not all patients respond to ICIs, and substantial rates of treatment-related toxicities are observed, highlighting the need for identifying biomarkers that can aid patient stratification.

The studies presented in this thesis focus on elucidating the role of the UC tumor microenvironment (TME) in determining responses to pre-operative treatments, such as chemotherapy and ICIs. Through a comprehensive multi-omics approach, we quantified intrinsic and extrinsic characteristics of UC tumors collected from human samples. We associated them with clinical outcomes such as treatment response and treatment dynamics.

Targeted and successful cellular therapies for disease treatment require an extensive mapping of the complex structure and dynamics of molecular mechanisms which determine the behaviour and function of cell. CELL-seq is a genome-wide screening procedure measuring specific and targeted protein quantities as phenotypic readouts and is employed by the Netherlands Cancer Institute to analyze which genes regulate the protein state of interest. This research aims to explore the current compendium of CELL-seq screens that investigate a range of phenotypes, to create a mapping of gene-gene associations that share similar phenotypic profiles and elucidate biology that is hard to uncover with more conventional screening techniques.

We perform exploratory research to investigate the ability of the screen compendium to show network structures that reflect known biological processes. We find that with stringent requirements on interactions the screen compendium shows enrichment for a wide range of biological processes and known protein-protein interactions. We further conclude that the experimental design biases network behaviour and needs to be accounted for when constructing networks. We recommended a mutual k-nearest neighbor network construction approach, which yielded networks with the most biological relevance.
We compare the CELL-seq screens using findings from the approaches to the DepMap dataset, a well-known collection of synthetic lethality CRISPR screens, and find that the behaviour of these datasets is in many ways mirrored. We conclude that this is both due to the biology they represent and the differences in the number of screens in each dataset. Finally, we compare the coverage of biological processes between the HAP1 compendium and DepMap, and show large overlap in their coverage. Nonetheless, the differences they do show leads us to bring forward two hypotheses for gene-gene interactions that score strongly uniquely in the CELL-seq networks which are biologically plausible but are not found in DepMap or curated literature, warranting future investigations.

All code pertaining to the methods and figures in this work are hosted on GitLab by the High Performance Computing Facility of the Netherlands Cancer Institute. As such the code can be viewed by supervisors, but further details could be shared upon request.

The tumor composition of breast cancer determines how tumors behave. Yet, there is a limited understanding of the arrangement of tumor cells in relation to cells in the tumor microenvironment (TME). In this research, we have characterized distance relationships between 324 cell-type pairs in 749 tissue samples of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study using Weibull distribution estimations, summarizing comprehensive spatial relationships with two parameters. The research showcased the first application of the method to a dataset of this substantial size and a dataset acquired with imaging mass cytometry. We identified distinct spatial relationships among breast cancer subtypes, particularly for basal, HER2-enriched, and luminal A tumors. The spatial relationships indicate attractive and repulsive interactions between different cell types and define cellular arrangements regardless of cellular abundance.
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks.

KRAS mutations are very common in several different types of cancer. A promising targeted combination therapy using a MEK and HER inhibitor was proposed based on in vitro finding. The clinical results of this combination were found to be lacking due to emergent treatement resistance. Here we investigate what mechanism is causing this emergent resistance in KRAS mutant cancers. We propose a novel ODE model of the MAPK pathway that can be used to infer kinetic parameter estimates from a population of KRAS mutant cells under drug perturbation. Parameter estimates inferred from FRET biosensor data correctly predict protein activity in an external CyTOF validation dataset. However, the parameter estimates did not recapitulate the known gain-of-function in RAS activity that we would expect. From this we conclude that more experimental observation are required to elucidate the inner working of the resistance mechanism of KRAS mutant cancer to the proposed combination therapy.

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be essentially a unique disease, clinicians and researchers are moving towards a new paradigm, termed “precision medicine”, which consists of designing bespoke lines of treatment for each patient.

This paradigm-shift has been fueled by international consortia that have characterized large collections of tumors, thereby providing a vast reference for cancer heterogeneity. Two main strategies have been employed: sequencing of tumor biopsies directly extracted from patients or studying pre-clinical models, i.e., tumor cells cultured in artificial environments. While the first strategy generates clinically faithful data, the second strategy is flexible and cost-effective, and allows for the study of effects of various drugs at different concentrations.

Based on the large amount of data generated from pre-clinical models, computer
scientists have developed various machine learning algorithms to model drug response based on these data. However, these models do not take into account the complexity of human tumors and the differences between model systems and human tumors, and are therefore not directly applicable in a clinical setting. In this thesis, we aim at bridging this gap. Specifically, we develop algorithms to integrate and align data generated from the two aforementioned strategies with a goal to predict drug response in patients from datasets generated using pre-clinical models.