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.@en

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.