Exploring heterogeneity in colorectal cancer to improve tailored treatment and patient outcomes

This thesis explores heterogeneity among patients with colorectal cancer (CRC), with a focus on real-world data research to improve tailored treatment and outcomes. Each patient diagnosed with CRC presents with a distinct combination of genomic and clinical characteristics, and tailored treatment based on these characteristics should be used to improve outcomes for individual patients. Considerable heterogeneity between individual patient treatment trajectories is observed in this thesis, as well as significant differences between molecular subgroups in time on treatment, attrition rates, clinicopathological characteristics, and survival outcomes. A significant efficacy-effectiveness gap in patients treated within clinical trials compared to patients treated in daily clinical practice is showed. These results can contribute to the overarching debate of the righteous use of expensive systemic therapy. Large real-world data sets can be used to observe and analyze (causal) associations, as it is demonstrated in this thesis that specific type of CRCs occur more often in patients with transplantations. This could potentially impact the manner of screening in this population. International collaboration with common data models can be used to obtain even larger real-world data sets, although harmonization and overcoming restricted data availability is still needed. Lastly, this thesis described a successful pilot study of a potential new treatment paradigm in treating patients with metastatic CRC with single fraction sub-ablative radiotherapy in addition to standard of care systemic therapy. In the future, high-granular, high-quality, extensive linked datasets are needed to provide the infrastructure to use innovative methods for obtaining robust knowledge concerning individualized treatment effects and ultimately find the tailored treatment with the best positive impact on the health outcome for an individual patient.