The cellular demonstrator aims to champion the use of organoids to understand tumor drug resistance. Understanding and detecting existing or acquired drug resistance of tumors remains a major and largely unmet challenge. The average response rate to cancer drugs is only 25 to 30%. With the development of 3D organoid culturing technology, it has become possible to derive primary tumor organoid cultures from most types of common cancers. This provides a sophisticated tool to study the resistance of a large range of specific tumors. As a platform, this allows for highly reproducible, time-course multi-omics analyses on common tumors that are treated with clinically relevant drugs.
The individual demonstrator aims to use a multi-omics experimental design to realize deep understanding of rare genetic diseases, providing relevant insights into pathophysiology, diagnosis and treatment. The revolution in genomics (genome and exome sequencing) has realized diagnostic rates up to 60% (from 10-20% a decade ago) for these rare diseases. The challenge now is to diagnose the remaining fraction that does not display a convincingly diagnostic genomic variant. Specifically, the demonstrator will focus on immune and cognitive disorders.
The population demonstrator aims to establish digital proteomic and metabolomic fingerprints of individuals and link these to genomic data. The identification of over 10,000 genetic risk factors for many different diseases, in the many genome wide association studies in the last decade, means that it is now possible to predict disease risk on an individual basis. However, even for major genetically determined diseases like Alzheimer disease it is still virtually impossible to predict when a person will develop disease. A crucial addition of the proteomic and metabolic data will be to monitor the status of the disease process over time and thus reveal when (i.e., at what age) the disease is starting to develop. Based on this rich information and a X-omics experimental design, this demonstrator will construct digital molecular fingerprints over time using the unique longterm follow-up data of the Rotterdam Study and Life Lines. This will be highly relevant in the clinic as it will omit the collection of large and expensive sample-sizes, leading to the application of personalized medicine.