Genotoxic chemotherapies are a mainstay for the treatment of colorectal cancer (CRC). Genotoxic chemotherapies frequently fail since transcriptional responses leading to cancer cell death cannot be mounted. The transcription-independent activation of cell death by death receptor ligands is therefore a promising alternative, and 2nd generation TRAIL receptor agonists suitable for translational validation have now been bioengineered. To efficiently apply these agonists in a clinical setting, reliable markers that predict responsiveness to single and combination treatments are therefore required. Here, we will address these needs as an interdisciplinary team using a systems biology-driven strategy that outperforms classical biostatistical approaches. In preparatory work, we successfully combined mathematical modelling and apoptosis protein quantification to case-specifically predict the responsiveness of cell line models to TRAIL receptor agonists with high accuracy. Here we will validate and exploit this approach in a translational setting in primary and metastatic CRC (mCRC). Primary tumour tissues from CRC patients and patient-derived xenografts (PDX) from mCRC will be quantitatively analysed for regulators of TRAIL signalling. These data, together with information on apoptosis signalling topologies, will be used to computationally predict responsiveness of tumour tissues to 2nd generation TRAIL receptor agonists alone or in combination with standard 5FU-based chemotherapy. Predictions will be validated in ex vivo cultures of fresh CRC tumour tissues and in vivo mouse PDX mCRC studies. From this we will develop prototype stratification tools which will assist in the design of subsequent clinical trials by identifying patients likely to respond to TRAIL-based treatments. Our proposal builds on a substantial amount of promising preliminary data and will set an example for how innovative systems biological strategies can address significant clinical needs. Assessing novel treatment options and providing these in an optimised and personalised manner can benefit large numbers of CRC patients in the future.
