Most targeted cancer drugs aim to inhibit the oncogenic signals that drive cancer growth, pushing signalling to a toxic low level. In this industry-partnered studentship between the ICR and Merck, we propose to pursue a radically different approach to cancer therapy: instead of suppressing the signals that make cancers grow, we aim to “hyperactivate” those signals beyond an upper threshold of tolerability, thereby killing cancer cells by causing “too much” oncogenic signalling.

This studentship will focus on colorectal cancer, a common malignancy with limited treatment options for metastatic disease where outcomes remain poor. Dysregulation of the WNT signalling pathway is the initiating event of most colorectal cancers, and subsequent alterations of MAPK signalling (principally activating KRAS and BRAF mutations) are common. Here, we will explore hyperactivation of WNT and MAPK signalling as novel, counterintuitive, treatment modalities in colorectal cancer.

We propose an interdisciplinary approach combining data science, computational biology, and in vitro preclinical modelling approaches. We will 1) develop machine learning approaches to explore existing large-scale high-throughput drug screening datasets (e.g. DepMap) to find targets that cause hyper-activation. 2) Develop mathematical models to predict tumour evolution in response to hyper-stimulation and determine treatment regimes where this approach is likely to be efficacious, and 3) utilise cell and organoid models in vitro to empirically evaluate computational predictions.

This industry-linked studentship is partnered with Merck. The student will spend significant time in Merck laboratories, and will utilise existing compounds from the Merck portfolio to induce overstimulation in in vitro models.

Candidates must have, or be on track to receive, a First- or Upper Second- class Honours degree (or a Masters) in a Biological or Physical Sciences subject and have experience in either cellular and molecular biology or data science/bioinformatics/mathematical modelling. Ideally candidates will have some experience of both biology and mathematical sciences areas.