Multiple myeloma (MM) is a malignant disorder of the blood. In the U.K. approximately 5,000 people are diagnosed with MM annually and overall survival though acknowledged in real world regional data to be under 50% at five years5, survivorship has considerably improved over the last ten years, with expected 10 year survival of 50-60% in younger fitter patients. The current standard of care carries considerable treatment-related toxicities, with broad proteosome inhibitors (e.g. bortezomib), immunomodulatory drugs (e.g. lenalidomide) and autologous stem cell transplantation, but newer immunotherapies such as CAR-T and T-cell engager therapies are showing promising improvements. Despite these advances, all treatments carry not infrequent off-target toxicities, resulting in an unmet critical need for more targeted and better tolerated therapeutic approaches.
It is well established that the cyclin-dependent kinase subunit CKS1B is overexpressed and correlates with poor prognosis in MM regardless of cytogenetics, treatment protocol and autologous haematopoietic stem cell transplantation status. We hypothesise that CKS1-dependent proteostasis is a potential Achilles heel in MM akin to that which we and others have reported in solid tumours and other blood disorders, and by studying the multifaceted role of CKS1 in MM we can develop new ways to treat MM that will reduce the harsh nature of high-dose chemotherapy.
The prospective student will join a multidisciplinary team of clinicians and scientists working across Leeds and York. You will be trained in state-of-the-art technological approaches to study the protein biology of blood cells from bulk populations down to single cell resolution. There will also be a strong data science component to the PhD, which will provide excellent training across scientific modalities. Together, these fundamental methodologies will allow us to ask:
What are the functional dependencies of CKS1 in MM?
Development of new combinatorial targeting approaches to treat MM.
Pre-clinical testing of new combinatorial approaches in MM models and healthy stem cell systems.