Network Biology Lab
Our lab uses signalling pathways to guide the treatment of aggressive cancers.
Lab LeaderAssociate Professor David Croucher
We focus on combining computational approaches with novel proteomics and single-cell technologies, to understand how altered signalling pathway dynamics can lead to tumour progression and therapeutic resistance. We use these advances to improve outcomes for two aggressive cancers: triple negative breast cancer (TNBC) and the childhood cancer neuroblastoma.
The TNBC subtype comprises approximately 20% of all breast cancer cases and has only a 60% five-year survival rate – the worst of all breast cancer subtypes. This aggressive tumour type is characterised by frequent metastases and a high rate of relapse, yet there are currently no drugs to specifically treat metastatic TNBC. Our work aims to develop a world-first drug capable of preventing the growth of metastatic TNBC cells. We hope to achieve this by specifically dissecting the oncogenic signalling complexes required for the proliferation of TNBC cells within the metastatic niche. By exploiting these therapeutic opportunities, we aim to target TNBC in ways that may not otherwise be possible with standard therapeutic approaches.
Neuroblastoma is a tumour that develops within extracranial nerve tissue. It is the most common cancer in infants, frequently occurring in children younger than two years of age. Approximately half of all neuroblastoma patients present with an invasive, high-risk disease, and receive aggressive treatment including chemotherapy, surgical resection and radiotherapy. Despite this intensive treatment, the survival rate for high-risk neuroblastoma patients is still only about 50%. Additionally, for those that do survive, the exposure to intense chemotherapy at such a young age often results in long-term complications including deafness, infertility and a higher risk of secondary cancers. Our work utilises mathematical modelling of drug response signalling pathways that scales from the patient level through to single-cell resolution. We use these dynamic models to develop novel biomarker approaches, capable of predicting patient-specific drug responses and tailoring chemotherapy options to each individual patient tumour.