Dr Stephen John Sammut graduated in medicine from the University of Malta in 2008. He developed an interest in computational biology while reading medicine and was awarded two scholarships by the Wellcome Trust Sanger Institute in Cambridge to apply novel statistical methods for protein sequence classification and analysis. His work remains part of the InterPro consortium database, which is regarded as the gold standard resource for protein family and domain information.
After completing general medical training in Cambridge in 2012, Dr Sammut commenced NIHR-funded joint clinical and academic specialist training in Medical Oncology at Cambridge University Hospitals, the EMBL-European Bioinformatics Institute and the Cancer Research UK (CRUK) Cambridge Institute. Here, he co-developed a systems biology computational framework for analysing large-scale biophysical models within their anatomical context and designed computational algorithms that leverage network graph theory to identify druggable protein targets in breast cancer.
In 2014, Dr Sammut commenced a Wellcome Trust funded PhD in breast cancer genomics at the University of Cambridge and the CRUK Cambridge Institute. Here, he specialised in the molecular characterisation of early and metastatic breast cancer. Dr Sammut charted the molecular evolution of early breast cancer during treatment with neoadjuvant chemotherapy and showed that response to therapy was associated with distinct tumour ecosystem evolutionary trajectories. In addition, his work in metastatic breast cancer showed that the adaptive immune system co-evolves with the tumour genome, providing further support to the cancer immunoediting hypothesis. In recognition of his outstanding scientific and translational research, he was awarded the Milo Keynes Prize and the Salje Medal by the University of Cambridge.
Following completion of his PhD in 2018, Dr Sammut was awarded a postdoctoral Academic Clinical Lectureship in breast cancer by the University of Cambridge. Here, he characterised the biological processes associated with response to chemo- and targeted therapies in early breast cancer and developed the first machine learning framework that combined genomic, transcriptomic and digital pathology data from diagnostic cancer biopsies to predict response to therapy. This major advance in personalised precision breast cancer medicine resulted in a landmark publication in Nature, which was cited as one of the top 10 cancer research publications by the European Association for Cancer Researchers in 2022. In recognition of this work, Dr Sammut received several awards, including the Scholar-in-Training Award by the American Association for Cancer Researchers, the McElwain Prize by the UK Association for Cancer Physicians, and the Whitney-Wood Cancer Scholarship by the Royal College of Physicians.
Dr Sammut joined the ICR in November 2022, and his research interest lies in developing and implementing methods that enable the delivery of personalised cancer medicine, including the prediction of response to treatment by using dynamic biomarker technologies that integrate serially acquired multiplatform data to model tumour biology as it is perturbed by treatment.