A different kind of precision oncology expertise
Precision oncology sits between disciplines. Reading a case well requires molecular knowledge, computational method, clinical context and an honest assessment of the evidence — and the difficulty is that these are usually held by different people.
Molecular depth
Genomics, next-generation sequencing, germline and somatic profiling, transcriptomics, multi-omics, circulating tumour DNA, biomarkers and tumour evolution. Reading a molecular report well means understanding how the data were generated, what the assay can and cannot detect, and how confident any given call actually is.
Computational depth
Bioinformatics, statistical genomics, machine learning, natural language processing and data science. Molecular data at this scale is not read by eye. The methods used to process, filter, annotate and model it determine what conclusions are available at the end.
Clinical context
Molecular results interpreted in relation to diagnosis, pathology, disease course and treatment history. The same variant can carry very different weight in a treatment-naïve tumour and in one that has progressed through three lines of therapy.
Evidence integration
Relevant scientific literature, biomarker data, clinical evidence and trial landscapes. Evidence in oncology is uneven: strong for some alterations in some diseases, thin or preclinical for others. Saying which is which is part of the work.
Systems thinking
Building tools, workflows and institutions that make complex cancer intelligence more usable. A good interpretation that reaches no one changes nothing; the systems around the analysis matter as much as the analysis.
This combination of molecular, computational and translational thinking is the foundation of the work.
Every claim, with its source
Credentials, memberships and the research record are set out individually, with links to public profiles where they exist.