Failure to get drugs to market is high, and treatment effectiveness is often determined by waiting to observe effects on cancer progression and survival. In this theme, we aim to address this issue by identifying high-confidence drug targets, and opportunities for drug repurposing, using Mendelian Randomization (MR).

MR is a method that uses genetic variation to evaluate causal relationships; it has shown promise for correctly predicting outcomes of clinical trials. We will extend this method, which has been used primarily to establish effects on cancer incidence, to evaluate therapeutic effects on progression and survival after diagnosis.

We will also improve treatment decision-making through systematic biomarker discovery of disease prognosis and response to therapy. This will be achieved within relevant peripheral and biopsy tissues using state-of-the-art molecular profiling and machine learning techniques. 

This theme is chaired by Philip Haycock and James Yarmolinsky.