Translational medicine encompasses multiple areas of applied research, which work in concert to help us quickly analyze and interpret data from the lab, implement new insights in clinical trials and accelerate the pipeline to identify the right treatments, for the right patients, at the right time. One of the most critical pieces of this puzzle is our ability to generate, integrate, analyze and synthesize complex data sets to develop actionable insights and testable hypotheses that help drive discovery and clinical development.
One example of this is in immuno-oncology, where our Translational Bioinformatics team is using cutting-edge algorithms to sift through massive raw genomics data. Whole Exome Sequencing (WES) generates data on tumor and blood samples from clinical trials, which we can use to identify mutations present in the tumor and inherited variants present in normal tissues. This allows us to identify correlations between patterns of genes or mutations and responses to certain therapies, which can in turn guide treatment decisions for patients. With more than three billion letters in the human genome, this may seem like an impossible effort, but thanks to a number of new experimental and in silico technologies, we are able to generate and analyze unprecedented amounts