Currently, first line treatment fails for up to 2/3rds of cancer patients, and it takes up to 6 months to realize. We think there is a better way. The LiMiTeR trial is an ongoing effort to profile cancer liquid biopsies during treatment. Data are used to help us predict how tumors evolve in response to treatment and how we can adapt our treatment strategies for the best patient outcome.
Differences in our DNA underlie many aspects of human health; from rare genetic diseases to cancer. In this project, we build a new class of software for detecting DNA variants. Based on the same principles behind facial recognition, our technique can identify cancer variants with unparalleled accuracy. We hope that releasing this software for non-commercial use will lead to more successful targeted therapy and personalized cancer medicine.
Despite recent advances in the field of cancer therapy, first line treatments still fail for two out of three cancer patients. In this study, we show development of an open source tool to allow machine learning researchers to work on cancer genomic datasets. We use this tool to predict how effective treatment will be, with accuracies of >80%.