Prediction of the tumor mutation burden and its predictive value in UK biobank cancer patients: using machine learning algorithm
Principal Investigator: Professor In Ah Kim
Approved Research ID: 53279
Approval date: December 23rd 2019
In an advent of cancer immunotherapy, there has been active researches to predict the response of immunotherapy. Several researches reported that mutational profile derived from primary tumor tissue or blood samples were associated with the response of the immunotherapy. Mutational profile can be acquired through UK Biobank whole-exome sequencing data in cancer patients, and can be analyzed effectively by using machine learning algorithm. We will evaluate the prognostic and predictive value of mutational profile in terms of patient's survival. Then, we will develop machine learning algorithm to predict tumor mutation burden conveniently. These process would take 3 years. This research will support the less-invasive liquid biopsy from cancer patients who are indicated for immunotherapy. Machine learning algorithm from current study can be helpful to predict the response of cancer therapy and patient's survival.