Checkpoint inhibitors yield a significant clinical benefit for a subset of cancer patients. Given the high cost of these therapies and the time required to determine whether a therapy is efficacious, tests that can identify patients who are most likely to benefit are urgently needed. Supported by a strong biological rationale, tumor mutational load has emerged as a robust determinant of clinical benefit for multiple checkpoint inhibitors in multiple cancer types. However, existing approaches for assessment of tumor mutational load are expensive and rely on tumor specimens that are not readily available or may yield insufficient material for mutational load analyses. Therefore, Personal Genome Diagnostics proposes to develop MutatorDetect, a non-invasive, cost effective method that can accurately identify late stage cancer patients whose tumors have high mutational load, regardless of the availability of tumor specimens. The specific aims include the design, development, preliminary analytical/clinical validation of MutatorDetect. If successful, MutatorDetect can accurately assess the mutational load of tumors non-invasively. Further studies are warranted to examine MutatorDetect for its clinical validity in large cohorts of late stage cancer patients to evaluate its predictive value for identification of patients that are likely to respond to checkpoint inhibitors.