Automated Dashboards for the Identification of Pathogenic Circulating Tumor DNA Mutations in Longitudinal Blood Draws of Cancer Patients

Abstract

The longitudinal monitoring of patient circulating tumor DNA (ctDNA) provides a powerful method for tracking the progression, remission, and recurrence of several types of cancer. Often, clinical and research approaches involve the manual review of individual liquid biopsy reports after sampling and genomic testing. Here, we describe a process developed to integrate techniques utilized in data science within a cancer research framework. Using data collection, an analysis that classifies genetic cancer mutations as pathogenic, and a patient matching methodology that identifies the same donor within all liquid biopsy reports, the manual work for research personnel is drastically reduced. Automated dashboards provide longitudinal views of patient data for research studies to investigate tumor progression and treatment efficacy via the identification of ctDNA variant allele frequencies over time.

Keywords

• liquid biopsy
• metastasis
• next-generation sequencing