Circulating Tumor DNA

Circulating tumor DNA (ctDNA) refers to fragments of DNA that are shed by tumor cells into the bloodstream through processes including apoptosis, necrosis, and active secretion. These fragments typically range from 120 to 200 base pairs in length and carry the same genetic mutations, methylation patterns, and structural alterations as the tumor from which they originate. Unlike a tissue biopsy, ctDNA can be obtained from a standard blood draw, enabling non-invasive access to a tumor's molecular profile.

The concentration of ctDNA in blood varies widely depending on tumor type, stage, and location. In advanced solid tumors, ctDNA may constitute a measurable fraction of total cell-free DNA (cfDNA), while in early-stage cancers, its fraction can be extremely low — sometimes below 0.01% of total cfDNA. This has driven significant advances in ultra-sensitive sequencing technologies and bioinformatic methods designed to detect tumor-derived fragments against a high background of normal cell-free DNA.

ctDNA is distinct from other circulating nucleic acids and should not be confused with total cell-free DNA, which includes DNA released from normal cells during routine tissue turnover. The tumor-specific mutations present in ctDNA are what distinguish it as a cancer-associated biomarker and underpin its utility in clinical and research settings.

Prevena Health is investigating whether continuous biomarker surveillance may enhance the clinical utility of ctDNA analysis. Current approaches to ctDNA testing rely on single-point blood draws, which capture a snapshot of circulating DNA at one moment in time. Prevena's platform aims to support longitudinal monitoring that may reveal trends, rate-of-change patterns, and transient signal fluctuations that point-in-time measurements could miss.

By exploring continuous sampling through a skin-worn biosensor, Prevena is researching whether frequent ctDNA measurements could support earlier identification of molecular changes associated with tumor dynamics. This approach may complement existing diagnostic workflows and provide additional temporal context for clinicians and researchers working in oncology.