Blood-Based ctDNA Test Predicts Lung Cancer Treatment Success Better Than Scans

Standard imaging scans have long been the primary tool for assessing whether a cancer treatment is working, but they have a fundamental blind spot: patients with radiologically stable disease often have very different underlying tumor trajectories that scans cannot distinguish. A more sensitive, dynamic molecular tool is needed.

This study introduced MinerVa-Delta, a novel algorithm designed to quantify ctDNA dynamics in blood samples using a personalized variant-tracking strategy. Tumor-associated mutations were first identified from pre-treatment plasma using a 769-gene next-generation sequencing panel, and changes in those same variants were then measured in post-treatment blood draws after two treatment cycles.

In a discovery cohort of 227 patients with advanced lung squamous cell carcinoma receiving either PD-1 checkpoint immunotherapy combined with chemotherapy or chemotherapy alone, patients classified as molecular responders — those with a MinerVa-Delta score below 30% — showed dramatically improved progression-free survival (HR 0.19) and overall survival (HR 0.24) compared to non-responders. These results were independently validated in a separate cohort of 97 patients.

One of the most clinically important findings was MinerVa-Delta's ability to stratify patients with radiologic stable disease — a traditionally ambiguous group — identifying those who were genuinely benefiting from therapy versus those who were not, despite similar imaging appearances.

This approach could meaningfully improve clinical decision-making by allowing oncologists to detect treatment failure or response earlier and more precisely than imaging alone permits. Key caveats include the study's focus on a single histology in a predominantly Asian cohort, the summary being based on the abstract only, and the involvement of a biotechnology company in the research team, which introduces potential commercial bias.