MinerVa algorithm: advances in MRD detection in lung cancer ✨🔬

Improving the signal acquisition efficiency of circulating tumor DNA (ctDNA) and the accuracy of low-frequency mutation authentication are key challenges in detecting minimal residual disease (MRD) in solid tumors. A recent study developed a new bioinformatics algorithm for MRD, namely Multi-variant Confidence Analysis (MinerVa), and tested it on both simulated ctDNA standards and DNA samples from patients with early-stage non-small cell lung cancer (NSCLC).

The results show that MinerVa's ability to track multiple variants ranged from 99.62% to 99.70% specificity, with signals detectable at variant abundances as low as 6.3 × 10 -5 when tracking 30 variants. Furthermore, in a cohort of 27 patients with NSCLC, the specificity of ctDNA-MRD for monitoring recurrence reached 100%, with a sensitivity of 78.6%.

These results indicate that MinerVa effectively captures ctDNA signals in blood samples and shows high accuracy in detecting MRD. This represents a promising advance in cancer diagnosis and patient monitoring, with potential implications for improving treatment outcomes. 🌟💉