Artificial intelligence–driven microsatellite instability profiling reveals distinctive genetic features in patients with lung cancer

Background

Microsatellite instability (MSI) has emerged as a predictive biomarker for immunotherapy response in various cancers, but its role in non–small cell lung cancer (NSCLC) is not fully understood.

Methods

The authors used the bioinformatics tool MIAmS to assess microsatellite status from next-generation sequencing (NGS) data using a tailored microsatellite score. Immunohistochemistry (IHC) assays were also performed to evaluate the correspondence between MSI and deficient mismatch repair (dMMR) status. A retrospective analysis of 1547 lung cancer patients was conducted, focusing on those with an MSI phenotype. Clinical characteristics, co-occurring molecular alterations, tumor mutation burden (TMB), and homologous recombination deficiency (HRD) status were evaluated in this subset.

Results

Of the 1547 patients analyzed, eight (0.52%) were identified as having MSI through MIAmS, with six (0.39%) of these cases also being dMMR on IHC. All patients with dMMR had an MS score ≥2 and a history of smoking. Most patients showed loss of MLH1 and PMS2 staining on IHC. No correlation was found between MSI status and programmed death-ligand 1 expression, although all MSI patients exhibited high TMB, averaging 21.4 ± 5.6 mutations per megabase.

Discussion

MSI/dMMR in lung cancer is exceedingly rare, affecting less than 1% of cases. NGS-based analysis combined with bioinformatics tools provides a robust method to identify MSI/dMMR patients, potentially guiding immunotherapy decisions. This comprehensive approach integrates molecular genotyping and MSI detection, offering personalized treatment options for lung cancer patients. NGS-based MSI testing is emerging as the preferred method for detecting microsatellite instability in various tumor types, including rare cancers.