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

Abstract

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.

Keywords: microsatellite instability; neuroendocrine tumors; next‐generation sequencing; non–small cell lung cancer.

FIGURE 1

Workflow of the study. A double‐library NGS analysis was conducted to assess the MSI status of 1547 lung cancer patients. High patients were identified with an MS_score >0. Among these patients, six had an MSI/dMMR phenotype, including five cases of NSCLC (0.34%) and one case of LCNEC (2.3%). dMMR indicates deficient mismatch repair; LCNEC, large cell neuroendocrine carcinoma of the lung; MSI, microsatellite instability; NGS, next‐generation sequencing; NSCLC, non–small cell lung cancer.

FIGURE 2

MMR immunohistochemistry results. (A) Lung adenocarcinoma, resection specimen, ×400. Loss of nuclear expression of the functional heterodimers MLH1 and PMS2. MS_score = 4. (B) Brain metastasis from lung adenocarcinoma, resection specimen, ×400. Isolated loss of nuclear MSH6 expression. Note the persistent expression of MSH2, which is a functional heterodimer. MS_score = 4. (C) Cervical lymph node metastasis from lung adenocarcinoma, biopsy needles, ×100. Loss of nuclear expression of MLH1 and PMS2. MS_score = 4. (D) Bone metastasis from a lung large‐cell neuroendocrine carcinoma, biopsy needles, ×100. Loss of nuclear expression of MLH1 and PMS2. MS_score = 2. For each figure, internal controls are represented by normal immunostaining of stromal cells and lymphocytes for the MMR proteins tested. MMR indicates mismatch repair.

FIGURE 3

Clinicopathological and genomic characteristics. HR indicates homologous recombination; HRD, homologous recombination deficiency; LCNEC, large cell neuroendocrine carcinoma of the lung; MMR, mismatch repair; NSCLC, non–small cell lung cancer; PDL1, programmed death‐ligand 1; TMB, tumor mutational burden; VUS, variant of unknown significance.