⚡ Quick Summary
This study utilized artificial intelligence to profile microsatellite instability (MSI) in lung cancer patients, revealing that only 0.52% of the analyzed cases exhibited MSI. The findings suggest that MSI/dMMR is rare in lung cancer, but the integration of next-generation sequencing (NGS) with bioinformatics tools could enhance personalized treatment strategies.
🔍 Key Details
- 📊 Dataset: 1547 lung cancer patients
- 🧩 Features used: Next-generation sequencing (NGS) data and immunohistochemistry (IHC)
- ⚙️ Technology: MIAmS bioinformatics tool for MSI assessment
- 🏆 Key findings: 0.52% of patients had MSI; 0.39% were dMMR
🔑 Key Takeaways
- 📊 MSI/dMMR is exceedingly rare in lung cancer, affecting less than 1% of cases.
- 💡 NGS-based analysis combined with bioinformatics tools offers a robust method for identifying MSI/dMMR patients.
- 👩🔬 All MSI patients exhibited a high tumor mutation burden (TMB), averaging 21.4 ± 5.6 mutations per megabase.
- 🏥 Most patients with dMMR showed loss of MLH1 and PMS2 staining on IHC.
- 🌍 The study highlights the potential for personalized treatment options in lung cancer through MSI profiling.
- 🔍 No correlation was found between MSI status and programmed death-ligand 1 expression.
- 🧬 This research emphasizes the importance of integrating molecular genotyping with MSI detection.
📚 Background
Microsatellite instability (MSI) has gained recognition as a predictive biomarker for immunotherapy response across various cancers. However, its role in non-small cell lung cancer (NSCLC) remains inadequately understood. This study aims to clarify the significance of MSI in lung cancer and explore its potential implications for treatment strategies.
🗒️ Study
The research involved a retrospective analysis of 1547 lung cancer patients, focusing on those with an MSI phenotype. The authors employed the MIAmS bioinformatics tool to assess microsatellite status from NGS data, complemented by IHC assays to evaluate the correspondence between MSI and deficient mismatch repair (dMMR) status.
📈 Results
Among the 1547 patients analyzed, only 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. Notably, no correlation was found between MSI status and programmed death-ligand 1 expression, although all MSI patients exhibited a high TMB.
🌍 Impact and Implications
The findings of this study underscore the rarity of MSI/dMMR in lung cancer, which could influence treatment decisions for a small subset of patients. The integration of NGS-based analysis with bioinformatics tools not only enhances the identification of MSI/dMMR patients but also paves the way for personalized treatment options. This approach could significantly impact the management of lung cancer, particularly in guiding immunotherapy decisions.
🔮 Conclusion
This study highlights the potential of artificial intelligence in advancing our understanding of microsatellite instability in lung cancer. By utilizing NGS and bioinformatics, healthcare professionals can better identify patients who may benefit from targeted therapies. As research continues to evolve, the integration of these technologies promises to enhance personalized treatment strategies in oncology.
💬 Your comments
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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.
Author: [‘Thomas QD’, ‘Vendrell JA’, ‘Khellaf L’, ‘Cavaillon S’, ‘Quantin X’, ‘Solassol J’, ‘Cabello-Aguilar S’]
Journal: Cancer
Citation: Thomas QD, et al. Artificial intelligence-driven microsatellite instability profiling reveals distinctive genetic features in patients with lung cancer. Artificial intelligence-driven microsatellite instability profiling reveals distinctive genetic features in patients with lung cancer. 2025; 131:e35882. doi: 10.1002/cncr.35882