2024 Asia Conference on Lung Cancer (ACLC) - Poster & ePosters-PP01.32

PP01.32 - Li Li

Pdf Summary

The study, conducted by researchers Li Li and Shuanghu Yuan, aimed to identify single nucleotide polymorphisms (SNPs) in DNA double-strand break repair (DSBR) genes associated with increased susceptibility to radiation-induced pneumonitis (RIP) and esophagitis (RIE) in thoracic cancer patients receiving definitive chemoradiotherapy (dCRT). Data from 536 patients with small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), and esophageal squamous cell carcinoma (ESCC) were analyzed.<br /><br />Results showed the occurrence of high-grade RIP in 16 SCLC, 21 NSCLC, and 15 ESCC patients, and high-grade RIE in 2 SCLC, 8 NSCLC, and 16 ESCC patients. Specific SNPs, such as XRCC3 (rs200230392), XRCC4 (rs3777015), and XRCC5 (rs3218739), were significantly associated with higher risk of severe radiation-induced thoracic toxicity (RITT), while SNPs in ATM (rs141761716), LIG4 (rs1805388), and BRCA2 (rs3783265) correlated with severe RIP. Particularly, SNPs in BRCA1 (rs149328571) and BRCA2 (rs201181613) were linked to severe RIE.<br /><br />The study underscores that genetic variants in DSBR genes contribute significantly to RIP and RIE in patients, suggesting these findings could enhance personalized treatment planning and risk stratification for thoracic cancer patients undergoing radiation therapy. Statistical analysis, including univariate and multivariate approaches, confirmed these SNPs' associations with increased RITT risk, emphasizing their potential role in predictive modeling.<br /><br />This research holds importance as understanding genetic predispositions to radiation-induced side effects can tailor therapeutic approaches, minimize toxicity, and improve patient outcomes in thoracic oncology.

Keywords

single nucleotide polymorphisms

DNA double-strand break repair

radiation-induced pneumonitis

radiation-induced esophagitis

thoracic cancer

chemoradiotherapy

genetic variants

personalized treatment

predictive modeling

thoracic oncology