201. PREDICTION OF LYMPH NODE METASTASIS IN PATIENTS WITH ESOPHAGEAL SQUAMOUS CELL CARCINOMA USING GENETIC SIGNATURES FROM LARGE-SCALE GENOMIC REPERTOIRES. (24th September 2022) - Kang, Xiaozheng; Wan, Zhiyi; Zhang, Ruixiang; Zhang, Enli; Zhao, Yahui; Qi, Shu; Chen, Xiankai; Qin, Jianjun; Li, Yin
Abstract: The presence of lymph node metastasis puts patients with resectable esophageal squamous cell carcinoma (ESCC) at a high risk of disease recurrence, and these patients would benefit from neoadjuvant treatment. However, current clinical staging is not enough to capture all patients with high-risk disease, and novel diagnostic tools are required to improve the accuracy of treatment planning. We tried to explore a better prediction using genomic features from large-scale gene mutation data. The major public data repositories of whole-exome sequencing for ESCC were involved in this study. The absolute differences in gene mutation frequency were calculated using Fisher's exact test in nodal negative versus nodal positive patients. The classification of gene mutation status was dichotomized as wild-type and mutant type. To find a reliable gene panel that could provide sufficient population coverage, we set the cut-off level above 85% in both nodal phenotypes. The genomic classifier for nodal metastasis risk prediction was further generated by LASSO logistic regression analysis with 10-fold cross-validation based on the topmost significantly different genes. Above the threshold values, a total of 120 differentially mutated genes were identified in the 561 ESCC genomic sequencing data. The population coverage rates were 85.97% in the nodal positive group (n = 335) and 88.05% in the nodal negative group (n = 226), respectively. According to the LASSO logistic regression approach, a panel of 112 topmost relevant genes (termed LNM112) was selected. The receiver operating characteristic curve of the LNM112 predictive model was plotted with an area under the curve value of 0.95 (95% CI: 0.93–0.96). The sensitivity and specificity of the LNM112 model were 96.72% (324/335) and 71.24% (161/226), respectively. We identified a genomic signature (112 genes) from the large-scale genomic repertoires capable of predicting lymph node metastasis in ESCC. It can be incorporated into the current staging modalities to help inform treatment decisions, and external validation is warranted in different populations.