Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics
Abstract Background The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next‐generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis us...
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Wiley
2024-07-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70039 |
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| author | Wei Guo Tong Lu Yang Song Anqi Li Xijia Feng Dingpei Han Yuqin Cao Debin Sun Xiaoli Gong Chengqiang Li Runsen Jin Hailei Du Kai Chen Jie Xiang Junbiao Hang Gang Chen Hecheng Li |
| author_facet | Wei Guo Tong Lu Yang Song Anqi Li Xijia Feng Dingpei Han Yuqin Cao Debin Sun Xiaoli Gong Chengqiang Li Runsen Jin Hailei Du Kai Chen Jie Xiang Junbiao Hang Gang Chen Hecheng Li |
| author_sort | Wei Guo |
| collection | DOAJ |
| description | Abstract Background The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next‐generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed. Methods Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort. Results Twenty‐four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN‐positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild‐type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780). Conclusion This study suggests that the integration of genomic profiling and clinical features identifies early‐invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions. |
| format | Article |
| id | doaj-art-fac37f1b26834cab8874aa152ec763b5 |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-fac37f1b26834cab8874aa152ec763b52025-08-20T02:34:44ZengWileyCancer Medicine2045-76342024-07-011314n/an/a10.1002/cam4.70039Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristicsWei Guo0Tong Lu1Yang Song2Anqi Li3Xijia Feng4Dingpei Han5Yuqin Cao6Debin Sun7Xiaoli Gong8Chengqiang Li9Runsen Jin10Hailei Du11Kai Chen12Jie Xiang13Junbiao Hang14Gang Chen15Hecheng Li16Department of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Huashan Hospital, Fudan University Shanghai ChinaDepartment of Pathology Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaGenecast Biotechnology Co., Ltd Wuxi ChinaGenecast Biotechnology Co., Ltd Wuxi ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Surgery Huashan Hospital, Fudan University Shanghai ChinaDepartment of Thoracic Surgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaAbstract Background The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next‐generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed. Methods Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort. Results Twenty‐four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN‐positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild‐type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780). Conclusion This study suggests that the integration of genomic profiling and clinical features identifies early‐invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.https://doi.org/10.1002/cam4.70039lung adenocarcinomalymph node metastasisnext‐generation sequencingprediction model |
| spellingShingle | Wei Guo Tong Lu Yang Song Anqi Li Xijia Feng Dingpei Han Yuqin Cao Debin Sun Xiaoli Gong Chengqiang Li Runsen Jin Hailei Du Kai Chen Jie Xiang Junbiao Hang Gang Chen Hecheng Li Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics Cancer Medicine lung adenocarcinoma lymph node metastasis next‐generation sequencing prediction model |
| title | Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| title_full | Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| title_fullStr | Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| title_full_unstemmed | Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| title_short | Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| title_sort | lymph node metastasis in early invasive lung adenocarcinoma prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics |
| topic | lung adenocarcinoma lymph node metastasis next‐generation sequencing prediction model |
| url | https://doi.org/10.1002/cam4.70039 |
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