Zaoqu Liu1,2,3, Hui Xu4,5,6, Siyuan Weng4,5,6, Chunguang Guo7, Qin Dang8, Yuyuan Zhang4, Yuqing Ren9, Long Liu10, Libo Wang10, Xiaoyong Ge4, Zhe Xing11, Jian Zhang12, Peng Luo12, Xinwei Han13,14,15. 1. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. liuzaoqu@163.comm. 2. Interventional Institute of Zhengzhou University, Zhengzhou, 450052, Henan, China. liuzaoqu@163.comm. 3. Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, 450052, Henan, China. liuzaoqu@163.comm. 4. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. 5. Interventional Institute of Zhengzhou University, Zhengzhou, 450052, Henan, China. 6. Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, 450052, Henan, China. 7. Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. 8. Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. 9. Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 10. Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. 11. Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 12. Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. 13. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. fcchanxw@zzu.edu.cn. 14. Interventional Institute of Zhengzhou University, Zhengzhou, 450052, Henan, China. fcchanxw@zzu.edu.cn. 15. Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, 450052, Henan, China. fcchanxw@zzu.edu.cn.
Abstract
BACKGROUND: Although immunotherapy and targeted treatments have dramatically improved the survival of melanoma patients, the intra- or intertumoral heterogeneity and drug resistance have hindered the further expansion of clinical benefits. METHODS: The 96 combination frames constructed by ten machine learning algorithms identified a prognostic consensus signature based on 1002 melanoma samples from nine independent cohorts. Clinical features and 26 published signatures were employed to compare the predictive performance of our model. RESULTS: A machine learning-based prognostic signature (MLPS) with the highest average C-index was developed via 96 algorithm combinations. The MLPS has a stable and excellent predictive performance for overall survival, superior to common clinical traits and 26 collected signatures. The low MLPS group with a better prognosis had significantly enriched immune-related pathways, tending to be an immune-hot phenotype and possessing potential immunotherapeutic responses to anti-PD-1, anti-CTLA-4, and MAGE-A3. On the contrary, the high MLPS group with more complex genomic alterations and poorer prognoses is more sensitive to the BRAF inhibitor dabrafenib, confirmed in patients with BRAF mutations. CONCLUSION: MLPS could independently and stably predict the prognosis of melanoma, considered a promising biomarker to identify patients suitable for immunotherapy and those with BRAF mutations who would benefit from dabrafenib.
BACKGROUND: Although immunotherapy and targeted treatments have dramatically improved the survival of melanoma patients, the intra- or intertumoral heterogeneity and drug resistance have hindered the further expansion of clinical benefits. METHODS: The 96 combination frames constructed by ten machine learning algorithms identified a prognostic consensus signature based on 1002 melanoma samples from nine independent cohorts. Clinical features and 26 published signatures were employed to compare the predictive performance of our model. RESULTS: A machine learning-based prognostic signature (MLPS) with the highest average C-index was developed via 96 algorithm combinations. The MLPS has a stable and excellent predictive performance for overall survival, superior to common clinical traits and 26 collected signatures. The low MLPS group with a better prognosis had significantly enriched immune-related pathways, tending to be an immune-hot phenotype and possessing potential immunotherapeutic responses to anti-PD-1, anti-CTLA-4, and MAGE-A3. On the contrary, the high MLPS group with more complex genomic alterations and poorer prognoses is more sensitive to the BRAF inhibitor dabrafenib, confirmed in patients with BRAF mutations. CONCLUSION: MLPS could independently and stably predict the prognosis of melanoma, considered a promising biomarker to identify patients suitable for immunotherapy and those with BRAF mutations who would benefit from dabrafenib.
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