Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management

• Published in: Cancer Immunology Immunotherapy Vol. 72; no. 3; pp. 599 - 615
• Main Authors: Liu, Zaoqu, Xu, Hui, Weng, Siyuan, Guo, Chunguang, Dang, Qin, Zhang, Yuyuan, Ren, Yuqing, Liu, Long, Wang, Libo, Ge, Xiaoyong, Xing, Zhe, Zhang, Jian, Luo, Peng, Han, Xinwei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Cancer Research; CTLA-4 protein; Drug resistance; Humans; Imidazoles - therapeutic use; Immunology; Immunotherapy; Learning algorithms; Machine learning; Medical prognosis; Medicine; Medicine & Public Health; Melanoma; Melanoma - drug therapy; Mutation; Oncology; Original; Original Article; Patients; PD-1 protein; Phenotypes; Prognosis; Proto-Oncogene Proteins B-raf - genetics; Survival; Prognosis; Treatment; Multi-omics; Machine learning; Melanoma
• ISSN: 0340-7004; 1432-0851; 1432-0851
• DOI: 10.1007/s00262-022-03279-1

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.