Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses

Journal of Big Data

Research

Nan Zhang¹  Hao Zhang²  Ran Zhou³  Xisong Liang⁴  Bo Zhang⁴  Ziyu Dai⁴  Jie Wen⁴  Xun Zhang⁴  Zeyu Wang⁴  Quan Cheng⁵  Peng Luo⁶  Qi Zhang⁷  Shuyu Li⁸  Xue Yang⁸  Hanning Li⁸  Zhifang Yang⁹  Wantao Wu^1,10  Zirui Li^1,11 

[1] College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China;Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China;Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China;Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK;National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China;Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China;National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China;Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China;National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China;Department of Neurosurgery, Xiangya Hospital, Central South University, 410008, Changsha, Hunan, P. R. China;Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China;Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College of Huazhong, University of Science and Technology, 430030, Wuhan, Hubei, P.R. China;Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China;Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China;Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong, University of Science and Technology, 430030, Wuhan, Hubei, P.R. China;National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China;Department of Oncology, Xiangya Hospital, Central South University, Changsha, China;School of Artificial Intelligence and Computer Science, Jiangnan University, Jiangsu, China;

关键词: Triple-negative breast cancer;    Machine learning;    Immunotherapy;    Immune infiltrating cell;    Prognosis;   

DOI  :  10.1186/s40537-023-00809-1

 received in 2023-02-21, accepted in 2023-08-07,  发布年份 2023

来源: Springer

【 摘 要 】

Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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