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

作者全名："Li, Shuyu; Zhang, Nan; Zhang, Hao; Zhou, Ran; Li, Zirui; Yang, Xue; Wu, Wantao; Li, Hanning; Luo, Peng; Wang, Zeyu; Dai, Ziyu; Liang, Xisong; Wen, Jie; Zhang, Xun; Zhang, Bo; Cheng, Quan; Zhang, Qi; Yang, Zhifang"

作者地址："[Li, Shuyu; Yang, Xue; Li, Hanning; Yang, Zhifang] Huazhong Univ Sci & Technol, Tongji Hosp, Dept Thyroid & Breast Surg, Tongji Med Coll, Wuhan, Peoples R China; [Zhang, Nan] Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Wuhan, Peoples R China; [Zhang, Hao] Chongqing Med Univ, Affiliated Hosp 2, Dept Neurosurg, Chongqing, Peoples R China; [Zhou, Ran; Wang, Zeyu; Dai, Ziyu; Liang, Xisong; Wen, Jie; Zhang, Xun; Zhang, Bo; Cheng, Quan] Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha, Peoples R China; [Zhou, Ran] Univ Manchester, Fac Biol Med & Hlth, Div Neurosci & Expt Psychol, Manchester, England; [Zhou, Ran; Wu, Wantao; Wang, Zeyu; Dai, Ziyu; Liang, Xisong; Wen, Jie; Zhang, Xun; Zhang, Bo; Cheng, Quan] Cent South Univ, Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Changsha, Peoples R China; [Li, Zirui] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China; [Wu, Wantao] Cent South Univ, Xiangya Hosp, Dept Oncol, Changsha, Peoples R China; [Luo, Peng] Southern Med Univ, Zhujiang Hosp, Dept Oncol, Guangzhou, Peoples R China; [Zhang, Qi] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Plast Surg, Wuhan, Peoples R China; [Cheng, Quan] Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha 410008, Hunan, Peoples R China; [Zhang, Qi] Univ Sci & Technol, Tongji Hosp, Tongji Med Coll Huazhong, Dept Plast & Cosmet Surg, Wuhan 430030, Hubei, Peoples R China; [Yang, Zhifang] Univ Sci & Technol, Tongji Hosp, Tongji Med Coll Huazhong, Dept Thyroid & Breast Surg, Wuhan 430030, Hubei, Peoples R China"

通信作者："Yang, ZF (通讯作者)，Huazhong Univ Sci & Technol, Tongji Hosp, Dept Thyroid & Breast Surg, Tongji Med Coll, Wuhan, Peoples R China.; Cheng, Q (通讯作者)，Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha, Peoples R China.; Cheng, Q (通讯作者)，Cent South Univ, Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Changsha, Peoples R China.; Zhang, Q (通讯作者)，Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Plast Surg, Wuhan, Peoples R China.; Cheng, Q (通讯作者)，Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha 410008, Hunan, Peoples R China.; Zhang, Q (通讯作者)，Univ Sci & Technol, Tongji Hosp, Tongji Med Coll Huazhong, Dept Plast & Cosmet Surg, Wuhan 430030, Hubei, Peoples R China.; Yang, ZF (通讯作者)，Univ Sci & Technol, Tongji Hosp, Tongji Med Coll Huazhong, Dept Thyroid & Breast Surg, Wuhan 430030, Hubei, Peoples R China."

来源：JOURNAL OF BIG DATA

ESI学科分类：　

WOS号：WOS:001054752100001

JCR分区：Q1

影响因子：8.6

年份：2023

卷号：10

期号：1

开始页：　

结束页：　

文献类型：Article

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

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."

基金机构："The author expresses gratitude to the public databases, websites, and software used in the paper. We are grateful to the High Performance Computing Center of Central South University for partial support of this work.; High Performance Computing Center of Central South University"

基金资助正文："The author expresses gratitude to the public databases, websites, and software used in the paper. We are grateful to the High Performance Computing Center of Central South University for partial support of this work."