The prognostic landscape of interactive biological processes presents treatment responses in cancerResearch in context
Bin He,
Rui Gao,
Dekang Lv,
Yalu Wen,
Luyao Song,
Xi Wang,
Suxia Lin,
Qitao Huang,
Ziqian Deng,
Zifeng Wang,
Min Yan,
Feimeng Zheng,
Eric W.-F. Lam,
Keith W. Kelley,
Zhiguang Li,
Quentin Liu
Affiliations
Bin He
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Rui Gao
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China; Department of Medical Oncology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 510275, PR China
Dekang Lv
Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China
Yalu Wen
Department of Statistics, University of Auckland, New Zealand
Luyao Song
Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China
Xi Wang
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Suxia Lin
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Qitao Huang
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Ziqian Deng
Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China
Zifeng Wang
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Min Yan
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Feimeng Zheng
Department of Medical Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, PR China
Eric W.-F. Lam
Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London, W12 ONN, UK
Keith W. Kelley
Laboratory of Immunophysiology, Department of Animal Sciences, College of ACES and Department of Pathology, College of Medicine, University of Illinois at Urbana-Champaign, Urbana, USA
Zhiguang Li
Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China; Correspondence to: Zhiguang Li, Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China
Quentin Liu
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China; Institute of Cancer Stem Cell, Dalian Medical University, Dalian 116044, PR China; Correspondence to: Quentin Liu, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
Background: Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response. Methods: The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses. Findings: The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-proliferation treatment. Moreover, while high immune activities were associated with favorable prognosis, enhanced lipid metabolism signatures distinguished immunotherapy resistant patients. Interpretation: These findings between prognosis and treatment response provide further insights into patient stratification for precision treatments, providing opportunities for further experimental and clinical validations. Fund: National Natural Science Foundation, Innovative Research Team in University of Ministry of Education of China, National Key Research and Development Program, Natural Science Foundation of Guangdong, Science and Technology Planning Project of Guangzhou, MRC, CRUK, Breast Cancer Now, Imperial ECMC, NIHR Imperial BRC and NIH. Keywords: Prognosis, Biological processes, Treatment response, Cell-proliferation, Immune processes
