State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
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.
EBioMedicine. 2019 Mar;41:120-133. doi: 10.1016/j.ebiom.2019.01.064. Epub 2019 Feb 22.
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.
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.
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.
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.
差异基因表达模式通常被用作生物标志物，以预测异质性肿瘤的治疗反应。然而，反应生物标志物与治疗靶向生物学过程之间的联系仍知之甚少。在这里，我们开发了一种预后指导方法来确定治疗反应的决定因素。
通过整合~26000 例 39 种恶性肿瘤的转录组和临床结果，评估了生物过程的预后。使用 39 种恶性肿瘤的基因预后评分（GEO 数据集）来检查预后，并且选择 TCGA 数据集进行验证。使用 Oncomine 和 GEO 数据集来建立和验证治疗反应的转录特征。
建立了 39 种恶性肿瘤的生物过程预后图谱。值得注意的是，生物过程的预后在癌症类型之间存在差异，并且这些预后模式背后的转录特征区分了针对特定生物学过程的治疗反应。应用这一指标，我们发现低肿瘤增殖率预示着良好的预后，而升高的细胞应激反应特征则预示着对抗增殖治疗的耐药性。此外，尽管高免疫活性与良好的预后相关，但增强的脂质代谢特征区分了免疫治疗耐药患者。
这些预后与治疗反应之间的发现为精准治疗的患者分层提供了进一步的见解，为进一步的实验和临床验证提供了机会。资助：国家自然科学基金、教育部创新团队发展计划、国家重点研发计划、广东省自然科学基金、广州市科技计划项目、MRC、CRUK、乳腺癌现在、帝国 ECMC、NIHR 帝国 BRC 和 NIH。
