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Abstract：
BACKGROUND: The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification.
METHODS: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation.
RESULTS: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum.
CONCLUSIONS: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection.
BACKGROUND: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).
METHODS: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation.
RESULTS: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum.
CONCLUSIONS: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection.
BACKGROUND: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).
摘要：
背景：在免疫治疗时代，肿瘤的高度异质性和肿瘤微环境（TME）的复杂性极大地影响了肿瘤的发展和癌症的预后。在这项研究中,我们旨在描绘肺腺癌（LUAD）的单细胞特征景观，并开发出整合肿瘤异质性和TME的综合特征，用于预后分层。
方法：对来自14例LUAD患者的肿瘤组织和匹配的正常组织进行单细胞标记逆转录测序（STRT-seq），用于免疫景观描述和候选关键基因选择用于签名构建。进行Kaplan-Meier存活分析和体外细胞实验以确认基因功能。获得了来自11个独立队列的1949名患者的转录组学概况，其中包括9个公共数据集和2个内部队列用于验证。
结果：我们选择了11个与细胞间相互作用密切相关的关键基因，肿瘤发展，T细胞表型转化,和Ma/Mo细胞分布，包括HLA-DPB1，FAM83A，ITGB4,OAS1,FHL2,S100P,FSCN1,SFTPD,SPP1，DBH-AS1，CST3，并建立了整合的11个基因签名，将患者分为高评分或低评分组，以评估预后的好坏。此外，通过11个独立的队列验证了签名的预后预测效能，免疫治疗预测效能也通过我们接受免疫治疗治疗的内部队列得到验证.此外,体外细胞实验和药物敏感性预测进一步证实了该特征在整个RNA谱谱中的基因功能和普适性.
结论：这种单细胞特征的11个基因标记可能为预后分层和治疗选择提供潜在指导。
背景：国家重点研发项目（2022YFC2505004，2022YFC2505000至Z.W.和J.W.）提供了对该研究的支持，北京市自然科学基金(7242114,J.X.),国家自然科学基金(82102886,J.X.,81871889和82072586至Z.W.)，北京新星计划（20220484119至J.X.），国家自然科学基金总纲(J.W.82272796)，国家自然科学基金专项计划(J.W.82241229)，CAMS医学创新基金（2021-1-I2M-012，2022-I2M-1-009至Z.W.和J.W.），北京市自然科学基金(7212084至Z.W.),CAMS肺癌转化研究重点实验室（2018PT31035至J.W.），爱友基金会（KY201701toJ.W.）。中国医学科学院肿瘤医院肿瘤内科重点基金(CICAMS-MOCP2022003toJ.X.).
方法：对来自14例LUAD患者的肿瘤组织和匹配的正常组织进行单细胞标记逆转录测序（STRT-seq），用于免疫景观描述和候选关键基因选择用于签名构建。进行Kaplan-Meier存活分析和体外细胞实验以确认基因功能。获得了来自11个独立队列的1949名患者的转录组学概况，其中包括9个公共数据集和2个内部队列用于验证。
结果：我们选择了11个与细胞间相互作用密切相关的关键基因，肿瘤发展，T细胞表型转化,和Ma/Mo细胞分布，包括HLA-DPB1，FAM83A，ITGB4,OAS1,FHL2,S100P,FSCN1,SFTPD,SPP1，DBH-AS1，CST3，并建立了整合的11个基因签名，将患者分为高评分或低评分组，以评估预后的好坏。此外，通过11个独立的队列验证了签名的预后预测效能，免疫治疗预测效能也通过我们接受免疫治疗治疗的内部队列得到验证.此外,体外细胞实验和药物敏感性预测进一步证实了该特征在整个RNA谱谱中的基因功能和普适性.
结论：这种单细胞特征的11个基因标记可能为预后分层和治疗选择提供潜在指导。
背景：国家重点研发项目（2022YFC2505004，2022YFC2505000至Z.W.和J.W.）提供了对该研究的支持，北京市自然科学基金(7242114,J.X.),国家自然科学基金(82102886,J.X.,81871889和82072586至Z.W.)，北京新星计划（20220484119至J.X.），国家自然科学基金总纲(J.W.82272796)，国家自然科学基金专项计划(J.W.82241229)，CAMS医学创新基金（2021-1-I2M-012，2022-I2M-1-009至Z.W.和J.W.），北京市自然科学基金(7212084至Z.W.),CAMS肺癌转化研究重点实验室（2018PT31035至J.W.），爱友基金会（KY201701toJ.W.）。中国医学科学院肿瘤医院肿瘤内科重点基金(CICAMS-MOCP2022003toJ.X.).
