AbstractTumor microenvironment heterogeneity in hepatocellular carcinoma (HCC) on a spatial single-cell resolution is unclear. Here, we conducted co-detection by indexing to profile the spatial heterogeneity of 401 HCC samples with 36 biomarkers. By parsing the spatial tumor ecosystem of liver cancer, we identified spatial patterns with distinct prognosis and genomic and molecular features, and unveiled the progressive role of vimentin (VIM)high macrophages.

摘要肝细胞癌（HCC）在空间单细胞分辨率上的肿瘤微环境异质性尚不清楚。在这里，我们通过索引进行共检测，以分析401个HCC样本与36个生物标志物的空间异质性。通过解析肝癌的空间肿瘤生态系统，我们确定了具有不同预后和基因组和分子特征的空间模式，并揭示了波形蛋白（VIM）高巨噬细胞的进展作用。

Integration analysis with eight independent cohorts demonstrated that the spatial co-occurrence of VIMhigh macrophages and regulatory T cells promotes tumor progression and favors immunotherapy. Functional studies further demonstrated that VIMhigh macrophages enhance the immune-suppressive activity of regulatory T cells by mechanistically increasing the secretion of interleukin-1β.

与八个独立队列的整合分析表明，VIMhigh巨噬细胞和调节性T细胞的空间共存促进了肿瘤的进展，并有利于免疫治疗。功能研究进一步表明，VIMhigh巨噬细胞通过机械增加白细胞介素-1β的分泌来增强调节性T细胞的免疫抑制活性。

Our data provide deep insights into the heterogeneity of tumor microenvironment architecture and unveil the critical role of VIMhigh macrophages during HCC progression, which holds potential for personalized cancer prevention and drug discovery and reinforces the need to resolve spatial-informed features for cancer treatment..

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Fig. 1: Study design and cell type and phenotype definition.Fig. 2: A spatial single-cell phenotypic atlas of HCC.Fig. 3: Definition of tumor patterns in the HCC samples.Fig. 4: SPs with distinct prognosis in HCC.Fig. 5: The discriminate features of SPs.Fig. 6: Co-occurrence of macrophage_VIM+ and Treg cells associated with tumor malignancy behaviors.Fig.

图1：研究设计和细胞类型和表型定义。图2:HCC的空间单细胞表型图谱。图3:HCC样品中肿瘤模式的定义。图4:HCC预后明显的SPs。图5:SP的鉴别特征。图6：与肿瘤恶性行为相关的巨噬细胞VIM+和Treg细胞的共同出现。图。

7: Co-occurrence of macrophage_VIM+ and Treg cells favors immunotherapy in HCC.Fig. 8: VIMhigh macrophages promote the immune-suppressive ability of Treg cells via IL-1β..

7： 巨噬细胞VIM+和Treg细胞的共同出现有利于HCC的免疫治疗。图8:VIMhigh巨噬细胞通过IL-1β促进Treg细胞的免疫抑制能力。。

Data availability

数据可用性

The WES data that support the findings of this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession no. PRJNA1056508. The RNA-seq data that support the findings of this study have been deposited in the SRA under accession no. PRJCA022445 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA006377).

支持本研究结果的WES数据已保存在NCBI序列读取档案（SRA）中，登录号为PRJNA1056508。支持这项研究结果的RNA-seq数据已保存在SRA中，登录号为PRJCA022445(https://ngdc.cncb.ac.cn/gsa-human/browse/HRA006377)。

The ST data generated in this study are publicly available at Mendeley Data (https://data.mendeley.com/datasets/cvnx8jd6jw/1). All raw output files resulting from cell segmentation on the CODEX raw images are publicly available at Mendeley Data (https://data.mendeley.com/datasets/6w2k42kytt/1). The dataset derived from this resource, which supports the findings of this study, is publicly available at Mendeley Data (https://data.mendeley.com/datasets/km2df7y256/2).

这项研究中产生的ST数据可在Mendeley data上公开获得(https://data.mendeley.com/datasets/cvnx8jd6jw/1)。所有由CODEX原始图像上的细胞分割产生的原始输出文件都可以在Mendeley Data上公开获得(https://data.mendeley.com/datasets/6w2k42kytt/1)。来自该资源的数据集支持了这项研究的结果，可在Mendeley Data上公开获得(https://data.mendeley.com/datasets/km2df7y256/2)。

The published scRNA-seq dataset (cohort 3) has been deposited in the China National GeneBank DataBase under accession no. CNP0000650 (ref. 31). The published ST datasets HCC-R1 and HCC-R2 are available from the corresponding author of the original study upon request34. The published ST datasets HCC-1-N, HCC-5-A and HC-4-L have been deposited in the Genome Sequence Archive under accession no.

已发布的scRNA-seq数据集（队列3）已保存在中国国家基因库数据库中，登录号为CNP0000650（参考文献31）。已发布的ST数据集HCC-R1和HCC-R2可应要求从原始研究的通讯作者处获得34。已发布的ST数据集HCC-1-N，HCC-5-A和HC-4-L已以登录号保藏在基因组序列档案中。

HRA000437 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000437) (ref. 32). The published RNA-seq datasets HCCDB6 and HCCDB15 can be downloaded from http://lifeome.net/database/hccdb/home.html (ref. 3). The published RNA-seq datasets for the EHBH cohort are available from the corresponding author of the original study upon reasonable request32.

HRA000437(https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000437)（参考文献32）。已发布的RNA-seq数据集HCCDB6和HCCDB15可以从http://lifeome.net/database/hccdb/home.html（参考文献3）。EHBH队列的已发布RNA-seq数据集可根据合理的要求从原始研究的通讯作者处获得32。

The Molecular Signatures Database can be accessed at https://www.gsea-msigdb.org/gsea/msigdb. The source data for Figs. 1–8 and Extended Data Figs. 1–10 have been provided as Source Data files. All other data supporting the findings of this study are available from the .

分子签名数据库可以访问https://www.gsea-msigdb.org/gsea/msigdb.。支持这项研究结果的所有其他数据都可以从中获得。

Code availability

代码可用性

The code generated in this study is publicly available at Github (https://github.com/sanrishiguang/ChenleiLab_CODEX_HCC).

这项研究中生成的代码可以在Github上公开获得(https://github.com/sanrishiguang/ChenleiLab_CODEX_HCC)。

ReferencesSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).Article

ReferencesSung，H.等人，《2020年全球癌症统计：全球癌症协会对185个国家36种癌症的全球发病率和死亡率的估计》。CA Cancer J.Clin。71209-249（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Llovet, J. M. et al. Hepatocellular carcinoma. Nat. Rev. Dis. Primers 7, 6 (2021).Article

Llovet，J.M.等人。肝细胞癌。自然版本Dis。引物7、6（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Llovet, J. M., Montal, R., Sia, D. & Finn, R. S. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 15, 599–616 (2018).Article

Llovet，J.M.，Montal，R.，Sia，D。＆Finn，R.S。肝细胞癌的分子疗法和精准医学。国家修订临床。Oncol公司。15599-616（2018）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Llovet, J. M. et al. Immunotherapies for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 19, 151–172 (2022).Article

Llovet，J.M.等人。肝细胞癌的免疫疗法。国家修订临床。Oncol公司。19151-172（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Xue, R. et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612, 141–147 (2022).Article

薛，R。等。肝肿瘤免疫微环境亚型和中性粒细胞异质性。自然612141-147（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459 (2020).Article

。细胞181442-459（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Zhang, Q. et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829–845 (2019).Article

Zhang，Q。等。肝细胞癌中单个免疫细胞的景观和动力学。细胞179829-845（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Liu, Y.-M. et al. Combined single-cell and spatial transcriptomics reveal the metabolic evolvement of breast cancer during early dissemination. Adv. Sci. 10, e2205395 (2023).Article

Liu，Y.-M.等人结合单细胞和空间转录组学揭示了乳腺癌早期传播过程中的代谢演变。高级科学。10，e2205395（2023）。文章

Google Scholar

谷歌学者

Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).Article

Longo，S.K.，Guo，M.G.，Ji，A.L。和Khavari，P.A。整合单细胞和空间转录组学以阐明细胞间组织动力学。Genet自然Rev。22627-644（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).Article

Moncada，R。等人整合基于微阵列的空间转录组学和单细胞RNA-seq揭示了胰腺导管腺癌的组织结构。美国国家生物技术公司。38333-342（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, e31657 (2018).Article

Lin，J.-R.等人。使用t-CyCIF和常规光学显微镜对人体组织和肿瘤进行高度多重免疫荧光成像。eLife 7，e31657（2018）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).Article

Giesen，C.等人。通过质量细胞术以亚细胞分辨率对肿瘤组织进行高度多重成像。《自然方法》11417–422（2014）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Angelo, M. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).Article

Angelo，M。人乳腺肿瘤的多重离子束成像。《自然医学杂志》20436-442（2014）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).Article

Keren，L。等人。通过多重离子束成像揭示的三阴性乳腺癌中结构化的肿瘤免疫微环境。细胞1741373-1387（2018）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018).Article

Goltsev，Y。等人。用CODEX多重成像对小鼠脾脏结构进行深度分析。细胞174968-981（2018）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).Article

Jackson，H.W.等人，《乳腺癌的单细胞病理学景观》。。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).Article

Schürch，C.M.等人协调的细胞邻域在结直肠癌侵袭前沿协调抗肿瘤免疫。细胞1821341-1359（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Risom, T. et al. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell 185, 299–310 (2022).Article

Risom，T。等人。向浸润性乳腺癌的转变与肿瘤基质的结构和组成的进行性变化有关。细胞185299-310（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sheng, J. et al. Topological analysis of hepatocellular carcinoma tumour microenvironment based on imaging mass cytometry reveals cellular neighbourhood regulated reversely by macrophages with different ontogeny. Gut 71, 1176–1191 (2022).Article

Sheng，J。等人。基于成像质量细胞术的肝细胞癌肿瘤微环境拓扑分析揭示了具有不同个体发育的巨噬细胞反向调节的细胞邻域。肠道711176-1191（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Makino, Y. et al. Constitutive activation of the tumor suppressor p53 in hepatocytes paradoxically promotes non-cell autonomous liver carcinogenesis. Cancer Res. 82, 2860–2873 (2022).Article

Makino，Y。等人。肝细胞中肿瘤抑制因子p53的组成型激活矛盾地促进了非细胞自主性肝癌的发生。癌症研究822860-2873（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Yang, J. Hepatocellular carcinoma and macrophage interaction induced tumor immunosuppression via Treg requires TLR4 signaling. 18, 2938–2947 (2012).Yeung, O. W. H. et al. Alternatively activated (M2) macrophages promote tumour growth and invasiveness in hepatocellular carcinoma. J. Hepatol.

Yang，J。肝细胞癌和巨噬细胞相互作用通过Treg诱导的肿瘤免疫抑制需要TLR4信号传导。182938-2947（2012）。Yeung，O.W.H.等人。活化的（M2）巨噬细胞促进肝细胞癌的肿瘤生长和侵袭。J、 肝病。

62, 607–616 (2015).Article .

62607-616（2015）。文章。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Gao, Q. et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell 179, 561–577 (2019).Article

Gao，Q。等人。HBV相关肝细胞癌的综合蛋白质基因组学表征。。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Scire, J. et al. estimateR: an R package to estimate and monitor the effective reproductive number. BMC Bioinformatics 24, 310 (2023).Article

Scire，J。等人。估计值：用于估计和监测有效生殖数的R包。BMC生物信息学24310（2023）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).Article

。基因组生物学。17218（2016）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. in Cancer Systems Biology (ed. Von Stechow, L.) 243–259 (Springer, 2018).Llovet, J. M. et al. Molecular pathogenesis and systemic therapies for hepatocellular carcinoma. Nat. Cancer 3, 386–401 (2022).Article

Chen，B.，Khodadoust，M.S.，Liu，C.L.，Newman，A.M。和Alizadeh，A.A.《癌症系统生物学》（ed.Von Stechow，L.）243-259（Springer，2018）。Llovet，J.M.等。肝细胞癌的分子发病机制和全身治疗。《自然癌症》3386-401（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sia, D. et al. Integrative molecular analysis of intrahepatic cholangiocarcinoma reveals 2 classes that have different outcomes. Gastroenterology 144, 829–840 (2013).Article

Sia，D。等人。肝内胆管癌的综合分子分析揭示了两类具有不同结果的疾病。胃肠病学144829-840（2013）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Hoshida, Y. et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 69, 7385–7392 (2009).Article

Hoshida，Y。等人。整合转录组分析揭示了人类肝细胞癌的常见分子亚类。癌症研究697385-7392（2009）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Chiang, D. Y. et al. Focal gains of VEGFA and molecular classification of hepatocellular carcinoma. Cancer Res. 68, 6779–6788 (2008).Article

Chiang，D.Y.等。VEGFA的局部获得和肝细胞癌的分子分类。癌症研究686779-6788（2008）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sun, Y. et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 184, 404–421 (2021).Article

Sun，Y。等。早期复发性肝细胞癌生态系统的单细胞景观。细胞184404-421（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Wu, R. et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci. Adv. 7, eabg3750 (2021).Article

Wu，R。等。原发性肝癌空间结构的综合分析。科学。广告7，eabg3750（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lian, Q. et al. HCCDB: a database of hepatocellular carcinoma expression atlas. Genomics Proteomics Bioinformatics 16, 269–275 (2018).Article

Lian，Q。等。HCCDB：肝细胞癌表达图谱数据库。基因组学蛋白质组学生物信息学16269-275（2018）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wang, Y.-F. et al. Spatial maps of hepatocellular carcinoma transcriptomes reveal spatial expression patterns in tumor immune microenvironment. Theranostics 12, 4163–4180 (2022).Article

Wang，Y.-F.等人。肝细胞癌转录组的空间图谱揭示了肿瘤免疫微环境中的空间表达模式。。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mittal, V. Epithelial mesenchymal transition in tumor metastasis. Annu. Rev. Pathol. 13, 395–412 (2018).Article

Mittal，V。肿瘤转移中的上皮-间质转化。。Pathol牧师。13395-412（2018）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).Article

Browaeys，R.，Saelens，W。＆Saeys，Y。NicheNet：通过将配体连接到靶基因来模拟细胞间通讯。自然方法17159-162（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Plitas, G. & Rudensky, A. Y. Regulatory T cells in cancer. Annu. Rev. Cancer Biol. 4, 459–477 (2020).Article

Plitas，G。＆Rudensky，A.Y。癌症中的调节性T细胞。。癌症生物学评论。4459-477（2020）。文章

Google Scholar

谷歌学者

Caronni, N. et al. IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer. Nature 623, 415–422 (2023).Article

Caronni，N。等人。IL-1β+巨噬细胞促进胰腺癌的致病性炎症。自然623415-422（2023）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Mair, F. et al. Extricating human tumour immune alterations from tissue inflammation. Nature 605, 728–735 (2022).Article

Mair，F.等人，《从组织炎症中摆脱人类肿瘤免疫改变》。《自然》605728-735（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lindau, D., Gielen, P., Kroesen, M., Wesseling, P. & Adema, G. J. The immunosuppressive tumour network: myeloid-derived suppressor cells, regulatory T cells and natural killer T cells. Immunology 138, 105–115 (2013).Article

Lindau，D.，Gielen，P.，Kroesen，M.，Wesseling，P。＆Adema，G.J。免疫抑制肿瘤网络：骨髓来源的抑制细胞，调节性T细胞和自然杀伤T细胞。。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Xiao, X. et al. PD-1hi identifies a novel regulatory B-cell population in human hepatoma that promotes disease progression. Cancer Discov. 6, 546–559 (2016).Article

Xiao，X。等人PD-1hi在人类肝癌中鉴定出一种新的调节性B细胞群，可促进疾病进展。癌症发现。6546-559（2016）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Mor-Vaknin, N., Punturieri, A., Sitwala, K. & Markovitz, D. M. Vimentin is secreted by activated macrophages. Nat. Cell Biol. 5, 59–63 (2003).Article

Mor Vaknin，N.，Punturieri，A.，Sitwala，K。＆Markovitz，D.M。波形蛋白由活化的巨噬细胞分泌。自然细胞生物学。5，59-63（2003）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).Article

Cabrita，R。等人。三级淋巴结构可改善黑色素瘤的免疫治疗和存活率。自然577561-565（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Helmink, B. A. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).Article

Helmink，B.A.等人B细胞和三级淋巴结构促进免疫治疗反应。自然577549-555（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zhu, A. X. et al. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat. Med. 28, 1599–1611 (2022).Article

Zhu，A.X.等。晚期肝细胞癌对阿替唑珠单抗联合贝伐单抗的临床反应和耐药性的分子相关性。《自然医学》281599-1611（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Wang, D.-R., Wu, X.-L. & Sun, Y.-L. Therapeutic targets and biomarkers of tumor immunotherapy: response versus non-response. Signal Transduct. Target. Ther. 7, 331 (2022).Article

Wang，D.-R.，Wu，X.-L。和Sun，Y.-L。肿瘤免疫治疗的治疗靶点和生物标志物：反应与无反应。信号传输管。目标。他们。7331（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Ruiz de Galarreta, M. et al. β-catenin activation promotes immune escape and resistance to anti-PD-1 therapy in hepatocellular carcinoma. Cancer Discov. 9, 1124–1141 (2019).Article

Ruiz de Galarreta，M。等人。β-连环蛋白激活促进肝细胞癌的免疫逃逸和抗PD-1治疗的抗性。癌症发现。91124-1141（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).Article

Eisenhauer，E.A.等人，《实体瘤新的反应评估标准：修订的RECIST指南》（1.1版）。《欧洲癌症杂志》45228-247（2009）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).Article

。科学。代表716878（2017）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. in Medical Image Computing and Computer Assisted Intervention-MICCAI 2018 (eds Frangi, A. F. et al.) 265–273 (Springer, 2018).Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Schmidt，U.，Weigert，M.，Broaddus，C。＆Myers，G。医学图像计算和计算机辅助干预MICCAI 2018（eds Frangi，A.F。等人）265-273（Springer，2018）。Leek，J.T.，Johnson，W.E.，Parker，H.S.，Jaffe，A.E.＆Storey，J.D。用于消除高通量实验中批次效应和其他不必要变化的sva包。

Bioinformatics 28, 882–883 (2012).Article .

生物信息学28882-883（2012）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article

Li，H。＆Durbin，R。使用Burrows-Wheeler变换进行快速准确的短读比对。生物信息学251754-1760（2009）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).Article

DePristo，M.A。等人。使用下一代DNA测序数据进行变异发现和基因分型的框架。纳特·吉内特。43491-498（2011）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).Article

Wang，K.，Li，M。＆Hakonarson，H。ANNOVAR：高通量测序数据中遗传变异的功能注释。核酸研究38，e164（2010）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).Article

Talevich，E.，Shain，A.H.，Botton，T。＆Bastian，B.C。CNVkit：靶向DNA测序的全基因组拷贝数检测和可视化。。生物学12，e1004873（2016）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).Article

Mermel，C。H。等人，GISTIC2.0促进了人类癌症中局灶性体细胞拷贝数改变靶标的敏感和可靠定位。基因组生物学。。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Krueger, F. et al. FelixKrueger/TrimGalore: v0.6.10 - add default decompression path. Zenodo https://doi.org/10.5281/zenodo.7598955 (2023).Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).Article

Krueger，F。等人。FelixKrueger/TrimGalore:v0.6.10-添加默认解压缩路径。泽诺多https://doi.org/10.5281/zenodo.7598955（2023年）。Dobin，A。等人STAR：超快通用RNA-seq比对仪。生物信息学29,15-21（2013）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).Article

Liao，Y.，Smyth，G.K。＆Shi，W。featureCounts：一种有效的通用程序，用于将序列读数分配给基因组特征。生物信息学30923-930（2014）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Haas, B. J. et al. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol. 20, 213 (2019).Article

Haas，B.J.等人。通过读取映射和基于从头融合转录本组装的方法对融合转录本检测的准确性评估。基因组生物学。20213（2019）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article

Love，M.I.，Huber，W。＆Anders，S。用DESeq2缓和了RNA-seq数据的倍数变化和分散估计。基因组生物学。15550（2014）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).CAS

。创新2100141（2021）。中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zeng, D. et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol. 12, 687975 (2021).Article

Zeng，D。et al。IOBR：解码肿瘤微环境和特征的多组学免疫肿瘤学生物学研究。正面。免疫。12687975（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sia, D. et al. Identification of an immune-specific class of hepatocellular carcinoma, based on molecular features. Gastroenterology 153, 812–826 (2017).Article

Sia，D.等人。基于分子特征鉴定免疫特异性肝细胞癌。胃肠病学153812-826（2017）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Hoshida, Y. Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS ONE 5, e15543 (2010).Article

Hoshida，Y。最近模板预测：基于单样本的灵活类别预测和置信度评估。PLoS ONE 5，e15543（2010）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356 (2017).Article

郑，C。等。通过单细胞测序揭示肝癌中浸润性T细胞的景观。细胞1691342-1356（2017）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Collison, L. W. & Vignali, D. A. A. in Regulatory T Cells (eds Kassiotis, G. & Liston, A.) 21–37 (Humana Press, 2011).Download referencesAcknowledgementsThis work was supported by the National Key R&D Program of China (no. 2023YFC2507500 to L.C.), the National Natural Science Foundation of China (no.

Collison，L.W。＆Vignali，D.A.A。在调节性T细胞中（eds Kassiotis，G。＆Liston，A。）21-37（Humana Press，2011）。下载参考文献致谢这项工作得到了中国国家重点研发计划（L.C.编号2023YFC2507500），国家自然科学基金（no。

U21A20376 to L.C., no. 82273277 to X.Q., no. 81988101 to H.W. and no. 82372872 to S.Y.), the National Science Foundation of Shanghai (no. 21XD1404600 to L.C., no. 21JC1406600 to H.W., no. 22140901000 to L.C. and no. 21DZ2291900 to H.W.) and the Shanghai Institute of Chinese Engineering Development Strategies (no.

U21A20376至L.C.，82273277至X.Q.，81988101至H.W.，82372872至S.Y.），上海国家科学基金（21XD1404600至L.C.，21JC1406600至H.W.，22140901000至L.C.，21DZ2291900至H.W.）和上海中国工程发展战略研究所（第。

23692123600 to H.W.). We thank the Shanghai Municipal Science and Technology Major Project for their support.Author informationAuthor notesThese authors contributed equally: Xinyao Qiu, Tao Zhou, Shuai Li, Jianmin Wu, Jing Tang.Authors and AffiliationsFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaXinyao Qiu, Shuai Yang, Hongyang Wang & Lei ChenNational Center for Liver Cancer, Shanghai, ChinaXinyao Qiu, Tao Zhou, Ji Hu, Siyun Shen & Lei ChenThe International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, ChinaTao Zhou, Ji Hu, Siyun Shen, Hongyang Wang & Lei ChenInstitute of Metabolism and Integrative Biology, Fudan University, Shanghai, ChinaShuai Li, Jianmin Wu, Guosheng Ma, Kaiting Wang & Hongyang WangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaJing TangKey Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer, Ministry of Education, Shanghai, ChinaHongyang Wang & Lei ChenShanghai Key Laboratory of Hepatobili.

23692123600至H.W.）。我们感谢上海市科技重大项目的支持。作者信息作者注意到这些作者做出了同样的贡献：邱新尧，周涛，李帅，吴建民，汤静。作者和单位复旦大学上海医学院肿瘤科复旦大学上海癌症中心，上海，中国邱新尧，杨帅，王红阳和陈磊国家肝癌中心，上海，邱新尧，周涛，胡吉，沈思云和陈磊海军医大学东肝胆外科医院信号转导国际合作实验室，上海，周涛，胡吉，沈思云，王红阳和陈磊复旦大学代谢与整合生物学研究所，上海，李帅，吴建民，马国胜，王开亭和王红阳癌症中心，华中同济医学院联合医院中国科学技术大学，武汉，教育部肝癌信号调控与靶向治疗重点实验室，上海，中国，王红阳和陈磊上海肝胆重点实验室。

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PubMed Google ScholarContributionsX.Q. and J.W. constructed the HCC tissue microarray, conducted the CODEX-related experiments and segmented the images. X.Q., T.Z. and S.L. analyzed the data. X.Q. and T.Z. organized the figures. X.Q. wrote the manuscript. X.Q., S.L. and J.W. conducted the in vitro experiments.

PubMed谷歌学术贡献x。Q、 。十、 Q.，T.Z.和S.L.分析了数据。十、 Q.和T.Z.组织了这些数字。十、 Q.写了手稿。十、 Q.，S.L.和J.W.进行了体外实验。

G.M. conducted the WES and RNA-seq analyses of the FFPE tissues. J.W. and S.L. collected the clinical information. K.W., S.S., S.Y. and J.H. collected the clinical information. J.T. collected the samples with immunotherapies, analyzed the data produced during the revision process and helped revise the manuscript.

G、 M.对FFPE组织进行了WES和RNA-seq分析。J、 W.和S.L.收集了临床信息。K、 W.，S.S.，S.Y.和J.H.收集了临床信息。J、 T.用免疫疗法收集样本，分析修订过程中产生的数据，并帮助修订稿件。

H.W. and L.C. designed the research, supervised the study and revised the manuscript.Corresponding authorsCorrespondence to.

H、 W.和L.C.设计了研究，监督了研究并修改了手稿。通讯作者通讯。

Hongyang Wang or Lei Chen.Ethics declarations

王红阳或陈雷。道德宣言

Competing interests

相互竞争的利益

The authors declare no competing interests.

作者声明没有利益冲突。

Peer review

同行评审

Peer review information

同行评审信息

Nature Cancer thanks Bertram Bengsch, Garry Nolan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

《自然癌症》感谢伯特伦·本奇（BertramBengsch）、加里·诺兰（GarryNolan）和另一位匿名审稿人对这项工作的同行评议做出的贡献。

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Single-cell phenotyping in HCC samples.a, Other markers in the CODEX panel besides cell type recognition markers were listed.

Additional informationPublisher的noteSpringer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。扩展数据扩展数据图1 HCC样品中的单细胞表型。a，除了细胞类型识别标记之外，还列出了CODEX面板中的其他标记。

b, UMAP plot of the 13 markers used for cell type definition. For each UMAP plot, 16215 cells were plotted. c, The UMAP of each TMA before batch effect correction. A total of 1048575 cells was plotted. d, The UMAP of each TMA after batch effect correction. A total of 1048575 cells was plotted. e, Clustering by Rphenograph with 36 markers.

b、 用于细胞类型定义的13个标记的UMAP图。对于每个UMAP图，绘制了16215个细胞。c、 批次效应校正之前每个TMA的UMAP。绘制了总共1048575个细胞。d、 批次效应校正后每个TMA的UMAP。绘制了总共1048575个细胞。e、 通过具有36个标记的Rphenograph聚类。

Heatmap showing the z-scored mean marker expression for each cluster. f, Clustering by Rphenograph with 13 cell type recognition markers. Heatmap showing the z-scored mean marker expression for each cluster. The identified cell type was annotated below. g-j, The recognition of cell phenotypes in macrophages (g), tumor cells (h), CD4 + T cells (i), and CD8 + T cells (j) respectively.

热图显示每个簇的z得分平均标记表达。f、 通过具有13个细胞类型识别标记的Rphenograph聚类。热图显示每个簇的z得分平均标记表达。鉴定出的细胞类型注释如下。。

Indicated functional markers were used to cluster cells. Heatmap showing the z-scored mean marker expression level for each cell phenotype in indicated cell type. The percentage of each cell phenotype in indicated cell type was presented as a bar plot.Source dataExtended Data Fig. 2 Stratifying the HCC samples based on cell type composition.a, Hierarchical clustering on cell type percentage defined 4 groups annotated by different colors.

。热图显示了指定细胞类型中每种细胞表型的z评分平均标记表达水平。所示细胞类型中每种细胞表型的百分比以条形图表示。源数据扩展数据图2根据细胞类型组成对HCC样本进行分层。a，细胞类型百分比的层次聚类定义了4个由不同颜色注释的组。

The top panel showed the composition of cell types in each sample, followed by detailed annotations of clinical variables. Two-sided Chi-squared Test. b, The Kaplan–Meier survival curves of the groups were drawn and log-rank tests were done to investigate the overall significance. c, The Kaplan–Meier survival curv.

上图显示了每个样品中细胞类型的组成，然后是临床变量的详细注释。双侧卡方检验。b、 绘制各组的Kaplan–Meier生存曲线，并进行对数秩检验以调查总体显着性。c、 卡普兰-迈耶生存曲线。

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