第一部分 乳腺癌患者免疫微环境的预后指示意义

        第一章 一种免疫相关的乳腺癌预后模型

背景：虽然经典的乳腺癌分子亚型已被广泛应用于临床诊断并协助临床治疗决策，但作为一种高度异质性的恶性肿瘤，乳腺癌的经典分子分型的定义仅基于肿瘤本身，不足以准确预测乳腺癌患者的预后。越来越多的研究表明，肿瘤微环境中的各种免疫细胞在肿瘤发生发展和预后转归中起着至关重要的作用。

目的：在本研究中，我们旨在开发一种免疫微环境相关的免疫预后模型，以对乳腺癌患者的预后提供指示意义。

材料及方法：本部分研究共纳入43名乳腺癌患者，对入组患者肿瘤组织进行总RNA提取及转录组测序（本部分定义为BRCA_OURS）。BRCA_OURS和来自TCGA数据库中的932例乳腺癌组织转录组数据（本部分定义为BRCA_TCGA）作为训练集，对乳腺癌患者免疫基因表达谱进行一致性聚类分为免疫浸润高组和免疫浸润低组，并比较组间差异基因作为建模候选基因。利用Lasso(Least absolute shrinkage and selection operation)-Cox的方法进一步构建与乳腺癌患者总生存率（overall survival, OS）相关的免疫预后风险模型，计算训练集中免疫相关评分，并根据数据集评分中位值将患者分为高风险组和低风险组，进行预后验证并检验模型效能。同时，在多个外部数据集中验证其可重复性。

结果：在乳腺癌训练集BRCA_OURS和BRCA_TCGA中,构建基于10个基因的免疫风险预后模型，包括1个危险性因素（IL10）和9个保护性基因（C14orf79、C1orf168、C1orf226、CELSR2、FABP7、FGFBP1、KLRB1、PLEKHO1和RAC2），危险性因素与患者预后不良有关，而保护性基因的高表达与患者预后较好相关。根据免疫相关风险评分，训练集乳腺癌患者分为高风险组和低风险组，Kaplan-Meier生存曲线提示高风险组患者的总生存期、无进展生存期和无病生存期等多个生存指标显著缩短。此外，我们发现高风险组患者的恶性进展可能与几种重要的肿瘤生物学途径有关，包括更多的高尔基体囊泡介导的转运被激活、剧烈的DNA双链断裂和基因组不稳定性，这些途径可能是促进肿瘤生长和转移的关键。

结论：利用转录组描绘乳腺癌免疫微环境，我们认为乳腺癌患者的的免疫微环境是患者预后转归因素的一个重要决定因素。作为有效的风险分层工具，免疫相关预后风险模型可能为乳腺癌患者的临床预后评估提供指示。

关键词：乳腺癌；转录组；肿瘤微环境；预后；免疫相关模型

l  第二章  基于T细胞受体库的乳腺癌预后模型和免疫反应

背景：T细胞是介导适应性免疫反应的活跃细胞群，也是体液免疫系统激活反应的重要组成部分。抗肿瘤免疫反应由多克隆T细胞介导，T细胞识别具有不同亲和力的肿瘤抗原并进行不同程度的克隆扩增。T细胞受体测序（T cell repertoire sequencing, TCR-Seq）为描述T细胞的适应性免疫反应的多样性提供了一种途径。

目的：为了更好地了解乳腺癌患者免疫微环境中的T细胞的丰度及多样性对肿瘤发生发展的影响，我们收集了43名患者的肿瘤组织和对应患者外周血液样本，整合并分析乳腺癌患者RNA测序（RNA sequencing, RNA-Seq）和TCR-Seq表达谱，以期获得更多预后治疗提示。

材料及方法：我们通过相关性分析将TCR多样性指标—标化香农-威纳指数映射到患者转录组表达谱，并进一步在TCGA测试集中使用LASSO（Least absolute shrinkage and selection operation）-Cox回归构建乳腺癌预后预测风险模型。根据TCR多样性风险指数评分，将乳腺癌患者分为高风险组和低风险组，并基于转录组和免疫组对其免疫景观进行进一步评估与比较，以及患者对免疫检查点的反应性。

结果：相比于肿瘤组织，乳腺癌患者外周血中T细胞克隆更丰富。相比于非三阴性乳腺癌患者，三阴性乳腺癌患者的肿瘤组织中T细胞克隆更丰富。通过将TCR免疫组库映射到转录组，我们在乳腺癌患者样本中开发了一个由19个基因组成的TCR多样性指数风险模型。该TCR多样性指数特异性反应CD8+T细胞丰度及功能。与低风险组相比，TCR多样性指数更高的高风险组的乳腺癌患者总生存期、疾病特异性生存期及无病生存期显著缩短。同时，TCR多样性指数风险评分越高，TCR免疫组库多样性越低，乳腺癌患者对免疫检查点的反应较差，预后生存越差。

结论：我们的研究首次将乳腺癌患者TCR表达谱与转录组相结合，并提出了一种预测乳腺癌预后和免疫应答的免疫模型。

关键词：T细胞受体库；转录组；TCR多样性指数风险评分；预后；乳腺癌

l  第二部分 基于中性粒细胞胞外诱捕网的泛癌预后模型验证

背景：中性粒细胞胞外诱捕网（Neutrophil extracellular traps, NETs）最初提出时，被认为是机体的中性粒细胞用于捕获入侵微生物的一种防御机制所释放的中性粒细胞衍生物。越来越多的研究表明，NETs在肿瘤的进展和转移中起着至关重要的作用。转录组分析为NETs与泛癌患者临床结果之间的联系提供了一个途径。

目的：聚焦于肿瘤免疫微环境中的中性粒细胞在患者预后预示的差异性提示意义，借助于中性粒细胞衍生物NETs探索中性粒细胞在预后预示的一致性因素，通过生物信息学结合临床免疫组化验证挖掘NETs泛癌预后模型。

材料及方法：从USCS Xena下载TCGA泛癌原发灶的转录组表达谱数据，根据从多项研究收集到的69个中性粒细胞和NETs生成相关标记物，使用Lasso （Least absolute shrinkage and selection operation）回归降维减少特征基因数量，进一步使用Cox回归构建以泛癌患者疾病特异性生存期（disease specific survival, DSS）为模型预测目的，构建一个由19个基因组成的泛癌患者预后NETs评分模型。此外，从多个数据平台下载10个独立数据集用于验证NETs评分预后模型。基因本体富集（Gene Ontology, GO）分析用于注释NETs相关通路功能富集。免疫组织化学（Immunochemistry, IHC）评估NETs特征性基因在不同类型肿瘤患者的临床组织样本中的表达，包括肺腺癌（lung adenocarcinoma, LUAD, n=58）、结直肠癌（colon adenocarcinoma, COAD, n=93）、肾透明细胞癌（renal cell adenocarcinoma, KIRC, n=90）和三阴性乳腺癌（triple negative breast cancer, TNBC, n=80）样本。

结果：根据19个基因的NETs模型计算出每位患者的NETs评分。在大多数实体瘤中，NETs评分被认为是一个危险性因素，NETs评分越高的患者生存期显著缩短。此外，我们发现NETs生成与多个肿瘤恶性生物学过程密切相关，如上皮间质转化（R=0.7444，p<0.0001），血管生成（R = 0.5369, p<0.0001）和肿瘤细胞增殖（R = 0.3835, p<0.0001）。在IHC临床队列中，髓过氧化物酶（myeloperoxidase, MPO）作为NETs预后模型的基因，也是NETs形成的代表性基因，与多种肿瘤患者较差的临床预后密切相关。

结论：我们的研究首次提出组成性和互补性的生物标记物代表NETs形成能力预测泛癌患者预后进展。整合转录组分析加上临床队列样本的验证可能有助于NETs特征性生物标记物的发现与临床转化。

关键词：NETs，泛癌，转录组，预后

 Part I The prognostic significance of tumor immune microenvironment of breast cancer patients

 Chapter I An immune-related signature to improve prognosis prediction of breast cancer

Background: Increasing evidence suggests that immune cells in the tumor microenvironment (TME) play a paramount role in driving poor prognosis. Herein, we aimed to develop a TME-associated, immune-related signature to improve the prognosis prediction of Breast Cancer (BRCA).

Methods: BRCA_OURS enriched transcriptomic RNA sequencing (RNA-seq) of tumor tissue were acquired from 43 breast cancer patients before any treatment. Based on the immune gene profiles of 43 patients from BRCA_OURS and 932 BRCA patients from The Cancer Genome Atlas (TCGA), we performed consensus clustering to evaluate the immune infiltration of patients, determining a robust immune-related signature that was significantly associated with the overall survival (OS) in the training series. We further confirmed their reproductivity in an external validation series, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort.

Results: Based on this 10-gene immune-related signature, the training series patients were grouped into the high-risk and low-risk groups with significant OS, progression-free survival, and disease-free survival. The high-risk group is likely to have inferior outcomes to the low-risk group. Correspondingly, we found the high-risk score group was related to several important cancer-associated pathways, including deregulation of cellular energetics, genome instability, mobilizing more Golgi vesicle-mediated transport, and intensive DNA double-strand breaking. That holds the key to further growing and metastasizing.

Conclusions: Based on the immune infiltration of each BRCA patient, we developed and validated an immune-related signature with the different performance of prognosis prediction. Our results demonstrated immunological heterogeneity within BRCA and provided an efficient stratification tool for the clinical prognosis assessment of BRCA patients.

Keywords: Breast cancer, RNA-seq, overall survival, immune-related signature, tumor microenvironment (TME)

 Chapter II A novel signature based on TCR repertoire to predict the prognosis and immune response for breast cancer

Background: Breast cancer (BC) is the most common cancer in females. Although immunotherapy has revolutionized the treatment of other intractable cancers, only a partial BC responds to immune checkpoint inhibitors. To better understand the T cells in the immune microenvironment of BC, we collected 43 patients for tumor tissue and peripheral blood to obtain and analyze the transcriptome and TCR repertoire profiling.

Methods: We connected TCR diversity with expression profile via correlation analysis and further used the LASSO-COX algorithm to construct a signature to predict the prognosis of BC. Based on the TCR index score, patients were divided into high- and low-risk groups. Various evaluations for the immune landscape were further used to assess the characteristics of the immune microenvironment in each group both in TCR profile and transcriptome.

Results: We constructed a 19-gene TCR-related signature for patients with BC in the transcriptome. TCR index was representative of the reactive CD8+ T cell function. Compared to the low-risk group, the patients with the high TCR index had significantly shorter survival indicators, minor immune scores, less TCR diversity, lower clonotypes, and inferior response to the immune checkpoints.

Conclusions: Our study, for the first time, integrated tumor TCR profiling with transcriptome and proposed an immune signature that predicted the prognosis and immune response for breast cancer.

Keywords: TCR repertoire, transcriptome, TCR index, prognosis, breast cancer.

l  Part II A signature for pan-cancer prognosis based on neutrophil extracellular traps

Background: Neutrophil extracellular traps (NETs) were originally thought to be formed by neutrophils to trap invading microorganisms as a defense mechanism. Increasing studies have shown that NETs play a pivotal role in tumor progression and diffusion. In this case, transcriptome analysis provides an opportunity to unearth the association between NETs and clinical outcomes of pan-cancer patients.

Methods: The transcriptome sequencing data of The Cancer Genome Atlas Program pan-cancer primary focus was obtained from UCSC Xena, and a 19-gene NETs score was then constructed using the LASSO Cox regression model based on the expression levels of 69 NETs initial biomarkers we collected from multi-studies. In addition, 10 datasets covering multiple cancer types from other databases were collected and used to validate the signature. Gene ontology enrichment analyses were used to annotate the functions of NETs-related pathways. Immunohistochemistry (IHC) was implemented to evaluate the role of NETs-related genes in clinical patients across types of tumors, including lung adenocarcinoma (n = 58), colorectal carcinoma (n = 93), kidney renal clear cell carcinoma (n = 90), and triple-negative breast cancer (n = 80).

Results: The NETs score was calculated based on 19-NETs related genes according to the LASSO Cox model. The NETs scores were considered a hazardous factor in most cancer types, with a higher score indicating a more adverse outcome. In addition, we found that NETs were significantly correlated to various malignant biological processes, such as the epithelial to mesenchymal transition (R = 0.7444, p<0.0001), angiogenesis (R = 0.5369, p<0.0001), and tumor cell proliferation (R = 0.3835, p<0.0001). Furthermore, in IHC cohorts of a variety of tumors, myeloperoxidase, a gene involved in the model and a classical delegate of NETs formation, was associated with poor clinical outcomes.

Conclusions: Collectively, these constitutive and complementary biomarkers represented the ability of NETs formation to predict the development of patients’ progression. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical application.

Keywords: pan-cancer, NETs-related signature, transcriptome, prognosis.