RNAseq tumor immunity analysis安装分析全过程笔记

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Tutorial of RNA-seq tumor immunity analysis

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook.

https://liulab-dfci.github.io/RIMA/

RIMA的安装

只能安装在非M1的电脑上的linux系统上。（M1 Macbook的虚拟机也不行）

Ubuntu 更改文件夹权限

另一种方法 1.切换root账户

2.切换到你，想更换的路径下 3.输入命令 -R：递归修改目录下所有的拥有者 hxls:拥有者名字 gcn/:更改目录

Ubuntu修改文件（文件夹）的拥有者，成功！

4.更改用户组与上同理

Ubuntu修改文件（文件夹）的用户组，成功！ https://blog.csdn.net/qq_41238579/article/details/102778243

流程大纲

预处理（Preprocessing）:

STAR (Spliced Transcript Alignment to a Reference): 用于将测序数据进行比对，将已知的转录本与测序数据配对，以识别基因组中的剪接区域。

Salmon: Gene Quantification: 用于基因表达量的量化，可以估计基因在样本中的表达水平。

RSeQC (High Throughput Sequence Data Evaluation): 高通量测序数据评估工具，用于检查RNA-seq数据的质量和特性。

batch_removal (Remove Batch Effects Using Limma): 使用Limma工具去除批次效应，以确保实验数据的一致性。

差异表达分析（Differential Expression）:

DESeq2 (Gene Differential Expression Analysis): 用于鉴定基因在不同条件下的差异表达，帮助理解基因在不同生物学状态下的调控。

GSEA (Gene Set Enrichment Analysis): 用于评估基因集在两个实验条件之间的差异，有助于理解不同基因集的富集情况。

ssGSEA (Single-sample GSEA): 单样本基因集富集分析，用于在单个样本中评估基因集的富集程度。

免疫库分析（Immune Repertoire）:

TRUST4 (TCR and BCR Sequence Analysis): 用于分析T细胞受体（TCR）和B细胞受体（BCR）的序列，了解免疫系统中的多样性和变化。

免疫浸润分析（Immune Infiltration）:

ImmuneDeconv (Cell Components Estimation): 用于估计样本中不同免疫细胞类型的相对丰度，以了解免疫细胞的浸润情况。

免疫反应分析（Immune Response）:

MSIsensor2 (Microsatellite Instability (MSI) Detection): 用于检测微卫星不稳定性，这与一些肿瘤类型的免疫治疗响应相关。

TIDEpy (T cell dysfunction and exclusion prediction): 用于预测T细胞的功能障碍和排斥情况，这对于免疫疗法的研究和预测患者响应至关重要。

基因融合分析（Fusion）:

STAR-Fusion (Identify the fusion gene pairs): 用于检测基因融合事件，即基因结构变异，这在一些肿瘤中很常见。

微生物组分析（Microbiome）:

Centrifuge (Bacterial Abundance Detection): 用于检测和评估样本中细菌的相对丰度，有助于了解微生物组的结构。

新抗原分析（Neo-Antigen）:

arcasHLA (HLA Class I and Class II Genotyping): 用于分析和预测HLA（人类白细胞抗原）类型，这对于免疫治疗和新抗原检测非常重要。

文章阅读

In the past two decades, it has become clear that the immune system plays a significant role in tumour progression and metastasis.

Cancer immunotherapies harness a patient’s innate and adaptive immune system to attack cancer cells.

These immunotherapies include

(i)Immune checkpoint blockade (ICB) therapy targeting cytotoxic T lymphocyte-associated protein (CTLA)-4, programmed death 1 (PD-1) and programmed death-ligand 1 (ii)Adoptive T cell transfer of tumour infiltrating lymphocytes (iii)Chimeric antigen receptor T cells (iv)Personalized cancer vaccines.

Cancer immunotherapy treatments have shown durable remission and clinical success in various cancer types. However, patient outcomes are heterogeneous and vary considerably. Many studies have been conducted to identify molecular features associated with tumour immunity and immunotherapy response.

These molecular features include

(i) genetic markers 遗传标志：指基因组中可用于识别特定遗传特征的DNA序列。 (ii) gene expression signatures 基因表达特征：指特定基因在细胞或组织中表达的模式。 (iii) measures of tumour immune infiltration 肿瘤免疫浸润的测量：评估免疫细胞在肿瘤组织中的存在和活性。 (iv) immune receptor repertoires 免疫受体库：指T细胞或B细胞表面免疫受体的多样性和复杂性。 (v) characteristics of the microbiome. 微生物组特征：指宿主体内（如肠道）微生物的组成和功能。

First, tumour mutation burden is a well-known genetic marker of immunotherapy response, because a large tumour mutation burden is associated with better ICB patient outcomes in non-small cell lung cancer and metastatic melanoma. In addition, strong MHC binding affinity and T cell recognition of missense mutation-derived neoantigens have also been correlated with positive survival.

肿瘤突变负荷（Tumor Mutational Burden, TMB）是免疫治疗反应的已知遗传标志。高肿瘤突变负荷与非小细胞肺癌和转移性黑色素瘤患者在免疫检查点阻断（ICB）治疗中的更好疗效相关。这意味着，肿瘤中遗传突变数量较多的患者可能更能从ICB治疗中受益

"肿瘤突变负荷"是指肿瘤细胞中的突变数量。在免疫治疗中，如果肿瘤突变负荷高，说明突变较多，这通常与免疫治疗效果较好相关，特别是在非小细胞肺癌和转移性黑色素瘤治疗中。简单来说，就是肿瘤中突变越多，免疫治疗的效果可能越好。

当肿瘤细胞发生更多突变时，它们产生的异常蛋白（称为新抗原）数量增加。这些新抗原被人体免疫系统识别为外来物质，从而激活免疫系统攻击肿瘤细胞。因此，突变量较多的肿瘤更可能引起强烈的免疫反应，使免疫治疗效果更佳。简言之，突变越多，肿瘤细胞被免疫系统识别和攻击的可能性越大。

"T cell recognition of missense mutation-derived neoantigens" 指的是T细胞识别由错义突变产生的新抗原。错义突变是指DNA序列中的突变导致蛋白质的氨基酸序列发生改变，从而产生新抗原。这些新抗原由于在正常细胞中不存在，因此能被免疫系统的T细胞识别并作为外来物质进行攻击。这种识别过程在癌症免疫治疗中非常重要。

Second, in addition to cancer genetic markers, immune-related gene expression signatures have also been shown to have prognostic and predictive value for tumour immunity and immunotherapy response. Rooney quantified tumour cytolytic activity from granzyme A and perforin gene expression and correlated this measure with a survival benefit and improved prognosis. Ayers et developed a 28-gene interferon-γ (INF-γ) signature predictive of anti-PD1 response that encompasses genes related to antigen presentation, chemokine expression, cytolytic activity and adaptive immune resistance.

Third, measures of tumour immune infiltration also have predictive power for tumour immunity: Gentles revealed that tumour-associated leukocytes and prognostic genes are associated with tumour heterogeneity and cancer outcomes, and Thorsson et al.9 integrated The Cancer Genome Atlas (TCGA) pan-cancer tumour gene expression profiles and identified six immune subtypes discriminated by tumour microenvironment (TME) features and survival outcomes.

Fourth, profiling the immune repertoires of T cell receptors (TCRs) and B cell receptors (BCRs) helps elucidate the mechanisms of T and B cell tumour immunity: Zhang et al.10 revealed the effect of T and B cell clonal expansion in a TCGA acute myeloid leukaemia dataset, showing that highly expanded IgA2 B cells were associated with overall survival; Hopkins et al.11 showed that the clonality of the T cell receptor repertoire is associated with patient survival and outcomes in antiCTLA4– and anti-PD1–treated pancreatic ductal adenocarcinoma; and Tumeh et al.12 also found a broader T cell repertoire inside the tumour of metastatic melanoma patients who responded to anti-PD1 therapy than in patients who did not.

Finally, the microbiome also influences the host immune system and may contribute to cancer diagnosis and prognosis. For instance, Poore et al.13 examined microbial reads from TCGA transcriptome data and found tumour-specific microbial signatures in tissue and blood samples, providing novel insights into the potential of microbiome-based cancer diagnostics. Furthermore, Gopalakrishnan et al.14 found that higher gut microbiome diversity is associated with an improved response to anti-PD1 immunotherapy in metastatic melanoma.