肿瘤免疫治疗相关组学数据集

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受多年好友启发，准备收集整理一下肿瘤免疫治疗相关的单细胞数据集也做一个网页工具。因为看到了他们在在国际著名肿瘤免疫学期刊 Journal for Immunotherapy of Cancer（JITC） 上发表题为“TCCIA: A Comprehensive Resource for Exploring CircRNA in Cancer Immunotherapy“的研究成果。
for Exploring CircRNA in Cancer Immunotherapy
然后，我搜了一下自己电脑里面的存货，发现其实并不多：

  • 2020-GSE145281-单细胞-IMvigor210-免疫治疗队列转录组
  • 2021-PRJEB40416-胃癌-免疫治疗-单细胞
  • 2023-GSE205506-中山六院-肠癌-免疫治疗-单细胞
  • 2023-GSE207422-肺癌-免疫治疗-BD单细胞
  • 2023-CNP0004138-空间单细胞-癌症-免疫治疗
    然后又在多个交流群简单的征集了一下，发现大家给出来了的资源都其实并不是专注于肿瘤免疫治疗，而是肿瘤本身，或者说是肿瘤微环境话题的：
  1. http://tiger.canceromics.org/#/home
  2. https://immucanscdb.vital-it.ch/
  3. http://ctrdb.cloudna.cn/home
  4. http://tisch.comp-genomics.org/home/
  5. http://bioinfo.life.hust.edu.cn/ICBatlas/#!/

    Tumor Immunotherapy Gene Expression Resource

    为每个数据集提供了表达量矩阵和表型信息的下载途径，详见：http://tiger.canceromics.org/#/download

  • TIGER contains bulk transcriptome data for 1508 tumor samples with immunotherapy clinical outcome
  • 11,057 tumor/normal samples from TCGA,
  • cell transcriptome data for 2,116,945 cells of 655 samples, among which 119,039 cells of 63 samples have immunotherapy clinical data.

    IMMUcan SingleCell RNAseq Database

    为每个数据集提供了h5ad格式下载，并且有 Gene X vs Gene Y expression 的网页浏览方式供探索对应的数据集。

    144 publications in total, 78 filtered
( latest release @ 2022-05-11 12:12:52 )

    每个数据集是完全独立的探索，没有联合分析或者其它丰富的可视化。

    CTR-DB (Cancer Treatment Response gene signature DataBase)

    主要是传统的表达量芯片，每个数据集被重新编号了，详见：http://ctrdb.cloudna.cn/Analyze
    | Data type | Number |
    | :————————————————— | :——- |
    | Cancer type | 28 |
    | Therapeutic regimen | 275 |
    | Drug | 123 |
    | Sample | 5139 |
    | CTR-DB dataset | 626 |
    | CTR-DB dataset with sample size>=20 | 74 |
    | Source dataset | 83 |
    | Source reference | 47 |

    Tumor Immune Single-cell Hub 2 (TISCH2)

    是专注于肿瘤没有微环境的单细胞数据集的归纳整理
    目前 ： 190 datasets， 6,297,320 cells
    TISCH2 provides detailed cell-type annotation at the single-cell level, enabling the exploration of TME across different cancer types.
    详见；http://tisch.comp-genomics.org/home/

    Immune Checkpoint Blockade therapy Atlas (ICBatlas)

    主要是：

  • the expression between ICB groups of Response versus Non-Response (R vs NR)
  • groups between Pre-treatment and On-treatment (Pre vs On).
    其中：
  • Response was based on Response Evaluation Criteria in Solid Tumors (RECIST) v1.1.
  • Patients who experienced complete response (CR) or partial response (PR) were classified as responders (R);
  • patients who experienced stable disease (SD) or progressive disease (PD) were classified as non-responders (NR).
    文章：An-Yuan Guo. ICBatlas: A Comprehensive Resource for Depicting Immune Checkpoint Blockade Therapy Characteristics from Transcriptome Profiles. Cancer Immunol Res 10, 1398–1406 (2022). Download PDF
    Data Summary
  1. Source: TCGA, ArrayExpress, NCBI GEO, NCBI SRA, PubMed, and dbGaP.
  2. Datasets: 25 datasets (RNA-seq: 19, Microarray: 6).
  3. Samples: 1515 samples (Pre: 1361 [R: 536, NR: 825], On: 154).
  4. Cancer Types: 9 types (SKCM, NSCLC, RCC, UC, HCC, GBM, HNSCC, MPM and GC).
  5. Antibodies: anti-PD1/PDL1, anti-CTLA4, and anti-PD1 + anti-CTLA4.
    数据集列表详见：https://guolab.wchscu.cn/ICBatlas//#!/document

    ICBcomb, an Expression Database for Immune Checkpoint Blockade Combination Therapy

    2024年1月，华中科学技术大学郭安源教授团队首次搭建了一个免疫检查点阻断联合治疗基因表达数据库：ICBcomb (http://bioinfo.life.hust.edu.cn/ICBcomb/)。通过比较ICB治疗组、其他药物治疗组和联合治疗组的差异，分析接受ICB联合治疗的人类和小鼠转录组学特征并对其进行全面描述。该数据库用户界面友好，可从特定疾病、基因、通路、自定义基因集和免疫细胞等维度供用户进行信息检索与浏览。

    所以求助广大网友

    可以看到，上面的绝大部分数据资源都不是我们理想的肿瘤免疫治疗相关组学数据集，更别说专注于单细胞了。而个人力量是有限的，海量的网友们肯定是或多或少看到过一些数据集，文章，或者数据库资源链接，都可以分享一下！

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