RELB重编程耗竭肿瘤浸润淋巴细胞以改进过继细胞治疗 [TCR-Seq]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303438

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据说明

Tumor-infiltrating lymphocytes (TILs) are a promising autologous cell therapy to treat solid tumors. TILs are manufactured by expanding and reinfusing tumor-reactive T cells from tumor biopsies. Efficacy of TIL therapies has been limited by the heterogeneity of expanded TIL products and the high prevalence of dysfunctional exhausted CD8+ T cells (TEX). A subset of CD8+ TILs that are double-positive (DP) for CD103 and CD39 is enriched for tumor-reactive TILs across multiple cancer types, but also is susceptible to the TEX state. We screened overexpression of all human transcription factors (TFs) to discover master regulators of expansion in DP TILs from non-small cell lung cancer patients. RELB emerged as the dominant hit driving proliferation of DP TILs despite exhaustion induced by repeat stimulation, with a skew towards CD8+ cells. TCR-seq showed maintenance of TCR diversity from the initial TCR repertoire after multiple days of in vitro expansion driven by RELB. Transcriptome profiling of multiple RELB-expressing TIL subtypes revealed a shift towards a memory/costimulatory-like phenotype. Using a HER2-targeting CAR and tumor co-culture model, RELB conferred improved persistence after multiple tumor challenges in vitro and improved solid tumor control in mouse xenografts in vivo. Finally, co-culture of RELB-overexpressing TILs with patient-matched tumor organoids showed an increase in TIL product polyfunctionality, tumor reactivity, and tumor killing.

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。