GSE302247：DPP4恢复促进KRAS-LKB1突变肺癌抗肿瘤免疫

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE302247

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

KRAS proto-oncogene, GTPase (KRAS)-liver kinase B1 (LKB1)-mutant (KL) non-small cell lung cancer (NSCLC), characterized by a profoundly immunosuppressive tumor microenvironment (TME), is highly resistant to immune checkpoint inhibitors (ICIs). Despite their high tumor mutation burden, KL tumors exhibit low programmed cell death ligand 1 (PD-L1) expression, reduced immune cell infiltration, and suppressed immune signaling pathways. Through systematic genomic analyses, we found that LKB1 loss suppresses dipeptidyl peptidase 4 (DPP4) expression and activity in KRAS-mutant lung cancer. Therefore, we evaluated the therapeutic potential of restoring DPP4 function to improve the immune response in KL lung cancer using patient-derived tumor samples and syngeneic mouse models. Restoration of DPP4 expression reprogrammed the TME and significantly increased immune-related gene signatures, including those involved in T cell migration and natural killer (NK) cell activation. Indeed, DPP4 restoration in three-dimensional microfluidic models enhanced NK cell chemotaxis and activity in targeting tumor spheroids. Furthermore, DPP4 restoration in syngeneic KL murine models synergized with anti-PD-1 therapy to achieve significant tumor regression. These findings suggest that LKB1 loss suppresses DPP4 expression, contributing to the immunosuppressive characteristics of the TME in KL-NSCLC cells, whereas, restoring DPP4 expression promotes NK cell recruitment, facilitates immune activation, and enhances the effects of anti-PD-1 therapy. These results highlight DPP4 as a key immune modulator and a promising therapeutic target, offering a novel strategy to overcome immune resistance and enhance immunotherapy outcomes in this challenging subset of lung cancer.

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。