合成齐墩果烷三萜类化合物抑制肿瘤生长并促进抗肿瘤免疫应答且不依赖癌症KEAP1突变状态

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE308273

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

KEAP1 negatively regulates the cytoprotective factor NRF2 and is commonly inactivated in lung cancer cells. Loss of function KEAP1 mutations in cancer cells contribute to NRF2 activation and tumor immune evasion through immunosuppression and drug resistance. Counterintuitively, treatment with synthetic oleanane triterpenoids, potent NRF2 activa-tors, reduces preclinical tumor burden. This suggests the functional target of these drugs in cancer models is not the cancer cells but another tumor immune microenvironment (TIME) cell population. The anti-tumor potential of cells within the TIME, particularly macrophages, is potentiated by triterpenoid treatment in cancers with wild-type KEAP1 status. As KEAP1-mutant cancers show reduced tumor immune responses, triterpenoid-mediated immune stimulation may particularly benefit these cases, but this has not been investigated. To characterize the immunomodulatory effects of triterpenoids in KEAP1-mutant lung cancer, we studied tumor-educated bone marrow-derived macrophages (TE-BMDMs) and lung cancer models treated with the triterpenoids CDDO-Me or omaveloxolone. RNA-sequencing of TE-BMDMs cultured in KEAP1 KO compared to WT cancer-conditioned media had enhanced tumor-promoting phenotypes which reversed with CDDO-Me treatment. Similarly, subcutaneous KEAP1 KO tumors were larger and more immune-suppressed compared to WT tumors. Both CDDO-Me and omaveloxolone reduced tumor burden and improved immune cell phenotypes within the TIME, independent of KEAP1 mutational status.

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。