增强免疫治疗结局：非小细胞肺癌空间多组学预测模型 [GeoMx DSP]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292098

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

Non–small cell lung cancer (NSCLC) has an increasing number of targeted and systemic therapies allowing subsets of patients to achieve long–term durable benefit. Fundamental to understanding responses to a given therapy is comprehensive molecular characterization of the underlying tumor immune microenvironment (TIME). The TIME may inform models to predict immunotherapy outcomes and features to delineate therapeutic responses and clinical endpoints. We hypothesize that an integrated multi–omics approach will uncover interactions within the NSCLC TIME and identify novel biomarkers that are predictive of immunotherapy responses, thus aiding precision oncology. To develop a spatially resolved TIME model for NSCLC immunotherapy, we utilized a multi–omics approach, combining spatial mapping of protein expression at the single–cell resolution by Phenocycler Fusion (PCF) and multi–cellular readout whole transcriptome profiling at cellular compartment resolution by Digital Spatial Profiling (DSP–GeoMx–WTA). This approach facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first–line immunotherapy. We studied two independent cohorts of advanced NSCLC tissue samples, treated with PD–1–based immunotherapies. We derived gene signatures from cell type signatures to predict treatment outcomes using a multistage Least Absolute Shrinkage and Selection Operator (LASSO) approach. Our spatial proteomic analysis identified three distinct cell types, proliferating tumor cells, granulocytes, and vessels, associated with resistance to immunotherapy. A high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the Yale training cohort and 1.8 (p = 0.05) in the UQ validation cohort. In the response cell–type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells had an HR of 0.4 (p = 0.019) in the training cohort and 0.49 (p = 0.036) in the validation. In the transcriptomic analysis, gene signatures derived

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。