人胶质母细胞瘤活体肿瘤代谢成像数据集

数据集获取（Figshare）

• 源站标题：DataSheet1_Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early.docx
• DOI：10.3389/fonc.2022.969812.s003
• 发布日期：2025-04-24
• 文件数：1 个
• 文件总体积：4.66 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Metabolic-imaging_of_human_glioblastoma_live_tumors_A_new_precision-medicine_approach_to_predict_tumor_treatment_response_early_docx/28853447

命令行下载

curl -L -o "DataSheet1_Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early.docx" "https://ndownloader.figshare.com/files/53944442"

文件清单

• DataSheet1_Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early.docx（4.66 MB）

文件总体积 4.66 MB（源站 API 实测）。

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。

数据说明

BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients.
MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were performed to characterize tumor cases stratification.
ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non-Responder tumors were identified. FLIM-based metabolic tumor stratification was well reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17-gene molecular signature associated with survival. The number of MGMT gene promoter methylated tumors was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker.
ConclusionsFor the first time, FLIM-based metabolic imaging was used on live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tam

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.969812.s003（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Metabolic-imaging_of_human_glioblastoma_live_tumors_A_new_precision-medicine_approach_to_predict_tumor_treatment_response_early_docx/28853447）。