晚期结直肠癌患者来源类器官预测个体化疗反应的活体生物样本库

数据集获取（Figshare）

• 源站标题：Application of tumoroids derived from advanced colorectal cancer patients to predict individual response to chemotherapy
• DOI：10.6084/m9.figshare.19354979.v1
• 发布日期：2022-03-14
• 文件数：3 个
• 文件总体积：6.99 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Application_of_tumoroids_derived_from_advanced_colorectal_cancer_patients_to_predict_individual_response_to_chemotherapy/19354979

命令行下载

curl -L -o "yjoc_a_2045827_sm4978.xlsx" "https://ndownloader.figshare.com/files/34377689"
curl -L -o "yjoc_a_2045827_sm4954.docx" "https://ndownloader.figshare.com/files/34377692"
curl -L -o "yjoc_a_2045827_sm4944.doc" "https://ndownloader.figshare.com/files/34377695"

文件清单

• yjoc_a_2045827_sm4978.xlsx（13.55 KB）
• yjoc_a_2045827_sm4954.docx（17.81 KB）
• yjoc_a_2045827_sm4944.doc（6.96 MB）

文件总体积 6.99 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

Therapeutic approaches of advanced colorectal cancer are more complex, here we present a living biobank of patient-derived tumoroids from advanced colorectal cancer patients and show examples of how these tumoroids can be used to to simulate cancer behavior ex vivo and provide more evidence for tumoroids could be utilized as a predictive platform during chemotherapy treatment to identify the chemotherapy response. Morphological, histological and genomic characterization analysis of colorectal cancer tumoroids was conducted. Further, we treated colorectal cancer tumoroids with different drugs to detect cellular activities to evaluate drug sensitivity using CellTiter-Glo 3 D cell viability assay. Then the drug sensitivity of tumoroids was compared with clinical outcomes. Our results implied that tumoroids recapitulated the histological features of the original tumours and genotypic profiling of tumoroids showed a high-level of similarity to the matched primary tumours. Dose-response curves, area under the curve and tumour inhibitory rate of each therapeutic profiling calculations in tumoroids demonstrated a great diversity and we gained 88.24% match ratio between the sensitivity data of tumoroids with their paired patients’’ clinical outcomes. tumour inhibitory rate of each treatment parameters in tumoroids performed positive correlation with progression-free survival while area under the curve of each treatment parameters performed negative correlation with progression-free survival of the corresponding patients. In summary, We presented a living biobank of tumoroids from advanced colorectal cancer patients and show tumoroids got great potential for predicting clinical responses to chemotherapy treatment of advanced colorectal cancer.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.19354979.v1（source: https://tandf.figshare.com/articles/dataset/Application_of_tumoroids_derived_from_advanced_colorectal_cancer_patients_to_predict_individual_response_to_chemotherapy/19354979）。