生物信息学辅助分析下一代测序数据用于胰腺癌精准医学的数据集

数据集获取（Figshare）

• 源站标题：Dataset for: Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer
• DOI：10.6084/m9.figshare.5311114.v1
• 发布日期：2017-08-15
• 文件数：2 个
• 文件总体积：610.38 KB
• 源站页面：https://wiley.figshare.com/articles/dataset/Dataset_for_Bioinformatory-assisted_analysis_of_next-generation_sequencing_data_for_precision_medicine_in_pancreatic_cancer/5311114

命令行下载

curl -L -o "TableS2_GermlineVariants.xlsx" "https://ndownloader.figshare.com/files/9103246"
curl -L -o "TableS3_GenList4TME.xlsx" "https://ndownloader.figshare.com/files/9103249"

文件清单

• TableS2_GermlineVariants.xlsx（13.27 KB）
• TableS3_GenList4TME.xlsx（597.12 KB）

文件总体积 610.38 KB（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

Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly due to chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence-based software that analyzes Next-generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity. Tissue from 14 patients with PDAC was sequenced using NGS with a 620 gene panel. FASTQ files were fed into the TreatmentMAP software. The results were compared with chemotherapy in the patients, including all side effects. No changes in therapy were made. Known driver mutations for PDAC were confirmed, e.g. KRAS, TP53. Software analysis revealed positive biomarkers for predicted effective and ineffective treatments in all patients. At least one biomarker associated with increased toxicity could be detected in all patients. Patients had been receiving one of the currently approved chemotherapy agents. In two patients, toxicity could have been correctly predicted by the software analysis. Results suggest NGS, in combination with an evidence-based software, could be conducted within a two-week period - thus being feasible for clinical routine. Therapy recommendations were principally off-label use. Based on the predominant KRAS mutations, other drugs were predicted to be ineffective. The pharmacogenomic biomarkers indicative of increased toxicity could be retrospectively linked to reported negative side effects in the respective patients. Finally, the occurrence of somatic and germline mutations in cancer syndrome-associated genes is noteworthy, despite a high frequency of these particular variants in the background population. These results suggest software-analysis of NGS data provides evidence-based information on effective, ineffective, and toxic drugs, potentially forming the basis for precision cancer medicine in PDAC.

许可

CC0

引用

学术使用请引用 DOI 10.6084/m9.figshare.5311114.v1（source: https://wiley.figshare.com/articles/dataset/Dataset_for_Bioinformatory-assisted_analysis_of_next-generation_sequencing_data_for_precision_medicine_in_pancreatic_cancer/5311114）。