临床肿瘤样本中关键癌症基因突变谱分析

数据集获取（Figshare）

• 源站标题：Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples
• DOI：10.1371/journal.pone.0007887
• 发布日期：2009-11-18
• 文件数：4 个
• 文件总体积：288.26 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Profiling_Critical_Cancer_Gene_Mutations_in_Clinical_Tumor_Samples/145660

命令行下载

curl -L -o "Methods_S1.doc" "https://ndownloader.figshare.com/files/434293"
curl -L -o "Table_S1.xls" "https://ndownloader.figshare.com/files/434398"
curl -L -o "Table_S2.xls" "https://ndownloader.figshare.com/files/434419"
curl -L -o "Figure_S1.ppt" "https://ndownloader.figshare.com/files/434434"

文件清单

• Methods_S1.doc（70.14 KB）
• Table_S1.xls（128.00 KB）
• Table_S2.xls（28.16 KB）
• Figure_S1.ppt（61.95 KB）

文件总体积 288.26 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

Background
Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting.
Methodology
We developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact.
Conclusions
Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0007887（source: https://plos.figshare.com/articles/dataset/Profiling_Critical_Cancer_Gene_Mutations_in_Clinical_Tumor_Samples/145660）。