犬类癌症前瞻性分子谱分析数据集

数据集获取（Figshare）

• 源站标题：Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials
• DOI：10.1371/journal.pone.0090028
• 发布日期：2014-03-17
• 文件数：6 个
• 文件总体积：1.13 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Prospective_Molecular_Profiling_of_Canine_Cancers_Provides_a_Clinically_Relevant_Comparative_Model_for_Evaluating_Personalized_Medicine_PMed_Trials_/964580

命令行下载

curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/1423580"
curl -L -o "Table_S1.docx" "https://ndownloader.figshare.com/files/1423581"
curl -L -o "Table_S2.docx" "https://ndownloader.figshare.com/files/1423582"
curl -L -o "Table_S3.docx" "https://ndownloader.figshare.com/files/1423583"
curl -L -o "Table_S4.docx" "https://ndownloader.figshare.com/files/1423584"
curl -L -o "Table_S5.docx" "https://ndownloader.figshare.com/files/1423585"

文件清单

• Figure_S1.tif（1.02 MB）
• Table_S1.docx（24.01 KB）
• Table_S2.docx（27.82 KB）
• Table_S3.docx（19.19 KB）
• Table_S4.docx（16.92 KB）
• Table_S5.docx（14.90 KB）

文件总体积 1.13 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

Background
Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.
Methodology
A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type.
Conclusions
Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0090028（source: https://plos.figshare.com/articles/dataset/_Prospective_Molecular_Profiling_of_Canine_Cancers_Provides_a_Clinically_Relevant_Comparative_Model_for_Evaluating_Personalized_Medicine_PMed_Trials_/964580）。