数据表1：利用AI在精准医学中的力量：基于NGS的结直肠癌队列治疗见解

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.pdf
• DOI：10.3389/fonc.2024.1407465.s002
• 发布日期：2024-10-07
• 文件数：1 个
• 文件总体积：971.57 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Harnessing_the_power_of_AI_in_precision_medicine_NGS-based_therapeutic_insights_for_colorectal_cancer_cohort_pdf/27177063

命令行下载

curl -L -o "Data Sheet 1_Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.pdf" "https://ndownloader.figshare.com/files/49633884"

文件清单

• Data Sheet 1_Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.pdf（971.57 KB）

文件总体积 971.57 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

PurposeDeveloping innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients.
MethodsWe analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs.
ResultsWe identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications.
ConclusionOur study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2024.1407465.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Harnessing_the_power_of_AI_in_precision_medicine_NGS-based_therapeutic_insights_for_colorectal_cancer_cohort_pdf/27177063）。