整合组织学与基因组特征预测早期非小细胞肺癌肿瘤突变负荷表2

数据集获取（Figshare）

• 源站标题：Table_2_Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer.xlsx
• DOI：10.3389/fonc.2020.608989.s006
• 发布日期：2021-04-30
• 文件数：1 个
• 文件总体积：19.02 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Integrating_Histologic_and_Genomic_Characteristics_to_Predict_Tumor_Mutation_Burden_of_Early-Stage_Non-Small-Cell_Lung_Cancer_xlsx/14519322

命令行下载

curl -L -o "Table_2_Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer.xlsx" "https://ndownloader.figshare.com/files/27825495"

文件清单

• Table_2_Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer.xlsx（19.02 KB）

文件总体积 19.02 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

Tumor mutation burden (TMB) serves as an effective biomarker predicting efficacy of mono-immunotherapy for non-small cell lung cancer (NSCLC). Establishing a precise TMB predicting model is essential to select which populations are likely to respond to immunotherapy or prognosis and to maximize the benefits of treatment. In this study, available Formalin-fixed paraffin embedded tumor tissues were collected from 499 patients with NSCLC. Targeted sequencing of 636 cancer related genes was performed, and TMB was calculated. Distribution of TMB was significantly (p < 0.001) correlated with sex, clinical features (pathological/histological subtype, pathological stage, lymph node metastasis, and lympho-vascular invasion). It was also significantly (p < 0.001) associated with mutations in genes like TP53, EGFR, PIK3CA, KRAS, EPHA3, TSHZ3, FAT3, NAV3, KEAP1, NFE2L2, PTPRD, LRRK2, STK11, NF1, KMT2D, and GRIN2A. No significant correlations were found between TMB and age, neuro-invasion (p = 0.125), and tumor location (p = 0.696). Patients with KRAS p.G12 mutations and FAT3 missense mutations were associated (p < 0.001) with TMB. TP53 mutations also influence TMB distribution (P < 0.001). TMB was reversely related to EGFR mutations (P < 0.001) but did not differ by mutation types. According to multivariate logistic regression model, genomic parameters could effectively construct model predicting TMB, which may be improved by introducing clinical information. Our study demonstrates that genomic together with clinical features yielded a better reliable model predicting TMB-high status. A simplified model consisting of less than 20 genes and couples of clinical parameters were sought to be useful to provide TMB status with less cost and waiting time.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2020.608989.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Integrating_Histologic_and_Genomic_Characteristics_to_Predict_Tumor_Mutation_Burden_of_Early-Stage_Non-Small-Cell_Lung_Cancer_xlsx/14519322）。