联合GM-CSF免疫治疗与溶瘤病毒治疗成功的决定因素（基于计算机模拟个性化治疗）
基于计算机模拟的联合GM-CSF免疫治疗与溶瘤病毒治疗的个性化模型数据，用于确定晚期黑色素瘤联合疗法的成功决定因素。
基本信息
资源简介
联合GM-CSF免疫治疗与溶瘤病毒治疗成功的决定因素（基于计算机模拟个性化治疗）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 6 个文件、合计 4.36 MB，主题涵盖 T-VEC therapy、individualize combination immuno、oncolytic virotherapy success、exogenous cytokine immunotherapy、patient outcomes，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization》。配套论文《Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization》发表于 PLOS Computational Biology（2019），DOI：10.1371/journal.pcbi.1007495。源站首次发布：2019-11-27。
【数据内容】
源站原始描述：Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolyti…
【数据结构与技术规格】
文件数 6 个，合计 4.36 MB。pcbi.1007495.s001.pdf：application/pdf，2.15 MB。pcbi.1007495.s002.tiff：image/tiff，460.18 KB。pcbi.1007495.s003.tiff：image/tiff，185.04 KB。数据集 DOI：10.1371/journal.pcbi.1007495。
【主题与分类】
源站学科分类：Space Science、Medicine、Immunology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：T-VEC therapy、individualize combination immuno、oncolytic virotherapy success、exogenous cytokine immunotherapy、patient outcomes、combination oncolytic virotherapy、combination GM-CSF immunotherapy、clinically-actionable dosing strategies。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Determinants_of_combination_GM-CSF_immunotherapy_and_oncolytic_virotherapy_success_identified_through_i_in_silico_i_treatment_personalization/11286455
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Cassidy, Tyler; Craig, Morgan (2019). Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization. PLOS Computational Biology. Dataset
【使用注意】
· 源站最后修改于 2023-06-01，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through <i>in silico</i> treatment personalization
- DOI：10.1371/journal.pcbi.1007495
- 发布日期：2019-11-27
- 文件数：6 个
- 文件总体积：4.36 MB
- 源站页面：https://plos.figshare.com/articles/dataset/Determinants_of_combination_GM-CSF_immunotherapy_and_oncolytic_virotherapy_success_identified_through_i_in_silico_i_treatment_personalization/11286455
命令行下载
curl -L -o "pcbi.1007495.s001.pdf" "https://ndownloader.figshare.com/files/19988522"
curl -L -o "pcbi.1007495.s002.tiff" "https://ndownloader.figshare.com/files/19988525"
curl -L -o "pcbi.1007495.s003.tiff" "https://ndownloader.figshare.com/files/19988528"
curl -L -o "pcbi.1007495.s004.tiff" "https://ndownloader.figshare.com/files/19988531"
curl -L -o "pcbi.1007495.s005.tiff" "https://ndownloader.figshare.com/files/19988534"
curl -L -o "pcbi.1007495.s006.pdf" "https://ndownloader.figshare.com/files/19988537"
文件清单
pcbi.1007495.s001.pdf（2.15 MB）pcbi.1007495.s002.tiff（460.18 KB）pcbi.1007495.s003.tiff（185.04 KB）pcbi.1007495.s004.tiff（1.00 MB）pcbi.1007495.s005.tiff（497.90 KB）pcbi.1007495.s006.pdf（63.03 KB）
文件总体积 4.36 MB（源站 API 实测）。
数据说明
Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pcbi.1007495（source: https://plos.figshare.com/articles/dataset/Determinants_of_combination_GM-CSF_immunotherapy_and_oncolytic_virotherapy_success_identified_through_i_in_silico_i_treatment_personalization/11286455）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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