Integrating cancer datasets for predictive model development and training

整合癌症数据集以进行预测模型开发和训练

基本信息

批准号：
8896931
负责人：
Stephen Henry Friend
金额：
$ 16.71万
依托单位：
SAGE BIONETWORKS
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-06-03 至 2015-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8896931
关键词：
Archives Area Cancer Biology Cancer Death Rates Cardiovascular Diseases Characteristics Clinical Clinical Data Common Neoplasm Communities Complex Computational Biology Coupled DNA Data Data Set Data Sources Disease Drug Targeting Early Diagnosis Early Intervention Education Education and Outreach Environment Fellowship Program Fred Hutchinson Cancer Research Center Functional disorder Future Generations Genetic Transcription Genomics Goals Grant Individual Institutes Link Malignant Neoplasms Medical Research Mentors Mentorship Messenger RNA Meta-Analysis Methods Mission Modeling Molecular Netherlands Normal tissue morphology Outcome Patients Postdoctoral Fellow Predictive Value Process Prospective Studies Research Personnel Sampling Scientist Series Site Source System Systems Biology Technology Testing Training Validation Variant Vision Work cancer therapy cohort experience human disease mathematical model model development molecular phenotype network models oncology predictive modeling programs research study tool trait tumor

项目摘要

DESCRIPTION (provided by applicant): Our proposal for a Sage CCSB, "Integrating cancer datasets for predictive model development and training," has as its central scientific theme the generation of a set of probabilistic causal models for a series of tumor types from numerous collaborators. By selecting sample sets with different clinical outcomes, the resultant Sage models will have applications impacting cancer biology, early intervention, and cancer treatments. The Sage CCSB leverages the extensive work done at Rosetta/Merck on predictive models in numerous disease areas, which has been gifted to a new nonprofit medical research organization, "Sage Bionetworks." The Sage CCSB operational model contains a core platform of curated data, mathematical models and experienced investigators mentoring postdoctoral trainees/fellows. The data comes from collaborators and consists of DNA variation data, RNA expression data and clinical outcomes. The trainees will collate and annotate the genotypic, intermediate molecular phenotype, and clinical end point data from at least five different tumor-type cohorts and develop models that can predict potential new cancer targets, markers for early detection, and clinical outcomes. They will do externships at other sites (CCSBs), where they will build additional models of their data and facilitate reciprocal exchange of ideas. The trainees will delineate specifications for tools that will make the access to these models more scalable. Validation of their hypotheses will be performed at the Fred Hutchinson Cancer Research Center and the Netherlands Cancer Institute. This post-doctoral program will provide a unique training and mentorship environment in cancer systems biology and facilitate interactions between CCSBs and NCI.

描述（由申请人提供）：我们对SAGE CCSB的提议，“整合用于预测模型开发和培训的癌症数据集”，它是其中心科学主题，是从众多合作者中为一系列肿瘤类型的一系列概率因果模型的产生。通过选择具有不同临床结果的样品集，由此产生的鼠尾草模型将具有影响癌症生物学，早期干预和癌症治疗的应用。 SAGE CCSB利用了Rosetta/Merck在许多疾病领域的预测模型上所做的广泛工作，该模型已赠予新的非营利医学研究组织“ Sage Bionetworks”。 SAGE CCSB运营模型包含一个核心数据，数学模型和经验丰富的研究人员指导博士后学员/研究员的核心平台。数据来自合作者，由DNA变异数据，RNA表达数据和临床结果组成。受训者将从至少五个不同的肿瘤类型同类群中对基因型，中间分子表型和临床终点数据进行整理和注释，并开发可以预测潜在的新癌症靶标的，早期检测标记和临床结果的模型。他们将在其他站点（CCSB）进行实习，在那里他们将构建数据的其他模型，并促进互惠交流的想法。受训者将描述有关工具的规格，这些工具将使对这些模型的访问更加可扩展。其假设的验证将在弗雷德·哈钦森癌症研究中心和荷兰癌症研究所进行。该博士后计划将在癌症系统生物学中提供独特的培训和指导环境，并促进CCSB与NCI之间的相互作用。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

IGF-1R targeting increases the antitumor effects of DNA-damaging agents in SCLC model: an opportunity to increase the efficacy of standard therapy.

DOI：
10.1158/1535-7163.mct-12-1067
发表时间：
2013-07
期刊：
Molecular cancer therapeutics
影响因子：
5.7
作者：
Ferté C;Loriot Y;Clémenson C;Commo F;Gombos A;Bibault JE;Fumagalli I;Hamama S;Auger N;Lahon B;Chargari C;Calderaro J;Soria JC;Deutsch E
通讯作者：
Deutsch E

hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests.

DOI：
10.1098/rsfs.2013.0011
发表时间：
2013-08-06
期刊：
Interface focus
影响因子：
4.4
作者：
Jang IS;Margolin A;Califano A
通讯作者：
Califano A

Tumor growth rate is an early indicator of antitumor drug activity in phase I clinical trials.

DOI：
10.1158/1078-0432.ccr-13-2098
发表时间：
2014-01-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
0
作者：
Ferté C;Fernandez M;Hollebecque A;Koscielny S;Levy A;Massard C;Balheda R;Bot B;Gomez-Roca C;Dromain C;Ammari S;Soria JC
通讯作者：
Soria JC

Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors.