MOSAIC: Biospecimen Core
MOSAIC:生物样本核心
基本信息
- 批准号:10729425
- 负责人:
- 金额:$ 39.88万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-18 至 2028-08-31
- 项目状态:未结题
- 来源:
- 关键词:Academic Medical CentersAftercareArizonaAtlasesBiologyBiopsyCell LineageCellsClinicClinicalComplexComputer ModelsConsentDataDetectionFreezingGlioblastomaGliomaGoalsHistologicHistologyImageImage Guided BiopsyImmunohistochemistryMagnetic Resonance ImagingMalignant NeoplasmsMapsMathematicsMissionModelingMolecularMolecular AnalysisOncogenesOncologyOperative Surgical ProceduresParaffin EmbeddingPathologyPatientsPhenotypeProcessRNARunningSamplingScanningSiteSlideSubgroupSystemSystems AnalysisTissue StainsTissue imagingTissuesTrainingValidationdata integrationgene panelimaging facilitiesin vivomachine learning modelmathematical modelradiological imagingrecruitsingle cell sequencingsingle-cell RNA sequencingtissue preparationtooltranscriptome sequencingtreatment response
项目摘要
SUMMARY: BIOSPECIMEN CORE
The major objective of the Biospecimen Core is to obtain, process, image, and analyze glioblastoma (GBM)
patients’ biopsies for use in both Projects of our Mayo/Columbia CSBC Mathematical Oncology Systems
Analysis Imaging Center (MOSAIC). Patient biopsies are central to our CSBC’s primary goal: building a
conceptual framework to understand sensitive response to therapy through the represented tissue states in
clinical and experimental settings. The duties of the Biospecimen Core are spread across both sites: Mayo
Clinic Arizona (MCA) and Columbia University Medical Center (CUMC).
The Biospecimen Core will assist the Center by operating under two specific aims. In Aim 1, we collect, process,
and analyze image-guided biopsies from pre-and post-treatment GBM. For Aim 2, we perform histopathological,
immunohistochemical (multiplex-IHC), and molecular analyses (bulk RNA-seq, scRNA-seq) of image-guided
biopsies.
The Biospecimen Core will provide support for both projects.
Project 1: Targeting Glioma Tissue States will utilize the RNAseq and single cell sequencing data from the
biopsies collected and analyzed by the Biospecimen Core to assess the compositional landscape of
glioblastoma along with the cross-talk between different subgroups that influence phenotype.
Project 2: Imaging the Dynamic Tissue State in Patients In Vivo will utilize the bulk histology and RNAseq
along with the imaging of the image-localized biopsies collected from the Biospecimen Core. Project 2 will
also incorporate these histologic and molecular cell lineage data into ML and mechanistic models that map the
relationship of magnetic resonance imaging (MRI) features to cellular composition. These data will drive the
training and validation of machine learning models and the parameterization of mechanistic mathematical
models.
The data generated by the Biospecimen Core will be transferred to the Data Integration and Computation
Core, where it will be stored, further processed including batch correction and atlas alignment, and integrated
into computational modeling tools.
摘要:生物样本核心
生物样本核心的主要目标是获取、处理、成像和分析胶质母细胞瘤 (GBM)
患者活检用于梅奥/哥伦比亚 CSBC 数学肿瘤系统的两个项目
分析成像中心 (MOSAIC) 患者活检是我们 CSBC 的主要目标的核心:建立一个
通过所代表的组织状态来理解对治疗的敏感反应的概念框架
生物样本核心的职责分布在两个地点:梅奥。
亚利桑那诊所 (MCA) 和哥伦比亚大学医学中心 (CUMC)。
生物样本核心将通过在两个具体目标下运作来协助该中心:在目标 1 中,我们收集、处理、
并分析治疗前和治疗后 GBM 的图像引导活检。对于目标 2,我们进行组织病理学、
图像引导的免疫组织化学(多重 IHC)和分子分析(bulk RNA-seq、scRNA-seq)
活检。
生物样本核心将为这两个项目提供支持。
项目 1:针对神经胶质瘤组织状态将利用来自
由生物样本核心收集和分析的活组织检查,以评估
胶质母细胞瘤以及影响表型的不同亚组之间的串扰。
项目 2:体内患者动态组织状态成像将利用大量组织学和 RNAseq
以及从生物样本核心项目 2 收集的图像局部活检的成像。
还将这些组织学和分子细胞谱系数据纳入机器学习和机械模型中,以映射
磁共振成像(MRI)特征与细胞组成的关系将推动研究的发展。
机器学习模型的训练和验证以及机械数学的参数化
模型。
Biospecimen Core生成的数据将传输至数据集成和计算
核心,将在其中存储、进一步处理,包括批量校正和图集对齐,以及集成
进入计算建模工具。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kristin R Swanson其他文献
Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma
生物信息深度神经网络提供胶质母细胞瘤治疗后瘤内异质性的定量评估
- DOI:
10.1101/2022.12.20.521086 - 发表时间:
2024-01-23 - 期刊:
- 影响因子:0
- 作者:
Hairong Wang;Michael G. Argenziano;H. Yoon;D. Boyett;A. Save;P.D. Petridis;William M Savage;P. Jackson;A. Hawkins;Nhan L Tran;Lel;S. Hu;Osama Al Dalahmah;Jeffrey N. Bruce;J. Grinb;Kristin R Swanson;P. Canoll;Jing Li - 通讯作者:
Jing Li
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
用于癌症诊断和预后的知识型机器学习:综述
- DOI:
10.48550/arxiv.2401.06406 - 发表时间:
2024-01-12 - 期刊:
- 影响因子:0
- 作者:
Lingchao Mao;Hairong Wang;Lel;S. Hu;Nhan L Tran;Peter D Canoll;Kristin R Swanson;Jing Li - 通讯作者:
Jing Li
Complementary role of mathematical modeling in preclinical glioblastoma: differentiating poor drug delivery from drug insensitivity
数学模型在临床前胶质母细胞瘤中的补充作用:区分药物输送不良和药物不敏感
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
J. Urcuyo;S. Massey;A. Hawkins;B. Marin;D. Burgenske;J. Sarkaria;Kristin R Swanson - 通讯作者:
Kristin R Swanson
Response to "Tumor cells in search for glutamate: an alternative explanation for increased invasiveness of IDH1 mutant gliomas".
对“肿瘤细胞寻找谷氨酸:IDH1 突变神经胶质瘤侵袭性增加的另一种解释”的回应。
- DOI:
10.1093/neuonc/nou290 - 发表时间:
2014 - 期刊:
- 影响因子:15.9
- 作者:
Andrew D. Trister;Jacob Scott;Russell Rockne;Kevin Yagle;S. Johnston;A. Hawkins;A. Baldock;Kristin R Swanson - 通讯作者:
Kristin R Swanson
Uncertainty Quantification in Radiogenomics: EGFR Amplification in Glioblastoma
放射基因组学中的不确定性定量:胶质母细胞瘤中的 EGFR 扩增
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Leland S. Hu;Lujia Wang;A. Hawkins;Jenny M. Eschbacher;K. Singleton;P. Jackson;K. Clark;Christopher P. Sereduk;Sen Peng;Panwen Wang;Junwen Wang;L. Baxter;Kris A. Smith;Gina L. Mazza;Ashley M. Stokes;B. Bendok;Richard S. Zimmerman;C. Krishna;Alyx Porter;M. Mrugala;J. Hoxworth;Teresa Wu;Nhan L Tran;Kristin R Swanson;Jing Li - 通讯作者:
Jing Li
Kristin R Swanson的其他文献
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{{ truncateString('Kristin R Swanson', 18)}}的其他基金
MOSAIC: Imaging Human Tissue State Dynamics In Vivo
MOSAIC:体内人体组织状态动态成像
- 批准号:
10729423 - 财政年份:2023
- 资助金额:
$ 39.88万 - 项目类别:
Project 1: Modeling the Interface between Non-invasive Imaging and Drug Distribution
项目 1:对无创成像和药物分配之间的接口进行建模
- 批准号:
9187652 - 财政年份:2016
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
8605773 - 财政年份:2012
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
8515534 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
7905757 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
8309373 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
8123111 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
E=mc2: Environment-Driven Mathematical Modeling for Clinical Cancer Imaging
E=mc2:环境驱动的临床癌症成像数学模型
- 批准号:
8555189 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual
评估和预测个体放射治疗反应的新工具
- 批准号:
7730125 - 财政年份:2009
- 资助金额:
$ 39.88万 - 项目类别:
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