An innovative integrated computational framework using gene signatures for patient stratification
使用基因特征进行患者分层的创新集成计算框架
基本信息
- 批准号:10586527
- 负责人:
- 金额:$ 46.46万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-12-08 至 2027-11-30
- 项目状态:未结题
- 来源:
- 关键词:Acute Myelocytic LeukemiaBasic Cancer ResearchBiological AssayBiological MarkersBreastCancer PatientCancer PrognosisClinicalCollectionCommunitiesComputer softwareDataData AnalysesData SetDevelopmentDiseaseDrug TargetingEffectivenessEpidermal Growth Factor ReceptorEpidermal Growth Factor Receptor Tyrosine Kinase InhibitorEstrogen receptor positiveEventFrequenciesGene DeletionGene ExpressionGene Expression AlterationGenesGenomicsGlioblastomaGoalsImmunotherapyJointsLinkLungMalignant NeoplasmsMalignant neoplasm of lungMalignant neoplasm of pancreasMetadataMethodsModelingMutateMutationNamesNon-Small-Cell Lung CarcinomaOncogenicOutputPathway interactionsPatient SelectionPatient-Focused OutcomesPatientsPilot ProjectsProbabilityRecurrenceResearchResourcesRiskSamplingSomatic MutationSource CodeTP53 geneThe Cancer Genome AtlasTherapeuticTransfusionTranslatingTreatment EfficacyWorkactionable mutationcancer genomicscancer typeclinical applicationclinical phenotypeclinical predictive modelcohortcomputer frameworkdesigndriver mutationgenetic signaturegenomic aberrationsgenomic biomarkerimmune cell infiltrateimprovedinnovationinterestmalignant breast neoplasmmelanomamutational statusoncotypepatient biomarkerspatient prognosispatient stratificationpersonalized cancer therapypersonalized medicineprecision oncologypredict clinical outcomepredictive modelingprognosticprototyperesponsetargeted treatmenttooltranscriptome sequencingtranscriptomicstranslational cancer researchtumortumor progression
项目摘要
Project Summary/Abstract
Cancer is a very heterogeneous disease with each patient being driven by a specific set of genomic
aberrations. As such, personalized treatment has been intensively investigated as a promising strategy
for further improving patient prognosis. To aid personalized treatment, both genomic and expression-
based biomarkers have been investigated. Somatic mutations and amplification/deletions of genes,
especially driver genes, have been used to predict cancer prognosis and to preselect patients for
targeted treatment. Despite some successful examples, the overall effectiveness of these genomic
biomarkers remains unclear. Similarly, many gene expression-based biomarkers have been proposed,
but only a few of them are translated into clinical applications. In this project, we propose a new strategy:
develop an innovative statistical framework that integrates genomic and transcriptomic data to define
gene signatures by modeling the quantitative relationships between genomic aberrations and gene
expression alterations. These signatures recapitulate the downstream oncogenic pathways underlying
driver genomic events, and importantly, can capture pathway de-regulation caused by other
mechanisms. We will use this framework to leverage a vast amount of existing cancer data created from
previous studies. Specifically, we will utilize the TCGA, ICGC and TARGET data to define a
comprehensive list of gen signatures to characterize all driver genomic aberrations in 6 cancer types,
including lung, breast, and pancreatic cancer, glioblastoma, melanoma, and acute myeloid leukemia.
These gene signatures will then be combined to build integrative models to predict clinical outcomes,
including patient prognosis and sensitivity to therapeutic treatment. We will further incorporate immune
infiltration scores and clinical factors to maximize the prediction power of these models. Following that,
we will utilize a collection of 85 cancer datasets with matched gene expression profiles and survival
information to develop prognostic prediction models. Outputs from these models can be used to stratify
patients for advising personalized treatment. In line with our long-term research interest, we will integrate
in-house and existing lung cancer data to develop an optimized model for predicting post-surgical
recurrence risk of patients with early-stage non-small cell lung cancer. The resulting software, source
code, gene signatures, prediction models and other resources from this project will be released in a
timely manner. These resources will benefit a broad scientific community in the filed of basic and
translational cancer research.
项目总结/摘要
癌症是一种非常异质性的疾病,每个患者都由一组特定的基因组驱动。
失常。因此,个性化治疗作为一种有前途的策略已被深入研究
以进一步改善患者预后。为了帮助个性化治疗,基因组和表达-
生物标志物的研究。基因的体细胞突变和扩增/缺失,
特别是驱动基因,已被用于预测癌症预后和预选患者,
针对性治疗。尽管有一些成功的例子,但这些基因组的整体有效性
生物标志物仍不清楚。类似地,已经提出了许多基于基因表达的生物标志物,
但只有少数被应用到临床上。在这个项目中,我们提出了一个新的战略:
开发一个创新的统计框架,整合基因组和转录组数据,
通过模拟基因组畸变和基因突变之间的定量关系,
表情变化这些特征概括了潜在的下游致癌途径,
驱动基因组事件,重要的是,可以捕获由其他基因引起的途径失调,
机制等我们将使用这个框架来利用大量现有的癌症数据,
以前的研究。具体来说,我们将利用TCGA、ICGC和TARGET数据定义一个
用于表征6种癌症类型中所有驱动基因组畸变的基因特征的综合列表,
包括肺癌、乳腺癌和胰腺癌、胶质母细胞瘤、黑素瘤和急性髓性白血病。
然后将这些基因特征组合起来,建立综合模型来预测临床结果,
包括患者预后和对治疗性治疗的敏感性。我们将进一步将免疫
浸润评分和临床因素,以最大限度地提高这些模型的预测能力。随后,
我们将利用85个癌症数据集的集合,这些数据集具有匹配的基因表达谱和生存率。
信息,以开发预后预测模型。这些模型的输出可用于分层
患者建议个性化治疗。根据我们的长期研究兴趣,我们将整合
内部和现有的肺癌数据,以开发用于预测手术后
早期非小细胞肺癌患者的复发风险。软件,源代码
该项目的代码、基因签名、预测模型和其他资源将以
适时这些资源将使基础科学领域的广大科学界受益,
转化癌症研究。
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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CHAO CHENG的其他文献
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{{ truncateString('CHAO CHENG', 18)}}的其他基金
Computational Identification of new candidate drugs for lung cancer treatment
肺癌治疗新候选药物的计算鉴定
- 批准号:
9888344 - 财政年份:2019
- 资助金额:
$ 46.46万 - 项目类别:
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