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

• 批准号: 10586527
• 负责人: CHAO CHENG
• 金额: $ 46.46万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10586527
• 关键词: Acute Myelocytic Leukemia; Basic Cancer Research; Biological Assay; Biological Markers; Breast; Cancer Patient; Cancer Prognosis; Clinical; Collection; Communities; Computer software; Data; Data Analyses; Data Set; Development; Disease; Drug Targeting; Effectiveness; Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Estrogen receptor positive; Event; Frequencies; Gene Deletion; Gene Expression; Gene Expression Alteration; Genes; Genomics; Glioblastoma; Goals; Immunotherapy; Joints; Link; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of pancreas; Metadata; Methods; Modeling; Mutate; Mutation; Names; Non-Small-Cell Lung Carcinoma; Oncogenic; Output; Pathway interactions; Patient Selection; Patient-Focused Outcomes; Patients; Pilot Projects; Probability; Recurrence; Research; Resources; Risk; Sampling; Somatic Mutation; Source Code; TP53 gene; The Cancer Genome Atlas; Therapeutic; Transfusion; Translating; Treatment Efficacy; Work; actionable mutation; cancer genomics; cancer type; clinical application; clinical phenotype; clinical predictive model; cohort; computer framework; design; driver mutation; genetic signature; genomic aberrations; genomic biomarker; immune cell infiltrate; improved; innovation; interest; malignant breast neoplasm; melanoma; mutational status; oncotype; patient biomarkers; patient prognosis; patient stratification; personalized cancer therapy; personalized medicine; precision oncology; predict clinical outcome; predictive modeling; prognostic; prototype; response; targeted treatment; tool; transcriptome sequencing; transcriptomics; translational cancer research; tumor; tumor progression

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.

项目概要/摘要 癌症是一种非常异质的疾病，每个患者都受到一组特定基因组的驱动 像差。因此，个性化治疗作为一种有前途的策略已得到深入研究 以进一步改善患者的预后。为了帮助基因组和表达方面的个性化治疗- 已对基于生物标志物进行了研究。体细胞突变和基因扩增/缺失， 特别是驱动基因，已被用于预测癌症预后并预选患者 针对性治疗。尽管有一些成功的例子，但这些基因组的总体有效性 生物标志物仍不清楚。同样，人们提出了许多基于基因表达的生物标志物， 但只有少数成果转化为临床应用。在这个项目中，我们提出了一个新的策略： 开发一个创新的统计框架，整合基因组和转录组数据来定义 通过对基因组畸变和基因之间的定量关系进行建模来获得基因特征 表达改变。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，可以捕获由其他因素引起的通路失调 机制。我们将使用这个框架来利用从以下来源创建的大量现有癌症数据： 以前的研究。具体来说，我们将利用 TCGA、ICGC 和 TARGET 数据来定义 基因签名的综合列表，用于表征 6 种癌症类型中所有驱动基因组畸变， 包括肺癌、乳腺癌、胰腺癌、胶质母细胞瘤、黑色素瘤和急性髓性白血病。 然后将这些基因特征结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的敏感性。我们将进一步将免疫纳入 浸润评分和临床因素可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，这些数据集具有匹配的基因表达谱和生存率 开发预后预测模型的信息。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开发用于预测术后的优化模型 早期非小细胞肺癌患者的复发风险。生成的软件，来源 该项目的代码、基因签名、预测模型和其他资源将在 及时的方式。这些资源将使基础和领域的广泛科学界受益。 转化癌症研究。