Detecting and locating cancer for patients with CT-detected lung nodules

为 CT 检测到肺结节的患者检测和定位癌症

基本信息

批准号：
10318013
负责人：
XIANGHONG Jasmine ZHOU
金额：
$ 66.52万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
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：
2022-09-14 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10318013
关键词：
Address Age Algorithms Base Pairing Benign Biological Assay Biological Markers Biopsy Blood Bypass Cancer Detection Cancer Patient Chest Clinical Cohort Studies Collection Complex Copy Number Polymorphism CpG Islands DNA DNA Methylation DNA methylation profiling Data Diagnosis Diagnostic Diagnostic tests Disease Epigenetic Process Ethnic Origin Etiology Gender Genetic Genomics Human Image Learning Lesion Life Lung Lung nodule Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Methods Methylation Modeling Names Neoplasm Metastasis Nodule Organ Pathogenesis Patients Plasma Primary Neoplasm Procedures Race Radiation Risk Sample Size Sampling Screening for cancer Signal Transduction Site Source Statistical Distributions System Technology Testing Tissues Training Tumor-Derived base bisulfite sequencing cancer diagnosis cancer type cell free DNA chest computed tomography cohort comorbidity computational platform computer framework cost cost effective former smoker genome wide methylation genome-wide high risk innovation low dose computed tomography lung cancer screening methylation biomarker methylome microbial microbial composition microbiome mortality patient population patient screening psychologic public health relevance repository screening tool tumor tumor DNA whole genome

Address Age Algorithms Base Pairing Benign Biological Assay Biological Markers Biopsy Blood Bypass Cancer Detection Cancer Patient Chest Clinical Cohort Studies Collection Complex Copy Number Polymorphism CpG Islands DNA DNA Methylation DNA methylation profiling Data Diagnosis Diagnostic Diagnostic tests Disease Epigenetic Process Ethnic Origin Etiology Gender Genetic Genomics Human Image Learning Lesion Life Lung Lung nodule Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Methods Methylation Modeling Names Neoplasm Metastasis Nodule Organ Pathogenesis Patients Plasma Primary Neoplasm Procedures Race Radiation Risk Sample Size Sampling Screening for cancer Signal Transduction Site Source Statistical Distributions System Technology Testing Tissues Training Tumor-Derived base bisulfite sequencing cancer diagnosis cancer type cell free DNA chest computed tomography cohort comorbidity computational platform computer framework cost cost effective former smoker genome wide methylation genome-wide high risk innovation low dose computed tomography lung cancer screening methylation biomarker methylome microbial microbial composition microbiome mortality patient population patient screening psychologic public health relevance repository screening tool tumor tumor DNA whole genome

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项目摘要

PROJECT SUMMARY Low-Dose Computed Tomography (LDCT) has been demonstrated to reduce lung cancer mortality by 20% for high-risk current and former smokers. However, 25% of the subjects in the NLST demonstrated abnormalities and a large fraction of those lesions were determined to be false-positives. There is an unmet need to accurately and non-invasively identify early-stage aggressive lung cancers and distinguish lesions that are life threatening from those that are not. Recently cell-free DNA (cfDNA) in human blood has emerged as an ideal source for cancer detection. In this proposal, we develop an integrated system, CancerRadar, consisting of (1) an experimental assay, cfMethyl-Seq, for cost-effective genome-wide methylation profiling of cfDNA, offering >10 fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in profiling CpG islands; and (2) a computational framework to extract various information from cfMethyl-Seq data, including cfDNA methylation, cfDNA fragment size, copy number variation (CNV), and microbial composition, and perform multi-feature ensemble learning for detecting malignant lung nodule and locating its primary tumor sites. We will validate CancerRadar with several clinical cohorts. Compared to the commonly used small panels focusing on one type of markers, CancerRadar profiles and integrates genome-wide profiles of multiple genetic/epigenetic features, therefore can robustly capture the very small proportion of tumor-derived cfDNA fragments, comprehensively diagnose patients with heterogeneous cancer pathogenesis, and learn and exploit newly significant features as training sample size grow.

项目摘要低剂量计算机断层扫描（LDCT）已被证明可将肺癌死亡率降低20％高风险当前和前吸烟者。但是，NLST中有25％的受试者表现出异常这些病变中的很大一部分被确定为假阳性。有未满足的需求准确，非侵入性地识别早期侵略性肺癌，并区分生命的病变受到那些没有的威胁。最近，人类血液中无细胞的DNA（CFDNA）已成为理想癌症检测的来源。在此提案中，我们开发了一个集成系统，即Cancerradar，由（1）组成一种实验性测定，CFMETHYL-SEQ，用于cfDNA的成本效益全基因组甲基化分析 >在分析CpG岛的整个基因组亚硫酸盐测序（WGB）上的10倍富集；（2）a 计算框架以从CFMETHYL-SEQ数据中提取各种信息，包括CFDNA甲基化， CFDNA片段尺寸，拷贝数变化（CNV）和微生物组成，并执行多功能用于检测恶性肺结核并定位其原发性肿瘤部位的合奏学习。我们将验证 Cancerradar与几个临床队列。与普遍使用的小面板相比标记，cancerradar剖面并整合了多种遗传/表观遗传特征的全基因组轮廓，因此，可以全面捕获肿瘤衍生的CFDNA片段的很小比例诊断患有异质性癌症发病机理的患者，并学习和利用新的重要特征培训样本量增长。