The Pulmonary Pre-malignancy Atlas in the Lung Adenocarcinoma Spectrum

肺癌是美国退伍军人癌症死亡的主要原因，尽管最近的治疗进展。的 国家肺筛查试验（NLST）为肺癌筛查的有效性提供了令人信服的证据， 使用低剂量计算机断层扫描（LDCT），以降低肺癌死亡率。筛查的好处， 然而，必须与潜在的危害相协调，包括高假阳性率和 过度诊断最近的研究表明，肺癌往往表现出显着的分子异质性 由于基因组突变，促进了细胞内不同细胞群的启动和扩增， 肿瘤，促进对靶向和免疫疗法的抗性，并影响患者存活。至关重要 表征和预测肿瘤行为，以便制定适当的早期肺部临床管理 癌我们通过全外显子组测序（WES）分析了NLST中的肺腺癌（LUAD）， 多重免疫荧光（MIF）。可用数据包括临床和结节信息、LDCT图像 在三个时间点获得，并长期临床随访，这使我们能够评估积极与 懒惰行为我们假设体细胞突变影响免疫反应，形成肿瘤 微环境（TME）和形态，可以通过从LDCT中提取的定量特征来描述。我们 目标是开发一种系统的方法，将分子特征和临床成像特征整合在一起， 利用NLST研究中收集的数据区分侵袭性和惰性早期肺癌 并通过对公开数据集的分析来验证调查结果。我们计划实现以下具体目标： 1)确定与侵袭性肿瘤行为相关的体细胞突变谱。除了分析 NLST数据，我们将扩展我们的分析，以公开可用的数据集，包括癌症基因组图谱 （TCGA）和斯坦福大学非小细胞肺癌放射基因组学数据集。从这两个数据集，我们将 选择与NLST队列具有等同临床特征的受试者。2)识别肿瘤微环境 NLST队列中与攻击行为相关的改变特征。我们将交叉验证 如Aim中所述，TME发现的特征来自公开数据集的转录组数据。 1.我们将通过利用转录组数据来估计TME中细胞类型的组成， 细胞谱系标记物，我们已经从肺癌的单细胞转录组数据中鉴定出来。3)整合 NLST中区分攻击性和惰性行为的LDCT特征和分子谱 队列。我们将利用机器学习技术从LDCT中提取并联合收割机特征， 和目标1和目标2所确定的TME特征。预测疾病的侵袭性将提高个性化 早期肺癌患者的治疗。

Lung cancer is the leading cause of cancer death among US Veterans despite recent therapeutic advances. The National Lung Screening Trial (NLST) has provided compelling evidence of the efficacy lung cancer screening, using low-dose computed tomography (LDCT), to reduce lung cancer mortality. The benefits of screening, however, must be reconciled with potential harms, including high false-positive rates and the possibility of overdiagnosis. Recent studies demonstrate that lung cancer often exhibits significant molecular heterogeneity because of genomic mutations, facilitating the initiation and expansion of diverse cell populations within the tumor, promoting resistance to targeted and immune therapies, and impacting patient survival. It is critical to characterize and predict tumor behavior in order to develop appropriate clinical management of early-stage lung cancer. We have profiled lung adenocarcinoma (LUAD) in the NLST by whole-exome sequencing (WES) and multiplex immunofluorescence (MIF). Available data includes clinical and nodule information, LDCT images acquired at three time points, and long-term clinical follow-up, which allows us to assess aggressive versus indolent behavior. We hypothesize that somatic mutations impact immune responses, shaping the tumor microenvironment (TME) and morphology, can be described by quantitative features extracted from LDCTs. Our goal is to develop a systematic approach integrating molecular signature and clinical imaging profiles to distinguish between aggressive and indolent early-stage lung cancer by utilizing data collected in the NLST study and validating findings through the analysis of publicly available data sets. We plan the following specific aims: 1) To identify the somatic mutation profiles associated with aggressive tumor behavior. In addition to analyzing the NLST data, we will extend our analysis to publicly available data sets, including The Cancer Genome Atlas (TCGA) and the Stanford Non-Small Cell Lung Cancer Radiogenomic dataset. From these two data sets, we will select subjects with equivalent clinical features to the NLST cohort. 2) To identify tumor microenvironment alteration characteristics associated with aggressive behavior in the NLST cohort. We will cross-validate our TME findings with the signatures derived from transcriptome data of publicly available data sets, as noted in Aim 1. We will deconvolute the transcriptome data to estimate the composition of cell types in TME by utilizing the cell lineage markers, which we have identified from single-cell transcriptome data of lung cancer. 3) To integrate the LDCT features and molecular profiles that differentiated aggressive from indolent behaviors in the NLST cohort. We will leverage machine learning techniques to extract and combine features from LDCT with genetic and TME signatures, as identified in Aim 1 and 2. Predicting disease aggressiveness will improve personalized patient therapy for early-stage lung cancer.