Integrating imaging and biopsy-derived molecular markers for the pre-surgical detection of indolent and aggressive early stage lung adenocarcinoma

整合成像和活检衍生的分子标记物，用于惰性和侵袭性早期肺腺癌的术前检测

• 批准号: 10737330
• 负责人: Marc Elliott Lenburg
• 金额: $ 70.49万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-10 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737330
• 关键词: Ablation; Academic Medical Centers; Adjuvant; Adoption; Behavior; Bioinformatics; Biological; Biological Markers; Biometry; Biopsy; Boston; Bronchoscopy; Cancer Detection; Cancer Etiology; Cessation of life; Chest; Clinical; Data; Detection; Diagnosis; Disease; Enrollment; Excision; Gene Combinations; Gene Expression; Genomics; Goals; Health Care Costs; Histologic; Histology; Histopathologic Grade; Hospitals; Image; Indolent; Interventional radiology; Lesion; Lung; Lung Adenocarcinoma; Malignant neoplasm of lung; Measures; Medical center; Modeling; Molecular Analysis; Molecular Biology; Morbidity - disease rate; Morphologic artifacts; Neoadjuvant Therapy; Operative Surgical Procedures; Outcome; Participant; Pathologic; Pathology; Patient observation; Patient-Focused Outcomes; Patients; Performance; Prognosis; Prospective cohort; Radiology Specialty; Recurrence; Research; Resected; Risk; Sampling; Specimen; Systemic Therapy; Testing; Texture; Time; Tissues; Training; Tumor Tissue; Universities; Validation; biomarker performance; cancer subtypes; cancer survival; cancer therapy; chest computed tomography; clinical care; clinical predictors; cohort; cost; deep learning; disorder risk; high risk; high risk population; improved; individual patient; individualized medicine; lung cancer screening; molecular marker; mortality; novel; overtreatment; patient subsets; personalized management; personalized screening; predictive marker; predictive modeling; primary endpoint; prospective; radiomics; secondary endpoint; survivorship; transcriptome sequencing; treatment strategy; tumor; tumor heterogeneity

ABSTRACT Lung adenocarcinoma (LUAD) is the most common lung cancer subtype diagnosed in the US; characterized by a broad spectrum of biological behaviors and clinical trajectories. Yet, LUAD is managed uniformly based on clinical stage, with the potential for under- and over-treatment of aggressive and indolent lesions, respectively. This contributes both to suboptimal lung cancer outcomes and unnecessary morbidity, mortality and healthcare costs. While histologic grade of resected tumors correlates with patient outcome, it is only available after surgical treatment and cannot be used to inform pre-surgery management or surgical planning. We have developed and validated CANARY, a radiomic biomarker that predicts LUAD aggressiveness. We have further developed two gene expression biomarkers from resected FFPE Stage I LUAD for predicting indolent or aggressive tumor histology. These gene expression biomarkers are insensitive to intratumoral heterogeneity, suggesting that they might retain good performance when measured in limited tissue available from small, presurgical biopsies. This is potentially transformative as histologic assessment of these small biopsies is frequently insufficient for predicting tumor aggressiveness. Our goal is to refine and validate these radiomic and gene expression biomarkers and then integrate them into a single model for detecting indolent and aggressive Stage I LUAD, which is supported by our preliminary data. To accomplish these goals, we will prospectively enroll a cohort of patients undergoing transthoracic or transbronchial biopsy for suspected lung cancer and collect additional specimens for research. In the subset of tumors who are later resected for Stage I LUAD, we will perform a central pathologic assessment of tumor grade. Predicting tumor histologic grade at resection will be the primary endpoint for assessing the performance of the integrated presurgical prediction model. Refinement of the radiomic biomarker will involve testing whether the addition of features extracted from the peri-nodular lung using deep learning can improve the prediction of the Stage I LUAD histologic grade. Refinement of the gene expression biomarker will involve determining their performance in biopsy tumor tissue relative to resected tumor tissue and optimizing the biomarkers for assessment in biopsies. Finally, we will develop and assess an integrated model combining both radiomics and gene expression. As a secondary endpoint, we will compare the association between recurrence free survival and predicted tumor grade vs. actual tumor grade at resection. An improved ability to predict tumor aggressiveness prior to treatment has the potential to transform the management of Stage I LUAD as it could allow clinicians and patients to confidently choose precisely tailored treatment. The team from Boston University, Boston Medical Center, Vanderbilt University Medical Center, and Lahey Hospital & Medical Center has the diverse expertise in lung cancer clinical care, advanced bronchoscopy, interventional radiology, histology, pathology, radiology, radiomics, molecular biology, genomics, bioinformatics, deep learning and biostatistics required to complete this project.

抽象的 肺腺癌（LUAD）是美国最常见的肺癌亚型。以 广泛的生物行为和临床轨迹。然而，卢德根据 临床阶段，分别对侵袭性和懒惰病变的不足和过度治疗的潜力。 这既有助于次优的肺癌结局和不必要的发病率，死亡率和医疗保健 费用。虽然切除的肿瘤的组织学等级与患者结局相关，但仅在手术后才能使用 治疗，不能用于为手术前的管理或手术计划提供信息。我们已经开发了 经过验证的金丝雀是一种预测luad侵略性的放射性生物标志物。我们进一步开发了两个 从切除的FFPE I期luAD预测酸性或攻击性肿瘤的基因表达生物标志物 组织学。这些基因表达生物标志物对肿瘤内异质性不敏感，表明它们 当在有限的组织中测量可从小型的，术前活检可用的组织中测量时，可能会保持良好的性能。这 由于这些小型活检的组织学评估常常不足以进行变化 预测肿瘤的侵略性。我们的目标是完善和验证这些放射线和基因表达 生物标志物，然后将它们整合到一个单个模型中，以检测懒惰和激进的阶段I Luad， 这是我们的初步数据支持的。为了实现这些目标，我们将前瞻性地注册 接受经胸腔或经支气管检查的患者可疑肺癌，并收集其他 研究标本。在后来切除的第I阶段的肿瘤子集中，我们将执行一个 肿瘤等级的中央病理评估。预测切除时肿瘤组织学级将是主要的 评估综合预性预测模型的性能的终点。改进 放射线生物标志物将涉及测试是否使用使用肺周围肺中提取的特征 深度学习可以改善I阶段I组织学等级的预测。基因的细化 表达生物标志物将涉及确定其在活检肿瘤组织中相对于切除的肿瘤的性能 组织并优化生物标志物进行活检评估。最后，我们将开发和评估 综合模型结合了放射线和基因表达。作为次要端点，我们将比较 无复发的生存与预测肿瘤级与切除术时实际肿瘤级之间的关联。 提高的预测肿瘤侵略性之前的能力有可能改变 I阶段的管理，因为它可以允许临床医生和患者自信地选择量身定制的 治疗。波士顿大学，波士顿医学中心，范德比尔特大学医学中心和 Lahey医院和医疗中心在高级支气管镜检查中具有多样的专业知识， 介入放射学，组织学，病理学，放射学，放射素学，分子生物学，基因组学，生物信息学，， 完成该项目所需的深度学习和生物统计学。