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.

摘要 肺腺癌(LUAD)是美国诊断的最常见的肺癌亚型；其特点是 广泛的生物学行为和临床轨迹。然而，LUAD是基于以下条件统一管理的 临床阶段，有可能对侵袭性和惰性皮损分别治疗不足和过度治疗。 这导致了肺癌预后不佳，以及不必要的发病率、死亡率和医疗保健。 成本。虽然切除肿瘤的组织学分级与患者的预后相关，但只有在手术后才能获得。 不能用于提供手术前管理或手术计划的信息。我们已经开发出了 经过验证的金丝雀，一种预测LUAD侵袭性的放射组学生物标志物。我们进一步开发了两个 切除FFPE I期LUAD预测惰性或侵袭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记物对肿瘤内的异质性不敏感，提示它们 在小的、术前活检可获得的有限组织中进行测量时，可能保持良好的性能。这 具有潜在的变革性，因为对这些小活检的组织学评估往往不足以 预测肿瘤的侵袭性。我们的目标是提炼和验证这些放射和基因表达 生物标志物，然后将它们集成到单个模型中，用于检测惰性和侵略性的第一阶段LUAD， 这得到了我们的初步数据的支持。为了实现这些目标，我们将前瞻性地招募一批 因疑似肺癌而接受经胸或经支气管活检的患者，并收集额外的 用于研究的标本。在后来切除I期LUAD的肿瘤亚群中，我们将进行 肿瘤分级的中央病理评估。在切除时预测肿瘤的组织学分级将是主要的 用于评估综合手术前预测模型性能的终点。改进了 放射组学生物标记物将涉及测试添加从结节周围肺提取的特征是否使用 深度学习可以提高对LUAD分期的预测。基因的提纯 表达生物标记物将涉及确定它们在活检肿瘤组织中相对于切除肿瘤的表现 并优化用于活组织检查评估的生物标记物。最后，我们将制定和评估 放射组学与基因表达相结合的综合模型。作为次要终端，我们将比较 无复发生存率与预测的肿瘤分级和切除时的实际肿瘤分级之间的关系。 一种在治疗前预测肿瘤侵袭性的改进能力有可能改变 I期LUAD的管理，因为它可以让临床医生和患者自信地选择精确定制的 治疗。来自波士顿大学、波士顿医学中心、范德比尔特大学医学中心和 莱希医院和医疗中心在肺癌临床护理、先进的支气管镜检查、 介入放射学、组织学、病理学、放射学、放射组学、分子生物学、基因组学、生物信息学 完成这个项目所需的深度学习和生物统计学。