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；其特点是 广泛的生物学行为和临床轨迹。而LUAD的统一管理是基于 临床阶段，分别有可能对侵袭性病变和惰性病变治疗不足和过度治疗。 这不仅会导致肺癌结果不佳，还会导致不必要的发病率、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 治疗，不能用于指导术前管理或手术计划。我们已经开发并 验证了 CANARY，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种 来自切除的 FFPE I 期 LUAD 的基因表达生物标志物，用于预测惰性或侵袭性肿瘤 组织学。这些基因表达生物标志物对瘤内异质性不敏感，表明它们 当在来自小型术前活检的有限组织中进行测量时，可能会保持良好的性能。这 具有潜在的变革性，因为这些小活检的组织学评估通常不足以 预测肿瘤的侵袭性。我们的目标是完善和验证这些放射组学和基因表达 生物标志物，然后将它们整合到一个模型中，用于检测惰性和攻击性 I 期 LUAD， 我们的初步数据支持了这一点。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。在随后因 I 期 LUAD 切除的肿瘤子集中，我们将进行 肿瘤分级的中心病理评估。预测切除时的肿瘤组织学分级将是首要任务 用于评估集成术前预测模型性能的终点。细化 放射组学生物标志物将涉及测试是否添加从结节周围肺提取的特征 深度学习可以改善 I 期 LUAD 组织学分级的预测。基因的精炼 表达生物标志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 组织并优化用于活检评估的生物标志物。最后，我们将开发并评估 结合放射组学和基因表达的综合模型。作为次要终点，我们将比较 无复发生存率与预测肿瘤分级与切除时实际肿瘤分级之间的关联。 在治疗前预测肿瘤侵袭性的能力提高有可能改变 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、 完成该项目所需的深度学习和生物统计学。