Imaging and circulating DNA markers to assess early response and predict treatment failure patterns in lung cancer

摘要 非小细胞肺癌(NSCLC)是美国和世界范围内的主要疾病负担。 大多数患者是在晚期确诊的。对于不能切除的局部晚期非小细胞肺癌， 标准护理是明确的同步放化疗。不幸的是，大多数患者 将通过标准治疗发展为局部地区或遥远的衰竭。大剂量放射治疗或 巩固化疗可以减少局部或远处复发，但也与 严重的毒性会导致发病率甚至死亡。几个随机的III期试验没有成功 未经选择的局部晚期非小细胞肺癌强化治疗对生存有好处 强调了目前“一刀切”治疗的局限性。生物标记物驱动的 方法将允许根据个性化的风险评估合理选择治疗方法 本地-地区性故障与远程故障。然而，目前的成像和基因组标记缺乏足够的 在预测相关结果方面的准确性。该项目的目标是开发和验证 定量成像生物标记物评估早期反应并与循环肿瘤DNA结合 预测局部晚期非小细胞肺癌治疗失败模式的分析。之前，我们开发了 一种基于FDG-PET和CT图像的肿瘤分割方法 明确的肿瘤亚区在非小细胞肺癌中具有预测意义。在这个项目中，我们将进一步提高 我们的肿瘤分割方法识别健壮的子区域，并提出新的图像特征来 通过空间显式分析来表征肿瘤内的空间异质性。严格的资格条件 将采用程序来识别生物标记物的可重复和可重现的图像特征 发现号。我们将开发一种预测性成像生物标记物，将治疗前和治疗中期结合起来 在回溯性患者队列中进行扫描，并在两个预期收集的 包括一项国家随机第二阶段试验的队列。最后，我们将把成像和循环结合起来 在统一的模型中进行肿瘤DNA分析，以进一步提高预测准确性。我们预计， 集成的生物标志物将允许可靠的、早期地预测局部-区域与远程故障，这已经 大剂量放射治疗与强化全身治疗之间的重要意义。如果 如果成功，建议的生物标记物将为个性化治疗提供一种合理的方法 最终改善局部晚期非小细胞肺癌的预后。

ABSTRACT Non-small cell lung cancer (NSCLC) is a major disease burden in the United States and worldwide. Most patients are diagnosed at an advanced stage. For unresectable locally advanced NSCLC, the standard of care is definitive concurrent chemoradiotherapy. Unfortunately, the majority of patients will develop local-regional or distant failure with standard treatment. High-dose radiotherapy or consolidation chemotherapy may reduce local or distant recurrence, but are also associated with significant toxicity leading to morbidity and even mortality. Several randomized phase III trials failed to show a survival benefit with intensified treatment given to unselected, locally advanced NSCLC populations, highlighting the limitations of current `one-size-fits-all' treatment. A biomarker-driven approach would allow rational treatment selection based on individualized assessment of risks of local-regional versus distant failure. However, current imaging and genomic markers lack sufficient accuracy in predicting relevant outcomes. The goal of this project is to develop and validate quantitative imaging biomarkers to evaluate early response and integrate with circulating tumor DNA analysis to predict patterns of treatment failure in locally advanced NSCLC. Previously, we developed a novel tumor partitioning method based on FDG-PET and CT images, which revealed spatially distinct tumor subregions with predictive significance in NSCLC. In this project, we will further improve our tumor partitioning method to identify robust subregions, and propose novel image features to characterize intratumoral spatial heterogeneity via spatially explicit analysis. A rigorous qualification procedure will be employed to identify repeatable and reproducible image features for biomarker discovery. We will develop a predictive imaging biomarker by incorporating pre and mid-treatment scans in a retrospective patient cohort, and independently test it in two prospectively collected cohorts including a national randomized phase II trial. Finally, we will combine imaging with circulating tumor DNA analysis in a unifying model to further improve predictive accuracy. We anticipate that the integrated biomarker will allow reliable, early prediction of local-regional vs distant failure, which has important implications for deciding treatment between high-dose RT vs intensive systemic therapy. If successful, the proposed biomarkers will afford a rational approach to individualized therapy and ultimately improve outcomes in locally advanced NSCLC.