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

成像和循环 DNA 标记物可评估肺癌的早期反应并预测治疗失败模式

基本信息

批准号：
10330010
负责人：
Maximilian Diehn
金额：
$ 53.76万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10330010
关键词：
Biological Markers Biopsy Blood Cancer Patient Clinical Consensus DNA Markers DNA analysis Data Set Diagnosis Disease Distant Distant Metastasis Dose Evaluation Exhibits Failure Future Genomics Genotype Goals Heterogeneity Image Image Analysis Individual Institution International Link Logistic Models Lung CAT Scan Malignant neoplasm of lung Methodology Methods Modeling Molecular Morbidity - disease rate Motion Mutation Non-Small-Cell Lung Carcinoma Outcome PET/CT scan Patient Selection Patients Pattern Phase II Clinical Trials Phenotype Physiological Population Prediction of Response to Therapy Procedures Process Radiation therapy Randomized Recurrence Reproducibility Research Project Grants Resistance Risk Assessment Sampling Biases Scanning Selection for Treatments Source Systemic Therapy Systemic disease Techniques Technology Testing Therapeutic Trials Tissues Toxic effect Training Treatment Failure Treatment outcome Uncertainty United States Unresectable Validation X-Ray Computed Tomography base biomarker discovery biomarker-driven burden of illness cancer genomics cancer type chemoradiation chemotherapy circulating DNA clinical translation clinically relevant cohort effective therapy fluorodeoxyglucose positron emission tomography genomic biomarker image registration imaging biomarker improved improved outcome individualized medicine ineffective therapies minimally invasive mortality novel personalized care phase II trial phase III trial prospective quantitative imaging radiation response reconstruction respiratory response serial imaging standard care standard of care success tool treatment response tumor tumor DNA tumor heterogeneity

项目摘要

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.

抽象的非小细胞肺癌（NSCLC）是美国和全世界的主要疾病负担。大多数患者确诊时已属晚期。对于不可切除的局部晚期 NSCLC，护理标准是明确的同步放化疗。不幸的是，大多数患者通过标准治疗将出现局部区域或远处的失败。高剂量放射治疗或巩固化疗可能会减少局部或远处复发，但也与显着的毒性导致发病甚至死亡。多项随机 III 期试验未能对未经选择的局部晚期 NSCLC 进行强化治疗显示出生存获益人群，凸显了当前“一刀切”治疗的局限性。生物标志物驱动方法将允许基于个体化风险评估的合理治疗选择局部-区域故障与远程故障。然而，目前的成像和基因组标记缺乏足够的预测相关结果的准确性。该项目的目标是开发和验证定量成像生物标志物，用于评估早期反应并与循环肿瘤 DNA 整合分析预测局部晚期 NSCLC 治疗失败的模式。此前，我们开发了一种基于 FDG-PET 和 CT 图像的新型肿瘤分区方法，该方法揭示了空间不同的肿瘤亚区域对 NSCLC 具有预测意义。在这个项目中，我们将进一步完善我们的肿瘤分区方法来识别稳健的子区域，并提出新的图像特征通过空间显式分析来表征肿瘤内空间异质性。严格的资质将采用程序来识别生物标志物的可重复和可再现的图像特征发现。我们将通过结合治疗前和治疗中来开发预测成像生物标志物对回顾性患者队列进行扫描，并在两个前瞻性收集的患者中进行独立测试队列包括一项全国随机 II 期试验。最后，我们将成像与循环相结合在统一模型中进行肿瘤 DNA 分析，进一步提高预测准确性。我们预计综合生物标志物将能够对局部-区域与远处的故障进行可靠、早期的预测，这已经对于决定高剂量放疗与强化全身治疗之间的治疗具有重要意义。如果如果成功的话，所提出的生物标志物将为个体化治疗提供合理的方法最终改善局部晚期非小细胞肺癌的预后。