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

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

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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）是美国和全球的主要疾病负担。大多数患者在高级阶段被诊断出。对于不可切除的本地NSCLC，护理标准是确定的同时进行的化学疗法。不幸的是，大多数患者将通过标准处理发展局部区域或遥远的失败。高剂量放射疗法或合并化疗可能会降低局部或遥远的复发，但也与显着的毒性导致发病甚至死亡率。几个随机阶段III试验未能通过对未选择的局部高级NSCLC的加强治疗显示生存益处种群强调了当前的“一定程度合适”处理的局限性。生物标志物驱动方法将允许基于个性化评估的风险评估局部区域与遥远的故障。但是，当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。该项目的目的是开发和验证定量成像生物标志物评估早期反应并与循环肿瘤DNA整合分析以预测局部晚期NSCLC的治疗失败模式。以前，我们开发了一种基于FDG-PET和CT图像的新型肿瘤分配方法，该方法在空间上揭示了在NSCLC中具有预测意义的独特肿瘤子区域。在这个项目中，我们将进一步改善我们识别强大子区域的肿瘤分配方法，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。严格的资格将采用程序来识别生物标志物的可重复和可重复的图像特征发现。我们将通过纳入预处理和中等处理来开发预测成像生物标志物在回顾性患者队列中进行扫描，并在两个前瞻性收集中独立测试包括国家随机II期试验在内的队列。最后，我们将成像与循环结合统一模型中的肿瘤DNA分析，以进一步提高预测精度。我们预计综合生物标志物将允许对地方区域与遥远的失败进行可靠的早期预测，在大剂量RT与强化全身疗法之间确定治疗的重要意义。如果成功，拟议的生物标志物将为个性化疗法提供合理的方法和最终改善了本地高级NSCLC的结果。