Predictive and Diagnostic Radiomic Signatures in Non-Small Cell Lung Cancer (NSCLC) on Immunotherapy

非小细胞肺癌 (NSCLC) 免疫治疗的预测和诊断放射学特征

基本信息

批准号：
10652449
负责人：
Sharyn Katz
金额：
$ 59.59万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-04 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10652449
关键词：
Address American College of Radiology Imaging Network Biological Markers Blood Vessels Cancer Patient Characteristics Clinical Clinical Management Clinical Trials Cytotoxic Chemotherapy Data Data Set Databases Detection Diagnostic Early treatment Eastern Cooperative Oncology Group Eye Goals Human Image Immunotherapeutic agent Immunotherapy Individual Institution Malignant Neoplasms Multi-Institutional Clinical Trial Mutation Non-Small-Cell Lung Carcinoma Outcome Patient Selection Patients Pattern Phenotype Population Prediction of Response to Therapy Research Scanning Selection for Treatments Standardization Structure Surface Time Tumor-Infiltrating Lymphocytes Tumor-infiltrating immune cells Validation X-Ray Computed Tomography anti-PD-1 cancer immunotherapy circulating DNA clinical predictive model clinical translation cohort diagnostic tool effective therapy empowerment improved information gathering novel novel therapeutics open source patient stratification pembrolizumab personalized diagnostics personalized management phenomics predictive marker predictive signature programmed cell death ligand 1 programmed cell death protein 1 prospective radiological imaging radiomics response software development therapeutic biomarker treatment response tumor tumor heterogeneity tumor microenvironment tumor progression user-friendly

项目摘要

PROJECT SUMMARY We propose to identify novel radiomic signatures of anti-programmed death ligand 1 (PDL1)/PD1 therapy response for non-small cell lung cancer (NSCLC) and evaluate how these signatures can augment established biomarkers. Immunotherapy has been rapidly integrated into NSCLC management due to dramatically improved response rates compared to conventional cytotoxic therapy and is now also accepted as 1st line therapy for selected populations. While stratification of patients based on tumor expression of PDL1 has improved therapy response rates, up to 30-40% of NSCLC patients still fail 1st line therapy with these agents, suggesting that new strategies are needed to more accurately select patients likely to benefit. While a radiomic approach has yet to be fully studied in the context of NSCLC immunotherapy, early evidence, including our preliminary data, suggests that radiomic features extracted from routine computed tomography (CT) capture important characteristics of the tumor phenotype, including vascular structure, intra-tumor heterogeneity, and immune infiltration of the tumor microenvironment, which could provide a powerful phenotypic approach to augment established biomarkers for anti-PDL1/PD1 therapy. We propose to perform the largest radiomics study conducted to date on immunotherapy for NSCLC, leveraging CT data from an existing institutional database (n=2095 patients) which includes biocorrelates of patients treated with anti-PD1/PDL1 therapy agents, and an on-going ECOG-ACRIN multi- institutional trial (n=846) to be used for independent validation. By pursuing this research, we will therefore aim to address this fundamental question: Can radiomic signatures augment established biomarkers, such as PDL1 expression, in predicting which patients are likely to benefit most from anti-PD1/PDL1 therapy? While most radiomics studies to date have focused on anti-PD1/PDL1 therapy for NSCLC in the non-1st line setting, we will seek to discover radiomic signatures specifically for 1st versus later line of immunotherapy, and we will examine such signatures both at baseline, prior to the initiation of therapy, as well as longitudinally during the course of therapy in association to tumor response, progression-free and overall survival. We will further correlate these signatures with known biomarkers of anti-PDL1 therapy response, including PDL1 expression, tumor mutational burden (TMB), circulating (ct)-DNA, and tumor-infiltrating lymphocytes (TILS), to better understand how radiomics can augment these established and emerging biomarkers in predicting anti- PD1/PDL1 therapy response. To discover these radiomic signatures, we will leverage the Cancer Phenomics Toolkit (CapTK), an open-source and highly-standardized software developed by our group, and will utilize a novel radiomic feature standardization approach, allowing us to incorporate CT scans acquired by variable acquisition. Together, these approaches will result in robust phenotypic radiomic signatures that will enable a more informed clinical management of patients selected for anti-PD1/PDL1 therapy by identifying more nearly effective and earlier therapy options.

项目概要我们建议鉴定抗程序性死亡配体 1 (PDL1)/PD1 疗法的新放射组学特征对非小细胞肺癌 (NSCLC) 的反应并评估这些特征如何增强已建立的生物标志物。由于显着改善，免疫疗法已迅速融入 NSCLC 治疗中与传统细胞毒疗法相比，反应率更高，现在也被接受为一线疗法选定的人群。虽然根据肿瘤 PDL1 表达对患者进行分层改善了治疗高达 30-40% 的 NSCLC 患者使用这些药物的一线治疗仍然失败，这表明新的需要采取策略来更准确地选择可能受益的患者。虽然放射组学方法尚未在 NSCLC 免疫治疗的背景下进行充分研究，早期证据，包括我们的初步数据表明从常规计算机断层扫描（CT）中提取的放射组学特征捕获了重要的特征肿瘤表型，包括血管结构、肿瘤内异质性和肿瘤的免疫浸润微环境，它可以提供强大的表型方法来增强已建立的生物标志物抗 PDL1/PD1 治疗。我们建议进行迄今为止最大规模的免疫治疗放射组学研究对于 NSCLC，利用现有机构数据库（n=2095 名患者）的 CT 数据，其中包括接受抗 PD1/PDL1 治疗药物治疗的患者的生物相关性，以及正在进行的 ECOG-ACRIN 多用于独立验证的机构试验（n=846）。因此，通过开展这项研究，我们的目标是解决这个基本问题：放射组学特征能否增强已建立的生物标志物，例如 PDL1 表达，预测哪些患者可能从抗 PD1/PDL1 治疗中获益最多？虽然迄今为止大多数放射组学研究都集中在非第一线非小细胞肺癌的抗 PD1/PDL1 治疗上在这种情况下，我们将寻求专门针对第一线和后续免疫疗法的放射组学特征，并且我们将在治疗开始前的基线以及治疗期间纵向检查这些特征与肿瘤反应、无进展生存和总生存相关的治疗过程。我们将进一步将这些特征与抗 PDL1 治疗反应的已知生物标志物相关联，包括 PDL1 表达，肿瘤突变负荷 (TMB)、循环 (ct)-DNA 和肿瘤浸润淋巴细胞 (TILS)，以更好地了解放射组学如何增强这些已建立的和新兴的生物标志物来预测抗- PD1/PDL1 治疗反应。为了发现这些放射组学特征，我们将利用癌症表型组学 Toolkit（CapTK）是我们团队开发的开源且高度标准化的软件，将利用新颖的放射组学特征标准化方法，使我们能够将通过变量获取的 CT 扫描纳入其中获得。总之，这些方法将产生强大的表型放射组学特征，从而使通过识别更接近的信息，对选择接受抗 PD1/PDL1 治疗的患者进行更明智的临床管理有效且早期的治疗选择。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography.

在计算机断层扫描上应用低等级放射组学表征的非小细胞肺癌 (NSCLC) 手动分割中观察者间变异的影响。

DOI：
发表时间：
2021-11-28
期刊：
影响因子：
5.2
作者：
Hershman, Michelle;Yousefi, Bardia;Serletti, Lacey;Galperin;Roshkovan, Leonid;Luna, José Marcio;Thompson, Jeffrey C;Aggarwal, Charu;Carpenter, Erica L;Kontos, Despina;Katz, Sharyn I
通讯作者：
Katz, Sharyn I

Are radiomic signatures ready for incorporation in the clinical pipeline?

放射组学特征是否已准备好纳入临床管道？