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

• 批准号: 10418808
• 负责人: Sharyn Katz
• 金额: $ 59.5万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 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/10418808
• 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; Patients; Pattern; Phenotype; Population; Prediction of Response to Therapy; Research; Scanning; Standardization; Structure; Surface; Time; Tumor-Infiltrating Lymphocytes; Tumor-infiltrating immune cells; Validation; X-Ray Computed Tomography; anti-PD-1; base; cancer immunotherapy; circulating DNA; clinical translation; cohort; diagnostic tool; effective therapy; improved; novel; novel therapeutics; open source; patient stratification; pembrolizumab; personalized diagnostics; personalized management; phenomics; predictive marker; predictive modeling; 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

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.

项目摘要 我们建议鉴定抗编程死亡配体1（PDL1）/PD1治疗的新型放射素特征 非小细胞肺癌（NSCLC）的反应，并评估这些特征如何增加建立 生物标志物。由于大大改善，免疫疗法已迅速整合到NSCLC管理中 与常规的细胞毒性疗法相比，应答率现在也被接受为 选定人群。而基于PDL1肿瘤表达的患者分层已改善治疗 回复率，多达30-40％的NSCLC患者仍未使用这些药物的第一线治疗，这表明新的 需要策略以更准确地选择可能受益的患者。虽然radiomic方法尚未 在NSCLC免疫疗法的背景下进行全面研究，包括我们的初步数据在内的早期证据表明 从常规计算机断层扫描（CT）提取的那种放射素特征捕获了重要特征 肿瘤表型，包括血管结构，肿瘤内异质性和免疫浸润 微环境可以为增强建立生物标志物的强大表型方法 抗PDL1/PD1治疗。我们建议执行迄今为止对免疫疗法进行的最大放射组学研究 对于NSCLC，利用现有机构数据库的CT数据（n = 2095名患者），其中包括 用抗PD1/PDL1治疗剂治疗的患者的生物体和持续的ECOG-ACRIN Multi-time 机构试验（n = 846）用于独立验证。通过进行这项研究，我们将瞄准 为了解决这个基本问题：放射线签名是否可以增加建立的生物标志物，例如 PDL1表达，预测哪些患者可能受益于抗PD1/PDL1治疗？ 迄今为止，大多数放射组学研究都集中在非第1行中NSCLC的抗PD1/PDL1治疗上 设置，我们将寻求发现专门针对第1和以后的免疫疗法系列的放射线特征，以及 我们将在启动治疗之前在基线时检查此类特征，并在 与肿瘤反应，无进展和总生存期相关的治疗过程。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， 肿瘤突变负担（TMB），循环（CT）-DNA和肿瘤浸润淋巴细胞（TILS），以更好 了解放射组学如何在预测抗 - PD1/PDL1治疗反应。为了发现这些放射线特征，我们将利用癌症现象学 Toolkit（Captk）是我们小组开发的开源和高度标准的软件，并将利用一个 新型的放射素特征标准化方法，使我们能够合并可变的CT扫描 获得。这些方法一起将导致强大的表型放射素特征，这将使A 通过识别更多的抗PD1/PDL1治疗患者的知情临床管理，通过识别更多 有效且早期的治疗选择。