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: 10316572
• 负责人: Sharyn Katz
• 金额: $ 60.85万
• 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/10316572
• 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; cancer therapy; circulating DNA; clinical translation; cohort; 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; tool; 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.

项目总结 我们建议鉴定抗程序性死亡配体1(PDL1)/PD1治疗的新的放射组学特征 对非小细胞肺癌(NSCLC)的反应，并评估这些信号如何增强已建立的 生物标志物。由于免疫疗法的显著改进，免疫疗法已迅速整合到非小细胞肺癌的治疗中 与传统的细胞毒治疗相比，有效率，现在也被接受为一线治疗 选定的种群。而根据PDL1的肿瘤表达对患者进行分层已经改善了治疗 应答率，高达30%-40%的非小细胞肺癌患者使用这些药物一线治疗仍然失败，这表明新的 需要更准确地选择可能受益的患者的策略。虽然放射组学的方法还没有 在非小细胞肺癌免疫疗法的背景下进行全面研究，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捉到了 肿瘤表型，包括血管结构、肿瘤内异质性和肿瘤的免疫浸润性 微环境，这可能提供一种强大的表型方法来增强已建立的生物标记物 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。 对于非小细胞肺癌，利用现有机构数据库(n=2095名患者)的CT数据，该数据库包括 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 机构试验(n=846)，用于独立验证。通过开展这项研究，我们将致力于 为了解决这个根本问题：放射性签名能否增强已建立的生物标记物，如 PDL1的表达，在预测哪些患者可能从抗PD1/PDL1治疗中受益最多？ 虽然到目前为止大多数放射组学研究都集中在非一线非小细胞肺癌的抗PD1/PDL1治疗上 背景下，我们将寻求发现专门针对第一和第二免疫疗法的放射学特征，以及 我们将在治疗开始前的基线和治疗期间的纵向检查这些信号。 治疗过程与肿瘤反应、无进展和总存活率有关。我们将进一步 将这些信号与已知的抗PDL1治疗反应的生物标记物相关联，包括PDL1表达， 肿瘤突变负荷(TMB)、循环(CT)-DNA和肿瘤浸润性淋巴细胞(TIL)，以更好地 了解放射组学如何增强这些已建立的和正在出现的生物标记物在预测抗- PD1/PDL1治疗反应。为了发现这些放射特征，我们将利用癌症表现学 工具包(CapTK)，这是我们团队开发的一个开源和高度标准化的软件，并将使用 新的放射学特征标准化方法，允许我们合并通过变量获取的CT扫描 收购。这些方法结合在一起，将产生稳健的表型放射组学特征，从而使 更知情的临床管理选择抗PD1/PDL1治疗的患者通过识别更接近 有效和早期的治疗选择。