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: 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
• 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

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 我们建议识别抗程序性死亡配体1（PDL 1）/PD 1治疗的新放射组学特征 非小细胞肺癌（NSCLC）的反应，并评估这些签名如何可以增加建立 生物标志物。免疫治疗已迅速整合到NSCLC管理中，这是因为免疫治疗显著改善了 与传统细胞毒性治疗相比，缓解率更高，现在也被接受为一线治疗， 选择人口。虽然基于PDL 1的肿瘤表达对患者进行分层改善了治疗， 在缓解率方面，高达30-40%的NSCLC患者仍然无法使用这些药物进行一线治疗，这表明新的 需要制定更准确地选择可能受益的患者的策略。虽然放射组学方法还没有 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 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，这可以提供一个强大的表型方法，以增加既定的生物标志物， 抗PDL 1/PD 1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例患者）的CT数据，包括 接受抗PD 1/PDL 1治疗药物治疗的患者的生物相关性，以及正在进行的ECOG-ACRIN多项 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 为了解决这个基本问题：放射组学签名是否可以增强已建立的生物标志物， PDL 1表达，预测哪些患者可能从抗PD 1/PDL 1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一线NSCLC的抗PD 1/PDL 1治疗上， 在这种情况下，我们将寻求发现放射组学特征，特别是针对第一线与后来的免疫治疗， 我们将在基线、治疗开始前以及治疗期间纵向检查这些特征。 与肿瘤缓解、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PDL 1治疗应答的已知生物标志物（包括PDL 1表达）相关联， 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和新兴的生物标志物，以预测抗- PD 1/PDL 1治疗应答。为了发现这些放射组学特征，我们将利用癌症表型组学 工具包（CapTK），一个开放源代码和高度标准化的软件开发的小组，并将利用一个 一种新的放射组学特征标准化方法，使我们能够将通过变量 采集总之，这些方法将产生稳健的表型放射组学特征，其将使得能够进行放射组学分析。 通过识别更接近于PD 1/PDL 1治疗的患者， 早期有效的治疗方案。

项目成果

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

• DOI: 10.3390/cancers13235985
• 发表时间: 2021-11-28
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Hershman M;Yousefi B;Serletti L;Galperin-Aizenberg M;Roshkovan L;Luna JM;Thompson JC;Aggarwal C;Carpenter EL;Kontos D;Katz SI
• 通讯作者: Katz SI