Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer

预测肺癌免疫治疗反应的新型放射组学

基本信息

批准号：
10699497
负责人：
Anant Madabhushi
金额：
$ 56.7万
依托单位：
EMORY UNIVERSITY
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：
2021-04-02 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10699497
关键词：
Adoption Aftercare Antitumor Response Biological Biological Markers Biopsy Cancer Patient Characteristics Clinical Clinical Trials Clinical Trials Cooperative Group Computer Vision Systems Computers Development Diagnosis Disease Progression Early identification Early treatment Eastern Cooperative Oncology Group Environment Goals Immune Immune checkpoint inhibitor Immune response Immune system Immunotherapeutic agent Immunotherapy Inflammatory Institution Letters Malignant Neoplasms Malignant neoplasm of lung Measurement Molecular Monitor Morphology Mutation Nature Neoadjuvant Therapy Nivolumab Nodule Non-Small-Cell Lung Carcinoma Nonmetastatic Outcome PD-1/PD-L1 Pathologic Pathway interactions Patients Pattern Pharmaceutical Preparations Pharmacologic Substance Phase Phenotype Predictive Value Publishing Radiology Specialty Reporting Resected Scanning Shapes Site Testing Texture Time Tissues Toxic effect Training Treatment outcome Tumor Biology Tumor-Infiltrating Lymphocytes Validation X-Ray Computed Tomography anti-PD-1 anti-PD-1/PD-L1 anti-PD-L1 therapy base cost imaging biomarker immunotherapy clinical trials immunotherapy trials industry partner inhibitor therapy non-invasive imaging novel phase III trial predicting response predictive marker primary endpoint prognostic prognostic of survival prognostic value programmed cell death ligand 1 prospective radiological imaging radiomics responders and non-responders response success survival outcome survival prediction tool treatment response tumor tumor behavior tumor heterogeneity

项目摘要

ABSTRACT: In 2019, an estimated 228,150 patients in the US are expected to be diagnosed with non-small cell lung cancer (NSCLC). A recent landmark development has been the approval of the immune checkpoint inhibitors (anti-PD-1 and anti-PD-L1) for the treatment of locally advanced and metastatic NSCLC. These immunotherapy (IO) drugs have an excellent toxicity profile and have the potential to induce durable clinically meaningful responses. However, only 1 in 5 NSCLC patients treated with IO will have a favorable response. Unfortunately, the current tissue based biomarker approach to selecting patients for these treatments is sub- optimal due to the dynamic nature of the interaction of the immune system with the tumor. Given the prohibitive costs associated with IO (>$200K/year per patient), there is a critical unmet need for predictive biomarkers to identify which patients will not benefit from IO. Additionally, the current clinical standard to evaluating tumor response (i.e. RECIST and irRC which evaluate change in tumor size and nodule disappearance) is sub-optimal in evaluating early clinical benefit from IO drugs. This is due at least in part to the fact that some patients undergoing IO present apparent disease progression (pseudo-progression) on post-treatment CT scans. Unlike the standard canon of radiomics (computer extracted features from radiographic scans) that assess textural or shape patterns, our group has been developing novel computer vision strategies to capture patterns of peri-tumoral heterogeneity (outside the tumor) and tumor vasculature from CT scans. In N>300 patients, our group has shown that (1) radiomics of vessel tortuosity on baseline, pre-treatment CT for NSCLC patients undergoing IO were significantly different between responders (less tortuous) and non-responders (more tortuous), (2) serial changes in these measurements were better predictors of early response to IO compared to clinical response criteria such as RECIST and irRC and (3) these radiomic attributes were associated with PD-L1 expression and degree of tumor infiltrating lymphocytes on baseline biopsies. Critically, these radiomic features predicted response for NSCLC patients treated with 3 different IO agents from 3 sites. In this project we will further develop vasculature, peri- and intra-tumoral radiomic features for monitoring and predicting benefit and early response for NSCLC patients treated with IO. We will uniquely train our radiomics using a set of N>180 resected NSCLC patients treated with first line IO and for whom we will have major pathologic response (MPR) as primary endpoint. In addition, we will establish the biological underpinnings of these predictive radiomic signatures by evaluating their association with the morphology, immune landscape (from biopsies) and molecular pathways of the tumor. In addition we have access to N>700 NSCLC patients treated on completed clinical trials via our industry partners (Astrazeneca, Bristol-Myers Squibb) for tool validation. Finally, we will deploy LunIOTx within the ECOG-5163 (INSIGNA) trial (N>600), the first time that radiomics will be evaluated within a prospective cooperative group clinical trial for IO.

摘要：2019 年，美国预计将有 228,150 名患者被诊断为非小细胞肺癌肺癌（NSCLC）。最近的一个里程碑式的发展是免疫检查点的批准抑制剂（抗PD-1和抗PD-L1）用于治疗局部晚期和转移性NSCLC。这些免疫治疗（IO）药物具有优异的毒性特征，并有可能诱导持久的临床疗效。有意义的回应。然而，接受 IO 治疗的 NSCLC 患者中只有五分之一会产生良好的反应。不幸的是，目前用于选择接受这些治疗的患者的基于组织的生物标志物方法是次要的。由于免疫系统与肿瘤相互作用的动态性质，这是最佳的。鉴于令人望而却步的与 IO 相关的成本（每位患者每年 > 20 万美元），对预测性生物标志物的迫切需求尚未得到满足确定哪些患者不会从 IO 中受益。此外，目前评估肿瘤的临床标准反应（即评估肿瘤大小变化和结节消失的 RECIST 和 irRC）不是最佳的评估 IO 药物的早期临床益处。这至少部分是由于一些患者接受 IO 治疗后 CT 扫描显示明显的疾病进展（假性进展）。与放射组学的标准规范（计算机从放射线扫描中提取特征）不同，评估纹理或形状模式，我们的团队一直在开发新颖的计算机视觉策略来捕捉 CT 扫描的肿瘤周围异质性（肿瘤外）和肿瘤脉管系统的模式。 N>300时对于患者，我们小组已证明 (1) NSCLC 治疗前 CT 基线上的血管迂曲放射组学接受 IO 的患者在有反应者（较少曲折）和无反应者之间存在显着差异（更曲折），（2）这些测量值的连续变化可以更好地预测 IO 的早期反应与 RECIST 和 irRC 等临床反应标准相比，(3) 这些放射组学属性与基线活检中 PD-L1 表达和肿瘤浸润淋巴细胞程度相关。关键的是，这些放射组学特征预测了来自 3 个部位的 3 种不同 IO 药物治疗的 NSCLC 患者的反应。在这个项目中，我们将进一步开发脉管系统、肿瘤周围和肿瘤内的放射组学特征以进行监测预测接受 IO 治疗的 NSCLC 患者的获益和早期反应。我们将独特地训练我们的放射组学使用一组 N>180 名接受一线 IO 治疗的切除 NSCLC 患者，我们将对他们进行主要研究病理反应（MPR）作为主要终点。此外，我们将建立以下生物学基础：通过评估这些预测性放射组学特征与形态、免疫景观的关联（来自活检）和肿瘤的分子途径。此外，我们还可以接触到 N>700 名 NSCLC 患者通过我们的行业合作伙伴（阿斯利康、百时美施贵宝）完成的临床试验进行治疗验证。最后，我们将在 ECOG-5163 (INSIGNA) 试验 (N>600) 中部署 LunIOTx，这是第一次放射组学将在 IO 的前瞻性合作组临床试验中进行评估。