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

• 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
• 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

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，预计美国将有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，并且具有诱导持久的临床免疫应答的潜力。 有意义的回答。然而，只有1/5的接受IO治疗的NSCLC患者会有良好的反应。 不幸的是，目前基于组织的生物标志物的方法来选择患者进行这些治疗是亚， 这是由于免疫系统与肿瘤相互作用的动态性质而最佳的。鉴于禁止 与IO相关的成本（每位患者> 20万美元/年），对预测生物标志物的关键需求尚未得到满足， 确定哪些患者不会从IO中受益。此外，目前评价肿瘤的临床标准 缓解（即评价肿瘤大小变化和结节消失的RECIST和irRC）为次优 评估IO药物的早期临床获益。这至少部分是由于一些患者 接受IO的患者在治疗后CT扫描上表现出明显的疾病进展（假进展）。 不像放射组学的标准规范（计算机从射线照相扫描中提取特征）， 评估纹理或形状模式，我们的团队一直在开发新的计算机视觉策略， 肿瘤周围异质性（肿瘤外）和来自CT扫描的肿瘤脉管系统的模式。单位：N>300 患者，我们的研究小组已经表明（1）基线血管迂曲度的放射组学，NSCLC治疗前CT 接受IO的患者在应答者（较少迂曲）和无应答者之间存在显著差异 (more迂曲），（2）这些测量值的连续变化是IO早期反应的更好预测因素 与临床反应标准如RECIST和irRC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。重要的是， 这些放射组学特征预测了用来自3个部位的3种不同IO试剂治疗的NSCLC患者的应答。 在这个项目中，我们将进一步发展血管，肿瘤和肿瘤内放射组学特征，用于监测 并预测IO治疗NSCLC患者的获益和早期反应。我们将以独特的方式训练我们的放射组学 使用一组N>180例接受一线IO治疗的切除NSCLC患者， 病理学缓解（MPR）作为主要终点。此外，我们将建立生物学基础， 通过评估这些预测性放射组学特征与形态学、免疫景观 (from活组织检查）和肿瘤的分子途径。此外，我们还接触了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）试验中部署LunIOTx（N>600），这是首次 放射组学将在IO的前瞻性合作组临床试验中进行评估。