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: 10703255
• 负责人: Anant Madabhushi
• 金额: --
• 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 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703255
• 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; Immuno-Chemotherapy; 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; Outcome; PD-1/PD-L1; Pathologic; Pathway interactions; Patient Selection; 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; cost; imaging biomarker; immune- related response criteria; 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; prognostication; 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，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站的批准 用于治疗局部晚期和转移性NSCLC的抑制剂(抗PD-1和抗PD-L1)。这些 免疫疗法(IO)药物具有极好的毒性特征，并有可能在临床上诱导持久 有意义的回应。然而，接受IO治疗的NSCLC患者中只有1/5会有良好的反应。 不幸的是，目前选择患者进行这些治疗的基于组织的生物标记物方法是次要的 由于免疫系统与肿瘤相互作用的动态性质，因此是最佳的。考虑到令人望而却步的 与IO相关的成本(每个患者每年20万美元)，对预测性生物标记物的严重需求尚未得到满足 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)不是最优的 在评估IO药物的早期临床益处方面。这至少在一定程度上是因为一些患者 接受IO治疗后的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的计算机视觉策略来捕获 CT扫描显示肿瘤周围(肿瘤外)的异质性和肿瘤血管的类型。在N&gt；300 本组研究表明：(1)非小细胞肺癌患者治疗前CT检查的基础上血管弯曲的放射组学研究 接受IO的患者在应答者(不那么曲折)和非应答者之间有显著差异 (更曲折)，(2)这些测量的系列变化更能预测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PD-L1表达和基线活检中肿瘤浸润性淋巴细胞的程度相关。关键是， 这些放射组学特征预测了来自3个部位的3种不同IO剂治疗NSCLC患者的疗效。 在这个项目中，我们将进一步开发用于监测的血管系统、肿瘤周围和肿瘤内的放射学特征。 并预测IO治疗非小细胞肺癌患者的益处和早期反应。我们将独一无二地训练我们的放射组学 使用一组N&gT；180名接受一线IO治疗的NSCLC患者，我们将为他们提供主要的 以病理反应(MPR)为主要终点。此外，我们将建立生物基础， 这些可预测的放射组学特征通过评估它们与形态、免疫环境 (来自活组织检查)和肿瘤的分子途径。此外，我们还可以接触到N&>700名非小细胞肺癌患者 通过我们的行业合作伙伴(阿斯利康、百时美施贵宝)对Tool进行已完成的临床试验 验证。最后，我们将在ECOG-5163(INSIGNA)试验(N&gT；600)中部署LUNIOTx，这是第一次 放射组学将在IO的预期合作小组临床试验中进行评估。