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，预计美国估计有228,150名患者被诊断为非小细胞 肺癌（NSCLC）。最近的具有里程碑意义的开发是获得免疫检查站的批准 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，并且有可能在临床上诱导耐用的毒性 有意义的回应。但是，只有5个接受IO治疗的NSCLC患者中只有1个会有良好的反应。 不幸的是，当前基于组织的生物标志物方法用于选择这些治疗的患者是亚下的 由于免疫系统与肿瘤的相互作用的动态性质，最佳。鉴于过度的 与IO相关的成本（每位患者$ 200k/年），预测性生物标志物的至关重要的需要 确定哪些患者不会从IO中受益。另外，当前评估肿瘤的临床标准 反应（即评估肿瘤大小和结节消失变化的恢复和IRRC）是最佳的 在评估IO药物的早期临床益处时。这至少部分是由于某些患者 在治疗后CT扫描中正在进行IO目前的明显疾病进展（伪产生）。 与标准的放射素学典范（从放射线扫描中提取的计算机提取特征）不同 评估纹理或形状模式，我们的小组一直在制定新颖的计算机视觉策略来捕获 CT扫描的肿瘤周期异质性（肿瘤之外）和肿瘤脉管系统的模式。在n> 300中 患者，我们的小组表明（1）基线上血管曲折的放射素学，NSCLC的预处理CT 反应者（曲折）和无反应者之间的IO患者显着差异 （更曲折），（2）这些测量值的序列变化是对IO早期响应的更好预测指标 与临床反应标准（例如recist和irrc）相比，（3）这些放射性属性是 与基线活检中的PD-L1表达和肿瘤浸润淋巴细胞的程度有关。批判性， 这些放射线特征预测了由3种位点的3种不同IO药物治疗的NSCLC患者的反应。 在这个项目中，我们将进一步开发用于监测的脉管系统，周日和肿瘤内放射线特征 并预测接受IO治疗的NSCLC患者的收益和早期反应。我们将唯一训练我们的放射线学 使用一组n> 180个切除的NSCLC患者，接受了第一行IO治疗，我们将拥有主要 病理反应（MPR）作为主要终点。此外，我们将建立 这些通过评估它们与形态，免疫景观的关联来通过评估它们的预测性放射线标志 （来自活检）和肿瘤的分子途径。另外，我们可以使用N> 700名NSCLC患者 通过我们的行业合作伙伴（Astrazeneca，Bristol-Myers Squibb）进行了完整的临床试验治疗 验证。最后，我们将在ECOG-5163（Insigna）试验（n> 600）中部署Luniotx，这是第一次 将在IO的前瞻性合作组临床试验中评估放射素学。