Integrated blood and radiomic subtyping to guide immunotherapy treatment selection and early response assessment in metastatic non-small cell lung cancer

综合血液和放射组学亚型，指导转移性非小细胞肺癌的免疫治疗选择和早期反应评估

• 批准号: 10734127
• 负责人: Natalie Vokes
• 金额: $ 67.66万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734127
• 关键词: Algorithms; Biological; Biological Assay; Biological Markers; Biology; Biopsy; Blood; Blood specimen; Cancer Biology; Cancer Center; Cancer Patient; Classification; Clinical; Clinical Data; Clinical Management; Code; Collaborations; Combined Modality Therapy; Coupling; DNA Sequence Alteration; DNA sequencing; Data; Data Science; Data Set; Databases; Decision Making; Development; FDA approved; Foundations; General Hospitals; Goals; Image; Image Analysis; Immune; Immune checkpoint inhibitor; Immunooncology; Immunotherapy; Investigation; Lesion; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Massachusetts; Measurable; Methylation; Modality; Modeling; Molecular; Molecular Profiling; Multiomic Data; Mutation; Non-Small-Cell Lung Carcinoma; Oncology; Organ; Outcome; PET/CT scan; Patients; Performance; Phase; Phenotype; Provider; Radiogenomics; Radiology Specialty; Recurrent disease; Regimen; Research; Scanning; Selection for Treatments; Site; Specimen; Subgroup; Techniques; Testing; Therapeutic; Thoracic Oncology; Tissues; Toxicity due to chemotherapy; Translating; Treatment Protocols; Tumor Biology; Validation; analytical tool; bench to bedside; blood treatment; cancer genomics; checkpoint therapy; chemotherapy; clinical assay development; clinical decision-making; clinical predictors; clinically relevant; cohort; data integration; deep learning; imaging biomarker; immune cell infiltrate; improved; improved outcome; individual patient; innovation; molecular phenotype; novel; outcome prediction; personalized immunotherapy; predicting response; predictive modeling; programmed cell death ligand 1; prospective; radiological imaging; radiomics; response; success; treatment response; treatment strategy; tumor; tumor DNA

ABSTRACT Immune checkpoint inhibitors (ICIs) have improved outcomes in metastatic non-small cell lung cancer (NSCLC), and providers may now choose between multiple first-line ICI-based regimens including ICI monotherapy and ICI with chemotherapy. However, this increase in options has complicated clinical management, with few biomarkers to guide upfront ICI treatment selection, and incomplete metrics for early on-treatment assessment of response to ICI therapy. Hence, there is an urgent need for novel analytics tools to optimize and personalize immunotherapy treatment strategies. While prior biomarker efforts have focused largely on tissue-based molecular profiling, these have demonstrated limited predictive power and are difficult to implement due to practical limitations in acquiring pre- and on-treatment tissue. In contrast, imaging and blood-based assays offer a unique and non-invasive mechanism by which the biology of the tumor and the changes on treatment can be studied and modeled. Thus, we propose an integrated radiomic-blood analysis to develop predictors of pre- and on-treatment response to guide the clinical management of NSCLC. Our primary goal is to develop radiomic- blood signatures for precision immunotherapy in advanced NSCLC by leveraging our expertise in data science, thoracic oncology, cancer genomics, computational oncology, clinical assay development, and established research collaborations. Our preliminary data demonstrates our success in utilizing multi-parametric profiling of circulating tumor DNA to identify molecular phenotypes associated with ICI outcome and disease recurrence, and in developing novel radiomic subtyping techniques with superior outcome prediction and demonstrated association with underlying lung cancer biology. Hence, we hypothesize that coupling radiomic and blood-based metrics can non-invasively inform therapeutic decision-making in NSCLC management while advancing our understanding of NSCLC biology. To advance this hypothesis, we have assembled a unique set of cohorts of metastatic NSCLC patients treated with ICI regimens with high-quality radiographic scans, blood samples, and molecular and clinical data: our in-house lung cancer database (GEMINI, n=5000); a validation dataset from our collaboration with the Massachusetts General Hospital (MGH) Cancer Center (MGH, n=600); the multicenter collaborative Stand Up 2 Cancer/Mark Foundation cohort (SU2C, n=400), and a prospective phase III ICI trial (LONESTAR, n=300). Our proposal builds on these unique cohorts and our promising preliminary data to construct predictive models to guide up-front ICI therapy selection and improve on-treatment response assessment, while complementary investigations will uncover the biology underlying these clinical predictors. A major strength of our proposal is our interdisciplinary team’s expertise in developing, validating, and translating these innovative predictive models toward highly relevant clinical questions. The development of integrative blood- and imaging-based radio-genomic biomarkers will help improve the clinical management of patients with metastatic NSCLC while helping progress the field toward a new era of non-invasive precision immunooncology.

抽象的 免疫检查点抑制剂（ICIs）改善了转移性非小细胞肺癌（NSCLC）的预后， 提供者现在可以在多种基于 ICI 的一线治疗方案之间进行选择，包括 ICI 单一疗法和 然而，这种选择的增加使临床治疗变得复杂，而且很少有这种治疗方法。 指导前期 ICI 治疗选择的生物标志物，以及早期治疗评估的不完整指标 因此，迫切需要新颖的分析工具来优化和个性化。 虽然之前的生物标志物研究主要集中在基于组织的治疗策略上。 分子谱分析，这些已表现出有限的预测能力，并且由于以下原因难以实施 相比之下，成像和基于血液的检测提供了获取治疗前和治疗中组织的实际限制。 一种独特的非侵入性机制，通过该机制可以了解肿瘤的生物学和治疗的变化 因此，我们提出了一种综合放射组学血液分析来开发预和模型的预测因子。 我们的主要目标是开发放射组学。 利用我们在数据科学方面的专业知识，为晚期 NSCLC 的精准免疫治疗提供血液特征， 胸部肿瘤学、癌症基因组学、计算肿瘤学、临床检测开发，并建立了 我们的初步数据表明我们在利用多参数分析方面取得了成功。 循环肿瘤 DNA 以确定与 ICI 结果和疾病复发相关的分子表型， 并开发具有卓越结果预测和证明的新型放射组学亚型技术 因此，我们追求放射组学和血液学的结合。 指标可以非侵入性地为 NSCLC 管理中的治疗决策提供信息，同时推进我们的研究 为了推进这一假设，我们收集了一组独特的队列。 采用 ICI 方案治疗的转移性 NSCLC 患者，通过高质量的放射线扫描、血液样本和 分子和临床数据：我们的内部肺癌数据库（GEMINI，n=5000）；来自我们的验证数据集； 与麻省总医院 (MGH) 癌症中心 (MGH, n=600) 合作； Stand Up 2 Cancer/Mark Foundation 协作队列（SU2C，n=400）和前瞻性 III 期 ICI 试验 （LONESTAR，n=300）。我们的建议建立在这些独特的队列和我们有希望的初步数据的基础上。 构建预测模型来指导前期 ICI 治疗选择并改善治疗反应 评估，而补充研究将揭示这些临床预测因素背后的生物学原理。 我们提案的主要优势是我们的跨学科团队在开发、验证和翻译方面的专业知识 这些创新的预测模型针对高度相关的临床问题的发展。 基于血液和成像的放射基因组生物标志物将有助于改善患有以下疾病的患者的临床管理 转移性非小细胞肺癌，同时帮助该领域迈向非侵入性精准免疫肿瘤学的新时代。