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.

摘要 免疫检查点抑制剂（ICI）改善了转移性非小细胞肺癌（NSCLC）的结局， 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单药治疗， ICI+化疗。然而，这种选择的增加使临床管理复杂化， 用于指导前期ICI治疗选择的生物标志物，以及用于早期治疗评估的不完整指标 对ICI疗法的反应。因此，迫切需要新的分析工具来优化和个性化 免疫治疗策略。虽然先前的生物标志物工作主要集中在基于组织的 分子谱分析，这些已经证明了有限的预测能力，并且由于 在获取治疗前和治疗中的组织方面的实际限制。相比之下，成像和基于血液的分析提供了 一种独特的非侵入性机制，通过这种机制，肿瘤的生物学和治疗变化可以被 研究和建模。因此，我们提出了一个综合的放射性血液分析，以发展预测的前和 治疗中的反应，以指导NSCLC的临床管理。我们的主要目标是发展放射性- 利用我们在数据科学方面的专业知识， 胸肿瘤学、癌症基因组学、计算肿瘤学、临床检测开发，以及已建立的 研究合作。我们的初步数据表明，我们成功地利用多参数分析， 循环肿瘤DNA以鉴定与ICI结果和疾病复发相关的分子表型， 并在开发新的放射组学分型技术，具有优越的上级结果预测和证明 与潜在的肺癌生物学之间的关系。因此，我们假设，将放射组学和基于血液的 指标可以非侵入性地告知NSCLC管理中的治疗决策，同时推进我们的 了解NSCLC生物学。为了推进这一假设，我们收集了一组独特的队列， 接受ICI方案治疗的转移性NSCLC患者，采用高质量的放射学扫描、血液样本， 分子和临床数据：我们的内部肺癌数据库（GEMINI，n=5000）;我们的验证数据集 与马萨诸塞州总医院（MGH）癌症中心（MGH，n=600）合作;多中心 协作Stand Up 2癌症/Mark基金会队列（SU 2C，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， 构建预测模型以指导前期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的生物学基础。一 我们的建议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，对高度相关的临床问题。一体化发展 基于血液和成像的放射性基因组生物标志物将有助于改善患者的临床管理， 转移性非小细胞肺癌，同时帮助该领域迈向无创精确免疫肿瘤学的新时代。