Project 2: Transcriptional Dynamics and Temporal Reprogramming During Radiation Treatment

项目 2：放射治疗期间的转录动力学和时间重编程

• 批准号: 10526304
• 负责人: Jacob Gardinier Scott
• 金额: $ 19.58万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-14 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10526304
• 关键词: Adopted; Affect; Alternative Therapies; Antibodies; Antibody-drug conjugates; Antineoplastic Agents; Basic Science; Behavior; Bioinformatics; Biological; Biological Assay; Biopsy; Bladder; Cancer Patient; Cell Line; Cells; Cisplatin; Clinic; Clinical; Combined Modality Therapy; Cytology; Data; Decision Making; Discipline; Disease; Evolution; Failure; Frequencies; Funding; Genetic Transcription; Genomics; Glean; Head and Neck Squamous Cell Carcinoma; Head and neck structure; Image; Immune checkpoint inhibitor; Immunotherapy; In Vitro; Intervention; Investigation; Knowledge; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Maps; Measures; Medical; Medical Oncology; Modality; Modernization; Modification; Molecular; Mutation; Nature; Nivolumab; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Pelvis; Pharmacologic Substance; Phase Transition; Physics; Play; Positron-Emission Tomography; Prediction of Response to Therapy; Primary Neoplasm; Radiation; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Radiobiology; Refractory; Regimen; Resistance; Role; Saliva; Sampling; Site; Specificity; Structure; Techniques; Technology; The Cancer Genome Atlas; Therapeutic; Time; Translating; Translations; Treatment Efficacy; Treatment Failure; Tumor Tissue; Urine; Variant; actionable mutation; arm; base; cell type; chemoradiation; chemotherapy; cohort; combinatorial; computer science; cone-beam computed tomography; cytotoxic; density; experience; genetic signature; human genome sequencing; in vivo; individual patient; insight; interest; novel; personalized medicine; phase II trial; radiation effect; radiation response; radiomics; response; standard of care; success; synergism; targeted treatment; transcriptomics; treatment choice; treatment response; tumor; tumor DNA

SUMMARY Radiation therapy (RT) is the single most utilized anti-cancer agent; nearly 70% of all cancer patients will receive radiation at some point in their cancer journey, and RT plays a crucial role in almost half of all cancer cures. The sequencing of the human genome, completed nearly 20 years ago, followed by the large scale cancer sequencing effort in The Cancer Genome Atlas (TCGA) have provided an unprecedented understanding of cancers in the primary and metastatic setting. In those same years, medical oncology has undergone three major phase transitions: targeted therapies have changed the way we think many diseases with specific actionable mutations; immunotherapy has revolutionized the treatment of many of those without; and antibody-drug conjugates have increased the specificity of our cytotoxics. RT treatment decision making, however, has not seen these same changes from biological influences, instead having relied on advances in medical physics and computer science to drive our advances. While the number of trials has ballooned in radiation oncology of late, spurred on by encouragement, and funding, from pharmaceutical companies interested in the synergy between novel (and profitable) compounds in the form of immune checkpoint inhibitors and antibody-drug-conjugates, with radiation, our understanding of the relative benefits and best choices for individual patients has not seen the same increases. In fact, we have struggled to parse out the differences between these novel combinations and standard chemoradiotherapy in phase II trials, largely because of the combinatorial nature of our trials, and the sheer number of open questions. In this project, we seek to make headway toward personalizing radiation therapy treatment choices. Using our experience in using gene signatures to predict individual patient radiation benefit, together with expertise in radiomics and genomics, we will use 4 carefully crafted cohorts to dissect out the relative contribution of radiation, standard chemotherapy, the immune checkpoint inhibitor Nivolumab and the antibody-drug conjugate Sacituzumab govitecan. Having chosen two disease sites which benefit from high (but not uniform) cure rates with standard cisplatin-radiation combination therapy (bladder and head and neck), we have structured two investigational trials to compare to standard therapy. In each trial (bladder, with SG+RT, and HNSCC with ICI+RT) we will compare and contrast the temporal changes in tumor transcriptomic and mutational state change in primary tumor tissue and surrogates from shed cells and circulating tumor DNA. The ‘ground truth’ of these genomics through time will be married to high temporal density radiomics features to allow for translation and generalization to all patients treated with modern technique. Through these complimentary - omic modalities, we aim to leverage our experience in creating signatures of therapeutic response to admit personalized treatment choice in the up front setting, and opportunities to change course using real-time information gleaned from daily imaging. To round out the project we will perform in vitro experimental evolution to uncover the molecular mechanisms underpinning therapeutic success and failure in each modality, and to derive signatures of alternative therapy sensitivity.

概括 放射治疗 (RT) 是最常用的抗癌药物；近 70% 的癌症患者将接受 在癌症治疗过程中的某个阶段，放射治疗在几乎一半的癌症治疗中发挥着至关重要的作用。这 人类基因组测序，近 20 年前完成，随后是大规模癌症 癌症基因组图谱 (TCGA) 中的测序工作提供了前所未有的了解 原发性和转移性癌症。同年，肿瘤内科经历了三大变革： 相变：靶向治疗改变了我们对许多疾病的看法，具有特定的可操作性 突变；免疫疗法彻底改变了许多没有免疫疗法的人的治疗方法；和抗体药物 缀合物增加了我们的细胞毒素的特异性。然而，RT 治疗决策并未 看到了生物影响带来的这些相同的变化，而不是依赖于医学物理学的进步和 计算机科学推动我们的进步。尽管最近放射肿瘤学领域的试验数量激增， 受到对两者之间的协同作用感兴趣的制药公司的鼓励和资助的推动 免疫检查点抑制剂和抗体药物偶联物形式的新型（且有利可图）化合物， 对于放射治疗，我们对个体患者的相对益处和最佳选择的了解还没有看到 同样增加。事实上，我们一直在努力解析这些新颖组合之间的差异 和 II 期试验中的标准放化疗，主要是因为我们试验的组合性质，以及 悬而未决的问题数量之多。在这个项目中，我们寻求在个性化辐射方面取得进展 疗法 治疗选择。利用我们在使用基因特征来预测个体患者辐射方面的经验 结合放射组学和基因组学的专业知识，我们将使用 4 个精心设计的队列来剖析 放疗、标准化疗、免疫检查点抑制剂纳武单抗和 抗体-药物偶联物 Sacituzumab govitecan。选择了两个受益于高水平的疾病部位 （但不统一）标准顺铂放射联合治疗（膀胱和头颈部）的治愈率， 我们组织了两项研究试验来与标准疗法进行比较。在每次试验中（膀胱，SG+RT， 和 HNSCC 与 ICI+RT），我们将比较和对比肿瘤转录组和 HNSCC 的时间变化 原发肿瘤组织以及脱落细胞和循环肿瘤 DNA 的替代物中的突变状态变化。这 随着时间的推移，这些基因组学的“基本事实”将与高时间密度放射组学特征结合起来，以允许 向所有接受现代技术治疗的患者进行翻译和推广。通过这些免费的 - 组学模式，我们的目标是利用我们在创建治疗反应特征方面的经验来承认 预先设置的个性化治疗选择，以及使用实时改变疗程的机会 从日常成像中收集的信息。为了完成该项目，我们将进行体外实验进化 揭示支撑每种治疗方式成功和失败的分子机制，并 得出替代疗法敏感性的特征。