Project 2: Transcriptional Dynamics and Temporal Reprogramming During Radiation Treatment

总结 放射治疗（RT）是最常用的抗癌药物;近70%的癌症患者将接受放射治疗。 放射治疗在他们的癌症旅程中的某个时刻起着至关重要的作用，而RT在几乎一半的癌症治疗中起着至关重要的作用。的 人类基因组测序，完成近20年前，其次是大规模的癌症， 癌症基因组图谱（TCGA）中的测序工作提供了前所未有的理解， 原发性和转移性癌症。在这些年里，医学肿瘤学经历了三次重大的变革， 相变：靶向治疗改变了我们对许多疾病的看法， 突变;免疫疗法彻底改变了许多没有突变的人的治疗;抗体药物 偶联物增加了我们的细胞毒素的特异性。然而，RT治疗决策并没有 从生物学的影响中看到了同样的变化，而不是依赖于医学物理学的进步， 计算机科学来推动我们的进步虽然最近放射肿瘤学的试验数量激增， 在制药公司的鼓励和资助下， 免疫检查点抑制剂和抗体-药物-缀合物形式的新的（和有益的）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理解还没有看到， 同样的增加。事实上，我们一直在努力分析这些新组合之间的差异， II期试验中的标准化放疗，主要是因为我们试验的组合性质， 开放式问题的数量。在这个项目中，我们寻求在个性化辐射方面取得进展 治疗选择。利用我们在使用基因特征预测个体患者辐射方面的经验 受益，加上放射组学和基因组学的专业知识，我们将使用4个精心制作的队列来解剖出 放疗、标准化疗、免疫检查点抑制剂Nivolumab和 抗体-药物缀合物Sacituzumab govitecan。选择了两个疾病的网站，受益于高 (but不均匀）标准顺铂-放射联合治疗（膀胱和头颈部）的治愈率， 我们已经组织了两个研究性试验来与标准治疗进行比较。在每项试验中（膀胱，SG+RT， 和HNSCC与ICI+RT），我们将比较和对比肿瘤转录组的时间变化， 原发性肿瘤组织中的突变状态变化以及来自脱落细胞和循环肿瘤DNA的替代物。的 这些基因组学随时间的“地面实况”将与高时间密度放射组学特征结合， 用于翻译和推广到所有接受现代技术治疗的患者。通过这些免费的- 我们的目标是利用我们在创建治疗反应签名方面的经验， 个性化的治疗选择，并有机会改变过程中使用实时 从日常成像中收集的信息。为了完成这个项目，我们将进行体外实验进化， 揭示每种模式治疗成功和失败的分子机制， 获得替代疗法敏感性的特征。

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.