Radiogenomic predictors of treatment response in head and neck squamous cell carcinoma

尽管最近在头颈部鳞状细胞癌（HNSCC）的治疗方面取得了进展， 由于治疗后疾病进展率高，总生存期（OS）仍然很差。缺乏 可靠的预后生物标志物限制了预测非人乳头瘤病毒疾病进展的能力 (HPV)介导的HNSCC。在治疗后疾病进展的情况下进行挽救手术的候选人中， 在初次放化疗中，5年OS估计仅为16%。放射组学，包括分析大的 许多定量肿瘤成像特征，已被用于开发基于图像的预后 模型Delta放射组学结合了治疗前和治疗后图像之间的差异， 捕获治疗反应的定量测量。解决对预后生物标志物的迫切需求 对于HNSCC患者，我们的目标是开发和验证放射基因组学模型，以预测2年的总体 使用肿瘤衍生的基因组生物标志物和衍生的δ放射组学特征 对比增强计算机断层扫描的结果先前的研究高度发达 使用delta放射组学的癌症治疗反应的准确模型，但不是在局部区域性的患者中 晚期HNSCC很少有研究评估可靠和一致的基因组和放射组预后 HNSCC中的表型。我们假设，一个多组学模型整合放射基因组学特征， 准确预测2年总体和无进展生存期。我们的目标是：1）开发一个完整的注释， 以患者为中心的临床结局和放射学数据集，以及患者的相关肿瘤样本 局部晚期HPV阴性HNSCC接受确定性放化疗治疗，2）发生 放射基因组学模型，使用肿瘤源性基因组生物标志物和Δ预测2年OS和PFS 来自治疗前和治疗后对比度增强计算机断层扫描的放射组学特征。我们将 利用定量组学核心提取放射组学特征并探索放射基因组学表型 与2年OS和PFS相关。我们将使用生物库和生物标志物验证核心来生成 HNSCC患者来源的组织微阵列，以鉴定突变并量化与以下相关的基因表达： 表皮生长因子受体、MAPK相关蛋白激酶2（MK2）和缺氧途径。与 病人和社区参与的核心，我们将确保病人的优先事项是一致的， 在设计中表现出来。长期目标是支持旨在早期识别患者的未来试验 疾病进展的高风险人群，他们可能会从精确治疗方法中受益。

Despite recent advances in treatment of head and neck squamous cell carcinoma (HNSCC), five-year overall survival (OS) has remained poor due to high rates of disease progression after treatment. A lack of reliable prognostic biomarkers limits ability to predict disease progression in non-human papillomavirus (HPV) mediated HNSCC. Among candidates for salvage surgery in the setting of disease progression after primary chemoradiotherapy, 5-year OS is estimated at just 16%. Radiomics, involving analysis of large numbers of quantitative tumor imaging features, has been used to develop image-based prognostic models. Delta radiomics incorporates differences between pre- and post-treatment images, thereby capturing quantitative measures of treatment response. To address critical need for prognostic biomarkers for patients with HNSCC, we aim to develop and validate radiogenomic models to predict 2-year overall and progression-free survival using tumor-derived genomic biomarkers and delta radiomic features derived from pre- and post-treatment contrast-enhanced computed tomography. Prior studies developed highly accurate models of cancer treatment response using delta radiomics, but not in patients with locoregionally advanced HNSCC. Few studies have evaluated reliable and concordant genomic and radiomic prognostic phenotypes in HNSCC. We hypothesize that a multi-‘omic model integrating radiogenomic features will accurately predict 2-year overall and progression-free survival. We aim to 1) develop a fully annotated, patient-centered, clinical outcome and radiographic dataset with associated tumor samples for patients with locoregionally advanced HPV-negative HNSCC treated with definitive chemoradiotherapy and 2) develop radiogenomic models to predict 2-year OS and PFS using tumor-derived genomic biomarkers and delta radiomic features derived from pre- and post-treatment contrast-enhanced computed tomography. We will engage the Quantitative ‘Omics Core to extract radiomic features and explore radiogenomic phenotypes associated with 2-year OS and PFS. We will use the Biobanking and Biomarker Validation core to generate a HNSCC patient-derived tissue microarray to identify mutations and quantify gene expression relevant to epidermal growth factor receptor, MAPK-associated protein kinase 2 (MK2) and hypoxia pathways. With the Patient and Community Engagement core, we will ensure that patient priorities are consistently represented in the design. Long-term goal is to support future trials aimed at early identification of patients at high-risk of disease progression who are likely to benefit from precision treatment approaches.