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

尽管头颈部鳞状细胞癌(HNSCC)的治疗最近取得了进展，但五年 由于治疗后疾病进展率很高，总体存活率(OS)仍然很低。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 人乳头瘤病毒(HPV)介导的HNSCC。在疾病进展的背景下进行抢救手术的候选人中 初级放化疗，5年OS估计只有16%。放射组学，涉及分析大型 许多定量的肿瘤成像特征已被用于开发基于图像的预后 模特们。Delta放射组学结合了治疗前后图像之间的差异，从而 获取治疗反应的量化措施。以满足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来预测总体2年 使用肿瘤衍生基因组生物标记物和Delta放射学特征的无进展生存期 来自治疗前和治疗后的对比增强计算机断层扫描。以前的研究发展得很快 使用Delta放射组学建立癌症治疗反应的准确模型，但不适用于局部区域的癌症患者 高级HNSCC。很少有研究评估可靠和一致的基因组和放射学预后。 HNSCC的表型。我们假设一个整合了放射基因组特征的多基因组模型将 准确预测两年总体无进展生存期。我们的目标是1)开发一种带完整注释的 以患者为中心，临床结果和放射学数据集与相关的肿瘤样本 明确的放化疗治疗局部晚期HPV阴性HNSCC和2)发展 使用肿瘤衍生基因组生物标记物和Delta预测2年OS和PFS的放射基因组模型 放射学特征来自治疗前和治疗后的对比增强计算机断层扫描。我们会 利用Quantity的OMICS核心提取放射组学特征并探索放射基因组表型 与2年操作系统和PFS相关联。我们将使用生物库和Biomarker验证核心来生成 一种HNSCC患者来源的组织微阵列，用于识别突变和量化相关基因表达 表皮生长因子受体、MAPK相关蛋白激酶2(MK2)与低氧途径。使用 患者和社区参与的核心，我们将确保患者的优先事项始终如一 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很可能受益于精确的治疗方法。

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.