Project 2: Predicting Treatment Responses Using Single Cell RNA Sequencing and Bioengineered Patient-derived Organotypic Models of HNC

项目概要-项目2 我们目前无法准确预测头颈癌（HNC）患者的治疗结果 这对临床医生来说是一个重大挑战，毫无疑问， 晚期疾病的自由生存率。目前，临床上没有使用预测性生物标志物来确定 因此，迫切需要开发新的生物标志物和工具来改善临床决策 制作。可以提供多个正交终点的功能性生物标志物非常适合于报告一个或多个生物标志物。 复杂和动态的环境，如肿瘤微环境（TME）。因此，我们打算 直接在肿瘤样品和生物工程患者特异性模型中研究HNC生物标志物， 创建一套新颖的端点。我们将使用最先进的单细胞RNA测序（scCITE-seq）、蛋白质测序和DNA测序。 来自肿瘤组织微阵列（TMA）的表达特征和循环肿瘤细胞（CTC）的分析。我们 将利用这一多组学、患者特异性数据集来识别和验证治疗疗效的特征， 对患者结局进行分层。然后，我们将测试使用患者特异性生物工程模型的可行性， 临床试验研究中的患者护理。HNC TME的生物工程模型完全由细胞衍生的 来自相同患者肿瘤样品，来自用于CITEseq、TMA和CTC分析的相同患者群组。 这些微型患者特异性（由个体患者自身细胞构建）生物工程模型概括了 TME结构，包含被含有成纤维细胞的基质包围的HNC上皮球状体， 免疫细胞和两侧的血液和淋巴微血管。我们的具体目标是：1）评估能力 HNC患者特异性生物工程模型，以预测治疗效果，在那里我们将建立患者特异性 用于22名HNC患者的生物工程模型（代表HPV阳性和HPV阴性疾病和患者 用（化疗）放疗或原发性放化疗的原发性手术治疗），并用相同的方法治疗 患者接受的治疗。模型中的治疗成功率将与实际患者相关 结果包括无进展生存期。2)使用scCITE-seq和TMA鉴定HNC生物标志物，其中 我们将进行scCITE-seq，并将基因表达和细胞群与患者结果相关联， 研究现有的推定生物标志物并鉴定额外的新生物标志物。生物标志物将进一步 在TMA和CTC中进行了调查。3)使用生物工程模型告知临床中的剂量递减 初步研究，其中组织将从24名HPV+ HNC患者的手术中获得，并用于患者特异性 生物工程模型的建立将处理模型以确定患者肿瘤的放射敏感性， 对50或60戈伊治疗组之间的中等风险患者进行分层。主要终点将侧重于 模型与次要终点（包括局部控制）整合的可行性。圆满完成 这些目标将为HNC患者的分层和改善临床决策提供强有力的新工具 这有助于为未来的HNC患者提供最有效的治疗选择。

PROJECT SUMMARY - PROJECT 2 Our current inability to accurately predict treatment outcomes for head and neck cancer (HNC) patients represents a major challenge for clinicians and undoubtedly contributes to both the poor overall and progression free survival rates in advanced disease. Currently no predictive biomarkers are used clinically for definitive therapies, thus, there is a compelling need to develop new biomarkers and tools to improve clinical decision making. Functional biomarkers that can provide multiple orthogonal endpoints are well suited to reporting on a complex and dynamic environment such as the tumor microenvironment (TME). For this reason, we intend to investigate HNC biomarkers both directly in tumor samples and in a bioengineered patient-specific model to create a novel suite of endpoints. We will use state of the art single cell RNA sequencing (scCITE-seq), protein expression signatures from tumor tissue microarrays (TMA’s) and analysis of circulating tumor cells (CTC’s). We will utilize this multi-omic, patient-specific data set to identify and validate signatures of treatment efficacy and stratify patient outcomes. We will then test the feasibility of using patient specific bioengineered models to inform patient care in a clinical pilot study. The bioengineered model of the HNC TME is made entirely of cells derived from the same patient tumor sample, from the same patient cohort used for CITEseq, TMA and CTC analysis. These microscale patient-specific (built from the individual patients own cells) bioengineered models recapitulate the TME architecture, containing a HNC epithelial spheroid surrounded by a matrix containing fibroblasts and immune cells and flanked by blood and lymphatic microvessels. Our specific aims are: 1) Evaluate the ability of HNC patient-specific bioengineered models to predict treatment efficacy, where we will build patient-specific bioengineered models for 22 HNC patients (representing HPV-positive and HPV-negative disease and patients treated with primary surgery with (chemo)radiation or primary chemoradiation) and treat them with the same treatment the patient receives. Metrics of treatment success in the models will be correlated with actual patient outcomes including progression free survival. 2) Identify HNC biomarkers using scCITE-seq and TMA, where we will perform scCITE-seq and will correlate gene expression and cell populations with patient outcomes to investigate existing putative biomarkers and identify additional novel biomarkers. Biomarkers will be further investigated in a TMA and in CTC’s. 3) Use of bioengineered models to inform dose de-escalation in a clinical pilot study, where tissue will be acquired from surgery from 24 HPV+ HNC patients and used for patient-specific bioengineered model creation. Models will be treated to determine the radiosensitivity of a patient’s tumor and to stratify intermediate risk patients between 50 or 60 Gy treatment groups. Primary endpoints will focus on feasibility of model integration with secondary endpoints including local control. The successful completion of these aims will provide powerful new tools for the stratification of HNC patients and improved clinical decision making to help inform the most effective treatment selections for individual HNC patients in the future.