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

项目摘要--项目2 我们目前无法准确预测头颈癌(HNC)患者的治疗结果 对临床医生来说是一项重大挑战，无疑是导致整体和进展不佳的原因 晚期疾病的自由存活率。目前尚无预测性生物标记物用于临床确诊 因此，迫切需要开发新的生物标记物和工具来改善临床决策 制作。可提供多个正交端点的功能生物标记物非常适合于报告 复杂而动态的环境，如肿瘤微环境(TME)。出于这个原因，我们打算 直接在肿瘤样本中和在特定于患者的生物工程模型中研究HNC生物标记物 创建一套新颖的终端。我们将使用最先进的单细胞RNA测序(scCITE-seq)，蛋白质 来自肿瘤组织微阵列(TMA‘s)和循环肿瘤细胞(CTC’s)的表达特征。我们 将利用这一多组、患者特定的数据集来识别和验证治疗效果和 对患者结果进行分层。然后，我们将测试使用患者特定的生物工程模型来告知 临床先导性研究中的病人护理。HNC TME的生物工程模型完全由来源于 来自同一患者肿瘤样本，来自用于CITEseq、TMA和CTC分析的同一患者队列。 这些针对患者的微型生物工程模型(由患者自身的细胞构建而成)概括了 TME结构，包含一个HNC上皮球，周围环绕着一个含有成纤维细胞和 免疫细胞，两侧是血液和淋巴微血管。我们的具体目标是：1)评估 HNC特定于患者的生物工程模型用于预测治疗效果，我们将在其中构建特定于患者的 22例HNC患者的生物工程模型(代表HPV阳性和HPV阴性的疾病和患者 用一次手术加(化疗)放疗或一次化疗放射治疗)，并用同样的方法治疗 病人接受的治疗。模型中治疗成功的衡量标准将与实际患者相关 结果包括无进展生存。2)使用scCITE-seq和TMA识别HNC生物标记物，其中 我们将进行scCITE-seq，并将基因表达和细胞群体与患者预后关联起来 调查现有的假定生物标记物，并确定其他新的生物标记物。生物标志物将进一步 在TMA和CTC中进行研究。3)在临床中使用生物工程模型来告知剂量降低 先导性研究，其中组织将从24名HPV HNC患者的手术中获得，并用于特定患者 生物工程模型创建。将对模型进行治疗，以确定患者肿瘤的放射敏感性 将中等风险患者分成50或60Gy射线治疗组。主要终端将重点放在 模型与次要终端集成的可行性，包括本地控制。圆满完成 这些目标将为HNC患者的分层和改进临床决策提供强大的新工具 以帮助为未来HNC患者的个人治疗选择提供最有效的信息。

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.