Evaluation of novel microscale cell culture platform for translational drug development in prostate cancer

前列腺癌是一种对退伍军人健康影响非常大的疾病。这不仅是因为 退伍军人被诊断出最常见的癌症，每年有近15,000例新诊断病例， 但退伍军人被诊断出患有前列腺癌的比率几乎是普通男性的两倍。 不幸的是，即使接受治疗，这些退伍军人中的许多人最终也会死于 他们的病。因此迫切需要新的有效的治疗方法来改善结果。 对于患有前列腺癌的退伍军人来说。尽管新前列腺癌研究的进展是 目前，这种疗法的发展受到当前临床前阶段的严重限制。 研究模型，这些模型在确定对以下疾病有效的治疗方法方面做得非常糟糕 临床层面。因此，我们开发了一种新型的开放式微流控细胞培养平台 使用患者来源的原代细胞在体外实现多种肿瘤模型的培养。中环 这一建议中的假设是，原代细胞衍生的多培养TME模型将 更接近于模拟患者的肿瘤生物学，并可以更好地预测临床疗效 前列腺癌比传统的临床前模型要好。这项提议的主要目标将是 通过三个特定的目标来检验这一假设：目标1：确定基因是否 前列腺肿瘤细胞在多培养堆叠模型中的表达更接近 与传统体外模型中的肿瘤细胞相比，与患者的表达谱相关联。 肿瘤细胞(细胞系和患者衍生的有机物)将在传统的体外培养平台上培养。 在单一培养和与原代巨噬细胞/癌症相关成纤维细胞共培养中。这个 同样的单一文化和共文化模式也将在堆叠中建立，同时增加一个 三种细胞群体的三代培养模型。然后对肿瘤细胞进行rna-seq。 从每一款车型。转录档案将与患者数据集进行比较，以确定哪些 模型与患者肿瘤关系最为密切。目标2：确定多元文化是否 堆叠的肿瘤模型可以更准确地预测患者的治疗效果 前列腺癌的体外模型比标准模型要好。在目标1中使用相同的模型，每个 MODEL将用3种已知对前列腺癌患者有效的疗法进行治疗 3种已知对前列腺癌患者无效的治疗方法。黄曲霉毒素的细胞毒作用 治疗方法将在每种模型中进行评估，并与临床试验数据进行比较，以确定哪种治疗方法 系统在临床层面上最准确地预测治疗效果。目标3：评估 患者衍生的TME模型是否可以预测治疗反应 多西他赛治疗退伍军人前列腺癌。合作文化模式将层层建立 使用前列腺癌退伍军人的肿瘤细胞系和原代单核细胞来源的巨噬细胞 癌症即将启动多西紫杉醇治疗。STACKS模型将接受多西紫杉醇治疗 并对治疗的细胞毒作用进行评估。来自每个患者派生的堆栈的数据 然后将模型与相应患者的多西紫杉醇反应进行比较，以确定 如果堆栈共培养模型可以预测患者的治疗反应。每个数据库中的数据 的目标进行分析，以确定多元培养模式是否更具生物学和临床意义 相关，如果STACKS平台是比传统体外平台更有效的工具 转化型前列腺癌研究。

Prostate cancer is a disease with a remarkably high impact on Veteran health. Not only is it the most common cancer diagnosed in Veterans, with nearly 15,000 new cases diagnosed each year, but Veteran men get diagnosed with prostate cancer at nearly twice the rate of the general population 1. Unfortunately, even with treatment, many of these Veterans will ultimately die from their disease. New, effective treatments are therefore desperately needed to improve outcomes for Veterans with prostate cancer. Although the development of new prostate cancer research is currently under way, the development of such therapies is heavily limited by current preclinical research models, which do a notoriously poor job of identifying therapies that will be effective at the clinical level. We have therefore developed a novel open microfluidic cell culture platform that enables multi-culture tumor models in vitro using primary, patient-derived cells. The central hypothesis in this proposal is that primary cell derived multi-culture TME models in Stacks will more closely model patient tumor biology and can better predict clinical therapeutic efficacy in prostate cancer than traditional preclinical models. The primary objective of this proposal will be to test this hypothesis through three Specific Aims: Aim 1: To determine whether the gene expression profiles of prostate tumor cells in multi-culture Stacks models more closely correlate with patient expression profiles than tumor cells in traditional in vitro models. Tumor cells (cell line and patient-derived organoids) will be cultured in traditional in vitro platforms in mono-culture and in co-culture with primary macrophages/cancer-associated fibroblasts. The same mono- and co-culture models will also be established in Stacks along with the addition of a tri-culture model with all 3 cell populations. RNA-seq will then be performed on the tumor cells from each model. Transcription profiles will be compared to patient datasets to determine which model most closely correlates with patient tumors. Aim 2: To establish whether multi-culture tumor models in Stacks can more accurately predict the efficacy of therapies in patients with prostate cancer than standard in vitro models. Using the same models in Aim 1, each model will be treated with 3 therapies known to be effective in patients with prostate cancer and 3 therapies known to be ineffective in patients with prostate cancer. The cytotoxic effect of the therapies will be evaluated in each model and compared to clinical trial data to determine which system most accurately predicts therapeutic efficacy at the clinical level. Aim 3: To evaluate whether patient-derived TME models in Stacks can predict therapeutic response to docetaxel in Veterans with prostate cancer. Co-culture models will be established in Stacks using tumor cell lines and primary monocyte-derived macrophages from Veterans with prostate cancer about to initiate docetaxel treatment. Stacks models will then be treated with docetaxel and evaluated for the cytotoxic effect of the treatment. The data from each patient-derived Stacks model will then be compared to the docetaxel response in the corresponding patient to determine if the Stacks co-culture models can predict therapeutic response in patients. The data from each of the Aims will be analyzed to determine if multi-culture models are more biologically and clinically relevant and if the Stacks platform is a more effective tool than traditional in vitro platforms for translational prostate cancer research.