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

用于前列腺癌转化药物开发的新型微型细胞培养平台的评估

• 批准号: 10588604
• 负责人: David Kosoff
• 金额: --
• 依托单位: WM S. MIDDLETON MEMORIAL VETERANS HOSP
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588604
• 关键词: Address; Animal Model; Animals; Area; Biological; Biological Models; Biology; Cancer Patient; Caring; Cell Count; Cell Culture Techniques; Cell Line; Cells; Clinical; Clinical Trials; Coculture Techniques; Data; Data Set; Development; Diagnosis; Disease; Effectiveness; Evaluation; Exhibits; Fibroblasts; Gene Expression; Gene Expression Profile; General Population; Health; Heterogeneity; Immunosuppression; In Vitro; Information Systems; Investigation; Investments; Laboratory Research; Macrophage; Malignant Neoplasms; Malignant neoplasm of prostate; Metastatic Prostate Cancer; Microfluidics; Modeling; Molecular; Occupations; Organoids; Patients; Phenotype; Physiology; Population; Pre-Clinical Model; Prediction of Response to Therapy; Prostate Cancer therapy; Research; Role; Signal Transduction; System; Testing; Therapeutic; Time; Treatment Efficacy; Tumor Biology; Tumor Cell Line; Veterans; anticancer research; cancer diagnosis; cancer therapy; cell type; clinical efficacy; clinical predictors; clinical translation; clinically relevant; cytotoxic; docetaxel; drug development; effective therapy; efficacy evaluation; improved; improved outcome; in vitro Model; insight; men; monocyte; mouse model; neoplastic cell; novel; physiologic model; pre-clinical; pre-clinical research; pre-clinical therapy; prostate cancer cell; response; therapy development; tool; transcriptome sequencing; treatment comparison; treatment effect; treatment response; tumor; tumor microenvironment

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.

前列腺癌是一种对退伍军人健康有很大影响的疾病。它不仅是 退伍军人中诊断出的最常见的癌症，每年诊断出近15，000例新病例， 但退伍军人被诊断出患有前列腺癌的几率几乎是普通人的两倍， 人口1.不幸的是，即使接受治疗，许多退伍军人最终也会死于 他们的疾病。因此，迫切需要新的有效治疗方法来改善结果 为患有前列腺癌的退伍军人服务。虽然新的前列腺癌研究的发展是 目前，这种疗法的发展受到当前临床前研究的严重限制。 研究模型，在确定有效的治疗方法方面， 临床水平。因此，我们开发了一种新的开放式微流体细胞培养平台， 能够在体外使用原代患者来源的细胞进行多培养肿瘤模型。中央 该提议中的假设是，在Stacks中的原代细胞衍生的多培养TME模型将 更紧密地模拟患者肿瘤生物学，并且可以更好地预测临床治疗效果， 与传统的临床前模型相比，本提案的主要目标是 通过三个具体目标来验证这一假设：目标1：确定该基因是否 前列腺肿瘤细胞在多培养Stacks模型中的表达谱更接近 与传统体外模型中的肿瘤细胞相比， 肿瘤细胞（细胞系和患者源性类器官）将在传统的体外平台中培养 在单一培养物中以及在与原代巨噬细胞/癌症相关成纤维细胞共培养物中。的 同样的单培养和共培养模型也将在Stacks中建立，沿着添加 具有所有3个细胞群体的三培养模型。然后将对肿瘤细胞进行RNA-seq 从每个模型。将转录谱与患者数据集进行比较，以确定 模型与患者肿瘤最密切相关。目标2：确定是否存在多元文化 Stacks中的肿瘤模型可以更准确地预测患者的治疗效果 与标准体外模型相比，使用目标1中的相同模型， 模型将用已知对前列腺癌患者有效的3种疗法进行治疗， 3种已知对前列腺癌患者无效的疗法。的细胞毒性作用 将在每个模型中评估治疗，并与临床试验数据进行比较，以确定 该系统最准确地预测了临床水平的治疗效果。目标3：评价 Stacks中的患者源性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将在堆栈中建立共培养模型 使用肿瘤细胞系和原发性单核细胞衍生的巨噬细胞从退伍军人前列腺 即将开始多西他赛治疗的癌症。然后将使用多西他赛治疗Stacks模型 并评价治疗的细胞毒性作用。来自每个患者的Stacks的数据 然后将模型与相应患者的多西他赛反应进行比较，以确定 Stacks共培养模型能否预测患者的治疗反应。每个数据 将分析的目的，以确定是否多文化的模型更具有生物学和临床 相关，如果Stacks平台是比传统体外平台更有效的工具， 转化前列腺癌研究。