Engineering 3D Osteosarcoma Models to Elucidate Biology and Inform Drug Discovery

骨肉瘤（OS）是一种侵袭性原发性骨癌，主要影响儿童和年轻人， 以高基因组复杂性为特征。目前的治疗依赖于化疗，但许多患者表现出 耐药性或发展为转移性疾病。目前用于OS研究的实验模型主要依赖于2D 单层培养或异种移植模型。然而，2D培养物培养通常不能保留肿瘤表型 和体内药物反应，而小鼠模型对于高通量药物筛选是昂贵且不切实际的。 最近，组织工程3D癌症模型已经成为新的癌症研究工具， 与2D培养物相比，它概括了体内肿瘤信号传导和药物反应。然而，大多数组织工程 迄今为止的癌症模型仅限于软组织。与软组织不同，骨的特征是高度- 矿化的细胞外基质（ECM）由70%的矿物质如羟基磷灰石（HA）晶体组成。 然而，骨矿物质在驱动OS进展和药物反应中的作用在很大程度上仍然未知。 此外，以前的OS研究依赖于一组已经培养了几十年的细胞系， 可能不再反映体内的生物学和药物反应。 可扩展的生理相关3D OS模型，具有高维测序工具以阐明OS 基因组异质性和耐药性，以及筛选新的组合疗法，使用多种 患者来源的OS细胞系。我们的3D模型专门设计用于高通量筛选， 并利用Yang（PI）实验室发明的专利微载体（µRB）支架。这种多PI 应用程序将汇集生物材料设计和3D肿瘤模型（杨实验室/斯坦福大学）的专业知识， 在患者来源的异种移植（PDX）细胞系，基因组学和OS的临床前治疗方面的专业知识（Sweet- Cordero lab/UCSF）。我们假设3D培养中的OS信号传导和药物反应可以通过 调整矿物质线索3D明胶µRB支架的类型和大小，以更好地模拟体内表型， 使用3D OS模型靶向识别信号的组合疗法将导致更好的治疗结果 对于OS in vivo。为了验证这些假设，我们将实现以下目标。目标1：开发3D OS模型， 使用多个OS PDX细胞深度表征OS信号传导和异质性的优化的小生境线索 线，并将结果与小鼠原位OS模型进行比较。目的2：利用3D OS模型来确定 参与介导OS中受体酪氨酸激酶表达的调节途径并鉴定先导药物 通过筛选一组靶向药物治疗的候选人。目的3：确定新的联合疗法， PDX OS细胞系，并使用3D OS模型阐明潜在的耐药机制。本研究将 将3D OS模型与PDX细胞系和高维测序专业知识相结合的先驱。成果 将显著推进对OS生物学和异质性的理解， 机制，并加速发现使用现有工具无法实现的联合疗法。

Osteosarcoma (OS) is an aggressive primary bone cancer that mainly affects children and young adults, and is characterized by high genomic complexity. Current treatment relies on chemotherapy, yet many patients exhibit resistance or develop metastatic disease. Current experimental models for OS research rely primarily on 2D monolayer culture or xenograft models. However, 2D cultures culture generally fail to retain tumor phenotypes and drug response in vivo, whereas mouse models are costly and impractical for high-throughput drug screening. Recently tissue engineered 3D cancer models have emerged as new cancer research tools, which better recapitulate in vivo tumor signaling and drug responses than 2D cultures. However, most tissue engineered cancer models to date are limited to soft tissues. Unlike soft tissues, bone is characterized by a highly- mineralized extracellular matrix (ECM) comprised of 70% minerals such as hydroxyapatite (HA) crystals. However, the role of bone mineral in driving OS progression and drug response remains largely unknown. Furthermore, previous OS studies rely on a narrow set of cell lines that have been in culture for decades, which may no longer reflect the biology and drug response in vivo The overall goal of this proposal is to integrate a scalable and physiologically relevant 3D OS model with high-dimensional sequencing tools to elucidate OS genomic heterogeneity and drug resistance, as well as screening novel combination therapies using multiple patient-derived OS cell lines. Our 3D models is specifically designed with high-throughput screening in mind, and leverages on a patented microribbon (µRB)-based scaffold invented by the Yang (PI) lab. This multi-PI application will bring together expertise in biomaterials design and 3D tumor models (Yang lab/Stanford) with expertise in patient-derived xenograft (PDX) cell lines, genomics and preclinical therapeutics of OS (Sweet- Cordero lab/UCSF). We hypothesize that OS signaling and drug responses in 3D culture can be modulated by tuning the type and size of mineral cues 3D gelatin µRB scaffolds to better mimic the in vivo phenotype, and combinational therapies that target identified signaling using 3D OS model will lead to better treatment outcomes for OS in vivo. To test these hypotheses, we will carry out the following aims. Aim 1: Develop 3D OS models with optimized niche cues for deep characterization of OS signaling and heterogeneity using multiple OS PDX cell lines and compare results to mouse orthotopic OS models. AIM 2: To harness 3D OS models to determine the regulatory pathways involved in mediating receptor tyrosine kinase expression in OS and identify lead drug candidates by screening a panel of targeted drug therapies. Aim 3: To identify novel combination therapies for PDX OS cell lines and elucidate potential drug resistance mechanisms using 3D OS models. This study will pioneer integrating 3D OS model with PDX cell lines and high-dimensional sequencing expertise. The outcomes would significantly advance the understanding of OS biology and heterogeneity, identifying drug resistance mechanisms, and accelerate discovery of combination therapies that cannot be achieved using existing tools.