Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools.
Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools.
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DOI：
10.4172/2155-9929.1000356
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发表时间：
2017-09-01
期刊：
影响因子：
--
通讯作者：
Goldman, Aaron
中科院分区：
文献类型：
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作者：
Dhandapani, Muthu;Goldman, Aaron
背景与目的：预测抗癌治疗的疗效是临床药物开发和治疗选择的关键。为了实现这一目标，科学家们需要能够可靠地筛选具有强大临床相关性的抗癌药物的临床前模型。然而，开发能够准确捕捉肿瘤生态系统多样性的模型，从而可靠地预测肿瘤对治疗的反应或抵抗，面临着越来越大的挑战。事实上，肿瘤是由恶性细胞、正常和异常基质、免疫细胞和含有趋化因子、细胞因子和生长因子的动态微环境组成的异质性景观。在这篇小型综述中，我们重点介绍了新兴的抗癌治疗临床前模型，这些模型试图解决肿瘤异质性带来的挑战，突出了反应和耐药的生物标志物。最近的发现：从二维和三维体外模型开始，我们讨论了类器官共培养如何加速了抗癌药物筛选的努力，并通过高通量平台提高了我们对各种“临床”疗效生物标志物的作用机制的基本理解。然后，提到存在的局限性，我们将重点放在体内和人体外植体技术和模型上，这些技术和模型使用天然微环境作为支架来构建固有的肿瘤异质性。重要的是，我们将讨论如何利用这些模型来理解癌症免疫疗法，这是一种新兴的治疗策略，旨在重新校准人体自身的免疫系统来对抗癌症。结论：在过去的几十年里，已经出现了许多模型系统来解决癌症药物开发的爆炸式市场。虽然目前所有的模型都提供了关于肿瘤生物学的重要信息，但每个模型都有其局限性。利用包含细胞异质性的临床前模型开始解决与预测新型抗癌药物临床疗效相关的一些潜在挑战。
BACKGROUND AND PURPOSE: Predicting the efficacy of anticancer therapy is the holy grail of drug development and treatment selection in the clinic. To achieve this goal, scientists require pre-clinical models that can reliably screen anticancer agents with robust clinical correlation. However, there is increasing challenge to develop models that can accurately capture the diversity of the tumor ecosystem, and therefore reliably predict how tumors respond or resistant to treatment. Indeed, tumors are made up of a heterogeneous landscape comprising malignant cells, normal and abnormal stroma, immune cells, and dynamic microenvironment containing chemokines, cytokines and growth factors. In this mini-review we present a focused, brief perspective on emerging preclinical models for anticancer therapy that attempt to address the challenge posed by tumor heterogeneity, highlighting biomarkers of response and resistance.RECENT FINDINGS: Starting from 2-dimensional and 3-dimensional in-vitro models, we discuss how organoid co-cultures have led to accelerated efforts in anti-cancer drug screening, and advanced our fundamental understanding for mechanisms of action using high-throughput platforms that interrogate various biomarkers of 'clinical' efficacy. Then, mentioning the limitations that exist, we focus on in-vivo and human explant technologies and models, which build-in intrinsic tumor heterogeneity using the native microenvironment as a scaffold. Importantly, we will address how these models can be harnessed to understand cancer immunotherapy, an emerging therapeutic strategy that seeks to recalibrate the body's own immune system to fight cancer.CONCLUSION: Over the past several decades, numerous model systems have emerged to address the exploding market of drug development for cancer. While all of the present models have contributed critical information about tumor biology, each one carries limitations. Harnessing pre-clinical models that incorporate cell heterogeneity is beginning to address some of the underlying challenges associated with predicting clinical efficacy of novel anticancer agents.