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Biologically-relevant 3D tumor arrays: Imaging-based methods for quantification of reproducible growth and analysis of treatment response

机译：生物相关的3D肿瘤阵列：基于成像的可再现性生长定量和治疗反应分析方法

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Three-dimensional in vitro tumor models have emerged as powerful research tools in cancer biology, though the vast potential of these systems as high-throughput, biologically relevant reporters of treatment response has yet to be adequately explored. Here, building on previous studies, we demonstrate the utility of using 3D models for ovarian and pancreatic cancers in conjunction with quantitative image processing to reveal aspects of growth behavior and treatment response that would not be evident without either modeling or quantitative analysis component. In this report we specifically focus on recent improvements in the imaging component of this integrative research platform and emphasize analysis to establish reproducible growth properties in 3D tumor arrays, a key consideration in establishing the utility of this platform as a reliable reporter of therapeutic response. Building on previous studies using automated segmentation of low magnification image fields containing large numbers of nodules to study size dependent treatment effects, we introduce an improvement to this method using multiresolution decomposition to remove gradient background from transmitted light images for more reliable feature identification. This approach facilitates the development of a new treatment response metric, disruption fraction (D/rac), which quantifies dose dependent distribution shifts from nodular fragmentation induced by cytotoxic therapies. Using this approach we show that PDT treatment is associated with significant dose-dependent increases in Dfrac, while this is not observed with carboplatin treatment. The ability to quantify this response to therapy could play a key role in design of combination regimens involving these two modalities.
机译：三维体外肿瘤模型已成为癌症生物学中强大的研究工具，尽管这些系统作为治疗研究反应的高通量，生物学相关报道者的巨大潜力尚未得到充分探索。在此，在先前研究的基础上，我们证明了将3D模型用于卵巢癌和胰腺癌以及定量图像处理的实用性，以揭示生长行为和治疗反应的各个方面，如果没有建模或定量分析组件，这些方面将是不明显的。在本报告中，我们特别关注此集成研究平台的成像组件的最新改进，并着重分析以在3D肿瘤阵列中建立可再现的生长特性，这是建立该平台作为治疗反应的可靠报告者的关键考虑因素。在以前的研究中，使用包含大量结节的低倍率图像场的自动分割来研究尺寸依赖性治疗效果，我们对这种方法进行了改进，采用多分辨率分解从透射光图像中去除梯度背景，从而实现了更可靠的特征识别。这种方法促进了新的治疗反应指标，即中断分数（D / rac）的发展，该指标量化了由细胞毒性疗法诱导的结节性碎裂的剂量依赖性分布变化。使用这种方法，我们显示PDT治疗与Dfrac的剂量依赖性显着增加相关，而卡铂治疗则未观察到。量化对治疗反应的能力可能在涉及这两种方式的联合方案设计中起关键作用。

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