3.8 Article

Fluorescence-Based Quantitative and Spatial Analysis of Tumour Spheroids: A Proposed Tool to Predict Patient-Specific Therapy Response

期刊

FRONTIERS IN DIGITAL HEALTH
卷 3, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fdgth.2021.668390

关键词

tumour spheroid; tumour micro-environment; spatial analysis; intra-tumoural heterogeneity; anti-cancer drug response; hypoxia; cell death; immune cell infiltration

资金

  1. Australian Government
  2. National Health and Medical Research Council [APP1084893]
  3. Meehan Project Grant [021174 2017002565]

向作者/读者索取更多资源

Tumour spheroids are valuable tools for pre-clinical assessment of anti-cancer treatments, capturing the intra-tumour microenvironment-driven heterogeneity. Quantitative and spatial assessment of spheroid images can provide insights into therapy response based on biological differences within the tumour cell subpopulations.
Tumour spheroids are widely used to pre-clinically assess anti-cancer treatments. They are an excellent compromise between the lack of microenvironment encountered in adherent cell culture conditions and the great complexity of in vivo animal models. Spheroids recapitulate intra-tumour microenvironment-driven heterogeneity, a pivotal aspect for therapy outcome that is, however, often overlooked. Likely due to their ease, most assays measure overall spheroid size and/or cell death as a readout. However, as different tumour cell subpopulations may show a different biology and therapy response, it is paramount to obtain information from these distinct regions within the spheroid. We describe here a methodology to quantitatively and spatially assess fluorescence-based microscopy spheroid images by semi-automated software-based analysis. This provides a fast assay that accounts for spatial biological differences that are driven by the tumour microenvironment. We outline the methodology using detection of hypoxia, cell death and PBMC infiltration as examples, and we propose this procedure as an exploratory approach to assist therapy response prediction for personalised medicine.

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