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Assessing a hyperspectral image analysis system to study tumor and immune cell spatial organizations within lung tumor microenvironments

University of British Columbia

Lung cancer is the leading cause of cancer-associated death worldwide. To improve clinical management, biological discovery underlying disease pathogenesis and improved sensitivity of early detection imaging modalities are required. Presently, therapeutic targeting immune components of the tumor microenvironment led to immunotherapies, potent therapeutic agents revolutionizing clinical practice. Increasingly recognized, the native spatial organization of tumor microenvironment immune cells could potentially serve as a surrogate for predicting prognosis and stratifying tumor immunogenicity. However, conventional methods, including histopathology and genomic profiling, are limited in measuring such parameters. Image analysis systems are being tested for these applications, but further development and optimization is required before clinical adoption. Dr. Calum MacAulay and Dr. Martial Guillaud’s research team at the British Columbia Cancer Research Centre (BCCRC) developed a novel hyperspectral image analysis system capable of spatially profiling tumor microenvironments in situ. Herein, this thesis reports the workflow optimization of this system prototyped with the lung adenocarcinoma (AC) microenvironment. This study was conducted with 21 primary lung AC. Adjacent tumor sections were stained for nuclei with hematoxylin and markers of tumor-infiltrating lymphocytes (CD3, CD79a, CD8) or adaptive immune resistance (PD1, PDL1, CD8) by multiplex immunohistochemistry. After a lung pathologist identified areas of interest on tissue sections, five representative regions within each area were imaged. Image stacks of sixteen different illumination wavelengths (from 420 to 720 nm) were then processed by spectral unmixing, segmented, and immunohistochemical marker positivity thresholds applied. Processed two-dimensional images were quantified for cell types and neighboring cell type spatial correlations. Additionally, three lung AC within this cohort were selected to test workflow of image registration software to interpolate cell spatial distributions between sections. The system was able to determine immune cell-to-cell spatial correlations and distribution within and across sections. Furthermore, technical factors were identified that affected the workflow of this system, including staining, image acquisition, segmentation, specialized equipment, and sampling strategy. Here we describe a valuable platform to quantitatively and spatially profile tumor heterogeneity that could then be used to correlate with lung cancer prognosis and treatment outcomes, although further optimization of this method is required.

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2018-02-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/64522

Interdisciplinary Oncology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/