Storey, Carl

Abstract Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 um section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and fail to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire intact NSCLC tissue samples. Methods: Human NSCLC tissue, stored frozen in OCT, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Whole, intact tissue samples, roughly 1-5mm3 in volume, were imaged at 2 microns/pixel resolution with an open-top light-sheet microscope (3Di, Alpenglow Biosciences). Smaller regions of interest (ROIs) with key pathologic features were reimaged at higher resolution, 0.17 microns/pixel, to reveal subnuclear features and for cell typing. Visualization was performed using Aivia software. Results: NSCLC tissue samples were successfully imaged in 3D. Low resolution images (2 microns/pixel) were obtained within 4-31 minutes, depending on the tissue volume. The 3D distribution of cancer cells, immune cells, vessels, and fibrosis varied substantially throughout the volume of the tissue. Recognizable histologic features, including nests of tumor cells surrounded by vasculature and immune cells, were readily visualized. Squamous and adenocarcinoma with its subtypes (solid, acinar, lepidic, and micropapillary) morphologies were recognizable in 2D optical sections of the 3D datasets. Imaging quality degraded in tissue deeper than 1 mm due to light scattering. Conclusion: We assessed intact NSCLC tissue samples measuring up to 5 mm3 using our custom light-sheet microscope and tissue clearing techniques. This novel method enables us to visualize key features of NSCLC such as the tumor interfaces, tertiary lymphoid structures, vessels and fibrosis in the entire tissue sample, preventing under sampling error, and potentially enabling new biologic insights. Next steps include segmentation and quantification of key tissue structures such as tumor volume, immune cell distribution, and fibrosis/immune cell exclusion. This proof-of-concept study provides motivation for further investigation into the significance of 3D tissue features in NSCLC tissue samples. Citation Format: Alexandra Alvarsson, Carl Storey, Brandy Olin Pope, Caleb Stoltzfus, Robert Vierkant, Jessica Tufariello, Aaron Bungum, Julia Naso, Cheuk Ki Chan, Eric Edell, Christopher Hartley, Janani Reisenauer, Nicholas Reder. 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6624.

2616 Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 µm section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and failure to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire, intact lung tissue samples. Herein, we present analysis methods for comprehensive 3D assessment of intact NSCLC tissue. Methods: Human NSCLC tissue, stored frozen in optimal cutting temperature (OCT) compound, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Twenty tissue samples measuring 1-5 mm3 in volume were imaged at 2 µm/pixel resolution with a hybrid open-top light-sheet microscope. Smaller regions of interest (ROIs) with interesting pathologic features were re-imaged at higher resolution, 0.17 µm/pixel. Visualization and analysis was performed using Aivia software, custom spatial analysis software and python scripts. Results: All NSCLC tissue samples were successfully imaged in 3D at both low and high resolution. Computational approaches, such as machine learning, were used to segment and classify tumor cells and lymphocytes within the high resolution ROIs. In one example we quantified a 0.0045 mm3 ROI containing 1x109 voxels. We segmented 1,474 unique objects, and classified 683 (46%) of the objects as lymphocytes and 309 (20%) of the cells as tumor cells. We found that on average the lymphocytes were within 12 µm of the nearest tumor cell. Additionally the tissue was broken into separate lymphocyte dense (58% of volume) and tumor dense regions (42% of volume). Further analysis is in progress to quantify more features, such as tertiary lymphoid structures, stromal cells, and fibrosis over the full 20 sample dataset. Conclusions: We successfully implemented 3D machine learning analysis pipelines using images from intact NSCLC tissue samples measuring up to 5 mm3. The imaging was performed using our custom hybrid open-top light-sheet microscope. This novel method enables us to identify and quantify key histological features such as tumor cells and lymphocytes within the tumor microenvironment. In addition, machine learning driven algorithms clustered cells into distinct tissue regions in 3D which enables a more precise assessment of disease state and heterogeneity. Further investigation is ongoing to link these results to patient outcomes.