Abstract 4172: Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling
Authors: Hernandez Sharia , Arrechedera Claudio J. , Rocha Pedro , Kostousov Larisa , Barnes Sean , Khaja Khan , Solis-Soto Luisa M. 
Published: March 2024

Background: Tissue-based high-plex platforms evaluate numerous biomarkers in limited tissue sections. Certain tissue features and assay technical limitations may jeopardize the accuracy of this analysis. In this study, we aim to determine tissue specific features that influence image-based region of interest (ROI) selection and segmentation for spatial profiling of carcinomas, and to develop pathology approaches to overcome them.

Methodology: A total of 317 samples from different carcinoma types processed with the GeoMx Digital spatial profiling protein assay protocol were assessed to identify deviations from original strategies for ROI placement and segmentation (Table 1). ROI were selected by a pathologist. The initial strategy for all cases was to place rectangles ROIs of 660*785 µm in tumor area and to segment based on morphology biomarkers as indicated in Table 1. Strategies to overcome technical and tissue specific features were recorded and quantified.

Results: A total of 2068 ROIs and 4422 AOIs were evaluated. In all the cases, tissue displayed specific features that limited planned ROI placement and segmentation. Table 1 describes these features by tissue type and profiling strategies. A total of 970 ROIs were solved using different annotation type or changes in segmentation strategy, 584 bypassed the limitations by changing the processing protocol; and 322 were not solved due to non-compliance with assay requirements.

Conclusions: We identified tissue features that limit the planned ROI selection and segmentation in spatial high-plex profiling, mainly due to tissue processing, distinct histology, type of sample, and presence of adjacent non-neoplastic tissue. A deep understanding of tumor architecture, biology and platform related technical limitations is needed to select areas of analysis for spatial profiling.

Table 1. ROI and segmentation strategy methodology and tissue specific features with pathology appro Histological type (Profiling segments) Anal Carcinoma (Tu/TME) Lung NSCLC (Tu/TME) Lung NSCLC TMA (Tu/Tcells/Mϕ) Lung NSCLC TMA (Tu/TME) CRC MT Tu/Immune Breast Ca Tu/Immune/else Pathology Approach (ROIs) Methodology Samples 48 33 80 80 6 70 Biopsy type Incisional (Core/other), Whole tissue Incisional (Core/other)Whole tissue TMA TMA Whole tissue Core Morphology markers/mIF Channels Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 CD68: Cy5/666 Syto13: FITC/525PanCK: Cy3/568CD20: Texas red/615CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: CD45: Texas red/615 Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 Strategy for segmentation Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+)TME (PanCK-) Tumor (PanCK+) T cell (CD3+) Mϕ (CD68+) Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+) Immune (CD45+) Tumor (PanCK+) Immune (CD45+) Else (PanCK-CD45-) Total ROIs/AOIs 224/435 144/252 80/218 82/162 944/1573 594/1782 Tissue specific features (ROI number/percentage) Folds (ROIs) 10 1 0 0 9 5 Draw Polygons (25) Adjacent* Non-Tumoral tissue 22 7 9 7 388 40 Draw Polygons (473) Tissue Derived Autofluorescence 3 105 0 1 0 45 Draw Polygons (13). New segment to exclude autofluorescence (141) Sample Size 26 19 1 2 5 138 Polygons (191) Necrosis 13 0 1 1 217 6 Polygons (238) Biological absence of morphology marker 10 0 0 0 0 5 Polygons non segmented for Tu and TME (15) Solid tumors with scant TME 0 21 0 0 0 9 Polygons non segmented for Tu and TME (30) Eosin autofluorescence 0 0 0 0 0 584 Stained new section with Validated PanCK in Cy5/666 channel (584) Failure to reach the minimum number of cell requirements 13 36 22 2 249 43 Excluded segments (322) New scan to select more areas (43)

Citation Format: Sharia Hernandez, Claudio J. Arrechedera, Pedro Rocha, Larisa Kostousov, Sean Barnes, Khan Khaja, Luisa M. Solis-Soto. Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4172.