Novotny, Brenna

Abstract Purpose: Tumor-infiltrating lymphocytes are associated with a decreased risk of recurrence in human papillomavirus–associated oropharyngeal squamous cell carcinoma. The composition and spatial distribution of tumor-infiltrating lymphocytes and tumor-infiltrating immune cells are not well characterized. Experimental Design: Formalin-fixed, paraffin-embedded primary and lymph node (LN) tumor tissues from 10 progressors (cases) and 10 matched nonprogressors (controls) were interrogated by imaging mass cytometry. Immune, stromal, and tumor cells were quantified from selected regions of interest using machine learning. Nearest neighbors, cell–cell interactions, and niche analyses were performed. Results: In primary regions of interest, immune cell, lymphocyte, T cell, CD8+ T cell, and innate cell prevalence was significantly greater in controls. High prevalence of immune cells, lymphocytes, innate cells, and CD4+ T cells in primary tissues was significantly associated with increased time to event (TTE). Although primary and LN prevalence of T cells, CD4+ T cells, CD8+ T cells, macrophages, and tumor cells were significantly correlated, differences in LNs were neither significant nor associated with TTE. Average distances between T cells and the nearest B cells and between lymphocytes and the nearest tumor cells were decreased in control primary tissues. Interactions between B and T cells were less organized in primary tissues from cases. A niche predominantly comprising lymphocytes was associated with longer TTE. Conclusions: In human papillomavirus–associated oropharyngeal squamous cell carcinoma, immune cell subset prevalence in primary tumors differs with outcome and is associated with TTE. Interactions between B cells and both T-cell subsets are associated with longer TTE, underscoring the importance of active intratumoral immune responses in outcomes.

Abstract Motivation Multiplex immunofluorescence (MxIF) enables the quantification of multiple protein markers at a single-cell level while preserving spatial information, offering a powerful tool for studying tissue microenvironments. However, the flexibility in MxIF panel design poses challenges in standardizing cell phenotyping. Results We present SpaFlow, an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and postclustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining the cell-type identification process in large datasets. SpaFlow’s robust quality control steps, including signal summation and cell density filtering, mitigate artifacts that may impact clustering accuracy. We demonstrate the utility of SpaFlow in a case study involving 297 ovarian tumor cores, where SpaFlow successfully identified biologically meaningful cell populations, including tumor-infiltrating lymphocytes, efficiently and rapidly. Additionally, SpaFlow’s reproducibility is validated using serial tonsil sections, confirming its capability to consistently identify distinctive cell populations across matched ROIs. Availability and implementation SpaFlow is freely available with detailed documentation and examples at https://github.com/dimi-lab/SpaFlow.

Abstract Multiplexed Immunofluorescence (MxIF) enables detailed immune cell phenotyping, providing critical insights into cell behavior within the tumor immune microenvironment (TIME). However, signal integrity can be compromised due to the complex cyclic staining processes inherent to MxIF. Hematoxylin and Eosin (H&E) staining, on the other hand, offers complementary information through its depiction of cell morphology and texture patterns and is often visually cross-referenced with MxIF in clinical settings. In this study, we proposed a novel framework to align H&E and MxIF images for precise cross-modal cell feature validation. Using cell detection outputs from each modality as anchors, we formulated the multimodal image registration problem as point set alignment. Coherent Point Drift (CPD) is employed for initial alignment, followed by Graph Matching (GM) for refinement. Evaluations on ovarian cancer tissue microarrays (TMAs) demonstrate that our method achieves high alignment accuracy, enabling reliable validation of cell-level features across modalities for both restained and serial sections. Our results indicate that restained H&E enhances confidence in findings derived from MxIF. Additionally, we demonstrated the feasibility of generating high-quality virtual H&E images from MxIF data when restained H&E is unavailable, offering a viable alternative for integrated multimodal analysis.