Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study.

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.

Phenotypic Characterization by Mass Cytometry of the Microenvironment in Ovarian Cancer and Impact of Tumor Dissociation Methods.

Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.

CD24-targeted fluorescence imaging in patient-derived xenograft models of high-grade serous ovarian carcinoma.

BACKGROUND: The survival rate of patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains disappointing. Clinically translatable orthotopic cell line xenograft models and patient-derived xenografts (PDXs) may aid the implementation of more personalised treatment approaches. Although orthotopic PDX reflecting heterogeneous molecular subtypes are considered the most relevant preclinical models, their use in therapeutic development is limited by lack of appropriate imaging modalities. METHODS: We developed novel orthotopic xenograft and PDX models for HGSOC, and applied a near-infrared fluorescently labelled monoclonal antibody targeting the cell surface antigen CD24 for non-invasive molecular imaging of epithelial ovarian cancer. CD24-Alexa Fluor 680 fluorescence imaging was compared to bioluminescence imaging in three orthotopic cell line xenograft models of ovarian cancer (OV-90luc+, Skov-3luc+ and Caov-3luc+, n = 3 per model). The application of fluorescence imaging to assess treatment efficacy was performed in carboplatin-paclitaxel treated orthotopic OV-90 xenografts (n = 10), before the probe was evaluated to detect disease progression in heterogenous PDX models (n = 7). FINDINGS: Application of the near-infrared probe, CD24-AF680, enabled both spatio-temporal visualisation of tumour development, and longitudinal therapy monitoring of orthotopic xenografts. Notably, CD24-AF680 facilitated imaging of multiple PDX models representing different histological subtypes of the disease. INTERPRETATION: The combined implementation of CD24-AF680 and orthotopic PDX models creates a state-of-the-art preclinical platform which will impact the identification and validation of new targeted therapies, fluorescence image-guided surgery, and ultimately the outcome for HGSOC patients. FUNDING: This study was supported by the H2020 program MSCA-ITN [675743], Helse Vest RHF, and Helse Bergen HF [911809, 911852, 912171, 240222, HV1269], as well as by The Norwegian Cancer Society [182735], and The Research Council of Norway through its Centers of excellence funding scheme [223250, 262652].