Association between parity and pregnancy-associated tumor features in high-grade serous ovarian cancer.

PURPOSE: High-grade serous ovarian cancer (HGSC) is the most common ovarian cancer subtype. Parity is an important risk-reducing factor, but the underlying mechanism behind the protective effect is unclear. Our aim was to study if the expression of hormones and proteins involved in pregnancy were affected by the woman's parity status, and if they may be associated with tumor stage and survival. METHODS: We evaluated expression of progesterone receptor (PR), progesterone receptor membrane component 1 (PGRMC1), relaxin-2, and transforming growth factor beta 1 (TGFß1) in tumor tissue from 92 women with HGSC parous (n = 73) and nulliparous (n = 19). Key findings were then evaluated in an independent expansion cohort of 49 patients. Survival rates by hormone/protein expression were illustrated using the Kaplan-Meier method. The independent prognostic value was tested by Cox regression, using models adjusted for established poor-prognostic factors (age at diagnosis, FIGO stage, type of surgery, and macroscopic residual tumor after surgery). RESULTS: HGSC tumors from parous women were PR positive (≥ 1% PR expression in tumor cells) more often than tumors from nulliparous women (42% vs. 16%; p-value 0.04), and having more children was associated with developing PR positive tumors [i.e., ≥ 3 children versus nulliparity, adjusted for age at diagnosis and stage: OR 4.31 (95% CI 1.12-19.69)]. A similar result was seen in the expansion cohort. Parity status had no impact on expression of PGRMC1, relaxin-2 and TGFß1. No associations were seen with tumor stage or survival. CONCLUSION: Tumors from parous women with HGSC expressed PR more often than tumors from nulliparous women, indicating that pregnancies might possibly have a long-lasting impact on ovarian cancer development.

Enhanced plant-derived vesicles for nucleotide delivery for cancer therapy.

Small RNAs (microRNAs [miRNAs] or small interfering RNAs [siRNAs]) are effective tools for cancer therapy, but many of the existing carriers for their delivery are limited by low bioavailability, insufficient loading, impaired transport across biological barriers, and low delivery into the tumor microenvironment. Extracellular vesicle (EV)-based communication in mammalian and plant systems is important for many physiological and pathological processes, and EVs show promise as carriers for RNA interference molecules. However, some fundamental issues limit their use, such as insufficient cargo loading and low potential for scaling production. Plant-derived vesicles (PDVs) are membrane-coated vesicles released in the apoplastic fluid of plants that contain biomolecules that play a role in several biological mechanisms. Here, we developed an alternative approach to deliver miRNA for cancer therapy using PDVs. We isolated vesicles from watermelon and formulated a hybrid, exosomal, polymeric system in which PDVs were combined with a dendrimer bound to miRNA146 mimic. Third generation PAMAM was chosen due to its high branching structure and versatility for loading molecules of interest. We performed several in vivo experiments to demonstrate the therapeutic efficacy of our compound and explored in vitro biological mechanisms underlying the anti-tumor effects of miRNA146, which are mostly related to its anti-angiogenic activity.

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping.

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of highresolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This approach enhances imaging speed without compromising image quality and ensures robust tissue segmentation. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology. It represents a significant leap forward from traditional histopathological methods, offering profound implications for cancer diagnostics and treatment decision-making.

Angiogenesis-Enabled Human Ovarian Tumor Microenvironment-Chip Evaluates Pathophysiology of Platelets in Microcirculation.

The tumor microenvironment (TME) promotes angiogenesis for its growth through the recruitment of multiple cells and signaling mechanisms. For example, TME actively recruits and activates platelets from the microcirculation to facilitate metastasis, but platelets may simultaneously also support tumor angiogenesis. Here, to model this complex pathophysiology within the TME that involves a signaling triad of cancer cells, sprouting endothelial cells, and platelets, an angiogenesis-enabled tumor microenvironment chip (aTME-Chip) is presented. This platform recapitulates the convergence of physiology of angiogenesis and platelet function within the ovarian TME and describes the contribution of platelets in promoting angiogenesis within an ovarian TME. By including three distinct human ovarian cancer cell-types, the aTME-Chip quantitatively reveals the following outcomes-first, introduction of platelets significantly increases angiogenesis; second, the temporal dynamics of angiogenic signaling is dependent on cancer cell type; and finally, tumor-educated platelets either activated exogenously by cancer cells or derived clinically from a cancer patient accelerate tumor angiogenesis. Further, analysis of effluents available from aTME-Chip validate functional outcomes by revealing changes in cytokine expression and several angiogenic and metastatic signaling pathways due to platelets. Collectively, this tumor microphysiological system may be deployed to derive antiangiogenic targets combined with antiplatelet treatments to arrest cancer metastasis.

EphA2- and HDAC-Targeted Combination Therapy in Endometrial Cancer.

Endometrial cancer is the most frequent malignant tumor of the female reproductive tract but lacks effective therapy. EphA2, a receptor tyrosine kinase, is overexpressed by various cancers including endometrial cancer and is associated with poor clinical outcomes. In preclinical models, EphA2-targeted drugs had modest efficacy. To discover potential synergistic partners for EphA2-targeted drugs, we performed a high-throughput drug screen and identified panobinostat, a histone deacetylase inhibitor, as a candidate. We hypothesized that combination therapy with an EphA2 inhibitor and panobinostat leads to synergistic cell death. Indeed, we found that the combination enhanced DNA damage, increased apoptosis, and decreased clonogenic survival in Ishikawa and Hec1A endometrial cancer cells and significantly reduced tumor burden in mouse models of endometrial carcinoma. Upon RNA sequencing, the combination was associated with downregulation of cell survival pathways, including senescence, cyclins, and cell cycle regulators. The Axl-PI3K-Akt-mTOR pathway was also decreased by combination therapy. Together, our results highlight EphA2 and histone deacetylase as promising therapeutic targets for endometrial cancer.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy.

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This optical photothermal infrared (O-PTIR) imaging technique provides a 10× enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that the enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present a statistically robust analysis from 78 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques with up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelia and stroma that exhibit efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology.

Molecular Analysis of Short- versus Long-Term Survivors of High-Grade Serous Ovarian Carcinoma.

Despite having similar histologic features, patients with high-grade serous ovarian carcinoma (HGSC) often experience highly variable outcomes. The underlying determinants for long-term survival (LTS, ≥10 years) versus short-term survival (STS, <3 years) are largely unknown. The present study sought to identify molecular predictors of LTS for women with HGSC. A cohort of 24 frozen HGSC samples was collected (12 LTS and 12 STS) and analyzed at DNA, RNA, and protein levels. OVCAR5 and OVCAR8 cell lines were used for in vitro validation studies. For in vivo studies, we injected OVCAR8 cells into the peritoneal cavity of female athymic nude mice. From RNAseq analysis, 11 genes were found to be differentially expressed between the STS and LTS groups (fold change > 2; false discovery rate < 0.01). In the subsequent validation cohort, transmembrane protein 62 (TMEM62) was found to be related to LTS. CIBERSORT analysis showed that T cells (follicular helper) were found at higher levels in tumors from LTS than STS groups. In vitro data using OVCAR5 and OVCAR8 cells showed decreased proliferation with TMEM62 overexpression and positive correlation with a longevity-regulating pathway (KEGG HSA04213) at the RNA level. In vivo analysis using the OVCAR8-TMEM62-TetON model showed decreased tumor burden in mice with high- vs. low-expressing TMEM62 tumors. Our results demonstrate that restoring TMEM62 may be a novel approach for treatment of HGSC. These findings may have implications for biomarker and intervention strategies to help improve patient outcomes

Molecular Profiles of Serum-Derived Extracellular Vesicles in High-Grade Serous Ovarian Cancer.

Patients with high-grade serous ovarian cancer (HGSC) who have no visible residual disease (R0) after primary surgery have the best clinical outcomes, followed by patients who undergo neoadjuvant chemotherapy (NACT) and have a response enabling interval cytoreductive surgery. Clinically useful biomarkers for predicting these outcomes are still lacking. Extracellular vesicles (EVs) have been recognized as liquid biopsy-based biomarkers for early cancer detection and disease surveillance in other disease settings. In this study, we performed extensive molecular characterization of serum-derived EVs and correlated the findings with therapeutic outcomes in patients with HGSC. Using EV-DNA whole-genome sequencing and EV-RNA sequencing, we identified distinct somatic EV-DNA alterations in cancer-hallmark genes and in ovarian cancer genes, as well as significantly altered oncogenic pathways between the R0 group and NACT groups. We also found significantly altered EV-RNA transcriptomic variations and enriched pathways between the groups. Taken together, our data suggest that the molecular characteristics of EVs could enable prediction of patients with HGSC who could undergo R0 surgery or respond to chemotherapy.

Novel Markers for Liquid Biopsies in Cancer Management: Circulating Platelets and Extracellular Vesicles.

Although radiologic imaging and histologic assessment of tumor tissues are classic approaches for diagnosis and monitoring of treatment response, they have many limitations. These include challenges in distinguishing benign from malignant masses, difficult access to the tumor, high cost of the procedures, and tumor heterogeneity. In this setting, liquid biopsy has emerged as a potential alternative for both diagnostic and monitoring purposes. The approaches to liquid biopsy include cell-free DNA/circulating tumor DNA, long and micro noncoding RNAs, proteins/peptides, carbohydrates/lectins, lipids, and metabolites. Other approaches include detection and analysis of circulating tumor cells, extracellular vesicles, and tumor-activated platelets. Ultimately, reliable use of liquid biopsies requires bioinformatics and statistical integration of multiple datasets to achieve approval in a Clinical Laboratory Improvement Amendments setting. This review provides a balanced and critical assessment of recent discoveries regarding tumor-derived biomarkers in liquid biopsies along with the potential and pitfalls for cancer detection and longitudinal monitoring.

Spatially resolved transcriptomics of high-grade serous ovarian carcinoma.

Bulk and single-cell RNA sequencing do not provide full characterization of tissue spatial diversity in cancer samples, and currently available in situ techniques (multiplex immunohistochemistry and imaging mass cytometry) allow for only limited analysis of a small number of targets. The current study represents the first comprehensive approach to spatial transcriptomics of high-grade serous ovarian carcinoma using intact tumor tissue. We selected a small cohort of patients with highly annotated high-grade serous ovarian carcinoma, categorized them by response to neoadjuvant chemotherapy (poor or excellent), and analyzed pre-treatment tumor tissue specimens. Our study uncovered extensive differences in tumor composition between the poor responders and excellent responders to chemotherapy, related to cell cluster organization and localization. This in-depth characterization of high-grade serous ovarian carcinoma tumor tissue from poor and excellent responders showed that spatial interactions between cell clusters may influence chemo-responsiveness more than cluster composition alone.

Targeting CCR2+ macrophages with BET inhibitor overcomes adaptive resistance to anti-VEGF therapy in ovarian cancer.

PURPOSE: Tumor-associated macrophages (TAMs) are known to contribute to adaptive resistance to anti-vascular endothelial growth factor (VEGF) antibody (AVA) therapy in ovarian cancer. BET (bromodomain and extra-terminal domain) inhibitors (BETi) may have unique roles in targeting TAMs. Our objective was to examine the effects of BETi on TAMs, especially in the context of enhancing the efficacy of AVA therapy. METHODS: We conducted a series of in vitro (MTT assay, apoptosis, flow cytometry, and RNA sequencing) and in vivo (xenograft ovarian cancer model) experiments to determine the biological effects of BETi combined with AVA in ovarian cancer. For statistical analysis, a two-tailed Student's t test (equal variance) or ANOVA was used for multiple groups' comparison, and p < 0.05 was considered significant. RESULTS: BETi resulted in a dose-dependent decrease in cell viability and induced apoptosis (p < 0.01) in ovarian cancer cells (SKOV3ip1, OVCAR5, and OVCAR8). Treatment with BETi significantly increased apoptosis in THP-1 monocytes and macrophages (PMA-differentiated THP-1; p < 0.01). Furthermore, BETi selectively induced greater apoptosis in M2-like macrophages (PMA and IL-4, IL-13-differentiated THP-1) (31.3%-36.1%) than in M1-like macrophages (PMA and LPS-differentiated THP-1) (12.4%-18.5%) (p < 0.01). Flow cytometry revealed that the percentage of M1-like macrophages (CD68+/CD80+) was significantly increased after treatment with low-dose BETi (ABBV-075 0.1 µM; p < 0.05), whereas the percentage of CD68+/CCR2+ macrophages was significantly decreased (p < 0.001); these findings suggest that BETi may selectively inhibit the survival of CCR2+ macrophages and re-polarize the macrophages into an M1-like phenotype. RNA-seq analysis revealed that BETi selectively targeted macrophage infiltration-related cytokines/chemokines in ovarian cancer (adjusted p < 0.05 and Log2 fold change ≥ 1.5). Finally, using in vivo ovarian cancer models, compared with control or monotherapy, the combination of BETi (ABBV-075) and bevacizumab resulted in greater inhibition of tumor growth and macrophage infiltration (p < 0.05) and longer survival of tumor-bearing mice (p < 0.001). CONCLUSIONS: Our findings indicate a previously unrecognized role for BETi in selectively targeting CCR2+ TAMs and enhancing the efficacy of AVA therapy in ovarian cancer.

Endothelial p130cas confers resistance to anti-angiogenesis therapy.

Anti-angiogenic therapies, such as anti-VEGF antibodies (AVAs), have shown promise in clinical settings. However, adaptive resistance to such therapies occurs frequently. We use orthotopic ovarian cancer models with AVA-adaptive resistance to investigate the underlying mechanisms. Genomic profiling of AVA-resistant tumors guides us to endothelial p130cas. We find that bevacizumab induces cleavage of VEGFR2 in endothelial cells by caspase-10 and that VEGFR2 fragments internalize into the nucleus and autophagosomes. Nuclear VEGFR2 and p130cas fragments, together with TNKS1BP1 (tankyrase-1-binding protein), initiate endothelial cell death. Blockade of autophagy in AVA-resistant endothelial cells retains VEGFR2 at the membrane with bevacizumab treatment. Targeting host p130cas with RGD (Arg-Gly-Asp)-tagged nanoparticles or genomic ablation of vascular p130cas in p130casflox/floxTie2Cre mice significantly extends the survival of mice with AVA-resistant ovarian tumors. Higher vascular p130cas is associated with shorter survival of individuals with ovarian cancer. Our findings identify opportunities for new strategies to overcome adaptive resistance to AVA therapy.

Immune microenvironment composition in high-grade serous ovarian cancers based on BRCA mutational status.

PURPOSE: An in-depth analysis of the tumor microenvironment of ovarian cancer is needed. The purpose of this study was to elucidate the architecture of the immune microenvironment of high-grade serous ovarian cancers (HGSCs) with or without BRCA1 and BRCA2 mutations. METHODS: A cohort of highly annotated HGSC patients with known germline BRCA1 and BRCA2 status was selected, and pretreatment tumor tissue specimens were analyzed with a multiplexed staining technique aimed at detecting lymphocytes, macrophages, and fibroblasts in the whole tumor area and in specific regions including epithelium, stroma, and perivascular areas. RESULTS: BRCA1- or BRCA2-mutated tumors showed a more immunogenic microenvironment, characterized by a higher abundance of CD8+ and PD-L1+ cells, than did tumors with wild-type BRCA1 and BRCA2. High numbers of PD-L1+ and PD-L1+CD8+ cells were prognostic for event-free survival (hazard ratio [HR]: 0.41, 95% CI 0.21-0.79, p = 0.008 and HR 0.49, 95% CI 0.26-0.91, p = 0.025, respectively), as were high numbers of epithelial PD-L1+ and FAP+PD-L1+ cells (HR 0.52, 95% CI 0.28-0.96, p = 0.037 and HR 0.27, 95% CI 0.08-0.87, p = 0.029) and CD8+ cells (HR 0.51, 95% CI 0.28-0.93, p = 0.027). CONCLUSIONS: This study reveals substantial differences between the immune microenvironment composition of germline BRCA-mutated and BRCA wild-type HGSC.

Stromal FAP is an independent poor prognosis marker in non-small cell lung adenocarcinoma and associated with p53 mutation.

OBJECTIVES: Fibroblasts regulate tumor growth and immune surveillance. Here, we study FAP, PDGFßR and α-SMA fibroblast markers in a well-annotated clinical cohort of non-small-cell lung cancer (NSCLC) for analyses of associations with immune cell infiltration, mutation status and survival. MATERIALS AND METHODS: A well-annotated NSCLC cohort was subjected to IHC analyses of stromal expression of FAP, PDGFßR and α-SMA and of stromal CD8 density. Fibroblast markers-related measurements were analyzed with regard to potential associations with CD8 density, cancer genetic driver mutations, survival and PD-L1 expression in the whole NSCLC cohort and in subsets of patients. RESULTS: High stromal FAP expression was identified as an independent poor prognostic marker in the whole study population (HR 1.481; 95 % CI, 1.012-2.167, p = 0.023) and in the histological subset of adenocarcinoma (HR 1.720; 95 % CI, 1.126-2.627, p = 0.012). Among patients with adenocarcinoma, a particularly strong association of FAP with poor survival was detected in patients with low stromal CD8 infiltration, and in other subpopulations identified by specific clinical characteristics; elderly patients, females, non-smokers and patients with normal ECOG performance status. α-SMA expression was negatively associated with CD8 infiltration in non-smokers, but none of the fibroblast markers expression was associated with CD8 density in the whole study population. Significant associations were detected between presence of p53 mutations and high α-SMA (p = 0.003) and FAP expression (p < 0.001). CONCLUSION: The study identifies FAP intensity as a candidate independent NSCLC prognostic biomarker. The study also suggests continued analyses of the relationships between genetic driver mutations and the composition of tumor stroma, as well as continued probing of marker-defined fibroblasts as NSCLC subset-specific modifiers of immune surveillance and outcome.

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed. METHODS: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns. FINDINGS: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival. INTERPRETATION: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance. FUNDING: The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden.

Prognostic Interactions between FAP+ Fibroblasts and CD8a+ T Cells in Colon Cancer.

Inter-case variations in immune cell and fibroblast composition are associated with prognosis in solid tumors, including colon cancer. A series of experimental studies suggest immune-modulatory roles of marker-defined fibroblast populations, including FAP-positive fibroblasts. These studies imply that the fibroblast status of tumors might affect the prognostic significance of immune-related features. Analyses of a population-based colon cancer cohort demonstrated good prognosis associations of FAP intensity and CD8a density. Notably, a significant prognostic interaction was detected between these markers (p = 0.013 in nonadjusted analyses and p = 0.003 in analyses adjusted for cofounding factors) in a manner where the good prognosis association of CD8 density was restricted to the FAP intensity-high group. This prognostic interaction was also detected in an independent randomized trial-derived colon cancer cohort (p = 0.048 in nonadjusted analyses). In the CD8-high group, FAP intensity was significantly associated with a higher total tumor density of FoxP3-positive immune cells and a higher ratio of epithelial-to-stromal density of CD8a T cells. The study presents findings relevant for the ongoing efforts to improve the prognostic performance of CD8-related markers and should be followed by additional validation studies. Furthermore, findings support, in general, earlier model-derived studies implying fibroblast subsets as clinically relevant modulators of immune surveillance. Finally, the associations between FAP intensity and specific immune features suggest mechanisms of fibroblast-immune crosstalk with therapeutic potential.

High density of stroma-localized CD11c-positive macrophages is associated with longer overall survival in high-grade serous ovarian cancer.

OBJECTIVE: Pre-clinical studies have identified marker- and tumor compartment-defined functionally distinct macrophage subsets. Our study analyzes marker-defined macrophage subsets in different tumor compartments of high-grade serous ovarian cancer (HGSC). METHODS: A discovery cohort (N = 113) was subjected to immunohistochemistry (IHC) analyses. CD68-positivity was confirmed for CD11c-, CD80- and CD163-positive cells. Subset-marker-positive cells were scored in the total tumor and in four tumor compartments. Correlation analyses investigated co-expression of subsets, relationship to CD8+ cells and survival associations. A validation cohort (N = 121) was used to confirm selected findings from the discovery cohort. RESULTS: CD163-positve cells was the most abundant subtype in all compartments. CD11c and CD163 subsets were strongly correlated with each other in stroma and epithelial areas, whereas CD80 and CD163 were correlated in epithelial areas. CD80 and CD11c in perivascular areas showed low correlations. Strong associations were detected between CD8 and CD80 in the tumor epithelium-dominated areas, and between CD8 and CD11c in stroma areas. High stromal CD11c density was associated with a longer median overall survival in the discovery cohort (HR 0.39; CI 95%, 0.23-0.68; p = 0.001) and in the validation cohort (HR 0.46; CI 95%, 0.22-0.93; p = 0.03). CONCLUSIONS: Our study supports the existence of clinically relevant marker- and localization defined macrophage subsets in HGSC, which are independently regulated. Moreover, it suggests stromal CD11c as a novel prognostic marker in HGSC.