Stromal Regions Research Articles

Evaluation of the Antioxidant and Antiangiogenic Activity of a Pomegranate Extract in BPH-1 Prostate Epithelial Cells.

Benign prostatic hypertrophy (BPH) is a noncancerous enlargement of the prostate gland that develops from hyper-proliferation of the stromal and epithelium region. Activation of pathways involving inflammation and oxidative stress can contribute to cell proliferation in BPH and tumorigenesis. Agricultural-waste-derived extracts have drawn the attention of researchers as they represent a valid and sustainable way to exploit waste production. Indeed, such extracts are rich in bioactive compounds and can provide health-promoting effects. In particular, extracts obtained from pomegranate wastes and by-products have been shown to exert antioxidant and anti-inflammatory effects. This study focused on the evaluation of the anti-angiogenic effects and chemopreventive action of a pomegranate extract (PWE) in cellular models of BPH. In our experimental conditions, we observed that PWE was able to significantly (p < 0.001) reduce the proliferation and migration rates (up to 60%), together with the clonogenic capacity of BPH-1 cells concomitantly with the reduction in inflammatory cytokines (e.g., IL-6, PGE2) and pro-angiogenic factor (VEGF-ADMA) release. Additionally, we demonstrated the ability of PWE in reducing angiogenesis in an in vitro model of BPH consisting in transferring BPH-1-cell-conditioned media to human endothelial H5V cells. Indeed, PWE was able to reduce tube formation in H5V cells through VEGF level reduction even at low concentrations. Overall, we confirmed that inhibition of angiogenesis may be an alternative therapeutic option to prevent neovascularization in prostate tissue with BPH and its transformation into malignant prostate cancer.

Use of AI-based second harmonic generation digital pathology for predicting patient survival for triple negative breast cancer patients.

e12526 Background: Extracellular matrix (ECM) in the stromal region has been known to be associated with tumorigenesis and metastasis in breast cancers. About 10-20% of all breast cancers are triple-negative breast cancers (TNBC), considered to be more aggressive with poorer prognosis than other types of breast cancer. AI-based Second Harmonic Generation (SHG) digital pathology, which is a fully quantitative fibrosis assessment, has been widely used innon-alcoholic steatohepatitis (NASH) and is FDA approved primary endpoint in NASH phase 2 clinical trials. In oncology, AI-based SHG digital pathology has demonstrated its relevance in survival prognosis for hepatocellular carcinoma and renal cell carcinoma after surgical treatment, based on analysis of tumor and non-tumor tissue collagen parameters. In this study, we study its potential applications for survival prognosis of TNBC patients. Methods: 68 patients with TNBC were treated by breast-cancer excision surgery. Tumor and non-tumor tissue from excised mass was imaged by SHG microscope (Genesis200, HistoIndex Pte. Ltd., Singapore). Regular follow-up was performed for patients post-operatively. A total of 33 collagen morphology features for collagen strings were quantified, such as length/width of strings, number of long/short/thick/thin strings, from tumour and non-tumour tissues. We used these collagen parameters to build two survival prediction models (RFS-index and OS-index) to predict patient’s recurrence-free survival (RFS) and overall survival (OS) years. The models were validated using the leave-one-out method. Results: Both RFS-index and OS-index were created using 10 specific collagen parameters chosen by sequential selection methods from overall 33 collagen parameters evaluated. 9 out of 10 parameters were selected from non-tumour tissue. The RFS-index can differentiate patients with RFS≥3 years (n = 36) and RFS &lt; 3 years (p &lt; 0.001) with cut-off value of RFS-index = 0.50. The OS-index can differentiate patients with OS≥5 years (n = 42) and OS &lt; 5 years (p &lt; 0.001) with cut-off value of RFS-index = 0.55. The log-rank test showed RFS-index (p = 0.026) and OS-index (p &lt; 0.001) can be used for prediction of disease-free and overall survival based on collagen parameters. Conclusions: SHG based AI-aided quantitative assessment of ECM collagen has been shown to correlate with survival rates of TNBC patients. This is a proof of concept study applying AI based digital pathology in helping predict survival rates for TNBC patients based on tumor and non-tumor tissue ECM collagen parameters. This could help identify patients with a higher risk of recurrence of disease and lower overall survival rates. Such patients can be followed-up more carefully, and be considered for earlier treatment interventions to improve their survival outcomes.

Tumor immune microenvironment in BRAF V600E mutant lung adenocarcinoma.

e21003 Background: The efficacy of immune checkpoint inhibitors (ICIs) is closely related to tumor immune microenvironment (TIME). However, little is known about the role of BRAF V600E mutation in the TIME of lung adenocarcinoma (LUAD). Methods: Here, we analyzed the TIME of LUAD with the BRAF V600E mutation using RNA sequencing and multiplex immunofluorescent (MIF) stained with CD8, CD68, CD163, CD57, PD-1, and PD-L1 antibodies. Patients were divided into the BRAF V600E (n = 31) and driver gene ( ALK, BRAF, EGFR, HER2, KRAS, MET, RET, and ROS1) wild-type groups (WT, n = 32). Results: There were no significant differences between the BRAF V600E and driver gene WT groups in terms of immunological signature scores, including T cell–inflamed gene expression profiles, adhesion molecules, chemokines, cytolytic activity, immunocostimulators, immunoinhibitors, MHC Class I, MHC Class II, MHC Non-Class, IFNg signature, tertiary lymphoid structure, and most of infiltrating immune cell subtypes. MIF showed that the proportions of PD-L1+ cells and CD68-associated tumor-associated macrophages (TAMs) were significantly higher in the BRAF V600E group in the tumor, stroma, and total regions. Notably, the majority of CD68-associated macrophages were CD68+CD163- TAMs. However, the proportions of CD8+, CD8+PD-L1+, and CD8+PD-1+ tumor-infiltrating lymphocytes (Tils) in the BRAF V600E and driver gene WT groups were all comparable in the tumor, stroma, and total region. Conclusions: The TIME was generally similar between the BRAF V600E mutant and driver gene wild-type groups, particularly in the proportion of CD8+ Tils. This may support that BRAF V600E mutation does not affect the efficacy of immune checkpoint inhibitors in LUAD.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Fibroblast Activation Protein-Targeting Minibody-IRDye700DX for Ablation of the Cancer-Associated Fibroblast with Photodynamic Therapy.

Fibroblast activation protein (FAP), expressed on cancer-associated fibroblasts, is a target for diagnosis and therapy in multiple tumour types. Strategies to systemically deplete FAP-expressing cells show efficacy; however, these induce toxicities, as FAP-expressing cells are found in normal tissues. FAP-targeted photodynamic therapy offers a solution, as it acts only locally and upon activation. Here, a FAP-binding minibody was conjugated to the chelator diethylenetriaminepentaacetic acid (DTPA) and the photosensitizer IRDye700DX (DTPA-700DX-MB). DTPA-700DX-MB showed efficient binding to FAP-overexpressing 3T3 murine fibroblasts (3T3-FAP) and induced the protein's dose-dependent cytotoxicity upon light exposure. Biodistribution of DTPA-700DX-MB in mice carrying either subcutaneous or orthotopic tumours of murine pancreatic ductal adenocarcinoma cells (PDAC299) showed maximal tumour uptake of 111In-labelled DTPA-700DX-MB at 24 h post injection. Co-injection with an excess DTPA-700DX-MB reduced uptake, and autoradiography correlated with FAP expression in the stromal tumour region. Finally, in vivo therapeutic efficacy was determined in two simultaneous subcutaneous PDAC299 tumours; only one was treated with 690 nm light. Upregulation of an apoptosis marker was only observed in the treated tumours. In conclusion, DTPA-700DX-MB binds to FAP-expressing cells and targets PDAC299 tumours in mice with good signal-to-background ratios. Furthermore, the induced apoptosis indicates the feasibility of targeted depletion of FAP-expressing cells with photodynamic therapy.

High expression of STAT3 within the tumour-associated stroma predicts poor outcome in breast cancer patients.

Triple-negative breast cancer (TNBC) patients have the poorest clinical outcomes compared to other molecular subtypes of breast cancer. IL6/JAK/STAT3 signalling is upregulated in breast cancer; however, there is limited evidence for its role in TNBC. This study aimed to assess the expression of IL6/JAK/STAT3 in TNBC as a prognostic biomarker. Tissue microarrays consisting of breast cancer specimens from a retrospective cohort (n = 850) were stained for IL6R, JAK1, JAK2 and STAT3 via immunohistochemistry. Staining intensity was assessed by weighted histoscore and analysed for association with survival/clinical characteristics. In a subset of patients (n = 14) bulk transcriptional profiling was performed using TempO-Seq. Nanostring GeoMx® digital spatial profiling was utilised to establish the differential spatial gene expression in high STAT3 tumours. In TNBC patients, high expression of stromal STAT3 was associated with reduced cancer-specific survival (HR = 2.202, 95% CI: 1.148-4.224, log rank p = 0.018). TNBC patients with high stromal STAT3 had reduced CD4+ T-cell infiltrates within the tumour (p = 0.001) and higher tumour budding (p = 0.003). Gene set enrichment analysis (GSEA) of bulk RNA sequencing showed high stromal STAT3 tumours were characterised by enrichment of IFNγ, upregulation of KRAS signalling and inflammatory signalling Hallmark pathways. GeoMx™ spatial profiling showed high stromal STAT3 samples. Pan cytokeratin (panCK)-negative regions were enriched for CD27 (p < 0.001), CD3 (p < 0.05) and CD8 (p < 0.001). In panCK-positive regions, high stromal STAT3 regions had higher expression of VEGFA (p < 0.05). High expression of IL6/JAK/STAT3 proteins was associated with poor prognosis and characterised by distinct underlying biology in TNBC.

CD8 + T cell infiltration is associated with improved survival and negatively correlates with hypoxia in clear cell ovarian cancer

Unlike other histological types of epithelial ovarian carcinoma, clear cell ovarian carcinoma (CCOC) has poor response to therapy. In many other carcinomas, expression of the hypoxia-related enzyme Carbonic anhydrase IX (CAIX) by cancer cells is associated with poor prognosis, while the presence of CD8 + tumor-infiltrating lymphocytes (TIL) is positively prognostic. We employed [18F]EF5-PET/CT imaging, transcriptome profiling, and spatially-resolved histological analysis to evaluate relationships between CAIX, CD8, and survival in CCOC. Tissue microarrays (TMAs) were evaluated for 218 cases in the Canadian COEUR study. Non-spatial relationships between CAIX and CD8 were investigated using Spearman rank correlation, negative binomial regression and gene set enrichment analysis. Spatial relationships at the cell level were investigated using the cross K-function. Survival analysis was used to assess the relationship of CAIX and CD8 with patient survival for 154 cases. CD8 + T cell infiltration positively predicted survival with estimated hazard ratio 0.974 (95% CI 0.950, 1000). The negative binomial regression analysis found a strong TMA effect (p-value < 0.0001). It also indicated a negative association between CD8 and CAIX overall (p-value = 0.0171) and in stroma (p-value = 0.0050) but not in tumor (p-value = 0.173). Examination of the spatial association between the locations of CD8 + T cells and CAIX cells found a significant amount of heterogeneity in the first TMA, while in the second TMA there was a clear signal indicating negative spatial association in stromal regions. These results suggest that hypoxia may contribute to immune exclusion, primarily mediated by effects in stroma.

Abstract CT197: Profiling of tumor infiltrating T cells in malignant pleural/peritoneal mesothelioma (MPM) and ovarian cancer patients as part of a Phase 1 clinical trial of gavo-cel (TC-210)

Abstract Gavocabtagene autoleucel (gavo-cel; TC-210) is a T cell receptor fusion construct (TRuC) cell therapy directed against mesothelin. As part of the phase 1 dose-escalation clinical trial (NCT03907852), patients with advanced solid tumors expressing mesothelin were administered a single infusion of gavo-cel. Thirty-two patients with late-stage disease (23 MPM, 8 ovarian and 1 cholangiocarcinoma) received gavo-cel at doses ranging from 5 × 107 - 5 × 108 TRuC cells/m2. The disease control rate (defined as achieving a response by RECISTv1.1 criteria or having stable disease [SD] for at least 12 weeks post-infusion) was 77%. Dose level 3 (1 × 108 TRuC cells/m2 following lymphodepletion with cyclophosphamide and fludarabine) was selected as the Recommended Phase 2 Dose (RP2D) for the ongoing phase 2 portion of the trial. Spatial distribution of immune cells, particularly T cells, in the tumor and stromal regions of tumor biopsies has been shown to identify patients that may respond to immunotherapies. To understand the tumor immune contexture, we profiled the T cell compartment in biopsies from five patients collected prior to and eight weeks post treatment using multiplexed immunofluorescence. We observed that the MPM patient with a partial response (PR) had a greater number of T cells at baseline in tumor biopsies when compared with the other SD patients. Notably, this patient with a PR, had a 3.6-fold increase in CD3+CD8+ T cell infiltrates into the tumor bed. In contrast, 2 SD patients with MPM had a minimal increase of T cells in the tumor compartment at 8 weeks post infusion. The density of TILs 8 weeks post infusion was much lower in the SD patients compared to the PR subject, indicating a potentially insufficient number of infiltrating T cells. Interestingly, 2/2 SD patients with ovarian cancer had a decrease in CD3+CD8+ T cells infiltrating into the tumor, consistent with an immune exclusion phenotype. The PR patient with MPM had minimal increase in PD-L1 and CD155 (a ligand for the inhibitory receptor, TIGIT) from negligible levels at baseline, whereas both SD patients with MPM had marked increases in PD-L1 and CD155, indicating potential resistance mechanisms. In summary, this preliminary data suggests although CD8+ TILs are increased following treatment with gavo-cel, the tumor regions in patients with SD show evidence of immune surveillance escape via PD-L1 and CD155 upregulation, thus acting as putative mechanisms of resistance. However, as seen in the PR patient with MPM there appears to be a critical number of TILs associating with response. We are currently evaluating the tumor immune contexture in phase 2, focusing on differences across tumor types with regards to TIL content, T cell influx, and correlations with response. We will also explore additional markers such as PD-1 and TIGIT, to profile the exhaustion state of immune cells and their spatial relationship with infiltrating gavo-cel T cells. Citation Format: Munisha Smalley, Michael Ross, Samantha F. Fowler, Lauren McLaughlin, Erin Jeter, Kevin Zikaras, Philip D. Bardwell, Alfonso Quintás-Cardama, Robert Tighe. Profiling of tumor infiltrating T cells in malignant pleural/peritoneal mesothelioma (MPM) and ovarian cancer patients as part of a Phase 1 clinical trial of gavo-cel (TC-210) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT197.

Abstract 3019: Density of T cell subsets in colorectal cancer in relation to disease-specific survival

Abstract Background. Prior studies have demonstrated that the overall density of T cells in colorectal tumors is favorably associated with colorectal cancer (CRC) survival; however, few studies have considered the potentially distinct roles of heterogeneous T cell subsets in different tissue regions in relation to CRC outcomes. Methods. We used data and colorectal tumor specimens from a subset of participants in three studies (total N=878). We profiled the in situ T cell landscape of CRC using digital imaging, machine learning, and a customized 9-plex multiplexed immunofluoresence panel with antibodies directed against CD3, CD4, CD8, CD45RA, CD45RO, FOXP3, and MKI67. This allowed us to determine the density of T cell subsets defined according to marker co-localization and according to tissue region (i.e., epithelial, stromal). We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of T cell subset densities in CRC with disease-specific survival, adjusting for age, sex, tumor site, microsatellite instability status, CpG island methylator phenotype status, status of somatic mutations in BRAF and KRAS, stage, and study. Results. Overall, the densities of CD3+CD4+ helper T cells and CD3+CD8+ cytotoxic T cells in CRC were favorably associated with CRC-specific survival. However, in analyses stratified by tissue region, the association of CD3+CD4+ helper T cell density with survival was limited to density in stromal regions (HR for highest vs. lowest density quartile=0.40, 95% CI: 0.26-0.63, ptrend&amp;lt;0.001), whereas the association with CD3+CD8+ cytotoxic T cells was limited to density in epithelial regions (HR=0.48, 95% CI: 0.31-0.76, ptrend=0.001). These significant associations persisted after mutually adjusting region-specific CD3+CD4+ and CD3+CD8+ T cell densities for each other. Associations of memory T cell subsets (i.e., CD3+CD4+CD45RO+ and CD3+CD8+ CD45RO+ T cells) with survival were more modest than associations with overall CD3+CD4+ and CD3+CD8+ T cell densities, and were similar in magnitude across stromal and epithelial tissue regions. Conclusions. The density of T cells in CRC tissue is significantly associated with CRC survival, independent of stage at diagnosis and other evaluated tumor markers; however, the T cell subsets that are associated with CRC outcomes differ according to tissue region. These findings highlight the differing roles of distinct T cell subsets in CRC with regard to cancer outcomes, and support the need for additional work identifying factors that contribute to T cell response profiles in CRC. Citation Format: Amanda I. Phipps, Jonathan Nowak, Yasutoshi Takashima, Daniel Buchanan, Andressa Dias Costa, Steven Gallinger, Jeroen Huyghe, Li Hsu, Conghui Qu, Claire Thomas, Sushma Thomas, Tomotaka Ugai, Ulrike Peters, Shuji Ogino. Density of T cell subsets in colorectal cancer in relation to disease-specific survival [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 3019.

Abstract 4625: A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis

Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). Understanding immune contexture requires multiplex staining, imaging, and analysis to obtain multi-marker phenotypes of specific cells and analyze their biodistribution in the TME. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and highplex imaging of FFPE tissues. FFPE tissue is autofluorescent, which limits the utility of immunofluorescence methods. Lung and colorectal tissue (and bone, skin, etc) are highly autofluorescent, and therefore good targets for IMC. However, developments in analysis software for highplex imagery have not kept pace with imaging advances. We present a comprehensive workflow designed specifically for highplex image analysis, covering tissue segmentation, cell segmentation based on IMC DNA images, cellular phenotyping, and spatial analyses. Lung and colorectal tissue sections with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers were imaged (Hyperion+™, Standard BioTools). Highplex image analysis (Phenoplex™, Visiopharm) was performed as a multi-step workflow in a single software package that includes: conversion of IMC images to pyramidal format; easy visualization methods for displaying different marker subsets; a paint-to-train algorithm for tissue segmentation (into tumor, stroma, blood vessels, etc.); deep-learning-based nuclear segmentation pre-trained on IMC DNA channels; cellular phenotyping based on thresholds based on visual assessment of positivity; spatial biodistribution metrics for cell populations; and a flexible set of outputs for downstream analysis. Tissue segmentation was used to divide the tissue into tumor, stromal, and tumor margin regions, and these regions were used to compare the immune contexture through a series of t-SNE images partitioned by spatial region. We demonstrate that a simple analysis workflow can be used for highplex images of different tissue types by users with no programming knowledge. Visualization templates for the marker subsets and the pre-trained IMC nuclear segmentation are reusable. A new tissue segmentation algorithm for each tissue type is required, as are new thresholds for biomarker positivity. Spatial biodistribution metrics, heatmaps and partitioned t-SNE plots were generated for each tissue type with a minimum of work. Highplex IMC imaging of lung and colorectal tumor samples is a simple and effective means of obtaining high-parameter images without interfering autofluorescence. Having a comprehensive workflow for the analysis of this complex data makes obtaining useful results from highplex images more accessible to biologists and immunologists by circumventing the requirement for expert programming for each specific application. Citation Format: Brenna O'Neill, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, Rasmus Norre Sorensen, Rasmus A. Lyngby, Alessandro Massaro, Andreas Hussing, Jeppe Thagaard, Johan Dore-Hansen, James Robert Mansfield. A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis. [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 4625.

Abstract 5393: Comparison of deep learning approaches applied to hematoxylin and eosin-stained whole slide images from women with benign breast disease to predict risk of developing invasive breast cancer

Abstract Purpose: To compare deep learning (DL) approaches applied to hematoxylin and eosin (H&amp;E)-stained whole slide images (WSIs) from women with benign breast disease (BBD) to predict risk of developing invasive breast cancer (BC). Method: Two deep convolutional neural networks (CNNs) based on a customized 16-layer CNN (known as VGG-16 by Visual Geometry Group, University of Oxford) and an automated CNN (Google’s AutoML) were trained using H&amp;E-stained WSIs to identify distinct histological features on diagnostic BBD biopsies that characterize BBD patients who were (cases, n=347) and were not (controls, n=347) subsequently diagnosed with invasive BC. The CNNs consisted of multiple convolutions, max pooling, fully connected, etc., layers. To incorporate our data into the VGG network, we customized the network architecture and hyperparameters to enhance the classification performances. For AutoML, we used the system's default network with standard hyperparameters. The trained model was then tested on a held-out set of 140 patients (70 cases and 70 controls). The quantitative performance was evaluated using accuracy (ACC), sensitivity (SE), precision (PR), area under the receiver operating characteristic curve (AUROC), etc. For qualitative results, we generated heatmaps using weights and feature maps from the final convolution layer of our customized CNN. Heatmaps were superimposed onto original H&amp;E images to highlight different unique features (such as pattern, texture, color, and morphology). Results: We found both deep learning methods to demonstrate remarkable ability in predicting case-control status in the held-out set (AUROC= 90% and 89% for customized CNN and AutoML, respectively). However, our customized CNN outperformed AutoML in terms of ACC (83.57% (95% confidence interval (CI): 76-89%) vs 77.86% (95%CI: 70-84%), respectively); SE (82.85% (95%CI: 72-91%) vs 77.86% (95%CI: 70-84%), respectively); PR (84.05% (95%CI: 73-92%) vs 81.97% (95%CI: 70-91%), respectively); F1 score (83.45% (95%CI: 76-89%) vs 76.34% (95%CI: 68-83%), respectively); as well as error rates (0.16% (95%CI: 0.11-0.24%) vs 0.22% (95%CI: 0.16-0.30%), respectively). Heatmaps revealed specific stromal and epithelial features that were distinct between case and control images. Conclusion: By using routinely available H&amp;E-stained WSIs, we developed a customized CNN that outperformed AutoML in distinguishing future BC cases from controls in a BBD population. The qualitative results identified stromal and epithelial regions in the BBD biopsies that were highly predictive of being a case versus control and vice versa thereby providing etiologic clues into breast cancer development following BBD. Future research will focus on leveraging DL to better understand the histologic basis of BBD progression to invasive BC. Citation Format: Monjoy Saha, Mustapha Abubakar, Thomas E. Rohan, Ruth M. Pfeiffer, Máire A. Duggan, Kathryn Richert-Boe, Jonine D. Figueroa, Jonas S. Almeida, Gretchen L. Gierach. Comparison of deep learning approaches applied to hematoxylin and eosin-stained whole slide images from women with benign breast disease to predict risk of developing invasive breast cancer. [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 5393.

Abstract 5407: A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Abstract Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&amp;E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&amp;E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&amp;E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues. Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [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 5407.

Abstract 6778: Spatial analysis of genomic signatures on colorectal cancer pathogenesis using the GeoMx® Digital Spatial Profiler

Abstract Background: The development of colorectal cancer (CRC) is a complex process, involving multiple sequential molecular aberrations that contribute to disease progression. Understanding the immunopathogenesis of CRC particularly within the spatial context of tumor microenvironment (TME) may elucidate the genomic and biological changes to improve patient prognosis for adjuvant therapies and identifying potential drug targets. However, this can be challenging, as genomic signatures from sub-cellular populations and varying levels of immune cell infiltration in TME may be lost in bulk sequencing. In this study, we used the NanoString GeoMx® Digital Spatial Profiler (DSP) to spatially select epithelial, proximal stromal and distal stromal regions of TMEs on a total of 10 FFPE samples (5 colon cancer patients and 5 healthy donor samples) and profiled up to 1,800 genes included in the Cancer Transcriptome Atlas (CTA) panel. Methods: Selection of regions of interest (ROI) were guided by pathology assessment of H&amp;E images and fluorescent markers (CD45, PanCK, Syto13). Tumor and stroma (proximal and distal) regions were profiled through geometric ROI selection in PanCK+ (epithelial) and CD45+ (stromal/immune) enriched areas respectively, followed by collection of indexed oligonucleotides and sequencing on NextSeq 550 Illumina instrument. Differential expression and pathways enrichment analyses was performed using R BioConductor package DESeq2 and DSP GeoMx analysis suite. A subset of samples were tested for bulk RNA expression using PanCancer IO360 panel on NanoString nCounter Flex system and data analyzed using nSolver software 3.0 and TIS algorithm App. Results: The CTA panel is designed to profile the global immune response and all aspects of tumor microenvironment biology. From differential gene expression analysis of epithelial and distal/proximal stromal ROIs, we have identified biomarkers such as SOX9, IRAK2 and NLRP3 that are specifically upregulated in CRC samples compared to the healthy cohort. Comprehensive pathways analysis revealed an over activation of NF-kB signaling, extracellular matrix organization and interferon signaling pathways in the CRC cohort. Suppression of the NF-kB signaling pathway is a potential therapeutic approach in the treatment of colon cancer. Conclusion: With this spatial study using GeoMx CTA panel, we have highlighted key genes and pathways involved in CRC in order to provide a much-needed understanding of the underlying mechanisms as well as immune signatures in the development of colon cancer. Citation Format: Xiang Li, Qinqin Zha, Brigitte Lovell, Jane Reed, Anna Juncker-Jensen, Tricia Peters, Lakshmi Chandramohan. Spatial analysis of genomic signatures on colorectal cancer pathogenesis using the GeoMx® Digital Spatial Profiler. [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 6778.

Abstract 4633: Quantitative spatial profiling of NSCLC subtypes across tumor stages using 6-plex multiplex imaging technology and AI-powered phenotyping analysis

Abstract Non-small cell lung cancer (NSCLC) is a complex disease with varying pathological subtypes including adenocarcinomas and squamous cell carcinomas. After diagnosis, tumor staging provides important information about the extent of cancer in the body and anticipated response to treatment. NSCLC patients can have impaired immune responses within the tumor microenvironment (TME), leading to a progression of tumor growth and poorer prognosis. Accurate cell phenotyping combined with spatial profiling of the immune contexture and checkpoint expression, can provide a deeper understanding of complex cellular interactions underpinning the tumor-immune response. The aim of this study was to utilize spatial multiplexed imaging technology and associated data analysis methods to profile the immune contexture, as well as their spatial interactions with the tumor, in a set of tissue cores covering a range of NSCLC subtypes and tumor staging. Formalin-fixed paraffin-embedded (FFPE) NSCLC tissue microarrays (TMA), comprised of n=41 cores containing a range of carcinomas and pathological Tumor-Node-Metastasis (pTNM) stages (I-IV), were stained on a Leica Bond RX™ using the Akoya PhenoCode™ Signature Immuno-contexture Human Protein Panel, which includes markers for CD8, CD68, PD-1, PD-L1, FoxP3, and PanCK as a tumor indicator. Stained TMAs were scanned at 20x magnification on a PhenoImager HT multispectral imaging system. Image analysis was performed using Visiopharm software. Deep learning algorithms were applied to segment tumor and stroma regions of interest (ROI) and to accurately detect and classify specific cell phenotypes. Cell object data files per core were exported for spatial and neighborhood analysis using OracleBio’s proprietary python-based program, PhenoXplore. Immune cell counts, phenotypes and spatial interactions were generated within tumor and stroma ROI per core. Data included total and negative cell phenotype counts, cell density in tumor and stroma, as well as cell distance and neighborhood spatial interactions per core across NSCLC subtypes and stages in the TMA set. The combination of high-quality staining provided by the PhenoCode™ panel, coupled with deep learning quantitative phenotyping and spatial pattern analysis enables detailed characterization of the complex cellular interactions, at both the functional and spatial level, within the TME of NSCLC tissue. Data generated will support a greater understanding of the complex cellular interactions that can contribute to progression of NSCLC and may help guide future precision medicine strategies. Citation Format: Lorcan Sherry, Nicole Couper, Bethany Remeniuk, Karen McClymont, Bei Hopkins, Natalie Monteiro, Gabriel Reines March, Darren Locke. Quantitative spatial profiling of NSCLC subtypes across tumor stages using 6-plex multiplex imaging technology and AI-powered phenotyping analysis. [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 4633.

MP17-14 IDENTIFYING CRIBRIFORM PATTERN IN MPMRI SCANS FROM 3D RECONSTRUCTED RALP SPECIMENS USING DEEP LEARNING

You have accessJournal of UrologyCME1 Apr 2023MP17-14 IDENTIFYING CRIBRIFORM PATTERN IN MPMRI SCANS FROM 3D RECONSTRUCTED RALP SPECIMENS USING DEEP LEARNING Ashwin Sachdeva, Dinisha Kadam, Avaneesh Meena, Nitin Singhal, Archie Parekh, Guillaume Antem, Yatin Jain, Pedro Oliveira, and Vijay Ramani Ashwin SachdevaAshwin Sachdeva More articles by this author , Dinisha KadamDinisha Kadam More articles by this author , Avaneesh MeenaAvaneesh Meena More articles by this author , Nitin SinghalNitin Singhal More articles by this author , Archie ParekhArchie Parekh More articles by this author , Guillaume AntemGuillaume Antem More articles by this author , Yatin JainYatin Jain More articles by this author , Pedro OliveiraPedro Oliveira More articles by this author , and Vijay RamaniVijay Ramani More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003237.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Amongst various Gleason 4 patterns, cribriform morphology (Gl4Cri) is unique, as it seems to correlate with an aggressive clinical course, distant metastases, and disease-specific mortality. However, the true value of Gl4Cri on PCa management has recently been challenged, given that cribriform morphology is frequently intermixed with other morphologic patterns (such as glomeruloid and intra-ductal carcinoma, IDC), and biopsy-based studies suffer from sampling bias. A methodology for precise identification and quantification of Gl4Cri would help inform the role of Gl4Cri in impacting PCa management (active surveillance vs surgery vs radiotherapy) and molecular germline testing. METHODS: Using deep learning based methods, we developed an algorithm for identification and quantification of cribriform and glomeruloid patterns in whole mount images of radical prostatectomy (RP) specimens. Whole-mount H&E-stained histopathological sections of formalin-fixed RP specimens showing a mixture of different GP4 subtypes were digitised. A neural network based on U-Net++ with EfficientNet backbone was trained to identify and separate cribriform and glomeruloid glands from benign, other tumour patterns, and stromal regions. Two pathologists annotated a training set of 13 RP samples and a validation set of 42 RP samples with perfect consensus. We then applied this new algorithm to label Gl4Cri on mpMRI images using reconstructed 3D histology, allowing the extent of Gl4Cri cancer to be mapped directly onto mpMRI for development of a radiomic signature. RESULTS: The model performed well on the validation set with AUCs (Area Under Curve) of 0.87 for cribriform and 0.81 for glomeruloid pattern identification. For the two patterns, the segmentation model achieved an F1-score of 0.83 and 0.80, respectively. Using deep learning, we developed a method for automated localisation of cribriform and glomeruloid growth patterns in H&E-stained RP whole slide images, which could precisely determine the proportion of each growth pattern on corresponding mpMRI scans. CONCLUSIONS: We developed a method for automated localisation of cribriform and glomeruloid growth patterns in radical prostatectomy images and demonstrated the ability to map these patterns within pre-biopsy mpMRI scans successfully. In the future, we intend to further improve the algorithm for IDC identification and conduct multicentre trials on larger data sets for validation of this approach. Source of Funding: NHS/Aira-Matrix Project PO01 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e218 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ashwin Sachdeva More articles by this author Dinisha Kadam More articles by this author Avaneesh Meena More articles by this author Nitin Singhal More articles by this author Archie Parekh More articles by this author Guillaume Antem More articles by this author Yatin Jain More articles by this author Pedro Oliveira More articles by this author Vijay Ramani More articles by this author Expand All Advertisement PDF downloadLoading ...

MP63-09 GENOMIC PROFILING OF UROTHELIAL CARCINOMA IN SITU OF BLADDER

You have accessJournal of UrologyCME1 Apr 2023MP63-09 GENOMIC PROFILING OF UROTHELIAL CARCINOMA IN SITU OF BLADDER Meenakshi Anurag, Trine Strandgaard, Sung Han Kim, Eva-Maria Comperat, Hikmat A AL-Ahmadie, Brant A Inman, Lars Dyrskjøt, and Seth Lerner Meenakshi AnuragMeenakshi Anurag More articles by this author , Trine StrandgaardTrine Strandgaard More articles by this author , Sung Han KimSung Han Kim More articles by this author , Eva-Maria ComperatEva-Maria Comperat More articles by this author , Hikmat A AL-AhmadieHikmat A AL-Ahmadie More articles by this author , Brant A InmanBrant A Inman More articles by this author , Lars DyrskjøtLars Dyrskjøt More articles by this author , and Seth LernerSeth Lerner More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003321.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urothelial carcinoma in situ (CIS) is a high-grade intraepithelial cancer whose natural history is associated with ahigh probability of progression. It can be broadly categorized by RNA expression into luminal and basal-likesubtypes, however the molecular drivers unique to CIS as compared to adjacent papillary tumor or normalurothelium are underexplored. Comprehensive genomic analysis of CIS and comparisons to papillary andprogressed tumors may help define progression pathways and BCG treatment response/resistance mechanisms. Todetermine feasibility and proof of concept, we performed molecular characterization of CIS, paired papillary tumorsand normal urothelium METHODS: TUR and matched bladder biopsies were obtained from 32 samples in 19 patients. A total of 10 CIS, 9 papillarytumor and 8 normal samples underwent whole transcriptome RNA sequencing. Wilcoxon test was used to filterdifferentially expressed genes. TCGA single sample classifier was used to assign molecular subtypes. Whole exomesequencing (WES) was performed for 19 patients with matched CIS and papillary tumor samples (nCIS=34 andntumor=33). Using multiplex IF and IHC analyses, 24 samples from 15 patients were analyzed for the presence ofcytotoxic T-cells (CTLs), T-helper cells, regulatory T-cells (Tregs), B-cells, M1 and M2 macrophages and PD-1 andPD-L1-expressing cells RESULTS: M:F ratio was 15:4 and the CIS TCGA subtypes were 3 basal, 2 luminal, 5 luminal infiltrative, 7 luminal papillary,and 1 neuronal. We identified a 46-gene signature of differentially expressed genes in CIS samples that includedknown druggable targets: MTOR, TYK2, AXIN1, CPT1B, GAK and PIEZO1 and selectively downregulated BRD2 andNDUFB2 (p<0.05). We assessed the robustness of these markers in an independent dataset from Dyrskjøt et al (GSE3167)and a reduced 36-gene signature accurately identified the CIS samples (AUC=0.90). We validatedselective overexpression of MTOR, GUSBP11, KMT2D, URB1 and under expression of HMGB1, RPS7, NDUFB4,BRD2, HNRNPK, CANX and HSP90B1in CIS samples. In the WES cohort, KDM6A was the most frequently mutatedgene across CIS and papillary tumor samples. When analyzing CIS samples alone, CCDC138 was the mostcommonly mutated gene. We observed clonality between CIS lesions and matched papillary tumors. We observedfewer immune cells within the tumorareas compared to stromal regions and significantly higher fractions of T-helper cells and B-cells in all stromal areas combined compared to areas of epithelial origin. Additionally, there wasa significantly larger fraction of PD1-expressing cells in CIS regions compared to papillary regions (p=0.031) CONCLUSIONS: We identified CIS lesions having a unique molecular signature and potentially actionable mutations. KDM6A andCCDC138 were commonly mutated, and levels of T-helper and B-cells were highest in stromal regions while CISharbored more PD1-expressing cells. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e874 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Meenakshi Anurag More articles by this author Trine Strandgaard More articles by this author Sung Han Kim More articles by this author Eva-Maria Comperat More articles by this author Hikmat A AL-Ahmadie More articles by this author Brant A Inman More articles by this author Lars Dyrskjøt More articles by this author Seth Lerner More articles by this author Expand All Advertisement PDF downloadLoading ...

Diagnosis and treatment of corneal ulcers in the course of systemic autoimmune diseases

Peripheral ulcerative keratitis is an inflammatory condition, usually crescent shaped and occurring in the corneal stromal region, where it is always associated with corneal stromal thinning. Peripheral ulcerative keratitis often occurs in the course of rheumatic diseases but is most commonly associated with rheumatoid arthritis. Peripheral ulcerative keratitis is also seen in granulomatosis with polyangiitis, polyarteritis nodosa, relapsing chondritis, and systemic lupus erythematosus. These diseases are analyzed in the article in relation to peripheral ulcerative keratitis with a discussion of individual treatment methods in specific cases.

Spatial analysis of the metastatic brain tumor immune and extracellular matrix microenvironment

Metastatic cancer is responsible for the overwhelming majority of cancer-related deaths, with metastatic tumors being the most common neoplasms affecting the central nervous system. One of the major factors regulating tumor biology is the tumor microenvironment. However, little is known about the cellular and non-cellular composition of metastatic brain tumors and how tumor cell ontogeny influences the metastatic brain tumor microenvironment. By integrating multiplex immunohistochemistry and histopathological analysis to investigate composition and the spatial relationship between neoplastic cells, infiltrating and brain resident immune cells and the extracellular matrix, we demonstrate that metastatic brain tumors exhibit differences in extracellular matrix deposition, compared with the most common primary brain tumor type, glioblastoma, and that the dominant immune cell types in metastatic brain tumors are immunosuppressive macrophages, which preferentially localize to extracellular matrix-rich stromal regions.

Abstract P5-02-09: Quantitative analysis of fiber-level collagen features in H&amp;E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ breast cancer

Abstract Background: Neoadjuvant treatment (NAT) combining chemotherapy and HER2-targeted agents is frequently administered to HER2-positive (HER2+) breast cancer (BC) patients, with some experiencing a pathological complete response (pCR) and others having residual disease measured by the residual cancer burden (RCB) score. Here, we use a physics-guided machine learning (ML)-based approach to extract fiber-level collagen features from hematoxylin and eosin (H&amp;E)-stained whole slide images (WSIs) and identify collagen-related associations with treatment response in HER2+ patients receiving NAT. Methods: Clinical data and specimens from stage II-III HER2+ BC patients enrolled on the De-escalation to Adjuvant Antibodies Post-pCR to Neoadjuvant THP (DAPHNe; NCT03716180) clinical trial and treated with neoadjuvant paclitaxel/trastuzumab/pertuzumab were analyzed. An ML-based model trained to identify regions of BC tissue as invasive carcinoma, ductal carcinoma in situ (DCIS), diffuse inflammatory infiltrate, stroma, necrosis, or normal tissue was deployed on WSIs of H&amp;E-stained diagnostic core needle biopsies (N=89) to generate tissue overlays. Additional tissue areas were computed from the tissue model predictions using heatmap transformation, including tumor nests (continuous regions predicted as invasive cancer epithelium or DCIS), tumor nest borders (stromal region boundaries 10 μm from tumor nests), and bulk tumor borders (stromal region boundaries 300 μm from aggregated tumor nests). A separate ML-based model trained to identify fiber-level collagen features in WSIs of H&amp;E-stained specimens was also deployed to generate collagen overlays. A fiber feature extraction pipeline was utilized to characterize properties of all identified collagen fibers in the WSI (on the order of hundreds of thousands per slide), including length, width, tortuosity, and angle. These fiber features were then assessed based on their position within the tumor (e.g. relative to the tumor nest border). Combinatorial features (e.g. angle of fibers with respect to tumor boundary) were then explored univariately for associations (N=609) with treatment response. Patients with pCR (RCB=0; N=53) were considered responders, while all other cases (RCBI-III; N=36) were designated non-responders. Due to the small size of the cohort analyzed here, raw p-values are reported. Results: Using estrogen receptor status as a clinical covariate, a logistic regression-based univariate analysis of 609 collagen-associated features revealed six features to strongly associate with pCR (p&amp;lt; 0.05, AUC≥0.75; Table 1). Notable feature themes were identified: 1) fiber tortuosity in tumor nest borders and tumor borders, 2) angle of fibers in tumor border with respect to tumor boundary, and 3) distribution patterns of fiber width in tumor nest borders. The presence of fibers perpendicular to tumor boundary tangents was negatively associated with pCR, as was higher fiber tortuosity and thickness in tumor nest borders. Conclusions: Improved prediction of response to NAT in patients with BC is needed to determine appropriate treatment strategies for each patient. Here, using ML-based models to identify tissue features and collagen fibers, we identify collagen-associated features, measured directly from WSIs of H&amp;E-stained diagnostic BC biopsies, that negatively correlate with pCR. Additional development of this strategy, including the addition of cell identification models and known clinical information, is underway to further refine this novel predictive model. Citation Format: Tan H. Nguyen, Mohammad Mirzadeh, Aaditya Prakash, Emma L. Krause, Jun Zhang, Michael Pyle, Esther R. Ogayo, Harry C. Cramer, Busem Binboga Kurt, Jacqueline Brosnan-Cashman, Michael G. Drage, Stuart Schnitt, Andrew H. Beck, Michael Montalto, Ilan Wapinski, Laura Chambre, Sara Tolaney, Adrienne Waks, Justin Lee, Elizabeth A. Mittendorf. Quantitative analysis of fiber-level collagen features in H&amp;E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-09.

Role of Nasal Fibroblasts in Airway Remodeling of Chronic Rhinosinusitis: The Modulating Functions Reexamined.

Chronic rhinosinusitis (CRS) is a multifactorial inflammatory disease of the nose and sinuses that affects more than 10% of the adult population worldwide. Currently, CRS is classified into endotypes according to the inflammatory response (Th1, Th2, and Th17) or the distribution of immune cells in the mucosa (eosinophilic and non-eosinophilic). CRS induces mucosal tissue remodeling. Extracellular matrix (ECM) accumulation, fibrin deposition, edema, immune cell infiltration, and angiogenesis are observed in the stromal region. Conversely, epithelial-to-mesenchymal transition (EMT), goblet cell hyperplasia, and increased epithelial permeability, hyperplasia, and metaplasia are found in the epithelium. Fibroblasts synthesize collagen and ECM, which create a structural skeleton of tissue and play an important role in the wound-healing process. This review discusses recent knowledge regarding the modulation of tissue remodeling by nasal fibroblasts in CRS.