Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa.

METHODS

We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy.

RESULTS

The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram.

CONCLUSIONS

The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Overexpression of Fibroblast Activation Protein (FAP) in stroma of proliferative inflammatory atrophy (PIA) and primary adenocarcinoma of the prostate

Fibroblast activation protein (FAP) is a serine protease upregulated at sites of tissue remodeling and cancer that represents a promising therapeutic and molecular imaging target. In prostate cancer, studies of FAP expression using tissue microarrays are conflicting, such that its clinical potential is unclear. Furthermore, little is known regarding FAP expression in benign prostatic tissues. Here we demonstrated, using a novel iterative multiplex IHC assay in standard tissue sections, that FAP was nearly absent in normal regions, but was increased consistently in regions of proliferative inflammatory atrophy (PIA). In carcinoma, FAP was expressed in all cases, but was highly heterogeneous. High FAP levels were associated with increased pathological stage and cribriform morphology. We verified that FAP levels in cancer correlated with CD163+ M2 macrophage density. In this first report to quantify FAP protein in benign prostate and primary tumors, using standard large tissue sections, we clarify that FAP is present in all primary prostatic carcinomas, supporting its potential clinical relevance. The finding of high levels of FAP within PIA supports the injury/regeneration model for its pathogenesis and suggests that it harbors a protumorigenic stroma. Yet, high levels of FAP in benign regions could lead to false positive FAP-based molecular imaging results in clinically localized prostate cancer.

Targeting the tumor microenvironment, a new therapeutic approach for prostate cancer

BACKGROUND

A growing number of studies have shown that in addition to adaptive immune cells such as CD8 + T cells and CD4 + T cells, various other cellular components within prostate cancer (PCa) tumor microenvironment (TME), mainly tumor-associated macrophages (TAMs), cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), have been increasingly recognized as important modulators of tumor progression and promising therapeutic targets.

OBJECTIVE

In this review, we aim to delineate the mechanisms by which TAMs, CAFs and MDSCs interact with PCa cells in the TME, summarize the therapeutic advancements targeting these cells and discuss potential new therapeutic avenues.

METHODS

We searched PubMed for relevant studies published through December 10 2023 on TAMs, CAFs and MDSCs in PCa.

RESULTS

TAMs, CAFs and MDSCs play a critical role in the tumorigenesis, progression, and metastasis of PCa. Moreover, they substantially mediate therapeutic resistance against conventional treatments including anti-androgen therapy, chemotherapy, and immunotherapy. Therapeutic interventions targeting these cellular components have demonstrated promising effects in preclinical models and several clinical trials for PCa, when administrated alone, or combined with other anti-cancer therapies. However, the lack of reliable biomarkers for patient selection and incomplete understanding of the mechanisms underlying the interactions between these cellular components and PCa cells hinder their clinical translation and utility.

CONCLUSION

New therapeutic strategies targeting TAMs, CAFs, and MDSCs in PCa hold promising prospects. Future research endeavors should focus on a more comprehensive exploration of the specific mechanisms by which these cells contribute to PCa, aiming to identify additional drug targets and conduct more clinical trials to validate the safety and efficacy of these treatment strategies.

Ratiometric Inclusion of Fibroblasts Promotes Both Castration-Resistant and Androgen-Dependent Tumorigenic Progression in Engineered Prostate Cancer Tissues

To investigate the ratiometric role of fibroblasts in prostate cancer (PCa) progression, this work establishes a matrix-inclusive, 3D engineered prostate cancer tissue (EPCaT) model that enables direct coculture of neuroendocrine-variant castration-resistant (CPRC-ne) or androgen-dependent (ADPC) PCa cells with tumor-supporting stromal cell types. Results show that the inclusion of fibroblasts within CRPC-ne and ADPC EPCaTs drives PCa aggression through significant matrix remodeling and increased proliferative cell populations. Interestingly, this is observed to a much greater degree in EPCaTs formed with a small number of fibroblasts relative to the number of PCa cells. Fibroblast coculture also results in ADPC behavior more similar to the aggressive CRPC-ne condition, suggesting fibroblasts play a role in elevating PCa disease state and may contribute to the ADPC to CRPC-ne switch. Bulk transcriptomic analyses additionally elucidate fibroblast-driven enrichment of hallmark gene sets associated with tumorigenic progression. Finally, the EPCaT model clinical relevancy is probed through a comparison to the Cancer Genome Atlas (TCGA) PCa patient cohort; notably, similar gene set enrichment is observed between EPCaT models and the patient primary tumor transcriptome. Taken together, study results demonstrate the potential of the EPCaT model to serve as a PCa-mimetic tool in future therapeutic development efforts.

Epithelial and Stromal Characteristics of Primary Tumors Predict the Bone Metastatic Subtype of Prostate Cancer and Patient Survival after Androgen-Deprivation Therapy

Simple Summary

Metastatic prostate cancer is a lethal disease and metastasis-specific treatments need to be developed. Mechanisms driving metastases and primary tumor growth could be different, but this is largely unexplored. We previously discovered that bone metastases can be separated into transcriptomic-based subtypes, showing different responses to standard androgen-deprivation therapy for metastatic prostate cancer. One subtype, named MetB, is particularly aggressive and has the worst prognosis. Here, we describe similarities and differences between primary tumors and their metastases, and specifically examine if the development of specific subtype of bone metastases can be predicted by analyzing the primary tumor. Results show that many aspects of prostate cancer bone metastases morphology are related to those in the primary tumor, while others are not. Importantly, men with primary tumors with high cell proliferation and low cellular PSA expression tend to develop metastases enriched for the MetB subtype, have poor prognosis, and need complementary treatment to standard hormone treatment.

Abstract

Prostate cancer (PC) bone metastases can be divided into transcriptomic subtypes, by us termed MetA-C. The MetB subtype, constituting about 20% of the cases, is characterized by high cell cycle activity, low androgen receptor (AR) activity, and a limited response to standard androgen deprivation therapy (ADT). Complementary treatments should preferably be introduced early on if the risk of developing metastases of the MetB subtype is predicted to behigh. In this study, we therefore examined if the bone metastatic subtype and patient outcome after ADT could be predicted by immunohistochemical analysis of epithelial and stromal cell markers in primary tumor biopsies obtained at diagnosis (n = 98). In this advanced patient group, primary tumor International Society of Urological Pathology (ISUP) grade was not associated with outcome or metastasis subtype. In contrast, high tumor cell Ki67 labeling (proliferation) in combination with low tumor cell immunoreactivity for PSA, and a low fraction of AR positive stroma cells in the primary tumors were prognostic for poor survival after ADT. Accordingly, the same tissue markers were associated with developing metastases enriched for the aggressive MetB subtype. The development of the contrasting MetA subtype, showing the best response to ADT, could be predicted by the opposite staining pattern. We conclude that outcome after ADT and metastasis subtype can, at least to some extent, be predicted by analysis of primary tumor characteristics, such as tumor cell proliferation and PSA expression, and AR expression in stromal cells.

Prostate Cancer Tumor Stroma: Responsibility in Tumor Biology, Diagnosis and Treatment

Simple Summary

The crosstalk between prostate stroma and its epithelium is essential to tissue homeostasis. Likewise, reciprocal signaling between tumor cells and the stromal compartment is required in tumor progression to facilitate or stimulate key processes such as cell proliferation and invasion. The aim of the present work was to review the current state of knowledge on the significance of tumor stroma in the genesis, progression and therapeutic response of prostate carcinoma. Additionally, we addressed the future therapeutic opportunities.

Abstract

Prostate cancer (PCa) is a common cancer among males globally, and its occurrence is growing worldwide. Clinical decisions about the combination of therapies are becoming highly relevant. However, this is a heterogeneous disease, ranging widely in prognosis. Therefore, new approaches are needed based on tumor biology, from which further prognostic assessments can be established and complementary strategies can be identified. The knowledge of both the morphological structure and functional biology of the PCa stroma compartment can provide new diagnostic, prognostic or therapeutic possibilities. In the present review, we analyzed the aspects related to the tumor stromal component (both acellular and cellular) in PCa, their influence on tumor behavior and the therapeutic response and their consideration as a new therapeutic target.

Tunable three-dimensional engineered prostate cancer tissues for in vitro recapitulation of heterogeneous in vivo prostate tumor stiffness

In this manuscript we report the establishment and characterization of a three-dimensional in vitro, coculture engineered prostate cancer tissue (EPCaT) disease model based upon and informed by our characterization of in vivo prostate cancer (PCa) xenograft tumor stiffness. In prostate cancer, tissue stiffness is known to impact changes in gene and protein expression, alter therapeutic response, and be positively correlated with an aggressive clinical presentation. To inform an appropriate stiffness range for our in vitro model, PC-3 prostate tumor xenografts were established. Tissue stiffness ranged from 95 to 6,750 Pa. Notably, xenograft cell seeding density significantly impacted tumor stiffness; a two-fold increase in the number of seeded cells not only widened the tissue stiffness range throughout the tumor but also resulted in significant spatial heterogeneity. To fabricate our in vitro EPCaT model, PC-3 castration-resistant prostate cancer cells were co-encapsulated with BJ-5ta fibroblasts within a poly(ethylene glycol)-fibrinogen matrix augmented with excess poly(ethylene glycol)-diacrylate to modulate the matrix mechanical properties. Encapsulated cells temporally remodeled their in vitro microenvironment and enrichment of gene sets associated with tumorigenic progression was observed in response to increased matrix stiffness. Through variation of matrix composition and culture duration, EPCaTs were tuned to mimic the wide range of biomechanical cues provided to PCa cells in vivo; collectively, a range of 50 to 10,000 Pa was achievable. Markedly, this also encompasses published clinical PCa stiffness data. Overall, this study serves to introduce our bioinspired, tunable EPCaT model and provide the foundation for future PCa progression and drug development studies. STATEMENT OF SIGNIFICANCE: The development of cancer models that mimic the native tumor microenvironment (TME) complexities is critical to not only develop effective drugs but also enhance our understanding of disease progression. Here we establish and characterize our 3D in vitro engineered prostate cancer tissue model with tunable matrix stiffness, that is inspired by this study's spatial characterization of in vivo prostate tumor xenograft stiffness. Notably, our model's mimicry of the TME is further augmented by the inclusion of matrix remodeling fibroblasts to introduce cancer-stromal cell-cell interactions. This study addresses a critical unmet need in the field by elucidating the prostate tumor xenograft stiffness range and establishing a foundation for recapitulating the biomechanics of site-of-origin and soft tissue metastatic prostate tumors in vitro.

Multiplex immunohistochemical phenotyping of T cells in primary prostate cancer

BACKGROUND

Most prostate cancers are "immune cold" and poorly responsive to immune checkpoint inhibitors. However, the mechanisms responsible for the lack of a robust antitumor adaptive immune response in the prostate are poorly understood, which hinders the development of novel immunotherapeutic approaches.

AIMS

Most inflammatory infiltrates in the prostate are centered around benign glands and stroma, which can confound the molecular characterization of the antitumor immune response. We sought to analytically validate a chromogenic-based multiplex immunohistochemistry (IHC) approach applicable to whole slide digital image analysis to quantify T cell subsets from the tumor microenvironment of primary prostatic adenocarcinomas. As an initial application, we tested the hypothesis that PTEN loss leads to an altered antitumor immune response by comparing matched regions of tumors within the same individual with and without PTEN loss.

MATERIALS & METHODS

Using the HALO Image Analysis Platform (Indica Labs), we trained a classifier to quantify the densities of eight T cell phenotypes separately in the tumor epithelial and stromal subcompartments.

RESULTS

The iterative chromogenic approach using 7 different antibodies on the same slide provides highly similar findings to results using individually stained slides with single antibodies. Our main findings in carcinomas (benign removed) include the following: i) CD4+ T cells are present at higher density than CD8+ T cells; ii) all T cell subsets are present at higher densities in the stromal compartment compared to the epithelial tumor compartment; iii) most CD4+ and CD8+ T cells are PD1+; iv) cancer foci with PTEN loss harbored increased numbers of T cells compared to regions without PTEN loss, in both stromal and epithelial compartments; and v) the increases in T cells in PTEN loss regions were associated with ERG gene fusion status.

DISCUSSION

This modular approach can apply to any IHC-validated antibody combination and sets the groundwork for more detailed spatial analyses.

CONCLUSION

Iterative chromogenic IHC can be used for whole slide analysis of prostate tissue samples and can complement transcriptomic results including those using single cell and spatial genomic approaches.

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.

Patterns of indolence in prostate cancer (Review)

Although prostate cancer is a major cause of cancer-related mortality worldwide, most patients will have a relatively indolent clinical course. Contrary to most other types of cancer, even the diagnosis of locally advanced or metastatic disease is not always lethal. The present review aimed to summarize what is known regarding the underlying mechanisms related to the indolent course of subsets of prostate cancer, at various stages. The data suggested that no specific gene alteration by itself was responsible for carcinogenesis or disease aggressiveness. However, pathway analysis identified genetic aberrations in multiple critical pathways that tend to accumulate over the course of the disease. The progression from indolence into aggressive disease is associated with a complex interplay in which genetic and epigenetic factors are involved. The effect of the immune tumor microenvironment is also very important. Emerging evidence has suggested that the upregulation of pathways related to cellular aging and senescence can identify patients with indolent disease. In addition, a number of tumors enter a long-lasting quiescent state. Further research will determine whether halting tumor evolution is a feasible option, and whether the life of patients can be markedly prolonged by inducing tumor senescence or long-term dormancy.

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer

Some clinically significant prostate cancers are missed by MRI. We asked whether the tumor stroma in surgically treated localized prostate cancer lesions positive or negative with MRI are different in their cellular and molecular properties, and whether the differences are reflected to the clinical course of the disease. We profiled the stromal and immune cell composition of MRI-classified tumor lesions by applying multiplexed fluorescence IHC (mfIHC) and automated image analysis in a clinical cohort of 343 patients (cohort I). We compared stromal variables between MRI-visible lesions, invisible lesions, and benign tissue and assessed the predictive significance for biochemical recurrence (BCR) and disease-specific survival (DSS) using Cox regression and log-rank analysis. Subsequently, we carried out a prognostic validation of the identified biomarkers in a population-based cohort of 319 patients (cohort II). MRI true-positive lesions are different from benign tissue and MRI false-negative lesions in their stromal composition. CD163⁺ cells (macrophages) and fibroblast activation protein (FAP)⁺ cells were more abundant in MRI true-positive than in MRI false-negative lesions or benign areas. In MRI true-visible lesions, a high proportion of stromal FAP⁺ cells was associated with PTEN status and increased immune infiltration (CD8⁺, CD163⁺), and predicted elevated risk for BCR. High FAP phenotype was confirmed to be a strong indicator of poor prognosis in two independent patient cohorts using also conventional IHC. The molecular composition of the tumor stroma may determine whether early prostate lesions are detectable by MRI and associates with survival after surgical treatment.

Significance

These findings may have a significant impact on clinical decision making as more radical treatments may be recommended for men with a combination of MRI-visible primary tumors and FAP⁺ tumor stroma.

An optimized MALDI MSI protocol for spatial detection of tryptic peptides in fresh frozen prostate tissue

MALDI MS imaging (MSI) is a powerful analytical tool for spatial peptide detection in heterogeneous tissues. Proper sample preparation is crucial to achieve high quality, reproducible measurements. Here we developed an optimized protocol for spatially resolved proteolytic peptide detection with MALDI time‐of‐flight MSI of fresh frozen prostate tissue sections. The parameters tested included four different tissue washes, four methods of protein denaturation, four methods of trypsin digestion (different trypsin densities, sprayers, and incubation times), and five matrix deposition methods (different sprayers, settings, and matrix concentrations). Evaluation criteria were the number of detected and excluded peaks, percentage of high mass peaks, signal‐to‐noise ratio, spatial localization, and average intensities of identified peptides, all of which were integrated into a weighted quality evaluation scoring system. Based on these scores, the optimized protocol included an ice‐cold EtOH+H₂O wash, a 5 min heating step at 95°C, tryptic digestion incubated for 17h at 37°C and CHCA matrix deposited at a final amount of 1.8 μg/mm². Including a heat‐induced protein denaturation step after tissue wash is a new methodological approach that could be useful also for other tissue types. This optimized protocol for spatial peptide detection using MALDI MSI facilitates future biomarker discovery in prostate cancer and may be useful in studies of other tissue types.

Prognostic implications of tumor-infiltrating T cells in early-stage endometrial cancer

Patients with endometrial cancer differ in terms of the extent of T-cell infiltration; however, the association between T-cell subpopulations and patient outcomes remains unexplored. We characterized 285 early-stage endometrial carcinoma samples for T-cell infiltrates in a tissue microarray format using multiplex fluorescent immunohistochemistry. The proportion of T cells and their subpopulations were associated with clinicopathological features and relapse-free survival outcomes. CD3+ CD4+ infiltrates were more abundant in the patients with higher grade or non-endometrioid histology. Cytotoxic T cells (CD25+, PD-1+, and PD-L1+) were strongly associated with longer relapse-free survival. Moreover, CD3+ PD-1+ stromal cells were independent of other immune T-cell populations and clinicopathological factors in predicting relapses. Patients with high stromal T-cell fraction of CD3+ PD-1+ cells were associated with a 5-year relapse-free survival rate of 93.7% compared to 79.0% in patients with low CD3+ PD-1+ fraction. Moreover, in patients classically linked to a favorable outcome (such as endometrioid subtype and low-grade tumors), the stromal CD3+ PD-1+ T-cell fraction remained prognostically significant. This study supports that T-cell infiltrates play a significant prognostic role in early-stage endometrial carcinoma. Specifically, CD3+ PD-1+ stromal cells emerge as a promising novel prognostic biomarker.

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

High tumor cell platelet-derived growth factor receptor beta expression is associated with shorter survival in malignant pleural epithelioid mesothelioma

Malignant pleural mesothelioma (MPM) has a rich stromal component containing mesenchymal fibroblasts. However, the properties and interplay of MPM tumor cells and their surrounding stromal fibroblasts are poorly characterized. Our objective was to spatially profile known mesenchymal markers in both tumor cells and associated fibroblasts and correlate their expression with patient survival. The primary study cohort consisted of 74 MPM patients, including 16 patients who survived at least 60 months. We analyzed location-specific tissue expression of seven fibroblast markers in clinical samples using multiplexed fluorescence immunohistochemistry (mfIHC) and digital image analysis. Effect on survival was assessed using Cox regression analyses. The outcome measurement was all-cause mortality. Univariate analysis revealed that high expression of secreted protein acidic and cysteine rich (SPARC) and fibroblast activation protein in stromal cells was associated with shorter survival. Importantly, high expression of platelet-derived growth factor receptor beta (PDGFRB) in tumor cells, but not in stromal cells, was associated with shorter survival (hazard ratio [HR] = 1.02, p < 0.001). A multivariable survival analysis adjusted for clinical parameters and stromal mfIHC markers revealed that tumor cell PDGFRB and stromal SPARC remained independently associated with survival (HR = 1.01, 95% confidence interval [CI] = 1.00-1.03 and HR = 1.05, 95% CI = 1.00-1.11, respectively). The prognostic effect of PDGFRB was validated with an artificial intelligence-based analysis method and further externally validated in another cohort of 117 MPM patients. In external validation, high tumor cell PDGFRB expression associated with shorter survival, especially in the epithelioid subtype. Our findings suggest PDGFRB and SPARC as potential markers for risk stratification and as targets for therapy.

DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.

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A novel method for automated annotation of immunohistochemistry images.

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Introduction of an uncertainty metric, the DeepHistoClass (DHC) confidence score.

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Increased accuracy of automated image predictions.

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Identification of manual annotation errors.

Overcoming stromal barriers to immuno-oncological responses via fibroblast activation protein-targeted therapy

The tumor microenvironment contributes to disease progression through multiple mechanisms, including immune suppression mediated in part by fibroblast activation protein (FAP)-expressing cells. Herein, a review of FAP biology is presented, supplemented with primary data. This includes FAP expression in prostate cancer and activation of latent reservoirs of TGF-β and VEGF to produce a positive feedback loop. This collectively suggests a normal wound repair process subverted during cancer pathophysiology. There has been immense interest in targeting FAP for diagnostic, monitoring and therapeutic purposes. Until recently, this development has outpaced an understanding of the biology; impeding optimal translation into the clinic. A summary of these applications is provided with an emphasis on eliminating tumor-infiltrating FAP-positive cells to overcome stromal barriers to immuno-oncological responses.

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Tumors strongly depend on their surrounding tumor microenvironment (TME) for growth and progression, since stromal elements are required to generate the optimal conditions for cancer cell proliferation, invasion, and possibly metastasis. Prostate cancer (PCa), though easily curable during primary stages, represents a clinical challenge in advanced stages because of the acquisition of resistance to anti-cancer treatments, especially androgen-deprivation therapies (ADT), which possibly lead to uncurable metastases such as those affecting the bone. An increasing number of studies is giving evidence that prostate TME components, especially cancer-associated fibroblasts (CAFs), which are the most abundant cell type, play a causal role in PCa since the very early disease stages, influencing therapy resistance and metastatic progression. This is highlighted by the prognostic value of the analysis of stromal markers, which may predict disease recurrence and metastasis. However, further investigations on the molecular mechanisms of tumor-stroma interactions are still needed to develop novel therapeutic approaches targeting stromal components. In this review, we report the current knowledge of the characteristics and functions of the stroma in prostate tumorigenesis, including relevant discussion of normal prostate homeostasis, chronic inflammatory conditions, pre-neoplastic lesions, and primary and metastatic tumors. Specifically, we focus on the role of CAFs, to point out their prognostic and therapeutic potential in PCa.

Alterations in the methylome of the stromal tumour microenvironment signal the presence and severity of prostate cancer

Background

Prostate cancer changes the phenotype of cells within the stromal microenvironment, including fibroblasts, which in turn promote tumour progression. Functional changes in prostate cancer-associated fibroblasts (CAFs) coincide with alterations in DNA methylation levels at loci-specific regulatory regions. Yet, it is not clear how these methylation changes compare across CAFs from different patients. Therefore, we examined the consistency and prognostic significance of genome-wide DNA methylation profiles between CAFs from patients with different grades of primary prostate cancer.

Results

We used Infinium MethylationEPIC BeadChips to evaluate genome-wide DNA methylation profiles from 18 matched CAFs and non-malignant prostate tissue fibroblasts (NPFs) from men with moderate to high grade prostate cancer, as well as five unmatched benign prostate tissue fibroblasts (BPFs) from men with benign prostatic hyperplasia. We identified two sets of differentially methylated regions (DMRs) in patient CAFs. One set of DMRs reproducibly differed between CAFs and fibroblasts from non-malignant tissue (NPFs and BPFs). Indeed, more than 1200 DMRs consistently changed in CAFs from every patient, regardless of tumour grade. The second set of DMRs varied between CAFs according to the severity of the tumour. Notably, hypomethylation of the EDARADD promoter occurred specifically in CAFs from high-grade tumours and correlated with increased transcript abundance and increased EDARADD staining in patient tissue. Across multiple cohorts, tumours with low EDARADD DNA methylation and high EDARADD mRNA expression were consistently associated with adverse clinical features and shorter recurrence free survival.

Conclusions

We identified a large set of DMRs that are commonly shared across CAFs regardless of tumour grade and outcome, demonstrating highly consistent epigenome changes in the prostate tumour microenvironment. Additionally, we found that CAFs from aggressive prostate cancers have discrete methylation differences compared to CAFs from moderate risk prostate cancer. Together, our data demonstrates that the methylome of the tumour microenvironment reflects both the presence and the severity of the prostate cancer and, therefore, may provide diagnostic and prognostic potential.

CD66b+ neutrophils and α-SMA+ fibroblasts predict clinical outcomes and benefits from postoperative chemotherapy in gastric adenocarcinoma

Background

Emerging evidence indicates that the tumor microenvironment (TME) influences tumor progression through the various cells it contains. Tumor‐associated neutrophils (TANs) and cancer‐associated fibroblasts (CAFs) are prominent constituents of diverse malignant solid tumors and are crucial in the TME and cancer evolution. However, the relationships and combined prognostic value of these two cell types are not known in gastric adenocarcinoma (GAC).

Materials and Methods

In total, 215 GAC patients who underwent curative surgery were enrolled. TANs were assessed by immunohistochemical staining for CD66b, and CAFs were evaluated by immunohistochemical staining for α‐smooth muscle actin (α‐SMA).

Results

The percentages of patients with high‐density TANs and CAFs in GAC tissue were 47.9% (103/215) and 43.3% (93/215), respectively. The densities of TANs and CAFs in GAC tissue samples were markedly elevated and independently correlated with GAC clinical outcomes. A strong correlation (R = .348, P < .001) was detected between TANs and CAFs in GAC. The combination of TANs and CAFs produced a more exact outcome than either factor alone. Patients with an α‐SMA^lowCD66b^high (hazard ratio [HR] = 1.791; 95% CI: 1.062‐3.021; P = .029), α‐SMA^highCD66b^low (HR = 2.402; 95% CI: 1.379‐4.183; P = .002), or α‐SMA^highCD66b^high (HR = 3.599; 95% CI: 2.330‐5.560; P < .001) phenotype were gradually correlated with poorer disease‐free survival than the subset of patients with an α‐SMA^lowCD66b^low phenotype. The same results were observed for disease‐specific survival in the subgroups. Noticeably, in stage II‐III patients with the α‐SMA^lowCD66b^low phenotype, an advantage was obtained with postoperative chemotherapeutics, and the risk of a poor prognosis was reduced compared with stage II‐III patients with the α‐SMA^lowCD66b^high, α‐SMA^highCD66b^low or α‐SMA^highCD66b^high phenotype (HR: 0.260, 95% CI: 0.124‐0.542, P < .001 for disease‐free survival; and HR: 0.258, 95% CI: 124‐0.538, P < .001 for disease‐specific survival).

Conclusion

Overall, we concluded that the combination of CD66b⁺ TANs and α‐SMA⁺ CAFs could be used as an independent factor for patient outcomes and to identify GAC patients who might benefit from the administration of postoperative chemotherapeutics.

Complexity of Neural Component of Tumor Microenvironment in Prostate Cancer

The tumor microenvironment (TME) plays an essential role in the development and progression of neoplasms. TME consists of the extracellular matrix and numerous specialized cells interacting with cancer cells by paracrine and autocrine mechanisms. Tumor axonogenesis and neoneurogenesis constitute a developing area of investigation. Prostate cancer (PC) is one of the most common malignancies in men worldwide. During the past years, more and more studies have shown that mechanisms leading to the development of PC are not confined only to the epithelial cancer cell, but also involve the tumor stroma. Different nerve types and neurotransmitters present within the TME are thought to be important factors in PC biology. Moreover, perineural invasion, which is a common way of PC spreading, in parallel creates the neural niche for malignant cells. Cancer neurobiology seems to have become a new discipline to explore the contribution of neoplastic cell interactions with the nervous system and the neural TME component, also to search for potential therapeutic targets in malignant tumors such as PC.

Fibroblast as a critical stromal cell type determining prognosis in prostate cancer

BACKGROUND

Tumor stroma associates with prostate cancer (PCa) progression, but its specific cellular composition and association to patient survival outcome have not been characterized.

METHODS

We analyzed stromal composition in human PCa using multiplex immunohistochemistry and quantitative, high-resolution image analysis in two retrospective, formalin-fixed paraffin embedded observational clinical cohorts (Cohort I, n = 117; Cohort II, n = 340) using PCa-specific mortality as outcome measurement.

RESULTS

A high proportion of fibroblasts associated with aggressive disease and castration-resistant prostate cancer (CRPC). In a multivariate analysis, increase in fibroblast proportion predicted poor cancer-specific outcome independently in the two clinical cohorts studied.

CONCLUSIONS

Fibroblasts were the most important cell type in determining prognosis in PCa and associated with CRPC. Thus, the stromal composition could be critically important in developing diagnostic and therapeutic approaches to aggressive prostate cancer.