Identification of prognostic molecular features in the reactive stroma of human breast and prostate cancer

Mesenteric fibrosis in patients with small intestinal neuroendocrine tumors is associated with enrichment of alpha-smooth muscle actin-positive fibrosis and COMP-expressing stromal cells

Neuroendocrine tumors of the small intestine (SI-NETs) often develop lymph node metastasis (LNM)-induced mesenteric fibrosis (MF). MF can cause intestinal obstruction as well as ischemia and render surgical resection technically challenging. The underlying pathomechanisms of MF are still not well understood. We examined mesenteric LNM and the surrounding stroma compartment from 24 SI-NET patients, including 11 with in situ presentation of strong MF (MF+) and 13 without MF (MF-). Differential gene expression was assessed with the HTG EdgeSeq Oncology Biomarker Panel comparing MF+ with MF- within LNM and paired stromal samples, respectively. Most interesting differentially expressed genes were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in combination with validation of associated protein levels utilizing immunohistochemistry (IHC) staining of MF+ and MF- formalin-fixed, paraffin-embedded (FFPE) patient samples. Overall, 14 genes measured with a 2549-gene expression panel were differentially expressed in MF+ patients compared to MF-. Of those, nine were differentially expressed genes in LNM and five genes in the stromal tissue (>2-fold change, p < .05). The top hits included increased COMP and COL11A1 expression in the stroma of MF+ patients compared to MF-, as well as decreased HMGA2, COL6A6, and SLC22A3 expression in LNM of MF+ patients compared to LNM of MF- patients. RT-qPCR confirmed high levels of COMP and COL11A1 in stroma samples of MF+ compared to MF- patients. IHC staining confirmed the enrichment of α-smooth muscle actin-positive fibrosis in MF+ compared to MF- patients with corresponding increase of COMP-expressing stromal cells in MF+. Since COMP is associated with the known driver for fibrosis development transforming growth factor beta and with a cancer-associated fibroblasts enriched environment, it seems to be a promising new target for MF research.

ADAMTS18 deficiency associates extracellular matrix dysfunction with a higher risk of HER2-positive mammary tumorigenesis and metastasis

BACKGROUND

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer accounts for about 20% of all breast cancer cases and is correlated with a high relapse rate and poor prognosis. ADAMTS18 is proposed as an important functional tumor suppressor gene involved in multiple malignancies, including breast cancer. It functions as an extracellular matrix (ECM) modifier. However, it remains unclear whether ADAMTS18 affects mammary tumorigenesis and malignant progression through its essential ECM regulatory function.

METHODS

To elucidate the role of ADAMTS18 in HER2-positive mammary tumorigenesis and metastasis in vivo, we compared the incidence of mammary tumor and metastasis between Adamts18-knockout (MMTV)-Her2/ErbB2/Neu+ transgenic mice (i.e., Her2t/w/Adamts18-/-) and Adamts18-wildtype (MMTV)-Her2/ErbB2/Neu+ transgenic mice (i.e., Her2t/w/Adamts18+/+). The underlying mechanisms by which ADAMTS18 regulates HER2-positive tumorigenesis and metastasis were investigated by pathology, cell culture, Western blot and immunochemistry.

RESULTS

Adamts18 mRNA is mainly expressed in myoepithelial cells of the mammary duct. ADAMTS18 deficiency leads to a significantly increased incidence of mammary tumors and metastasis, as well as mammary hyperplasia in mice, over 30 months of observation. The proliferation, migration and invasion capacities of primary Her2t/w/Adamts18-/- mammary tumor cells are significantly higher than those of primary Her2t/w/Adamts18+/+ mammary tumor cells in vitro. At 30 months of age, the expression levels of laminin (LNα5), fibronectin (FN) and type I collagen (ColI) in the mammary glands of Her2t/w/Adamts18-/- mice are significantly increased, and the activities of integrin-mediated PI3K/AKT, ERK and JNK signaling pathways are enhanced.

CONCLUSIONS

ADAMTS18 deficiency leads to alterations in mammary ECM components (e.g., LNα5, FN, ColI), which are associated with a higher risk of HER2-positive mammary tumorigenesis and metastasis.

Role of autophagy in cancer-associated fibroblast activation, signaling and metabolic reprograming

Tumors not only consist of cancerous cells, but they also harbor several normal-like cell types and non-cellular components. cancer-associated fibroblasts (CAFs) are one of these cellular components that are found predominantly in the tumor stroma. Autophagy is an intracellular degradation and quality control mechanism, and recent studies provided evidence that autophagy played a critical role in CAF formation, metabolic reprograming and tumor-stroma crosstalk. Therefore, shedding light on the autophagy and its role in CAF biology might help us better understand the roles of CAFs and the TME in cancer progression and may facilitate the exploitation of more efficient cancer diagnosis and treatment. Here, we provide an overview about the involvement of autophagy in CAF-related pathways, including transdifferentiation and activation of CAFs, and further discuss the implications of targeting tumor stroma as a treatment option.

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-β. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

Defined extracellular matrix compositions support stiffness-insensitive cell spreading and adhesion signaling

Integrin-dependent adhesion to the extracellular matrix (ECM) mediates mechanosensing and signaling in response to altered microenvironmental conditions. In order to provide tissue- and organ-specific cues, the ECM is composed of many different proteins that temper the mechanical properties and provide the necessary structural diversity. Despite most human tissues being soft, the prevailing view from predominantly in vitro studies is that increased stiffness triggers effective cell spreading and activation of mechanosensitive signaling pathways. To address the functional coupling of ECM composition and matrix rigidity on compliant substrates, we developed a matrix spot array system to screen cell phenotypes against different ECM mixtures on defined substrate stiffnesses at high resolution. We applied this system to both cancer and normal cells and surprisingly identified ECM mixtures that support stiffness-insensitive cell spreading on soft substrates. Employing the motor-clutch model to simulate cell adhesion on biochemically distinct soft substrates, with varying numbers of available ECM-integrin-cytoskeleton (clutch) connections, we identified conditions in which spreading would be supported on soft matrices. Combining simulations and experiments, we show that cell spreading on soft is supported by increased clutch engagement on specific ECM mixtures and even augmented by the partial inhibition of actomyosin contractility. Thus, "stiff-like" spreading on soft is determined by a balance of a cell's contractile and adhesive machinery. This provides a fundamental perspective for in vitro mechanobiology studies, identifying a mechanism through which cells spread, function, and signal effectively on soft substrates.

Novel CAF-identifiers via transcriptomic and protein level analysis in HNSC patients

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct "CAF-markers" which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

Exosomal MicroRNA and Protein Profiles of Hepatitis B Virus-Related Hepatocellular Carcinoma Cells

Infection with hepatitis B virus (HBV) is a main risk factor for hepatocellular carcinoma (HCC). Extracellular vesicles, such as exosomes, play an important role in tumor development and metastasis, including regulation of HBV-related HCC. In this study, we have characterized exosome microRNA and proteins released in vitro from hepatitis B virus (HBV)-related HCC cell lines SNU-423 and SNU-182 and immortalized normal hepatocyte cell lines (THLE2 and THLE3) using microRNA sequencing and mass spectrometry. Bioinformatics, including functional enrichment and network analysis, combined with survival analysis using data related to HCC in The Cancer Genome Atlas (TCGA) database, were applied to examine the prognostic significance of the results. More than 40 microRNAs and 200 proteins were significantly dysregulated (p < 0.05) in the exosomes released from HCC cells in comparison with the normal liver cells. The functional analysis of the differentially expressed exosomal miRNAs (i.e., mir-483, mir-133a, mir-34a, mir-155, mir-183, mir-182), their predicted targets, and exosomal differentially expressed proteins (i.e., POSTN, STAM, EXOC8, SNX9, COL1A2, IDH1, FN1) showed correlation with pathways associated with HBV, virus activity and invasion, exosome formation and adhesion, and exogenous protein binding. The results from this study may help in our understanding of the role of HBV infection in the development of HCC and in the development of new targets for treatment or non-invasive predictive biomarkers of HCC.

Differential translation of mRNA isoforms underlies oncogenic activation of cell cycle kinase Aurora A

Aurora Kinase A (AURKA) is an oncogenic kinase with major roles in mitosis, but also exerts cell cycle- and kinase-independent functions linked to cancer. Therefore, control of its expression, as well as its activity, is crucial. A short and a long 3'UTR isoform exist for AURKA mRNA, resulting from alternative polyadenylation (APA). We initially observed that in triple-negative breast cancer, where AURKA is typically overexpressed, the short isoform is predominant and this correlates with faster relapse times of patients. The short isoform is characterized by higher translational efficiency since translation and decay rate of the long isoform are targeted by hsa-let-7a tumor-suppressor miRNA. Additionally, hsa-let-7a regulates the cell cycle periodicity of translation of the long isoform, whereas the short isoform is translated highly and constantly throughout interphase. Finally, disrupted production of the long isoform led to an increase in proliferation and migration rates of cells. In summary, we uncovered a new mechanism dependent on the cooperation between APA and miRNA targeting likely to be a route of oncogenic activation of human AURKA.

What is new in cancer-associated fibroblast biomarkers?

The tumor microenvironment is one of the important drivers of tumor development. Cancer-associated fibroblasts (CAFs) are a major component of the tumor stroma and actively participate in tumor development, invasion, metastasis, drug resistance, and other biological behaviors. CAFs are a highly heterogeneous group of cells, a reflection of the diversity of their origin, biomarkers, and functions. The diversity of CAF origin determines the complexity of CAF biomarkers, and CAF subpopulations expressing different biomarkers may play contrasting roles in tumor progression. In this review, we provide an overview of these emerging CAF biomarkers and the biological functions that they suggest, which may give a better understanding of the relationship between CAFs and tumor cells and be of great significance for breakthroughs in precision targeted therapy for tumors. Video Abstract.

ATP8B1: A prognostic prostate cancer biomarker identified via genetic analysis

BACKGROUND

Controlling the asymmetric distribution of phospholipids across biological membranes plays a pivotal role in the life cycle of cells; one of the most important contributors that maintain this lipid asymmetry are phospholipid-transporting adenosine triphosphatases (ATPases). Although sufficient information regarding their association with cancer exists, there is limited evidence linking the genetic variants of phospholipid-transporting ATPase family genes to prostate cancer in humans.

METHODS

In this study, we investigated the association of 222 haplotype-tagging single-nucleotide polymorphisms (SNPs) in eight phospholipid-transporting ATPase genes with cancer-specific survival (CSS) and overall survival (OS) of 630 patients treated with androgen-deprivation therapy (ADT) for prostate cancer.

RESULTS

After multivariate Cox regression analysis and multiple testing correction, we found that ATP8B1 rs7239484 was remarkably associated with CSS and OS after ADT. A pooled analysis of multiple independent gene-expression datasets demonstrated that ATP8B1 was under-expressed in tumor tissues and that a higher ATP8B1 expression was associated with a better patient prognosis. Moreover, we established highly invasive sublines using two human prostate cancer cell lines to mimic cancer progression traits in vitro. The expression of ATP8B1 was consistently downregulated in both highly invasive sublines.

CONCLUSION

Our study indicates that rs7239484 is a prognostic factor for patients treated with ADT and that ATP8B1 can potentially attenuate prostate cancer progression.

FASN multi-omic characterization reveals metabolic heterogeneity in pancreatic and prostate adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) and prostate cancer (PCa) are among the most prevalent malignant tumors worldwide. There is now a comprehensive understanding of metabolic reprogramming as a hallmark of cancer. Fatty acid synthase (FASN) is a key regulator of the lipid metabolic network, providing energy to favor tumor proliferation and development. Whereas the biological role of FASN is known, its response and sensitivity to inhibition have not yet been fully established in these two cancer settings.

METHODS

To evaluate the association between FASN expression, methylation, prognosis, and mutational profile in PDAC and PCa, we interrogated public databases and surveyed online platforms using TCGA data. The STRING database was used to investigate FASN interactors, and the Gene Set Enrichment Analysis platform Reactome database was used to perform an enrichment analysis using data from RNA sequencing public databases of PDAC and PCa. In vitro models using PDAC and PCa cell lines were used to corroborate the expression of FASN, as shown by Western blot, and the effects of FASN inhibition on cell proliferation/cell cycle progression and mitochondrial respiration were investigated with MTT, colony formation assay, cell cycle analysis and MitoStress Test.

RESULTS

The expression of FASN was not modulated in PDAC compared to normal pancreatic tissues, while it was overexpressed in PCa, which also displayed a different level of promoter methylation. Based on tumor grade, FASN expression decreased in advanced stages of PDAC, but increased in PCa. A low incidence of FASN mutations was found for both tumors. FASN was overexpressed in PCa, despite not reaching statistical significance, and was associated with a worse prognosis than in PDAC. The biological role of FASN interactors correlated with lipid metabolism, and GSEA indicated that lipid-mediated mitochondrial respiration was enriched in PCa. Following validation of FASN overexpression in PCa compared to PDAC in vitro, we tested TVB-2640 as a FASN inhibitor. PCa proliferation arrest was modulated by FASN inhibition in a dose- and time-dependent manner, whereas PDAC proliferation was not altered. In line with this finding, mitochondrial respiration was found to be more affected in PCa than in PDAC. FASN inhibition interfered with metabolic signaling causing lipid accumulation and affecting cell viability with an impact on the replicative processes.

CONCLUSIONS

FASN exhibited differential expression patterns in PDAC and PCa, suggesting a different evolution during cancer progression. This was corroborated by the fact that both tumors responded differently to FASN inhibition in terms of proliferative potential and mitochondrial respiration, indicating that its use should reflect context specificity.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

Cancer-Associated Fibroblasts: Tumorigenicity and Targeting for Cancer Therapy

Simple Summary

Cancer-associated fibroblasts (CAFs) are found in the tumor microenvironment and exhibit several protumorigenic functions. Preclinical studies suggest that CAFs can be reduced, eliminated, or reprogrammed; however, clinical translation has not yet occurred. A better understanding of these cells and their functions will undoubtedly improve cancer treatments. In this review, we summarize current research, highlight major challenges, and discuss future opportunities for improving our knowledge of CAF biology and targeting.

Abstract

Cancer-associated fibroblasts (CAFs) are a heterogenous group of activated fibroblasts and a major component of the tumor stroma. CAFs may be derived from fibroblasts, epithelial cells, endothelial cells, cancer stem cells, adipocytes, pericytes, or stellate cells. These complex origins may underlie their functional diversity, which includes pro-tumorigenic roles in extracellular matrix remodeling, the suppression of anti-tumor immunity, and resistance to cancer therapy. Several methods for targeting CAFs to inhibit tumor progression and enhance anti-tumor immunity have recently been reported. While preclinical studies have shown promise, to date they have been unsuccessful in human clinical trials against melanoma, breast cancer, pancreas cancer, and colorectal cancers. This review summarizes recent and major advances in CAF-targeting therapies, including DNA-based vaccines, anti-CAF CAR-T cells, and modifying and reprogramming CAF functions. The challenges in developing effective anti-CAF treatment are highlighted, which include CAF heterogeneity and plasticity, the lack of specific target markers for CAFs, the limitations in animal models recapitulating the human cancer microenvironment, and the undesirable off-target and systemic side effects. Overcoming these challenges and expanding our understanding of the basic biology of CAFs is necessary for making progress towards safe and effective therapeutic strategies against cancers in human patients.

Potential of the Stromal Matricellular Protein Periostin as a Biomarker to Improve Risk Assessment in Prostate Cancer

Prostate cancer (PCa) ranges from indolent to aggressive tumors that may rapidly progress and metastasize. The switch to aggressive PCa is fostered by reactive stroma infiltrating tumor foci. Therefore, reactive stroma-based biomarkers may potentially improve the early detection of aggressive PCa, ameliorating disease classification. Gene expression profiles of PCa reactive fibroblasts highlighted the up-regulation of genes related to stroma deposition, including periostin and sparc. Here, the potential of periostin as a stromal biomarker has been investigated on PCa prostatectomies by immunohistochemistry. Moreover, circulating levels of periostin and sparc have been assessed in a low-risk PCa patient cohort enrolled in active surveillance (AS) by ELISA. We found that periostin is mainly expressed in the peritumoral stroma of prostatectomies, and its stromal expression correlates with PCa grade and aggressive disease features, such as the cribriform growth. Moreover, stromal periostin staining is associated with a shorter biochemical recurrence-free survival of PCa patients. Interestingly, the integration of periostin and sparc circulating levels into a model based on standard clinico-pathological variables improves its performance in predicting disease reclassification of AS patients. In this study, we provide the first evidence that circulating molecular biomarkers of PCa stroma may refine risk assessment and predict the reclassification of AS patients.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Gene Expression Profile of Stromal Factors in Cancer-Associated Fibroblasts from Prostate Cancer

Recent investigations point at the stromal microenvironment to assess additional diagnostic information and provide new therapeutic targets in cancer. The aim of the study was to contribute to the characterization of the phenotype of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa) compared with normal prostate-associated fibroblasts (NAFs) and fibroblasts from benign prostatic hyperplasia (BPH). Three patient populations were prospectively recruited: 23 patients with new localized PCa, 14 patients with advanced PCa treated with androgenic deprivation therapy (ADT), and 7 patients with BPH. Gene expression of 20 stroma-derived factors, including the androgen receptor (AR), chaperones (HSPA1A and HSF1), growth factors (FGF2, FGF7, FGF10, HGF, PDGFB, and TGFβ), proteins implicated in invasion (MMP2, MMP9, and MMP11), inflammation (IL6, IL17RB, NFκB, and STAT3), and in-stroma/epithelium interaction (CDH11, CXCL12, CXCL14, and FAP), was evaluated. Localized PCa CAFs showed a significant higher expression of FGF7, IL6, MMP2, and MMP11 compared with NAFs or IL17RB compared with BPH fibroblasts, but significantly lower expression of FGF10 and IL17RB compared with NAFs or CXCL14 compared with BPH fibroblasts. In addition, CAFs from ADT-resistant PCa showed significantly higher MMP11 and NFκB but significant lower TGFβ expression compared with CAFs from ADT-sensitive tumors. Our results contribute to defining the CAFs phenotypes associated to PCa progression, which may contribute to the diagnosis and design of alternative therapies in PCa.

Variational Bayes for high-dimensional proportional hazards models with applications within gene expression

MOTIVATION

Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense.

RESULTS

We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as sparse variational Bayes. Our method, based on a mean-field variational approximation, overcomes the high computational cost of Markov chain Monte Carlo, whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk.

AVAILABILITY AND IMPLEMENTATION

our method has been implemented as a freely available R package survival.svb (https://github.com/mkomod/survival.svb).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

Exploring the Wnt Pathway as a Therapeutic Target for Prostate Cancer

Aberrant activation of the Wnt pathway is emerging as a frequent event during prostate cancer that can facilitate tumor formation, progression, and therapeutic resistance. Recent discoveries indicate that targeting the Wnt pathway to treat prostate cancer may be efficacious. However, the functional consequence of activating the Wnt pathway during the different stages of prostate cancer progression remains unclear. Preclinical work investigating the efficacy of targeting Wnt signaling for the treatment of prostate cancer, both in primary and metastatic lesions, and improving our molecular understanding of treatment responses is crucial to identifying effective treatment strategies and biomarkers that help guide treatment decisions and improve patient care. In this review, we outline the type of genetic alterations that lead to activated Wnt signaling in prostate cancer, highlight the range of laboratory models used to study the role of Wnt genetic drivers in prostate cancer, and discuss new mechanistic insights into how the Wnt cascade facilitates prostate cancer growth, metastasis, and drug resistance.

Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.

Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer

BACKGROUND

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking.

METHODS

Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression.

RESULTS

The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine-cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix-receptor interaction. Protein-protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients.

CONCLUSIONS

This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

The role of R-spondin proteins in cancer biology

R-spondin (RSPO) proteins constitute a family of four secreted glycoproteins (RSPO1-4) that have appeared as multipotent signaling ligands. The best-known molecular function of RSPOs lie within their capacity to agonize the Wnt/β-catenin signaling pathway. As RSPOs act upon cognate receptors LGR4/5/6 that are typically expressed by stem cells and progenitor cells, RSPO proteins importantly potentiate Wnt/β-catenin signaling especially within these proliferative stem cell compartments. Since multiple organs express LGR4/5/6 receptors and RSPO ligands within their stem cell niches, RSPOs can exert an influential role in stem cell regulation throughout the body. Inherently, over the last decade a multitude of reports implicated the deregulation of RSPOs in cancer development. First, RSPO2 and RSPO3 gene fusions with concomitant enhanced expression have been identified in colon cancer patients, and proposed as an alternative driver of Wnt/β-catenin hyperactivation that earmarks cancer in the colorectal tract. Moreover, the causal oncogenic capacity of RSPO3 overactivation has been demonstrated in the mouse intestine. As a paradigm organ in this field, most of current knowledge about RSPOs in cancer is derived from studies in the intestinal tract. However, RSPO gene fusions as well as enhanced RSPO expression have been reported in multiple additional cancer types, affecting different organs that involve divergent stem cell hierarchies. Importantly, the emerging oncogenic role of RSPO and its potential clinical utility as a therapeutic target have been recognized and investigated in preclinical and clinical settings. This review provides a survey of current knowledge on the role of RSPOs in cancer biology, addressing the different organs implicated, and of efforts made to explore intervention opportunities in cancer cases with RSPO overrepresentation, including the potential utilization of RSPO as novel therapeutic target itself.

Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer

Distinct T cell infiltration patterns, i.e., immune infiltrated, excluded, and desert, result in different responses to cancer immunotherapies. However, the key determinants and biology underpinning these tumor immune phenotypes remain elusive. Here, we provide a high-resolution dissection of the entire tumor ecosystem through single-cell RNA-sequencing analysis of 15 ovarian tumors. Immune-desert tumors are characterized by unique tumor cell-intrinsic features, including metabolic pathways and low antigen presentation, and an enrichment of monocytes and immature macrophages. Immune-infiltrated and -excluded tumors differ markedly in their T cell composition and fibroblast subsets. Furthermore, our study reveals chemokine receptor-ligand interactions within and across compartments as potential mechanisms mediating immune cell infiltration, exemplified by the tumor cell-T cell cross talk via CXCL16-CXCR6 and stromal-immune cell cross talk via CXCL12/14-CXCR4. Our data highlight potential molecular mechanisms that shape the tumor immune phenotypes and may inform therapeutic strategies to improve clinical benefit from cancer immunotherapies.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Role of opiorphin genes in prostate cancer growth and progression

Background: We describe the first studies investigating a role for opiorphin genes (PROL1, SMR3A and SMR3B) in prostate cancer (PrCa). Materials & methods: Databases and PrCa tissue arrays were screened for opiorphin expression. Xenografted tumor growth of human PrCa cells overexpressing PROL1 was compared with controls in nude mice. Modulated gene expression by overexpression of PROL1 was determined by RNA sequencing. Results: PrCa is associated with overexpression of opiorphin genes. Xenografted androgen-sensitive PrCa cells overexpressing PROL1 developed into tumors in castrated male mice (in contrast to parental cells). PROL1 overexpression modulates expression of genes in angiogenesis, steroid and hypoxic response pathways. Conclusions: Opiorphins promote the development of androgen-insensitive PrCa and activate pathways that potentially overcome the hypoxic barrier generated during tumor growth.

Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge

INTRODUCTION

Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence, and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples.

AREAS COVERED

We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented.

EXPERT OPINION

The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.

Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer

Tumor stroma interaction is known to take a crucial role in cancer growth and progression. In the present study, it was performed gene expression analysis of stroma samples with ovarian and breast cancer through an integrative analysis framework to identify common critical biomolecules at multiomics levels. Gene expression datasets were statistically analyzed to identify common differentially expressed genes (DEGs) by comparing tumor stroma and normal stroma samples. The integrative analyses of DEGs indicated that there were 59 common core genes, which might be feasible to be potential marks for cancer stroma targeted strategies. Reporter molecules (i.e. receptor, transcription factors and miRNAs) were determined through a statistical test employing the hypergeometric probability density function. Afterward, the tumor microenvironment protein-protein interaction and the generic network were reconstructed by using identified reporter molecules and common core DEGs. Through a systems medicine approach, it was determined that hub biomolecules, AR, GATA2, miR-124, TOR1AIP1, ESR1, EGFR, STAT1, miR-192, GATA3, COL1A1, in tumor microenvironment generic network. These molecules were also identified as prognostic signatures in breast and ovarian tumor samples via survival analysis. According to literature searching, GATA2 and TORYAIP1 might represent potential biomarkers and candidate drug targets for the stroma targeted cancer therapy applications.

Evaluating the transcriptional fidelity of cancer models

BACKGROUND

Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors.

METHODS

We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence.

RESULTS

We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity.

CONCLUSIONS

CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

Periostin Short Fragment with Exon 17 via Aberrant Alternative Splicing Is Required for Breast Cancer Growth and Metastasis

BACKGROUND

Periostin (POSTN) is a 93 kDa matrix protein that helps to regulate collagen gene expression in the extracellular matrix. POSTN overexpression is a prognostic factor in malignant cancers; however, some researchers have observed it in the stroma, whereas others have reported it on tumors.

OBJECTIVE

This study aimed to investigate the function of POSTN on tumors.

METHODS AND RESULTS

We found that POSTN in cancer cells can be detected by using an antibody against the POSTN C-terminal region exon 17 (Ex17 antibody), but not with an antibody against the POSTN N-terminal region exon 12 (Ex12 antibody) in patients with breast cancer. In a fraction secreted from fibroblasts, LC-MS/MS analysis revealed a short fragment of POSTN of approximately 40 kDa with exon 17. In addition, molecular interaction analysis showed that POSTN with exon 17, but not POSTN without exon 17, bound specifically to wnt3a, and the Ex17 antibody inhibited the binding.

CONCLUSION

A short fragment of POSTN with exon 17, which originates in the fibroblasts, is transported to cancer cells, whereas POSTN fragments without exon 17 are retained in the stroma. The Ex17 antibody inhibits the binding between POSTN exon 17 and wnt3a.

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

BACKGROUND

The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.

METHODS

Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.

RESULTS

243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.

CONCLUSIONS

UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

Harnessing Extracellular Matrix Biology for Tumor Drug Delivery

The extracellular matrix (ECM) plays an active role in cell life through a tightly controlled reciprocal relationship maintained by several fibrous proteins, enzymes, receptors, and other components. It is also highly involved in cancer progression. Because of its role in cancer etiology, the ECM holds opportunities for cancer therapy on several fronts. There are targets in the tumor-associated ECM at the level of signaling molecules, enzyme expression, protein structure, receptor interactions, and others. In particular, the ECM is implicated in invasiveness of tumors through its signaling interactions with cells. By capitalizing on the biology of the tumor microenvironment and the opportunities it presents for intervention, the ECM has been investigated as a therapeutic target, to facilitate drug delivery, and as a prognostic or diagnostic marker for tumor progression and therapeutic intervention. This review summarizes the tumor ECM biology as it relates to drug delivery with emphasis on design parameters targeting the ECM.

microRNA-211-mediated targeting of the INHBA-TGF-β axis suppresses prostate tumor formation and growth

Prostate cancer (PCa) stem cells increase the sustainability of tumor growth, resulting in high relapse rates in patients with PCa. This goal of the present study was to elucidate the function of microRNA (miR)-211 in PCa stem cell activities. Based on the initial findings from the GSE26910 dataset, inhibin-β A (INHBA) was used for subsequent experiments, and miR-211 was then predicted as a candidate regulatory miR. Subsequently, INHBA and miR-211 were observed to be highly and poorly expressed in PCa tissues, respectively, and miR-211 negatively target INHBA. CD44⁺CD133⁺ cells were isolated, and both miR-211 and INHBA expression was altered in these cells to assess functional role of miR-211 and INHBA in PCa stem cells. Overexpression of miR-211 decreased expression of TGF-β1, TGF-β2, smad2, smad3, phosphorylated smad2 and smad3, and stem cell markers. miR-211 upregulation or INHBA knockdown resulted in reductions in the proliferation, invasion, colony-forming ability, sphere-forming ability, and stemness of PCa stem cells but enhanced their apoptosis in vitro. Furthermore, miR-211 upregulation or INHBA silencing decreased tumor growth and cell apoptosis in vivo. Taken together, these results indicate that upregulation of miR-211 has tumor-suppressive properties by inhibiting TGF-β pathway activation via INHBA in PCa stem cells.

The Landscape of Immune Cells Infiltrating in Prostate Cancer

Background

This study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa.

Methods

Infiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa.

Results

Ten immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the five- and 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible.

Conclusions

This study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients’ prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.

miR-203 inhibits cell proliferation and ERK pathway in prostate cancer by targeting IRS-1

INTRODUCTION

Prostate cancer (PCa) is one of the most common types of cancer in men. In the course of the development and progression of this disease, abnormal expression of miR-203 is usually accompanied. However, its role in prostate tumorigenesis and the underlying mechanism are poorly understood.

METHODS

Dual luciferase reporter gene analysis was used to detect miR-203 binding site in insulin receptor substrates 1 (IRS-1). Cell proliferation was assessed by MTT assay in PCa cells with either IRS-1 knockdown or miR-203 overexpression. IRS-1 and other proteins expression in PCa cells was assessed by Western Blot.

RESULTS

we found that the insulin receptor substrates 1 (IRS-1) is a novel target of miR-203 in PCa and miR-203 can specifically bind to the 3'UTR region of the IRS-1 thus suppresses its expression. Moreover, we demonstrate that miR-203 functions as a tumor suppressor by directly targeting IRS-1 to inhibit cell proliferation and migration which results in PCa cell cycle arrest. Importantly, miR-203 overexpression blocks ERK signalling pathway by down-regulating IRS-1 expression.

CONCLUSIONS

Our results show a novel link between miR-203 and IRS-1, and reveal the importance of strict control of IRS - 1 by miR-203 in the progression of PCa, suggesting miR-203 may act as a promising target for the diagnosis and treatment of advanced PCa.

Zonal regulation of collagen-type proteins and posttranslational modifications in prostatic benign and cancer tissues by imaging mass spectrometry

BACKGROUND

The emergence of reactive stroma is a hallmark of prostate cancer (PCa) progression and a potential source for prognostic and diagnostic markers of PCa. Collagen is a main component of reactive stroma and changes systematically and quantitatively to reflect the course of PCa, yet has remained undefined due to a lack of tools that can define collagen protein structure. Here we use a novel collagen-targeting proteomics approach to investigate zonal regulation of collagen-type proteins in PCa prostatectomies.

METHODS

Prostatectomies from nine patients were divided into zones containing 0%, 5%, 20%, 70% to 80% glandular tissue and 0%, 5%, 25%, 70% by mass of PCa tumor following the McNeal model. Tissue sections from zones were graded by a pathologist for Gleason score, percent tumor present, percent prostatic intraepithelial neoplasia and/or inflammation (INF). High-resolution accurate mass collagen targeting proteomics was done on a select subset of tissue sections from patient-matched tumor or nontumor zones. Imaging mass spectrometry was used to investigate collagen-type regulation corresponding to pathologist-defined regions.

RESULTS

Complex collagen proteomes were detected from all zones. COL17A and COL27A increased in zones of INF compared with zones with tumor present. COL3A1, COL4A5, and COL8A2 consistently increased in zones with tumor content, independent of tumor size. Collagen hydroxylation of proline (HYP) was altered in tumor zones compared with zones with INF and no tumor. COL3A1 and COL5A1 showed significant changes in HYP peptide ratios within tumor compared with zones of INF (2.59 ± 0.29, P value: .015; 3.75 ± 0.96 P value .036, respectively). By imaging mass spectrometry COL3A1 showed defined localization and regulation to tumor pathology. COL1A1 and COL1A2 showed gradient regulation corresponding to PCa pathology across zones. Pathologist-defined tumor regions showed significant increases in COL1A1 HYP modifications compared with COL1A2 HYP modifications. Certain COL1A1 and COL1A2 peptides could discriminate between pathologist-defined tumor and inflammatory regions.

CONCLUSIONS

Site-specific posttranslational regulation of collagen structure by proline hydroxylation may be involved in reactive stroma associated with PCa progression. Translational and posttranslational regulation of collagen protein structure has potential for new markers to understand PCa progression and outcomes.

Ion Channel Profiling in Prostate Cancer: Toward Cell Population-Specific Screening

In the last three decades, a growing number of studies have implicated ion channels in all essential processes of prostate carcinogenesis, including cell proliferation, apoptosis, migration, and angiogenesis. The changes in the expression of individual ion channels show a specific profile, making these proteins promising clinical biomarkers that may enable better molecular subtyping of the disease and lead to more rapid and accurate clinical decision-making. Expression profiles and channel function are mainly based on the tumoral tissue itself, in this case, the epithelial cancer cell population. To date, little data on the ion channel profile of the cancerous prostate stroma are available, even though tumor interactions with the microenvironment are crucial in carcinogenesis and each distinct population plays a specific role in tumor progression. In this review, we describe ion channel expression profiles specific for the distinct cell population of the tumor microenvironment (stromal, endothelial, neuronal, and neuroendocrine cell populations) and the technical approaches used for efficient separation and screening of these cell populations.

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10⁻³⁴ for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

A subset of activated fibroblasts is associated with distant relapse in early luminal breast cancer

Background

Early luminal breast cancer (BC) represents 70% of newly diagnosed BC cases. Among them, small (under 2 cm) BC without lymph node metastasis (classified as T1N0) have been rarely studied, as their prognosis is generally favorable. Nevertheless, up to 5% of luminal T1N0 BC patients relapse with distant metastases that ultimately prove fatal. The aim of our work was to identify the mechanisms involved in metastatic recurrence in these patients.

Methods

Our study addresses the role that autonomous and non-autonomous tumor cell features play with regard to distant recurrence in early luminal BC patients. We created a cohort of T1N0 luminal BC patients (tumors between 0.5–2 cm without lymph node metastasis) with metastatic recurrence (“cases”) and corresponding “controls” (without relapse) matched 1:1 on main prognostic factors: age, grade, and proliferation. We deciphered different characteristics of cancer cells and their tumor micro-environment (TME) by deep analyses using immunohistochemistry. We performed in vitro functional assays and highlighted a new mechanism of cooperation between cancer cells and one particular subset of cancer-associated fibroblasts (CAF).

Results

We found that specific TME features are indicative of relapse in early luminal BC. Indeed, quantitative histological analyses reveal that “cases” are characterized by significant accumulation of a particular CAF subset (CAF-S1) and decrease in CD4⁺ T lymphocytes, without any other association with immune cells. In multivariate analysis, TME features, in particular CAF-S1 enrichment, remain significantly associated with recurrence, thereby demonstrating their clinical relevance. Finally, by performing functional analyses, we demonstrated that CAF-S1 pro-metastatic activity is mediated by the CDH11/osteoblast cadherin, consistent with bones being a major site of metastases in luminal BC patients.

Conclusions

This study shows that distant recurrence in T1N0 BC is strongly associated with the presence of CAF-S1 fibroblasts. Moreover, we identify CDH11 as a key player in CAF-S1-mediated pro-metastatic activity. This is independent of tumor cells and represents a new prognostic factor. These results could assist clinicians in identifying luminal BC patients with high risk of relapse. Targeted therapies against CAF-S1 using anti-FAP antibody or CDH11-targeting compounds might help in preventing relapse for such patients with activated stroma.

Visceral obesity and cell cycle pathways serve as links in the association between bisphenol A exposure and breast cancer

It has been identified that bisphenol A (BPA) exposure causes developmental toxicity in breast cells. However, the exact molecular mechanisms underlying the association between exposure to BPA and breast cancer remain unclear. The aim of the present study was to investigate the BPA-regulated signaling pathways associated with the aggressiveness and the development of breast cancer. Microarray technology and functional gene set analyses were used to evaluate BPA and breast cancer-associated biomarkers and pathways in a discovery-driven manner. Using individual dataset analyses, it was indicated that two BPA-associated gene sets, the visceral obesity pathway, involved in visceral fat deposits and the metabolic syndrome, and the cell cycle pathway, involved in cyclins and cell cycle regulation, were significantly associated with a high grade of aggressiveness and the development of estrogen receptor (ER)-positive breast cancer (between P<0.05 and 0.0001). The pooled analysis indicated that the most significant pathway was G₁/S checkpoint regulation, and the cyclin and cell cycle regulation pathway for BPA-associated ER-positive cancer. Cancer cell signaling pathways were associated with healthy breast cells developing into breast cancer. The visceral obesity and the cell cycle pathways were indicated to link BPA exposure to breast cancer. The results of the present study demonstrate a significant association between breast cancer and BPA-regulated gene pathways.

A new computational drug repurposing method using established disease-drug pair knowledge

MOTIVATION

Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

RESULTS

We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing.

AVAILABILITY AND IMPLEMENTATION

The R scripts are available on demand from the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A paracrine activin A-mDia2 axis promotes squamous carcinogenesis via fibroblast reprogramming

Cancer‐associated fibroblasts (CAFs) are key regulators of tumorigenesis and promising targets for next‐generation therapies. We discovered that cancer cell‐derived activin A reprograms fibroblasts into pro‐tumorigenic CAFs. Mechanistically, this occurs via Smad2‐mediated transcriptional regulation of the formin mDia2, which directly promotes filopodia formation and cell migration. mDia2 also induces expression of CAF marker genes through prevention of p53 nuclear accumulation, resulting in the production of a pro‐tumorigenic matrisome and secretome. The translational relevance of this finding is reflected by activin A overexpression in tumor cells and of mDia2 in the stroma of skin cancer and other malignancies and the correlation of high activin A/mDia2 levels with poor patient survival. Blockade of this signaling axis using inhibitors of activin, activin receptors, or mDia2 suppressed cancer cell malignancy and squamous carcinogenesis in 3D organotypic cultures, ex vivo, and in vivo, providing a rationale for pharmacological inhibition of activin A‐mDia2 signaling in stratified cancer patients.

In search of definitions: Cancer-associated fibroblasts and their markers

The tumor microenvironment has been identified as one of the driving factors of tumor progression and invasion. Inside this microenvironment, cancer-associated fibroblasts (CAFs), a type of perpetually activated fibroblasts, have been implicated to have a strong tumor-modulating effect and play a key role in areas such as drug resistance. Identification of CAFs has typically been carried based on the expression of various "CAF markers", such as fibroblast activation protein alpha (FAP) and alpha smooth muscle actin (αSMA), which separates them from the larger pool of fibroblasts present in the body. However, as outlined in this Review, the expression of various commonly used fibroblast markers is extremely heterogeneous and varies strongly between different CAF subpopulations. As such, novel selection methods based on cellular function, as well as further characterizing research, are vital for the standardization of CAF identification in order to improve the cross-applicability of different research studies in the field. The aim of this review is to give a thorough overview of the commonly used fibroblast markers in the field and their various strengths and, more importantly, their weaknesses, as well as to highlight potential future avenues for CAF identification and targeting.

Know Thy Model: Charting Molecular Homology in Stromal Reprogramming Between Canine and Human Mammary Tumors

Spontaneous canine simple mammary tumors (CMTs) are often viewed as models of human breast cancer. Cancer-associated stroma (CAS) is central for initiation and progression of human cancer, and is likely to play a key role in canine tumors as well. Until recently, however, canine CAS in general, and in CMT in particular, lacked detailed characterization and it remained unclear how canine and human CAS compare. This void in knowledge regarding canine CAS and the resulting lack of unbiased cross-species analysis of molecular homologies and differences undermined the validity of the canine model for human disease. To assess stromal reprogramming in canine breast tumors, we have recently established a protocol to specifically isolate and analyze CAS and matched normal stroma from archival, formalin-fixed paraffin embedded (FFPE) clinical tumor samples using laser-capture microdissection followed by next-generation RNA-sequencing. Using this approach, we have analyzed stromal reprogramming in both malignant canine mammary carcinomas (mCAs) as well as benign canine mammary adenomas in a series of studies. Our results demonstrate strong stromal reprogramming in CMTs and identify high-grade molecular homology between human and canine CAS. Here, I aim to give a short background on the value of comparative oncology in general, and spontaneous CMT in particular. This will be followed by a concise review of the current knowledge of stromal reprogramming in both malignant canine mCA and benign adenoma. Finally, I will conclude with insights on highly conserved aspects of stromal reprogramming between CMT and human breast cancer that accentuate the relevance of CAS in CMT as a model for the human disease.

Addressing Patient Specificity in the Engineering of Tumor Models

Cancer treatment is challenged by the heterogeneous nature of cancer, where prognosis depends on tumor type and disease stage, as well as previous treatments. Optimal patient stratification is critical for the development and validation of effective treatments, yet pre-clinical model systems are lacking in the delivery of effective individualized platforms that reflect distinct patient-specific clinical situations. Advances in cancer cell biology, biofabrication, and microengineering technologies have led to the development of more complex in vitro three-dimensional (3D) models to act as drug testing platforms and to elucidate novel cancer mechanisms. Mostly, these strategies have enabled researchers to account for the tumor microenvironment context including tumor-stroma interactions, a key factor of heterogeneity that affects both progression and therapeutic resistance. This is aided by state-of-the-art biomaterials and tissue engineering technologies, coupled with reproducible and high-throughput platforms that enable modeling of relevant physical and chemical factors. Yet, the translation of these models and technologies has been impaired by neglecting to incorporate patient-derived cells or tissues, and largely focusing on immortalized cell lines instead, contributing to drug failure rates. While this is a necessary step to establish and validate new models, a paradigm shift is needed to enable the systematic inclusion of patient-derived materials in the design and use of such models. In this review, we first present an overview of the components responsible for heterogeneity in different tumor microenvironments. Next, we introduce the state-of-the-art of current in vitro 3D cancer models employing patient-derived materials in traditional scaffold-free approaches, followed by novel bioengineered scaffold-based approaches, and further supported by dynamic systems such as bioreactors, microfluidics, and tumor-on-a-chip devices. We critically discuss the challenges and clinical prospects of models that have succeeded in providing clinical relevance and impact, and present emerging concepts of novel cancer model systems that are addressing patient specificity, the next frontier to be tackled by the field.

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate‐selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal‐specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal‐specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R² of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Bioinformatics Analysis of Stromal Molecular Signatures Associated with Breast and Prostate Cancer

This study aimed to identify stromal molecular signatures associated with breast and prostate cancer. The microarray data GSE26910 was downloaded from Gene Expression Omnibus database, including six invasive breast tumor stroma, six matched normal controls, six invasive prostate tumor stroma, and six matched controls. The differentially expressed genes (DEGs) in invasive breast and prostate tumors stroma were, respectively, identified. Then common stromal genes (B_P.DEGs) were further screened. Protein-protein interaction (PPI) network was constructed and Gene Ontology analysis was performed. Besides, gene-chemical interactions were mapped in Comparative Toxicogenomics Database to screen the chemicals related to feature genes. The results showed that, in total, 16 B_P.DEGs were identified. Thereinto, only seven B_P.DEGs were mapped into PPI, and only four functional modules (adenylate cyclase activating polypeptide 1 (pituitary) receptor type I (ADCYAP1R1) module, aspartoacylase (ASPA) module, glutathione S-transferase mu 5 (GSTM5) module, and periplakin (PPL) module) were involved in important biological processes associated with cancer progression. In addition, the chemicals, such as dihydrotestosterone, apocarotenal, testosterone, and progesterone, were screened for the roles of feature genes in the progression of breast and prostate cancer. In conclusion, ADCYAP1R1, GSTM5, and PPL were stromal molecular signatures and might play a key role in the progression of breast and prostate cancer.

Targeting the tumour stroma to improve cancer therapy

Cancers are not composed merely of cancer cells alone; instead, they are complex 'ecosystems' comprising many different cell types and noncellular factors. The tumour stroma is a critical component of the tumour microenvironment, where it has crucial roles in tumour initiation, progression, and metastasis. Most anticancer therapies target cancer cells specifically, but the tumour stroma can promote the resistance of cancer cells to such therapies, eventually resulting in fatal disease. Therefore, novel treatment strategies should combine anticancer and antistromal agents. Herein, we provide an overview of the advances in understanding the complex cancer cell-tumour stroma interactions and discuss how this knowledge can result in more effective therapeutic strategies, which might ultimately improve patient outcomes.

Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories

Identifying the earliest somatic changes in prostate cancer can give important insights into tumor evolution and aids in stratifying high- from low-risk disease. We integrated whole genome, transcriptome and methylome analysis of early-onset prostate cancers (diagnosis ≤55 years). Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients. Our integrative analysis identified four molecular subgroups, including a particularly aggressive subgroup with recurrent duplications associated with increased expression of ESRP1, which we validate in 12,000 tissue microarray tumors. Finally, we combined the patterns of molecular co-occurrence and risk-based subgroup information to deconvolve the molecular and clinical trajectories of prostate cancer from single patient samples.