Prognostic stromal gene signatures in breast cancer

Breast pericytes: a newly identified driver of tumor cell proliferation

Introduction

Effective treatment of breast cancer remains a formidable challenge, partly due to our limited understanding of the complex microenvironmental factors that contribute to disease pathology. Among these factors are tissue-resident perivascular cells, which play crucial roles in shaping vascular basement membranes, maintaining vessel integrity, and communicating with adjacent endothelial cells. Despite their essential functions, perivascular cells have been relatively overlooked. Identifying them by immunostaining has been challenging due to their low abundance, inherent heterogeneity, and shared marker expression with other cell types. These challenges have hindered efforts to purify pericytes and generate primary cell models for studying their biology.

Methods

Using a recently developed FACS method, we successfully identified and purified each cell type from breast tissues, allowing us to deep-sequence their transcriptomes and generate primary cell models of each cell type-including pericytes. Here, we used these data to analyze cell-type-specific gene expression in tumors, which revealed a strong association between pericyte-specific genes and breast cancer patient mortality. To explore this association, we defined the heterogeneity of breast pericytes using single-cell RNA sequencing and identified a broad marker for visualizing perivascular cells in breast tumors.

Results

Remarkably, we discovered perivascular cells dissociated from vessels and emerged as a dominant mesenchymal cell type in a subset of breast tumors that contrasted with their normal perivascular location. Moreover, when we purified pericytes from the breast and cultured them alongside breast tumor cells, we discovered that they induced rapid tumor cell growth significantly greater than isogenic fibroblast controls.

Discussion

These findings identify perivascular cells as a key microenvironmental factor in breast cancer, highlighting the critical need for further research to explore their biology and identify specific stimulatory mechanisms that could be targeted therapeutically.

Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer.

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.

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.

Postpartum breast cancer has a distinct molecular profile that predicts poor outcomes

Young women's breast cancer (YWBC) has poor prognosis and known interactions with parity. Women diagnosed within 5-10 years of childbirth, defined as postpartum breast cancer (PPBC), have poorer prognosis compared to age, stage, and biologic subtype-matched nulliparous patients. Genomic differences that explain this poor prognosis remain unknown. In this study, using RNA expression data from clinically matched estrogen receptor positive (ER+) cases (n = 16), we observe that ER+ YWBC can be differentiated based on a postpartum or nulliparous diagnosis. The gene expression signatures of PPBC are consistent with increased cell cycle, T-cell activation and reduced estrogen receptor and TP53 signaling. When applied to a large YWBC cohort, these signatures for ER+ PPBC associate with significantly reduced 15-year survival rates in high compared to low expressing cases. Cumulatively these results provide evidence that PPBC is a unique entity within YWBC with poor prognostic phenotypes.

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a 'taxonomy-like' structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other '-omics', data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

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.

Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.

The clinical and molecular significance associated with STING signaling in breast cancer

STING signaling in cancer is a crucial component of response to immunotherapy and other anti-cancer treatments. Currently, there is no robust method of measuring STING activation in cancer. Here, we describe an immunohistochemistry-based assay with digital pathology assessment of STING in tumor cells. Using this novel approach in estrogen receptor-positive (ER+) and ER- breast cancer, we identify perinuclear-localized expression of STING (pnSTING) in ER+ cases as an independent predictor of good prognosis, associated with immune cell infiltration and upregulation of immune checkpoints. Tumors with low pnSTING are immunosuppressed with increased infiltration of "M2"-polarized macrophages. In ER- disease, pnSTING does not appear to have a significant prognostic role with STING uncoupled from interferon responses. Importantly, a gene signature defining low pnSTING expression is predictive of poor prognosis in independent ER+ datasets. Low pnSTING is associated with chromosomal instability, MYC amplification and mTOR signaling, suggesting novel therapeutic approaches for this subgroup.

The complex roles of efferocytosis in cancer development, metastasis, and treatment

When tumor cells are killed by targeted therapy, radiotherapy, or chemotherapy, they trigger their primary tumor by releasing pro-inflammatory cytokines. Microenvironmental interactions can also promote tumor heterogeneity and development. In this line, several immune cells within the tumor microenvironment, including macrophages, dendritic cells, regulatory T-cells, and CD8⁺ and CD4⁺ T cells, are involved in the clearance of apoptotic tumor cells through a process called efferocytosis. Although the efficiency of apoptotic tumor cell efferocytosis is positive under physiological conditions, there are controversies regarding its usefulness in treatment-induced apoptotic tumor cells (ATCs). Efferocytosis can show the limitation of cytotoxic treatments, such as chemotherapy and radiotherapy. Since cytotoxic treatments lead to extensive cell mortality, efferocytosis, and macrophage polarization toward an M2 phenotype, the immune response may get involved in tumor recurrence and metastasis. Tumor cells can use the anti-inflammatory effect of apoptotic tumor cell efferocytosis to induce an immunosuppressive condition that is tumor-tolerant. Since M2 polarization and efferocytosis are tumor-promoting processes, the receptors on macrophages act as potential targets for cancer therapy. Moreover, researchers have shown that efferocytosis-related molecules/pathways are potential targets for cancer therapy. These include phosphatidylserine and calreticulin, Tyro3, Axl, and Mer tyrosine kinase (MerTK), receptors of tyrosine kinase, indoleamine-2,3-dioxygenase 1, annexin V, CD47, TGF-β, IL-10, and macrophage phenotype switch are combined with conventional therapy, which can be more effective in cancer treatment. Thus, we set out to investigate the advantages and disadvantages of efferocytosis in treatment-induced apoptotic tumor cells.

Theranostic Advances in Breast Cancer in Nuclear Medicine

The implication of 'theranostic' refers to targeting an identical receptor for diagnostic and therapeutic purposes, by the same radioligand, simultaneously or separately. In regard to extensive efforts, many considerable theranostic tracers have been developed in recent years. Emerging evidence strongly demonstrates the tendency of nuclear medicine towards therapies based on a diagnosis. This review is focused on the examples of targeted radiopharmaceuticals for the imaging and therapy of breast cancer.

The clinical role of combined serum C1q and hsCRP in predicting coronary artery disease

OBJECTIVE

C1q has been shown to be associated with coronary heart disease (CAD) and can co-deposit with C-reactive protein (CRP) in atherosclerotic plaques. However, few studies have been conducted between C1q, CRP parameters and CAD. The aim of this study is to explore the relationship between C1q and CRP parameters and assess their clinical significance in CAD.

METHODS

238 total patients who underwent coronary artery angiography were enrolled and divided into control group (n = 65), stable CAD group (n = 47) and unstable angina group (UA group, n = 126). Patients' data were collected from self-administered questionnaires and electrical medical records. The severity of coronary stenosis was presented by Gensini score. The relationship between C1q, CRP parameters and CAD were evaluated by multivariate regression analysis and their predicting performance were assessed by ROC analysis and odds ratio analysis.

RESULTS

Compared with control group, C1q was showed significantly lower in stable CAD (P = 0.004) and UA groups (P = 0.008), while hsCRP was higher in UA group (P = 0.024). Serum C1q was weakly positively associated with hsCRP (r = 0.24, P < 0.001) but not correlated with Gensini score. Logistic regression identified C1q (OR: 0.87 per 10 mg/L, 95% CI: 0.79-0.95, P = 0.001) and hsCRP (OR: 1.08 mg/L, 95% CI: 1.01-1.15, P = 0.032) as independent determinants of CAD. Furthermore, combined C1q and hsCRP level showed higher discriminatory accuracy in predicting CAD than C1q (AUC: 0.676 vs 0.585, P = 0.101; NRI: 10.4%, P = 0.049; IDI: 3.9%, P < 0.001) or hsCRP (AUC: 0.676 vs 0.585, P = 0.101; NRI: 16.7%, P = 0.006; IDI: 5.8%, P < 0.001).

CONCLUSIONS

Reduced serum C1q and increased hsCRP are independently associated with CAD and could be potential predictors for CAD diagnosis. Furthermore, combined C1q and hsCRP showed better performance in predicting CAD than using single one.

Clear cell renal cell carcinoma ontogeny and mechanisms of lethality

The molecular features that define clear cell renal cell carcinoma (ccRCC) initiation and progression are being increasingly defined. The TRACERx Renal studies and others that have described the interaction between tumour genomics and remodelling of the tumour microenvironment provide important new insights into the molecular drivers underlying ccRCC ontogeny and progression. Our understanding of common genomic and chromosomal copy number abnormalities in ccRCC, including chromosome 3p loss, provides a mechanistic framework with which to organize these abnormalities into those that drive tumour initiation events, those that drive tumour progression and those that confer lethality. Truncal mutations in ccRCC, including those in VHL, SET2, PBRM1 and BAP1, may engender genomic instability and promote defects in DNA repair pathways. The molecular features that arise from these defects enable categorization of ccRCC into clinically and therapeutically relevant subtypes. Consideration of the interaction of these subtypes with the tumour microenvironment reveals that specific mutations seem to modulate immune cell populations in ccRCC tumours. These findings present opportunities for disease prevention, early detection, prognostication and treatment.

Identification of Prognostic Related Genes of Tumor Microenvironment Derived From Esophageal Cancer Patients

Background and Objective: Esophageal cancer (ESCA) is a commonly occurring cancer worldwide with poor survival and limited therapeutic options. Due to the lack of biomarkers that facilitate early detection, its treatment remains a great challenge. This study aims at identifying the tumor microenvironment (TME)-related genes, which might affect prognosis and accelerate clinical treatment for ESCA patients.

Methods: We integrated the expression profiles from ESCA patients in The Cancer Genome Atlas. Then, we determined the stromal and immune scores of each sample using the R package. The Gene Expression Omnibus database was used to validate the expression profile of the key genes.

Results: Tumor mutational burden showed a significant difference between the groups of ESCA patients with high and low ESTIMATE scores. We identified 859 intersection genes among patients with different immune and stromal scores. Moreover, gene ontology analysis demonstrated that these 859 intersection genes were closely related to adaptive immune response and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes showed the enrichment of cytokine-cytokine receptor interaction and chemokine signaling pathway in the TME. Furthermore, the protein–protein interaction network consisted of 175 nodes. We selected 35 hub genes, including ITGAM, CXCL10, CCR2, CCR5, and CCR1. Of these, 23 intersection genes predicted the overall survival rate. C1QA and FCER1G correlated with overall survival of the ESCA patients in the two databases.

Conclusion: We identified a set of stromal and immune score-related prognostic differentially expressed genes that could influence the complexity of the TME. C1QA and FCER1G were identified and validated with respect to their role in the progression of ESCA.

Omission of Chemotherapy in HR+/HER2- Early Invasive Breast Cancer Based on Combined 6-IHC Score?

PURPOSE

Current methods of judging whether HR+/HER2- breast cancer (BC) require adjuvant therapy, such as Ki67 and multigene prognostic tests, cannot balance accuracy with the price most patients can afford.

METHODS

A retrospective analysis of 330 HR+/HER2- BC patients was conducted. Six BC-related genes (Cathepsin L2, MMP11, CyclinB1, Aurora A, Survivin, and Ki67) were screened using univariate and multivariate COX regression, and correlate clinical follow-up with immunohistochemical expression (designated as 6-IHC). All the included patients were divided randomly at a 7:3 ratio into training and testing cohorts. The cutoff prognosis index (PI) of 6-IHC was determined by multivariate Cox risk regression analysis after calculating the PI of each patient in training cohort and confirmed in testing cohort. Kaplan-Meier (KM) method was used to analyze Disease-free survival (DFS) and overall survival (OS). Six-IHC score and other factors associated with survival benefit of adjuvant chemotherapy were compared with Ki67 index.

RESULTS

The receiver operating characteristic curve analysis showed that the patients can be divided into 6-IHC score "High" and "Low" risk groups. The 8-year DFS and OS of the KM curves showed that chemotherapy did not significantly improve the DFS in the 6-IHC score "Low" risk group (P= 0.830), but significantly improved the DFS in the 6-IHC score "High" risk group (P = 0.012).

CONCLUSIONS

Combined 6-IHC score could be a reliable tool in predicting cancer-specific recurrences and survival in HR+/HER2-breast cancer patients, with additional advantages over using immunohistochemical expression of Ki67.

Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis.

Anti gC1qR/p32/HABP1 Antibody Therapy Decreases Tumor Growth in an Orthotopic Murine Xenotransplant Model of Triple Negative Breast Cancer

gC1qR is highly expressed in breast cancer and plays a role in cancer cell proliferation. This study explored therapy with gC1qR monoclonal antibody 60.11, directed against the C1q binding domain of gC1qR, in a murine orthotopic xenotransplant model of triple negative breast cancer. MDA231 breast cancer cells were injected into the mammary fat pad of athymic nu/nu female mice. Mice were segregated into three groups (n = 5, each) and treated with the vehicle (group 1) or gC1qR antibody 60.11 (100 mg/kg) twice weekly, starting at day 3 post-implantation (group 2) or when the tumor volume reached 100 mm³ (group 3). At study termination (d = 35), the average tumor volume in the control group measured 895 ± 143 mm³, compared to 401 ± 48 mm³ and 701 ± 100 mm³ in groups 2 and 3, respectively (p < 0.05). Immunohistochemical staining of excised tumors revealed increased apoptosis (caspase 3 and TUNEL staining) in 60.11-treated mice compared to controls, and decreased angiogenesis (CD31 staining). Slightly decreased white blood cell counts were noted in 60.11-treated mice. Otherwise, no overt toxicities were observed. These data are the first to demonstrate an in vivo anti-tumor effect of 60.11 therapy in a mouse model of triple negative breast cancer.

Elastosis in ERα-positive male breast cancer

In female breast cancer (BC), elastosis is strongly related to estrogen receptor alpha (ERα) expression. Male breast cancers almost invariably express ERα; so, the aim of this study was to investigate elastosis frequency in invasive male BC as well as clinicopathological correlations, in comparison with females. A total of 177 male BC cases and 135 female BC cases were included, all ERα-positive and invasive carcinoma of no special type. Elastosis on H&E-stained slides was scored in a four-tiered system as elastosis grade (EG) 0 (no elastosis) to EG3 (high amount of elastosis). EG scores in male BC were correlated to histopathological characteristics and overall surviva and compared with female BC EG scores. Male BC showed some degree of elastosis in 26/117 cases (22.2%) with none showing EG3, while female BC cases showed elastosis in 89/135 cases (65.9%) with 21.5% showing EG3 (p < 0.001). This difference retained its significance in multivariate logistic regression. In male BC cases, no significant correlations were found between the amount of elastosis and age, grade, mitotic activity index, and PgR. In addition, no significant prognostic value of elastosis was seen. In conclusion, despite high ERα expression, male BC showed significantly less elastosis than female BC. Elastosis did not show clinicopathological correlations or prognostic value. Therefore, elastosis seems to be a less useful ERα tissue biomarker with less clinical significance in male BC compared with females, pointing towards important BC sex differences.

Prognostic implications of the expression levels of different immunoglobulin heavy chain-encoding RNAs in early breast cancer

The extent and composition of the immune response in a breast cancer is one important prognostic factor for the disease. The aim of the current work was to refine the analysis of the humoral component of an immune response in breast tumors by quantifying mRNA expression of different immunoglobulin classes and study their association with prognosis. We used RNA-Seq data from two local population-based breast cancer cohorts to determine the expression of IGJ and immunoglobulin heavy (IGH) chain-encoding RNAs. The association with prognosis was investigated and public data sets were used to corroborate the findings. Except for IGHE and IGHD, mRNAs encoding heavy chains were generally detected at substantial levels and correlated with other immune-related genes. High IGHG1 mRNA was associated with factors related to poor prognosis such as estrogen receptor negativity, HER2 amplification, and high grade, whereas high IGHA2 mRNA levels were primarily associated with lower age at diagnosis. High IGHA2 and IGJ mRNA levels were associated with a more favorable prognosis both in univariable and multivariable Cox models. When adjusting for other prognostic factors, high IGHG1 mRNA levels were positively associated with improved prognosis. To our knowledge, these results are the first to demonstrate that expression of individual Ig class types has prognostic implications in breast cancer.

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Background

Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.

Methods

Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

Results

Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.

Conclusion

Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.

Alterations in Immune-Related Genes as Potential Marker of Prognosis in Breast Cancer

The tumor microenvironment (TME) is a heterogeneous system that contributes to breast cancer progression. The Cancer Genome Atlas (TCGA) database provides global gene expression profiling data for further analysis of various malignancies, including breast cancer. Based on the ESTIMATE algorithm, immune and stromal scores were calculated according to immune or stromal components in the TME. We divided breast cancer cases into high- and low-score groups and identified differentially expressed genes (DEGs) that were significantly associated with overall survival. We performed enrichment analysis and constructed a protein-protein interaction network and found that the DEGs were mainly involved in primary immunodeficiency, T cell receptor signaling pathway and cytokine-cytokine receptor reaction. Furthermore, we explored the effect of aging on immune and stromal scores, which was validated by lower immune/stromal scores, lower infiltration of T cells and lower expression of immune checkpoints in the elder group. In conclusion, certain differentially expressed immune-related genes contribute to longer overall survival, and aging influences the immune microenvironment and immunotherapy efficacy by changing the tumor-infiltrating lymphocyte (TIL) abundance and checkpoint expression in breast cancer.

Breast cancer stromal clotting activation (Tissue Factor and thrombin): A pre-invasive phenomena that is prognostic in invasion

Background

Tumor stroma, of which fibroblasts are the most abundant cell, resembles a non‐healing wound, where a procoagulant environment creates a permissive milieu for cancer growth. We aimed to determine if tumor expression of coagulation factors (procoagulant phenotype), and systemic hypercoagulability, occur at the preinvasive (ductal carcinoma in situ; DCIS) stage and correlate with breast cancer subtype, disease‐free survival (DFS), and overall survival (OS).

Methods

In a prospective cohort of early breast cancer (DCIS, n = 76; invasive, n = 248) tumor, normal breast and plasma were examined. Fibroblast and epithelial expression of Tissue Factor (TF), thrombin, PAR1, PAR2, and plasma thrombin‐antithrombin (TAT) and D‐dimer were correlated with clinicopathological data, and 5‐year survival.

Results

Fibroblast expression of TF, thrombin, and PAR1 was increased in DCIS and invasive cancer compared to normal breast fibroblasts (P ≤ .003, all). Fibroblast TF, thrombin, PAR1, and PAR2 was increased in cancers with high Ki67, high grade, ER‐ (vs ER+), and HER2+ (vs HER2‐) (all P < .05). On univariate analysis, fibroblast TF expression was inversely associated with DFS (P = .04) and OS (P = .02). D‐dimer was higher in node positive (507 (CI: 411‐625) ng/mL, n = 68) vs negative patients (428 (CI: 387‐472) ng/mL, n = 171, P = .004) and inversely associated with OS (P = .047). On multivariate analysis, plasma TAT was associated with reduced OS (HR 3.26, CI 1.16‐3.1, P = .02), with a high plasma TAT (≥3.2 ng/mL) associated with > 3‐fold mortality risk compared to low TAT.

Conclusion

This demonstrates procoagulant phenotypic changes occur in fibroblasts at the preinvasive stage. Fibroblast procoagulant phenotype is associated with aggressive breast cancer subtypes and reduced survival. Coagulation may be a therapeutic target in breast cancer.

Targeting of the Cancer-Associated Fibroblast-T-Cell Axis in Solid Malignancies

The introduction of a wide range of immunotherapies in clinical practice has revolutionized the treatment of cancer in the last decade. The majority of these therapeutic modalities are centered on reinvigorating a tumor-reactive cytotoxic T-cell response. While impressive clinical successes are obtained, the majority of cancer patients still fail to show a clinical response, despite the fact that their tumors express antigens that can be recognized by the immune system. This is due to a series of other cellular actors, present in or attracted towards the tumor microenvironment, including regulatory T-cells, myeloid-derived suppressor cells and cancer-associated fibroblasts (CAFs). As the main cellular constituent of the tumor-associated stroma, CAFs form a heterogeneous group of cells which can drive cancer cell invasion but can also impair the migration and activation of T-cells through direct and indirect mechanisms. This singles CAFs out as an important next target for further optimization of T-cell based immunotherapies. Here, we review the recent literature on the role of CAFs in orchestrating T-cell activation and migration within the tumor microenvironment and discuss potential avenues for targeting the interactions between fibroblasts and T-cells.

Tumour infiltrating lymphocytes and immune-related genes as predictors of outcome in pancreatic adenocarcinoma

BACKGROUND

We investigated the correlation between pancreatic ductal adenocarcinoma patient prognosis and the presence of tumour infiltrating lymphocytes and expression of 521 immune system genes.

METHODS

Intratumoural CD3+, CD8+, and CD20+ lymphocytes were examined by immunohistochemistry in 12 PDAC patients with different outcomes who underwent pancreaticoduodenectomy. The results were correlated with gene expression profile using the digital multiplexed NanoString nCounter analysis system (NanoString Technologies, Seattle, WA, USA).

RESULTS

Twenty immune system genes were significantly differentially expressed in patients with a good prognosis relative to patients with a worse prognosis: TLR2 and TLR7 (Toll-like receptor superfamily); CD4, CD37, FOXP3, PTPRC (B cell and T cell signalling); IRF5, IRF8, STAT1, TFE3 (transcription factors); ANP32B, CCND3 (cell cycle); BTK (B cell development); TNF, TNFRF1A (TNF superfamily); HCK (leukocyte function); C1QA (complement system); BAX, PNMA1 (apoptosis); IKBKE (NFκB pathway). Differential expression was more than twice log 2 for TLR7, TNF, C1QA, FOXP3, and CD37.

DISCUSSION

Tumour infiltrating lymphocytes were present at higher levels in samples from patients with better prognosis. Our findings indicate that tumour infiltrating lymphocyte levels and expression level of the immune system genes listed above influence pancreatic ductal adenocarcinoma prognosis. This information could be used to improve selection of best responders to immune inhibitors.

Landscape of transcriptomic interactions between breast cancer and its microenvironment

Solid tumours comprise mixtures of tumour cells (TCs) and tumour-adjacent cells (TACs), and the intricate interconnections between these diverse populations shape the tumour’s microenvironment. Despite this complexity, clinical genomic profiling is typically performed from bulk samples, without distinguishing TCs from TACs. To better understand TC–TAC interactions, we computationally distinguish their transcriptomes in 1780 primary breast tumours. We show that TC and TAC mRNA abundances are divergently associated with clinical phenotypes, including tumour subtypes and patient survival. These differences reflect distinct responses of TCs and TACs to specific somatic driver mutations, particularly TP53. These data further elucidate how the molecular interplay between breast tumours and their microenvironment drives aggressive tumour phenotypes.

The transcriptomic profile of tumour-adjacent cells provides important information about tumour context but its clinical utility is unclear. Here, in breast cancer, Fox et al. show that the mRNA abundances of tumour and tumour-adjacent cells hold prognostic information.

Schlafen-11 expression is associated with immune signatures and basal-like phenotype in breast cancer

PURPOSE

Breast cancer (BC) is a heterogeneous disorder, with variable response to systemic chemotherapy. Likewise, BC shows highly complex immune activation patterns, only in part reflecting classical histopathological subtyping. Schlafen-11 (SLFN11) is a nuclear protein we independently described as causal factor of sensitivity to DNA damaging agents (DDA) in cancer cell line models. SLFN11 has been reported as a predictive biomarker for DDA and PARP inhibitors in human neoplasms. SLFN11 has been implicated in several immune processes such as thymocyte maturation and antiviral response through the activation of interferon signaling pathway, suggesting its potential relevance as a link between immunity and cancer. In the present work, we investigated the transcriptional landscape of SLFN11, its potential prognostic value, and the clinico-pathological associations with its variability in BC.

METHODS

We assessed SLFN11 determinants in a gene expression meta-set of 5061 breast cancer patients annotated with clinical data and multigene signatures.

RESULTS

We found that 537 transcripts are highly correlated with SLFN11, identifying "immune response", "lymphocyte activation", and "T cell activation" as top Gene Ontology processes. We established a strong association of SLFN11 with stromal signatures of basal-like phenotype and response to chemotherapy in estrogen receptor negative (ER-) BC. We identified a distinct subgroup of patients, characterized by high SLFN11 levels, ER- status, basal-like phenotype, immune activation, and younger age. Finally, we observed an independent positive predictive role for SLFN11 in BC.

CONCLUSIONS

Our findings are suggestive of a relevant role for SLFN11 in BC and its immune and molecular variability.

Is the Complement Protein C1q a Pro- or Anti-tumorigenic Factor? Bioinformatics Analysis Involving Human Carcinomas

C1q is the first subcomponent of the classical pathway of the complement system and belongs to the C1q/Tumor Necrosis Factor superfamily. C1q can perform a diverse range of immune and non-immune functions in a complement-dependent as well as -independent manner. Being a pattern recognition molecule of the innate immunity, C1q can recognize a number of self, non-self and altered-self ligands and bring about effector mechanisms designed to clear pathogens via opsonisation and inflammatory response. C1q is locally synthesized by macrophages and dendritic cells, and thus, can get involved in a range of biological processes, such as angiogenesis and tissue remodeling, immune modulation, and immunologic tolerance. The notion of C1q involvement in the pathogenesis of cancer is still evolving. C1q appears to have a dual role in cancer: tumor promoting as well as tumor-protective, depending on the context of the disease. In the current study, we performed a bioinformatics analysis to investigate whether C1q can serve as a potential prognostic marker for human carcinoma. We used the Oncomine database and the survival analysis platforms Kaplan-Meier plotter. Our results showed that high levels of C1q have a favorable prognostic index in basal-like breast cancer for disease-free survival, and in HER2-positive breast cancer for overall survival, while it showed a pro-tumorigenic role of C1q in lung adenocarcinoma, and in clear cell renal cell carcinoma. This in silico study, if validated via a retrospective study, can be a step forward in establishing C1q as a new tool as a prognostic biomarker for various carcinoma.

Clinical significance of 21-gene recurrence score assay for hormone receptor-positive, lymph node-negative breast cancer in early stage

OBJECTIVE

To identify the relationship between clinical pathological characteristics and the recurrence score (RS) on a 21-gene expression assay in patients with hormone receptor-positive, node-negative breast cancer, as well as the effect of RS on adjuvant decision-making.

METHODS

The retrospective study was conducted among luminal breast cancer patients admitted to Xijing Hospital between October 10, 2016, and September 14, 2018. Real-time PCR was used for 21-genome detection. Based on the calculated RS, participants were classified into low-risk, moderate-risk, and high-risk groups. Single-factor analysis and multiple logistic regression analysis were performed to explore independent predictors of high RS. Moreover, the effect of RS on adjuvant decision-making was studied.

RESULTS

Two hundred twenty-two patients with luminal breast cancer, aged 48.3 ± 9.66, were enrolled. Among them, 33.8% had low (13 ± 3.34), 45.5% intermediate (23 ± 3.65), and 20.7% high (37 ± 3.44) RS. According to the single-factor analysis, age, tumor size, Ki-67, molecular subtype, CK5/6 expression, E-cadherin level, and histological grade were positively associated with high RS. Multiple logistic analyses showed that tumor size and histological grade were independent variables that might predict high RS in patients with hormone receptor-positive, node-negative breast cancer. For adjuvant decision-making, the proportion of adjuvant chemotherapy in the intermediate-/high-risk groups was higher than that in the low-risk group, P < 0.001. Compared with the data worldwide, the changes of treatment selection in the present study were similar to those in Japan (23.0% vs. 26%) and America (23.0% vs. 23.0%). Considering the pathology types, 14.3% of patients with invasive breast cancer with lower RS changed treatment recommendations, predominantly from chemo-endocrine to endocrine treatment alone, whereas the percentage in intermediate/high RS groups was 8.1%.

CONCLUSIONS

Tumor size and histological grade were independent variables, predicting high risk in patients with hormone receptor-positive, node-negative breast cancer; 21-gene RS assessment was potentially a critical tool in guiding adjuvant decision-making in China.

Genomic signature of parity in the breast of premenopausal women

Background

Full-term pregnancy (FTP) at an early age confers long-term protection against breast cancer. Previously, we reported that a FTP imprints a specific gene expression profile in the breast of postmenopausal women. Herein, we evaluated gene expression changes induced by parity in the breast of premenopausal women.

Methods

Gene expression profiling of normal breast tissue from 30 nulliparous (NP) and 79 parous (P) premenopausal volunteers was performed using Affymetrix microarrays. In addition to a discovery/validation analysis, we conducted an analysis of gene expression differences in P vs. NP women as a function of time since last FTP. Finally, a laser capture microdissection substudy was performed to compare the gene expression profile in the whole breast biopsy with that in the epithelial and stromal tissues.

Results

Discovery/validation analysis identified 43 differentially expressed genes in P vs. NP breast. Analysis of expression as a function of time since FTP revealed 286 differentially expressed genes (238 up- and 48 downregulated) comparing all P vs. all NP, and/or P women whose last FTP was less than 5 years before biopsy vs. all NP women. The upregulated genes showed three expression patterns: (1) transient: genes upregulated after FTP but whose expression levels returned to NP levels. These genes were mainly related to immune response, specifically activation of T cells. (2) Long-term changing: genes upregulated following FTP, whose expression levels decreased with increasing time since FTP but did not return to NP levels. These were related to immune response and development. (3) Long-term constant: genes that remained upregulated in parous compared to nulliparous breast, independently of time since FTP. These were mainly involved in development/cell differentiation processes, and also chromatin remodeling. Lastly, we found that the gene expression in whole tissue was a weighted average of the expression in epithelial and stromal tissues.

Conclusions

Genes transiently activated by FTP may have a role in protecting the mammary gland against neoplastically transformed cells through activation of T cells. Furthermore, chromatin remodeling and cell differentiation, represented by the genes that are maintained upregulated long after the FTP, may be responsible for the lasting preventive effect against breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1128-x) contains supplementary material, which is available to authorized users.

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

Genome-wide association study of germline variants and breast cancer-specific mortality

BACKGROUND

We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry.

METHODS

Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP).

RESULTS

We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster.

CONCLUSIONS

We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Interactions between cancer-associated fibroblasts and tumor cells promote MCL-1 dependency in estrogen receptor-positive breast cancers

Selective inhibition of BCL-2 is expected to enhance therapeutic vulnerability in luminal estrogen receptor-positive breast cancers. We show here that the BCL-2 dependency of luminal tumor cells is nevertheless mitigated by breast cancer-associated fibroblasts (bCAFs) in a manner that defines MCL-1 as another critical therapeutic target. bCAFs favor MCL-1 expression and apoptotic resistance in luminal cancer cells in a IL-6 dependent manner while their own, robust, survival also relies on MCL-1. Studies based on ex vivo cultures of human luminal breast cancer tissues further argue that the contribution of stroma-derived signals to MCL-1 expression shapes BCL-2 dependency. Thus, MCL-1 inhibitors are beneficial for targeted apoptosis of breast tumor ecosystems, even in a subtype where MCL-1 dependency is not intrinsically driven by oncogenic pathways.

Macrophages attenuate the transcription of CYP1A1 in breast tumor cells and enhance their proliferation

While aberrant cells are routinely recognized and removed by immune cells, tumors eventually escape innate immune responses. Infiltrating immune cells are even corrupted by the tumor to acquire a tumor-supporting phenotype. In line, tumor-associated macrophages are well-characterized to promote tumor progression and high levels of tumor-infiltrating macrophages are a poor prognostic marker in breast cancer. Here, we aimed to further decipher the influence of macrophages on breast tumor cells and determined global gene expression changes in three-dimensional tumor spheroids upon infiltration of macrophages. While various tumor-associated mRNAs were upregulated, expression of the cytochrome P450 family member CYP1A1 was markedly attenuated. Repression of CYP1A1 in tumor cells was elicited by a macrophage-shaped tumor microenvironment rather than by direct tumor cell-macrophage contacts. In line with changes in RNA expression profiles, macrophages enhanced proliferation of the tumor cells. Enhanced proliferation and macrophage presence further correlated with reduced CYP1A1 expression in patient tumors when compared with normal tissue. These findings are of interest in the context of combinatory therapeutic approaches involving cytotoxic and immune-modulatory compounds.

Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics

Leveraging insights from genomic studies of patient tumors is limited by the discordance between these tumors and the cell line models used for functional studies. We integrate omics datasets using functional networks to identify gene modules reflecting variation between tumors and show that the structure of these modules can be evaluated in cell lines to discover clinically relevant biomarkers of therapeutic responses. Applied to breast cancer, we identify 219 gene modules that capture recurrent alterations and subtype patients and quantitate various cell types within the tumor microenvironment. Comparison of modules between tumors and cell lines reveals that many modules composed primarily of gene expression and methylation are poorly preserved. In contrast, preserved modules are highly predictive of drug responses in a manner that is robust and clinically relevant. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.

An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors

By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, TH1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. IS is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors.Significance: We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis. Cancer Discov; 8(9); 1142-55. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 1047.

Hyaluronan, Cancer-Associated Fibroblasts and the Tumor Microenvironment in Malignant Progression

This review summarizes the roles of CAFs in forming a “cancerized” fibrotic stroma favorable to tumor initiation and dissemination, in particular highlighting the functions of the extracellular matrix component hyaluronan (HA) in these processes. The structural complexity of the tumor and its host microenvironment is now well appreciated to be an important contributing factor to malignant progression and resistance-to-therapy. There are multiple components of this complexity, which include an extensive remodeling of the extracellular matrix (ECM) and associated biomechanical changes in tumor stroma. Tumor stroma is often fibrotic and rich in fibrillar type I collagen and hyaluronan (HA). Cancer-associated fibroblasts (CAFs) are a major source of this fibrotic ECM. CAFs organize collagen fibrils and these biomechanical alterations provide highways for invading carcinoma cells either under the guidance of CAFs or following their epithelial to mesenchymal transition (EMT). The increased HA metabolism of a tumor microenvironment instructs carcinoma initiation and dissemination by performing multiple functions. The key effects of HA reviewed here are its role in activating CAFs in pre-malignant and malignant stroma, and facilitating invasion by promoting motility of both CAFs and tumor cells, thus facilitating their invasion. Circulating CAFs (cCAFs) also form heterotypic clusters with circulating tumor cells (CTC), which are considered to be pre-cursors of metastatic colonies. cCAFs are likely required for extravasation of tumors cells and to form a metastatic niche suitable for new tumor colony growth. Therapeutic interventions designed to target both HA and CAFs in order to limit tumor spread and increase response to current therapies are discussed.

Complement Component C1q: Historical Perspective of a Functionally Versatile, and Structurally Unusual, Serum Protein

Complement component C1q plays an important recognition role in adaptive, and innate, immunity through its ability to interact, via its six globular head regions, with both immunoglobulin and non-immunoglobulin activators of the complement system, and also in the clearance of cell debris, and by playing a role in regulation of cellular events by interacting with a wide range of cell surface molecules. The presence of collagen-like triple-helical structures within C1q appears crucial to the presentation, and multivalent binding, of the globular heads of C1q to targets, and also to its association with the proenzyme complex of C1r₂–C1s₂, to yield the C1 complex. The possible role that movement of these collagen-like structures may play in the activation of the C1 complex is a controversial area, with there still being no definitive answer as to how the first C1r proenzyme molecule becomes activated within the C1 complex, thus allowing it to activate proenzyme C1s, and initiate and the consequent cascade of events in the activation of the classical pathway of complement. The globular heads of C1q are similar to domains found within the tumor necrosis factor (TNF) superfamily of proteins, and have been shown to bind to a very wide range of ligands. In addition to its well-defined roles in infection and immunity, a variety of other functions associated with C1q include possible roles, in the development of problems in the central nervous system, which occur with aging, and perhaps in the regulation of tumor growth.

An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies

BACKGROUND

Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.

METHODS

In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.

RESULTS

We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).

CONCLUSIONS

This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

Complement Protein C1q Binds to Hyaluronic Acid in the Malignant Pleural Mesothelioma Microenvironment and Promotes Tumor Growth

C1q is the first recognition subcomponent of the complement classical pathway, which acts toward the clearance of pathogens and apoptotic cells. C1q is also known to modulate a range of functions of immune and non-immune cells, and has been shown to be involved in placental development and sensorial synaptic pruning. We have recently shown that C1q can promote tumor by encouraging their adhesion, migration, and proliferation in addition to angiogenesis and metastasis. In this study, we have examined the role of human C1q in the microenvironment of malignant pleural mesothelioma (MPM), a rare form of cancer commonly associated with exposure to asbestos. We found that C1q was highly expressed in all MPM histotypes, particularly in epithelioid rather than in sarcomatoid histotype. C1q avidly bound high and low molecular weight hyaluronic acid (HA) via its globular domain. C1q bound to HA was able to induce adhesion and proliferation of mesothelioma cells (MES) via enhancement of ERK1/2, SAPK/JNK, and p38 phosphorylation; however, it did not activate the complement cascade. Consistent with the modular organization of the globular domain, we demonstrated that C1q may bind to HA through ghA module, whereas it may interact with human MES through the ghC. In conclusion, C1q highly expressed in MPM binds to HA and enhances the tumor growth promoting cell adhesion and proliferation. These data can help develop novel diagnostic markers and molecular targets for MPM.

Tissue and plasma levels of galectins in patients with high grade serous ovarian carcinoma as new predictive biomarkers

Galectins are moving closer to center stage in detecting glycosylation aberration in cancer cells. Here, we have investigated the expression of galectins in ovarian cancer (OC) and examined their potential as biomarkers in tissues and blood plasma samples of high grade serous ovarian carcinoma (HGSC) patients. In tissues, we found that increased protein expression of stromal gal-1 and epithelial gal-8/9 was associated with a poor response to treatment of HGSC patients. Gal-8/9 were both independent predictors of chemoresistance and overall survival (OS), respectively. This galectin signature increased the predictive value of the cancer antigen 125 (CA125) on 5-year disease-free survival (DFS), post-chemotherapy treatment and 5-year OS. In CA125^LOW patients, epithelial gal-9 was associated with a lower 5-year OS while stromal gal-1 and epithelial gal-8 were both associated with a lower 5-year DFS. Such negative predictive value of gal-8 and gal-9 was also found using plasma samples. In both cases, high plasma levels of gal-8 and gal-9 was associated with a lower OS and DFS. Overall, these data suggest that galectins may be promising biomarkers to identify subgroups of HGSC patients with poorer prognosis. Our study also contributes to better define the heterogeneity of the disease.

Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer

While progression from normal prostatic epithelium to invasive cancer is driven by molecular alterations, tumor cells and cells in the cancer microenvironment are co-dependent and co-evolve. Few human studies to date have focused on stroma. Here, we performed gene expression profiling of laser capture microdissected normal non-neoplastic prostate epithelial tissue and compared it to non-transformed and neoplastic low-grade and high-grade prostate epithelial tissue from radical prostatectomies, each with its immediately surrounding stroma. Whereas benign epithelium in prostates with and without tumor were similar in gene expression space, stroma away from tumor was significantly different from that in prostates without cancer. A stromal gene signature reflecting bone remodeling and immune-related pathways was upregulated in high compared to low-Gleason grade cases. In validation data, the signature discriminated cases that developed metastasis from those that did not. These data suggest that the microenvironment may influence prostate cancer initiation, maintenance, and metastatic progression.Stromal cells contribute to tumor development but the mechanisms regulating this process are still unclear. Here the authors analyze gene expression profiles in the prostate and show that stromal gene signature changes ahead of the epithelial gene signature as prostate cancer initiates and progresses.

Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis

Triple-negative breast cancer (TNBC) has attracted great attention due to its unique biology, poor prognosis, and aggressiveness. TNBC patients are more likely to suffer from metastasis. We screened and identified the TNBC-specific genes as potential biomarkers. A total of 167 breast cancer samples (45 TNBC and 122 non-TNBC) were used in the integrated analysis. Gene expression microarrays were used to screen the differentially expressed genes. We identified 65 core DEGs. According to the GO and KEGG analysis, the gene function enrichment in TNBC was revealed, such as basal cell carcinoma, prostate cancer, oocyte meiosis and choline metabolism in cancer pathways. Moreover, the PPI network reconstruction would benefit the screening of hubs. A RFS analysis of TNBC-specific genes was also conducted. RT-PCR was used to validate the expression pattern of hubs in TNBC. Finally, nine genes were identified and all of them were novel, specific and higher dysregulation expressed genes in TNBC. Such that, these genes will serve as potential biomarkers in TNBC and benefit further research in TNBC.

Identification and prognostic value of anterior gradient protein 2 expression in breast cancer based on tissue microarray

Breast cancer has attracted substantial attention as one of the major cancers causing death in women. It is crucial to find potential biomarkers of prognostic value in breast cancer. In this study, the expression pattern of anterior gradient protein 2 in breast cancer was identified based on the main molecular subgroups. Through analysis of 69 samples from the Gene Expression Omnibus database, we found that anterior gradient protein 2 expression was significantly higher in non-triple-negative breast cancer tissues compared with normal tissues and triple-negative breast cancer tissues (p < 0.05). The data from a total of 622 patients from The Cancer Genome Atlas were analysed. The data from The Cancer Genome Atlas and results from quantitative reverse transcription polymerase chain reaction also verified the anterior gradient protein 2 expression pattern. Furthermore, we performed immunohistochemical analysis. The quantification results revealed that anterior gradient protein 2 is highly expressed in non-triple-negative breast cancer (grade 3 excluded) and grade 1 + 2 (triple-negative breast cancer excluded) tumours compared with normal tissues. Anterior gradient protein 2 was significantly highly expressed in non-triple-negative breast cancer (grade 3 excluded) and non-triple-negative breast cancer tissues compared with triple-negative breast cancer tissues (p < 0.01). In addition, anterior gradient protein 2 was significantly highly expressed in grade 1 + 2 (triple-negative breast cancer excluded) and grade 1 + 2 tissues compared with grade 3 tissues (p < 0.05). Analysis by Fisher's exact test revealed that anterior gradient protein 2 expression was significantly associated with histologic type, histological grade, oestrogen status and progesterone status. Univariate analysis of clinicopathological variables showed that anterior gradient protein 2 expression, tumour size and lymph node status were significantly correlated with overall survival in patients with grade 1 and 2 tumours. Cox multivariate analysis revealed anterior gradient protein 2 as a putative independent indicator of unfavourable outcomes (p = 0.031). All these data clearly showed that anterior gradient protein 2 is highly expressed in breast cancer and can be regarded as a putative biomarker for breast cancer prognosis.

Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer

None of the clinically relevant gene expression signatures available for breast cancer were specifically developed to capture the influence of the microenvironment on tumor cells. Here, we attempted to build subtype‐specific signatures derived from an in vitro model reproducing tumor cell modifications after interaction with activated or normal stromal cells. Gene expression signatures derived from HER2+, luminal, and basal breast cancer cell lines (treated by normal fibroblasts or cancer‐associated fibroblasts conditioned media) were evaluated in clinical tumors by in silico analysis on published gene expression profiles (GEPs). Patients were classified as microenvironment‐positive (μENV+ve), that is, with tumors showing molecular profiles suggesting activation by the stroma, or microenvironment‐negative (μENV−ve) based on correlation of their tumors' GEP with the respective subtype‐specific signature. Patients with estrogen receptor alpha (ER)+/HER2−/μENV+ve tumors were characterized by 2.5‐fold higher risk of developing distant metastases (HR = 2.546; 95% CI: 1.751–3.701, P = 9.84E‐07), while μENV status did not affect, or only suggested the risk of distant metastases, in women with HER2+ (HR = 1.541; 95% CI: 0.788–3.012, P = 0.206) or ER‐/HER2− tumors (HR = 1.894; 95% CI: 0.938–3.824; P = 0.0747), respectively. In ER+/HER2− tumors, the μENV status remained significantly associated with metastatic progression (HR = 2.098; CI: 1.214–3.624; P = 0.00791) in multivariable analysis including size, age, and Genomic Grade Index. Validity of our in vitro model was also supported by in vitro biological endpoints such as cell growth (MTT assay) and migration/invasion (Transwell assay). In vitro‐derived gene signatures tracing the bidirectional interaction with cancer activated fibroblasts are subtype‐specific and add independent prognostic information to classical prognostic variables in women with ER+/HER2− tumors.

The past and future of breast cancer treatment-from the papyrus to individualised treatment approaches

Cancer is one of the oldest diseases ever described, since ancient Egypt there have always been attempts to treat and cure this illness. The growing body of knowledge about breast cancer biology and improvements in surgical and medical treatments has been built over time with contributions from many talented and enthusiastic physicians and researchers. Medical advances have changed the approach from a previously incurable condition, into a surgical disease. Further improvements in cancer biology have allowed the development of systemic treatments, hormonal therapies, and targeted drugs. The description of the molecular intrinsic subtypes of breast cancer clarified the understanding of breast cancer as a group of heterogeneous diseases, associated with different clinical outcomes, and therapeutic opportunities. This paper reviews how breast cancer treatment has improved since the earliest descriptions, in ancient times, and how future approaches, such as gene signatures, molecular profiling, and liquid biopsies, aim to further develop individualised treatments and improve treatment outcomes.

C1q: A fresh look upon an old molecule

Originally discovered as part of C1, the initiation component of the classical complement pathway, it is now appreciated that C1q regulates a variety of cellular processes independent of complement activation. C1q is a complex glycoprotein assembled from 18 polypeptide chains, with a C-terminal globular head region that mediates recognition of diverse molecular structures, and an N-terminal collagen-like tail that mediates immune effector mechanisms. C1q mediates a variety of immunoregulatory functions considered important in the prevention of autoimmunity such as the enhancement of phagocytosis, regulation of cytokine production by antigen presenting cells, and subsequent alteration in T-lymphocyte maturation. Furthermore, recent advances indicate additional roles for C1q in diverse physiologic and pathologic processes including pregnancy, tissue repair, and cancer. Finally, C1q is emerging as a critical component of neuronal network refinement and homeostatic regulation within the central nervous system. This review summarizes the classical functions of C1q and reviews novel discoveries within the field.