Extensive rewiring of epithelial-stromal co-expression networks in breast cancer

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Nf1 deficiency modulates the stromal environment in the pretumorigenic rat mammary gland

Background

Neurofibromin, coded by the NF1 tumor suppressor gene, is the main negative regulator of the RAS pathway and is frequently mutated in various cancers. Women with Neurofibromatosis Type I (NF1)-a tumor predisposition syndrome caused by a germline NF1 mutation-have an increased risk of developing aggressive breast cancer with poorer prognosis. The mechanism by which NF1 mutations lead to breast cancer tumorigenesis is not well understood. Therefore, the objective of this work was to identify stromal alterations before tumor formation that result in the increased risk and poorer outcome seen among NF1 patients with breast cancer.

Approach

To accurately model the germline monoallelic NF1 mutations in NF1 patients, we utilized an Nf1-deficient rat model with accelerated mammary development before presenting with highly penetrant breast cancer.

Results

We identified increased collagen content in Nf1-deficient rat mammary glands before tumor formation that correlated with age of tumor onset. Additionally, gene expression analysis revealed that Nf1-deficient mature adipocytes in the rat mammary gland have increased collagen expression and shifted to a fibroblast and preadipocyte expression profile. This alteration in lineage commitment was also observed with in vitro differentiation, however, flow cytometry analysis did not show a change in mammary adipose-derived mesenchymal stem cell abundance.

Conclusion

Collectively, this study uncovered the previously undescribed role of Nf1 in mammary collagen deposition and regulating adipocyte differentiation. In addition to unraveling the mechanism of tumor formation, further investigation of adipocytes and collagen modifications in preneoplastic mammary glands will create a foundation for developing early detection strategies of breast cancer among NF1 patients.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples.

Conclusion

This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Decoding the colorectal cancer ecosystem emphasizes the cooperative role of cancer cells, TAMs and CAFsin tumor progression

Background

Single-cell transcription data provided unprecedented molecular information, enabling us to directly encode the ecosystem of colorectal cancer (CRC). Characterization of the diversity of epithelial cells and how they cooperate with tumor microenvironment cells (TME) to endow CRC with aggressive characteristics at single-cell resolution is critical for the understanding of tumor progression mechanism.

Methods

In this study, we comprehensively analyzed the single-cell transcription data, bulk-RNA sequencing data and pathological tissue data. In detail, cellular heterogeneity of TME and epithelial cells were analyzed by unsupervised classification and consensus nonnegative matrix factorization analysis, respectively. Functional status of epithelial clusters was annotated by CancerSEA and its crosstalk with TME cells was investigated using CellPhoneDB and correlation analysis. Findings from single-cell transcription data were further validated in bulk-RNA sequencing data and pathological tissue data.

Results

A distinct cellular composition was observed between tumor and normal tissues, and tumors exhibited immunosuppressive phenotypes. Regarding epithelial cells, we identified one highly invasiveQuery cluster, C4, that correlated closely with tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs). Further analysis emphasized the TAMs subclass TAM1 and CAFs subclass S5 are closely related with C4.

Conclusions

In summary, our study elaborates on the cellular heterogeneity of CRC, revealing that TAMs and CAFs were critical for crosstalk network epithelial cells and TME cells. This in-depth understanding of cancer cell-TME network provided theoretical basis for the development of new drugs targeting this sophisticated network in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03661-8.

Local data commons: the sleeping beauty in the community of data commons

Background

Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC.

Results

This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples.

Conclusions

Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.

Characterisation of the Stromal Microenvironment in Lobular Breast Cancer

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer, and it exhibits a number of clinico-pathological characteristics distinct from the more common invasive ductal carcinoma (IDC). We set out to identify alterations in the tumor microenvironment (TME) of ILC. We used laser-capture microdissection to separate tumor epithelium from stroma in 23 ER+ ILC primary tumors. Gene expression analysis identified 45 genes involved in regulation of the extracellular matrix (ECM) that were enriched in the non-immune stroma of ILC, but not in non-immune stroma from ER+ IDC or normal breast. Of these, 10 were expressed in cancer-associated fibroblasts (CAFs) and were increased in ILC compared to IDC in bulk gene expression datasets, with PAPPA and TIMP2 being associated with better survival in ILC but not IDC. PAPPA, a gene involved in IGF-1 signaling, was the most enriched in the stroma compared to the tumor epithelial compartment in ILC. Analysis of PAPPA- and IGF1-associated genes identified a paracrine signaling pathway, and active PAPP-A was shown to be secreted from primary CAFs. This is the first study to demonstrate molecular differences in the TME between ILC and IDC identifying differences in matrix organization and growth factor signaling pathways.

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.

Deciphering cell-cell interactions and communication from gene expression

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Exploring chromatin conformation and gene co-expression through graph embedding

MOTIVATION

The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships.

RESULTS

To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis.

AVAILABILITY AND IMPLEMENTATION

The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Upregulation of lipid metabolism genes in the breast prior to cancer diagnosis

Histologically normal tissue adjacent to the tumor can provide insight of the microenvironmental alterations surrounding the cancerous lesion and affecting the progression of the disease. However, little is known about the molecular changes governing cancer initiation in cancer-free breast tissue. Here, we employed laser microdissection and whole-transcriptome profiling of the breast epithelium prior to and post tumor diagnosis to identify the earliest alterations in breast carcinogenesis. Furthermore, a comprehensive analysis of the three tissue compartments (microdissected epithelium, stroma, and adipose tissue) was performed on the breast donated by either healthy subjects or women prior to the clinical manifestation of cancer (labeled "susceptible normal tissue"). Although both susceptible and healthy breast tissues appeared histologically normal, the susceptible breast epithelium displayed a significant upregulation of genes involved in fatty acid uptake/transport (CD36 and AQP7), lipolysis (LIPE), and lipid peroxidation (AKR1C1). Upregulation of lipid metabolism- and fatty acid transport-related genes was observed also in the microdissected susceptible stromal and adipose tissue compartments, respectively, when compared with the matched healthy controls. Moreover, inter-compartmental co-expression analysis showed increased epithelium-adipose tissue crosstalk in the susceptible breasts as compared with healthy controls. Interestingly, reductions in natural killer (NK)-related gene signature and CD45+/CD20+ cell staining were also observed in the stromal compartment of susceptible breasts. Our study yields new insights into the cancer initiation process in the breast. The data suggest that in the early phase of cancer development, metabolic activation of the breast, together with increased epithelium-adipose tissue crosstalk may create a favorable environment for final cell transformation, proliferation, and survival.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment

The tumor environment is of vital importance for the incidence and development of colorectal cancer. Increasing evidence in recent years has elaborated the vital role of the tumor environment in cancer subtype classification and patient prognosis, but a comprehensive understanding of the colorectal tumor environment that is purely dependent on the stromal compartment is lacking. To decipher the tumor environment in colorectal cancer and explore the role of its immune context in cancer classification, we performed a gene expression microarray on the stromal compartment of colorectal cancer and adjacent normal tissues. Through the integrated analysis of our data with public gene expression microarray data of stromal and epithelial colorectal cancer tissues processed through laser capture microdissection, we identified four highly connected gene modules representing the biological features of four tissue compartments by applying a weighted gene coexpression network analysis algorithm and classified colorectal cancers into three immune subtypes by adopting a nearest template prediction algorithm. A systematic analysis of the four identified modules mainly reflected the close interplay between the biological changes of intrinsic and extrinsic characteristics at the initiation of colorectal cancer. Colorectal cancers were stratified into three immune subtypes based on gene templates identified from representative gene modules of the stromal compartment: active immune, active stroma, and mixed type. These immune subtypes differed by the immune cell infiltration pattern, expression of immune checkpoint inhibitors, mutation landscape, extent of mutation burden, extent of copy number burden, prognosis and chemotherapeutic sensitivity. Further analysis indicated that activation of the NF-kB signaling pathway was the major mechanism causing the no immune infiltration milieu in the active stroma subtype and that inhibitors of the NF-kB signaling pathway could be candidate drugs for treating patients with an active stroma. Overall, these results suggest that characterizing colorectal cancer by the tumor environment is of vital importance in predicting patients' clinical outcomes and helping guide precision and personalized treatment.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Stromal integrin α11 regulates PDGFR-β signaling and promotes breast cancer progression

Cancer-associated fibroblasts (CAFs) are key actors in modulating the progression of many solid tumors such as breast cancer (BC). Herein, we identify an integrin α11/PDGFRβ+ CAF subset displaying tumor-promoting features in BC. In the preclinical MMTV-PyMT mouse model, integrin α11-deficiency led to a drastic reduction of tumor progression and metastasis. A clear association between integrin α11 and PDGFRβ was found at both transcriptional and histological levels in BC specimens. High stromal integrin α11/PDGFRβ expression was associated with high grades and poorer clinical outcome in human BC patients. Functional assays using five CAF subpopulations (one murine, four human) revealed that integrin α11 promotes CAF invasion and CAF-induced tumor cell invasion upon PDGF-BB stimulation. Mechanistically, integrin α11 pro-invasive activity relies on its ability to interact with PDGFRβ in a ligand-dependent manner and to promote its downstream JNK activation, leading to the production of tenascin C, a pro-invasive matricellular protein. Pharmacological inhibition of PDGFRβ and JNK impaired tumor cell invasion induced by integrin α11-positive CAFs. Collectively, our study uncovers an integrin α11-positive subset of pro-tumoral CAFs that exploits PDGFRβ/JNK signalling axis to promote tumor invasiveness in BC.

The YB-1/EZH2/amphiregulin signaling axis mediates LPA-induced breast cancer cell invasion

Lysophosphatidic acid (LPA) has been known to induce epithelial-mesenchymal transition (EMT) to stimulate cancer cell invasion, and resveratrol (3,5,4'-trans-trihydroxystilbene; REV) suppresses the invasion and metastasis of various cancers. The current study aimed to identify the underlying mechanism by which LPA aggravates breast cancer cell invasion and the reversal of this phenomenon. Immunoblotting and quantitative RT-PCR analysis revealed that LPA induces amphiregulin (AREG) expression. Silencing of Y-box binding protein 1 (YB-1) or enhancer of zeste homolog 2 (EZH2) expression efficiently inhibited LPA-induced AREG expression. In addition, transfection of the cells with YB-1 siRNA abrogated LPA-induced EZH2 and AREG expression, leading to attenuation of breast cancer cell invasion. Furthermore, we observed that both REV and 5-fluorouracil (5-Fu) significantly reduce LPA-induced YB-1 phosphorylation and subsequent breast cancer invasion. Importantly, combined treatment of REV with 5-Fu showed more significant inhibition of LPA-induced breast cancer invasion compared to single treatment. Therefore, our data demonstrate that the YB-1/EZH2 signaling axis mediates LPA-induced AREG expression and breast cancer cell invasion and its inhibition by REV and 5-Fu, providing potential therapeutic targets and inhibition of breast cancer.

Network-based approach to identify principal isoforms among four cancer types

Protein isoforms are structurally similar proteins produced by alternative splicing of a single gene or genes from the same family. Isoforms of a protein can perform the same, similar, or even opposite biological functions. A previous study identified principal isoforms of proteins based on the extent of interactions per isoform in a functional relationship network, focusing on data from normal tissues. Additionally, the expression levels of specific isoforms of various genes associated with tumorigenesis and prognosis are frequently altered in tumors compared with those in normal tissues. In this study, we aimed to identify higher degree isoforms (HDIs) of multi-isoform genes (MIGs) in cancer by applying a meta-analytical framework to calculate co-expression between each pair of isoforms in two large datasets of RNA-seq profiles from breast cancer, lung cancer, leukemia, and colon cancer cell lines. Then, we compared HDIs with isoforms identified by proteomic data and prognostic and predictive evidence in various cancers. In addition, we separately analyzed the associations between HDIs and non-HDIs (nHDIs) of the same genes according to transcript expression and drug responses in various cancer type cell lines. Collectively, these results indicated the complex properties of HDIs per gene identified by cancer type-based isoform-isoform co-expression networks and showed the potential of HDIs as novel therapeutic targets for cancer treatment.

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes. In this study, we propose a disease gene prediction algorithm (called dgSeq) by combining protein-protein interaction (PPI) network, clinical RNA-Seq data, and Online Mendelian Inheritance in Man (OMIN) data. Our dgSeq constructs differential networks based on rewiring information calculated from clinical RNA-Seq data. To select balanced sets of non-disease genes (negative samples), a disease-gene network is also constructed from OMIM data. After features are extracted from the PPI networks and differential networks, the logistic regression classifiers are trained. Our dgSeq obtains AUC values of 0.88, 0.83, and 0.80 for identifying breast cancer genes, thyroid cancer genes, and Alzheimer's disease genes, respectively, which indicates its superiority to other three competing methods. Both gene set enrichment analysis and predicted results demonstrate that dgSeq can effectively predict new disease genes.

Overexpression of CDCA7 predicts poor prognosis and induces EZH2-mediated progression of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with high proliferative and metastatic phenotypes. CDCA7, a new member of the cell division cycle associated family of genes, is involved in embryonic development and dysregulated in various types of human cancer. However, the biological role and molecular mechanism of CDCA7 in TNBC have not been defined. Herein, we found that CDCA7 was preferentially and markedly expressed in TNBC cell lines and tissues. High expression of CDCA7 was associated with metastatic relapse status and predicted poorer disease-free survival in patients with TNBC. We observed that CDCA7 silencing in TNBC cell lines effectively impaired cell proliferation, invasion and migration in vitro. Importantly, depletion of CDCA7 strongly reduced the tumorigenicity and distant colonization capacities of TNBC cells in vivo. Furthermore, CDCA7 increased the expression of EZH2, a marker of aggressive breast cancer that is involved in tumor progression, by enhancing the transcriptional activity of its promoter. This increase in EZH2 expression was essential for the CDCA7-mediated effects on TNBC progression. Finally, our immunohistochemical analysis revealed that the CDCA7/EZH2 axis was clinical relevant. These findings suggest CDCA7 plays a crucial role in TNBC progression by transcriptionally upregulating EZH2 and might be a potential prognostic factor and therapeutic target in TNBC.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

PCP4/PEP19 upregulates aromatase gene expression via CYP19A1 promoter I.1 in human breast cancer SK-BR-3 cells

The Purkinje cell protein 4/peptide 19 (PCP4/PEP19) is a novel breast cancer cell expressing peptide, originally found in the neural cells as an anti-apoptotic factor, could inhibit cell apoptosis and enhance cell migration and invasion in human breast cancer cell lines. The expression of PCP4/PEP19 is induced by estrogens in estrogen receptor-positive (ER⁺) MCF-7 cells but also highly expressed in ER^- SK-BR-3 cells. In this study, we investigated the effects of PCP4/PEP19 on aromatase gene expression in MCF-7 and SK-BR-3 human breast cancer cells. In SK-BR-3 cells but not in MCF-7 cells, PCP4/PEP19 knockdown by siRNA silencing decreased the aromatase expression in gene transcriptional level. When PCP4/PEP19 was overexpressed by CMV promoter-driven PCP4/PEP19 expressing plasmid transfection, aromatase gene transcription increased in SK-BR-3 cells. This aromatase gene transcription is mainly mediated through promoter region PI.1, which is usually active in the placental tissue but not in the breast cancer tissue. These results indicate a new function of PCP4/PEP19 that would enhance aromatase gene upregulation to supply estrogens in heterogeneous cancer microenvironment.

CrosstalkNet: A Visualization Tool for Differential Co-expression Networks and Communities

Variations in physiological conditions can rewire molecular interactions between biological compartments, which can yield novel insights into gain or loss of interactions specific to perturbations of interest. Networks are a promising tool to elucidate intercellular interactions, yet exploration of these large-scale networks remains a challenge due to their high dimensionality. To retrieve and mine interactions, we developed CrosstalkNet, a user friendly, web-based network visualization tool that provides a statistical framework to infer condition-specific interactions coupled with a community detection algorithm for bipartite graphs to identify significantly dense subnetworks. As a case study, we used CrosstalkNet to mine a set of 54 and 22 gene-expression profiles from breast tumor and normal samples, respectively, with epithelial and stromal compartments extracted via laser microdissection. We show how CrosstalkNet can be used to explore large-scale co-expression networks and to obtain insights into the biological processes that govern cross-talk between different tumor compartments.Significance: This web application enables researchers to mine complex networks and to decipher novel biological processes in tumor epithelial-stroma cross-talk as well as in other studies of intercompartmental interactions. Cancer Res; 78(8); 2140-3. ©2018 AACR.

Network science in clinical trials: A patient-centered approach

There has been a paradigm shift in translational oncology with the advent of novel molecular diagnostic tools in the clinic. However, several challenges are associated with the integration of these sophisticated tools into clinical oncology and daily practice. High-throughput profiling at the DNA, RNA and protein levels (omics) generate a massive amount of data. The analysis and interpretation of these is non-trivial but will allow a more thorough understanding of cancer. Linear modelling of the data as it is often used today is likely to limit our understanding of cancer as a complex disease, and at times under-performs to capture a phenotype of interest. Network science and systems biology-based approaches, using machine learning and network science principles, that integrate multiple data sources, can uncover complex changes in a biological system. This approach will integrate a large number of potential biomarkers in preclinical studies to better inform therapeutic decisions and ultimately make substantial progress towards precision medicine. It will however require development of a new generation of clinical trials. Beyond discussing the challenges of high-throughput technologies, this review will develop a framework on how to implement a network science approach in new clinical trial designs in order to advance cancer care.

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. ©2017 AACR.

EZH2 protein expression in normal breast epithelium and risk of breast cancer: results from the Nurses' Health Studies

BACKGROUND

Enhancer of zeste homolog 2 (EZH2) is a polycomb-group protein that is involved in stem cell renewal and carcinogenesis. In breast cancer, increased EZH2 expression is associated with aggressiveness and has been suggested to identify normal breast epithelium at increased risk of breast cancer development. However, the association between EZH2 expression in benign breast tissue and breast cancer risk has not previously been evaluated in a large prospective cohort.

METHODS

We examined the association between EZH2 protein expression and subsequent breast cancer risk using logistic regression in a nested case-control study of benign breast disease (BBD) and breast cancer within the Nurses' Health Studies. EZH2 immunohistochemical expression in normal breast epithelium and stroma was evaluated by computational image analysis and its association with breast cancer risk was analyzed after adjusting for matching factors between cases and controls, the concomitant BBD diagnosis, and the Ki67 proliferation index.

RESULTS

Women with a breast biopsy in which more than 20% of normal epithelial cells expressed EZH2 had a significantly increased risk of developing breast cancer (odds ratio (OR) 2.95, 95% confidence interval (CI) 1.11-7.84) compared to women with less than 10% EZH2 epithelial expression. The risk of developing breast cancer increased for each 5% increase in EZH2 expression (OR 1.22, 95% CI 1.02-1.46, p value 0.026). Additionally, women with high EZH2 expression and low estrogen receptor (ER) expression had a 4-fold higher risk of breast cancer compared to women with low EZH2 and low ER expression (OR 4.02, 95% CI 1.29-12.59).

CONCLUSIONS

These results provide further evidence that EZH2 expression in the normal breast epithelium is independently associated with breast cancer risk and might be used to assist in risk stratification for women with benign breast biopsies.

A mechanically active heterotypic E-cadherin/N-cadherin adhesion enables fibroblasts to drive cancer cell invasion

Cancer-associated fibroblasts (CAFs) promote tumour invasion and metastasis. We show that CAFs exert a physical force on cancer cells that enables their collective invasion. Force transmission is mediated by a heterophilic adhesion involving N-cadherin at the CAF membrane and E-cadherin at the cancer cell membrane. This adhesion is mechanically active; when subjected to force it triggers β-catenin recruitment and adhesion reinforcement dependent on α-catenin/vinculin interaction. Impairment of E-cadherin/N-cadherin adhesion abrogates the ability of CAFs to guide collective cell migration and blocks cancer cell invasion. N-cadherin also mediates repolarization of the CAFs away from the cancer cells. In parallel, nectins and afadin are recruited to the cancer cell/CAF interface and CAF repolarization is afadin dependent. Heterotypic junctions between CAFs and cancer cells are observed in patient-derived material. Together, our findings show that a mechanically active heterophilic adhesion between CAFs and cancer cells enables cooperative tumour invasion.

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

The metastasis suppressor RARRES3 as an endogenous inhibitor of the immunoproteasome expression in breast cancer cells

In breast cancer metastasis, the dynamic continuum involving pro- and anti-inflammatory regulators can become compromised. Over 600 genes have been implicated in metastasis to bone, lung or brain but how these genes might contribute to perturbation of immune function is poorly understood. To gain insight, we adopted a gene co-expression network approach that draws on the functional parallels between naturally occurring bone marrow-derived mesenchymal stem cells (BM-MSCs) and cancer stem cells (CSCs). Our network analyses indicate a key role for metastasis suppressor RARRES3, including potential to regulate the immunoproteasome (IP), a specialized proteasome induced under inflammatory conditions. Knockdown of RARRES3 in near-normal mammary epithelial and breast cancer cell lines increases overall transcript and protein levels of the IP subunits, but not of their constitutively expressed counterparts. RARRES3 mRNA expression is controlled by interferon regulatory factor IRF1, an inducer of the IP, and is sensitive to depletion of the retinoid-related receptor RORA that regulates various physiological processes including immunity through modulation of gene expression. Collectively, these findings identify a novel regulatory role for RARRES3 as an endogenous inhibitor of IP expression, and contribute to our evolving understanding of potential pathways underlying breast cancer driven immune modulation.

Gene expression in local stroma reflects breast tumor states and predicts patient outcome

The surrounding microenvironment has been implicated in the progression of breast tumors to metastasis. However, the degree to which metastatic breast tumors locally reprogram stromal cells as they disrupt tissue boundaries is not well understood. We used species-specific RNA sequencing in a mouse xenograft model to determine how the metastasis suppressor RKIP influences transcription in a panel of paired tumor and stroma tissues. We find that gene expression in metastatic breast tumors is pervasively correlated with gene expression in local stroma of both mouse xenografts and human patients. Changes in stromal gene expression elicited by tumors better predicts subtype and patient survival than tumor gene expression, and genes with coordinated expression in both tissues predict metastasis-free survival. These observations support the use of stroma-based strategies for the diagnosis and prognosis of breast cancer.

Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue

The strength of associations between various exposures (e.g., diet, tobacco, chemopreventive agents) and colorectal cancer risk may partially depend on the complex interaction between epithelium and stroma across anatomic subsites. Currently, baseline data describing genome-wide coding and long noncoding gene expression profiles in the healthy colon specific to tissue type and location are lacking. Therefore, colonic mucosal biopsies from 10 healthy participants who were enrolled in a clinical study to evaluate effects of lignan supplementation on gut resiliency were used to characterize the site-specific global gene expression signatures associated with stromal vs. epithelial cells in the sigmoid colon and rectum. Using RNA-seq, we demonstrate that tissue type and location patterns of gene expression and upstream regulatory pathways are distinct. For example, consistent with a key role of stroma in the crypt niche, mRNAs associated with immunoregulatory and inflammatory processes (i.e., CXCL14, ANTXR1), smooth muscle contraction (CALD1), proliferation and apoptosis (GLP2R, IGFBP3), and modulation of extracellular matrix (MMP2, COL3A1, MFAP4) were all highly expressed in the stroma. In comparison, HOX genes (HOXA3, HOXD9, HOXD10, HOXD11, and HOXD-AS2, a HOXD cluster antisense RNA 2), and WNT5B expression were also significantly higher in sigmoid colon compared with the rectum. These findings provide strong impetus for considering colorectal tissue subtypes and location in future observational studies and clinical trials designed to evaluate the effects of exposures on colonic health.

Personalized Cancer Care: Risk Prediction, Early Diagnosis, Progression, and Therapy

At the annual prestigious International Symposium of the Fritz-Bender Foundation, Munich, 18-20 May, 2016, researchers, clinicians, and students discussed the state of the art and future perspectives of genomic medicine in cancer. Genomic medicine (also known as precision medicine/oncology) should help clinicians to provide a more precise diagnosis and therapy in oncology for individual patients. The meeting focused on next-generation sequencing methods, analytical computational analysis of big data, and data mining on the way to translational and evidence-based medicine. The meeting covered the social and ethical impact of genomic medicine as well as news and views on antibody targeting of intracellular proteins, on the architecture of intracellular proteins and their impact on carcinogenesis, and on the adaptation of tumor therapy in due consideration of tumor evolution. Subheadings like "Genetic Profiling of Patients and Risk Prediction," "Molecular Profiling of Tumors and Metastases," "Tumor-Host Microenvironment Interaction and Metabolism," and "Targeted Therapy" were subsumed under the main heading of "Personalized Cancer Care."

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

Extracellular Vesicles in Brain Tumor Progression

Brain tumors can be viewed as multicellular 'ecosystems' with increasingly recognized cellular complexity and systemic impact. While the emerging diversity of malignant disease entities affecting brain tissues is often described in reference to their signature alterations within the cellular genome and epigenome, arguably these cell-intrinsic changes can be regarded as hardwired adaptations to a variety of cell-extrinsic microenvironmental circumstances. Conversely, oncogenic events influence the microenvironment through their impact on the cellular secretome, including emission of membranous structures known as extracellular vesicles (EVs). EVs serve as unique carriers of bioactive lipids, secretable and non-secretable proteins, mRNA, non-coding RNA, and DNA and constitute pathway(s) of extracellular exit of molecules into the intercellular space, biofluids, and blood. EVs are also highly heterogeneous as reflected in their nomenclature (exosomes, microvesicles, microparticles) attempting to capture their diverse origin, as well as structural, molecular, and functional properties. While EVs may act as a mechanism of molecular expulsion, their non-random uptake by heterologous cellular recipients defines their unique roles in the intercellular communication, horizontal molecular transfer, and biological activity. In the central nervous system, EVs have been implicated as mediators of homeostasis and repair, while in cancer they may act as regulators of cell growth, clonogenicity, angiogenesis, thrombosis, and reciprocal tumor-stromal interactions. EVs produced by specific brain tumor cell types may contain the corresponding oncogenic drivers, such as epidermal growth factor receptor variant III (EGFRvIII) in glioblastoma (and hence are often referred to as 'oncosomes'). Through this mechanism, mutant oncoproteins and nucleic acids may be transferred horizontally between cellular populations altering their individual and collective phenotypes. Oncogenic pathways also impact the emission rates, types, cargo, and biogenesis of EVs, as reflected by preliminary analyses pointing to differences in profiles of EV-regulating genes (vesiculome) between molecular subtypes of glioblastoma, and in other brain tumors. Molecular regulators of vesiculation can also act as oncogenes. These intimate connections suggest the context-specific roles of different EV subsets in the progression of specific brain tumors. Advanced efforts are underway to capture these events through the use of EVs circulating in biofluids as biomarker reservoirs and to guide diagnostic and therapeutic decisions.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.

METHODS

We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.

RESULTS

Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).

CONCLUSIONS

A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00070278 ; 03/10/2003.