Matrisome AnalyzeR - a suite of tools to annotate and quantify ECM molecules in big datasets across organisms.

The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays crucial roles in all aspects of life - from orchestrating cell migration during development, to supporting tissue repair. It also plays critical roles in the etiology or progression of diseases. To study this compartment, we have previously defined the compendium of all genes encoding ECM and ECM-associated proteins for multiple organisms. We termed this compendium the 'matrisome' and further classified matrisome components into different structural or functional categories. This nomenclature is now largely adopted by the research community to annotate '-omics' datasets and has contributed to advance both fundamental and translational ECM research. Here, we report the development of Matrisome AnalyzeR, a suite of tools including a web-based application and an R package. The web application can be used by anyone interested in annotating, classifying and tabulating matrisome molecules in large datasets without requiring programming knowledge. The companion R package is available to more experienced users, interested in processing larger datasets or in additional data visualization options.

Ten Years of Extracellular Matrix Proteomics: Accomplishments, Challenges, and Future Perspectives.

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely posttranslationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome," of tissues. This first part of this review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Lastly, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. Mass spectrometry-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database.

The extracellular matrix (ECM) is a complex assembly of proteins that constitutes the scaffold organizing cells, tissues, and organs. Over the past decade, mass-spectrometry-based proteomics has become the method of choice to profile the composition of the ECM, or the matrisome, of tissues. To assist non-specialists with the reuse of ECM proteomic datasets, we released MatrisomeDB (https://matrisomedb.org) in 2020. Here, we report the expansion of the database to include 25 new curated studies on the ECM of 24 new tissues in addition to datasets on tissues previously included, more than doubling the size of the original database and achieving near-complete coverage of the in-silico predicted matrisome. We further enhanced data visualization by maps of peptides and post-translational-modifications detected onto domain-based representations and 3D structures of ECM proteins. We also referenced external resources to facilitate the design of targeted mass spectrometry assays. Last, we implemented an abstract-mining tool that generates an enrichment word cloud from abstracts of studies in which a queried protein is found with higher confidence and higher abundance relative to other studies in MatrisomeDB.

Hepatocellular carcinomas, exhibiting intratumor fibrosis, express cancer-specific extracellular matrix remodeling and WNT/TGFB signatures, associated with poor outcome.

BACKGROUND AND AIMS: HCC, the third leading cause of cancer-related death, arises in the context of liver fibrosis. Although HCC is generally poorly fibrogenic, some tumors harbor focal intratumor extracellular matrix (ECM) deposits called "fibrous nests." To date, the molecular composition and clinical relevance of these ECM deposits have not been fully defined. APPROACH AND RESULTS: We performed quantitative matrisome analysis by tandem mass tags mass spectrometry in 20 human cancer specific matrisome (HCCs) with high or low-grade intratumor fibrosis and matched nontumor tissues, as well as in 12 livers from mice treated with vehicle, carbon tetrachloride, or diethylnitrosamine. We found 94 ECM proteins differentially abundant between high and low-grade fibrous nests, including interstitial and basement membrane components, such as several collagens, glycoproteins, proteoglycans, enzymes involved in ECM stabilization and degradation, and growth factors. Pathway analysis revealed a metabolic switch in high-grade fibrosis, with enhanced glycolysis and decreased oxidative phosphorylation. Integrating the quantitative proteomics with transcriptomics from HCCs and nontumor livers (n = 2,285 samples), we identified a subgroup of fibrous nest HCCs, characterized by cancer-specific ECM remodeling, expression of the WNT/TGFB (S1) subclass signature, and poor patient outcome. Fibrous nest HCCs abundantly expressed an 11-fibrous-nest - protein signature, associated with poor patient outcome, by multivariate Cox analysis, and validated by multiplex immunohistochemistry. CONCLUSIONS: Matrisome analysis highlighted cancer-specific ECM deposits, typical of the WNT/TGFB HCC subclass, associated with poor patient outcomes. Hence, histologic reporting of intratumor fibrosis in HCC is of clinical relevance.

New Views of Old Proteins: Clarifying the Enigmatic Proteome.

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

Computational and experimental characterization of the novel ECM glycoprotein SNED1 and prediction of its interactome.

The extracellular matrix (ECM) is a complex meshwork of proteins and an essential component of multicellular life. We have recently reported the characterization of a novel ECM protein, SNED1, and showed that it promotes breast cancer metastasis and regulates craniofacial development. However, the mechanisms by which it does so remain unknown. ECM proteins exert their functions by binding to cell surface receptors and interacting with other ECM proteins, actions that we can predict using knowledge of protein's sequence, structure, and post-translational modifications. Here, we combined in-silico and in-vitro approaches to characterize the physico-chemical properties of SNED1 and infer its putative functions. To do so, we established a mammalian cell system to produce and purify SNED1 and its N-terminal fragment, which contains a NIDO domain, and demonstrated experimentally SNED1's potential to be glycosylated, phosphorylated, and incorporated into an insoluble ECM. We also determined the secondary and tertiary structures of SNED1 and its N-terminal fragment and obtained a model for its NIDO domain. Using computational predictions, we identified 114 proteins as putative SNED1 interactors, including the ECM protein fibronectin. Pathway analysis of the predicted SNED1 interactome further revealed that it may contribute to signaling through cell surface receptors, such as integrins, and participate in the regulation of ECM organization and developmental processes. Last, using fluorescence microscopy, we showed that SNED1 forms microfibrils within the ECM and partially colocalizes with fibronectin. Altogether, we provide a wealth of information on an understudied yet important ECM protein with the potential to decipher its pathophysiological functions.

MatrisomeDB: the ECM-protein knowledge database.

The extracellular matrix (ECM) is a complex and dynamic meshwork of cross-linked proteins that supports cell polarization and functions and tissue organization and homeostasis. Over the past few decades, mass-spectrometry-based proteomics has emerged as the method of choice to characterize the composition of the ECM of normal and diseased tissues. Here, we present a new release of MatrisomeDB, a searchable collection of curated proteomic data from 17 studies on the ECM of 15 different normal tissue types, six cancer types (different grades of breast cancers, colorectal cancer, melanoma, and insulinoma) and other diseases including vascular defects and lung and liver fibroses. MatrisomeDB (http://www.pepchem.org/matrisomedb) was built by retrieving raw mass spectrometry data files and reprocessing them using the same search parameters and criteria to allow for a more direct comparison between the different studies. The present release of MatrisomeDB includes 847 human and 791 mouse ECM proteoforms and over 350 000 human and 600 000 mouse ECM-derived peptide-to-spectrum matches. For each query, a hierarchically-clustered tissue distribution map, a peptide coverage map, and a list of post-translational modifications identified, are generated. MatrisomeDB is the most complete collection of ECM proteomic data to date and allows the building of a comprehensive ECM atlas.

Knockout of the gene encoding the extracellular matrix protein SNED1 results in early neonatal lethality and craniofacial malformations.

BACKGROUND: The extracellular matrix (ECM) is a fundamental component of multicellular organisms that orchestrates developmental processes and controls cell and tissue organization. We previously identified the novel ECM protein SNED1 as a promoter of breast cancer metastasis and showed that its level of expression negatively correlated with breast cancer patient survival. Here, we sought to identify the roles of SNED1 during murine development. RESULTS: We generated two novel Sned1 knockout mouse strains and showed that Sned1 is essential since homozygous ablation of the gene led to early neonatal lethality. Phenotypic analysis of the surviving knockout mice revealed a role for SNED1 in the development of craniofacial and skeletal structures since Sned1 knockout resulted in growth defects, nasal cavity occlusion, and craniofacial malformations. Sned1 is widely expressed in embryos, notably by cell populations undergoing epithelial-to-mesenchymal transition, such as the neural crest cells. We further show that mice with a neural-crest-cell-specific deletion of Sned1 survive, but display facial anomalies partly phenocopying the global knockout mice. CONCLUSIONS: Our results demonstrate requisite roles for SNED1 during development and neonatal survival. Importantly, the deletion of 2q37.3 in humans, a region that includes the SNED1 locus, has been associated with facial dysmorphism and short stature.

The cancer matrisome: From comprehensive characterization to biomarker discovery.

Tumor progression and dissemination critically depend on support from the tumor microenvironment, the ensemble of cellular and acellular components surrounding and interacting with tumor cells. The extracellular matrix (ECM), the complex scaffolding of hundreds of proteins organizing cells in tissues, is a major component of the tumor microenvironment. It orchestrates cellular processes including proliferation, migration, and invasion, that are highly dysregulated during cancer progression. Alterations in ECM abundance, integrity, and mechanical properties have been correlated with poorer prognosis for cancer patients. Yet the ECM proteome, or "matrisome," of tumors remained until recently largely unexplored. This review will present the recent developments in computational and proteomic technologies that have allowed the comprehensive characterization of the ECM of different tumor types and microenvironmental niches. These approaches have resulted in the definition of protein signatures distinguishing tumors from normal tissues, tumors of different stages, primary from secondary tumors, and tumors from other diseased states such as fibrosis. Moreover, recent studies have demonstrated that the levels of expression of certain genes encoding ECM and ECM-associated proteins is prognostic of cancer patient survival and can thus serve as biomarkers. Last, proteomic studies have permitted the identification of novel ECM proteins playing functional roles in cancer progression. Such proteins have the potential to be exploited as therapeutic targets.

MatrixDB: integration of new data with a focus on glycosaminoglycan interactions.

MatrixDB (http://matrixdb.univ-lyon1.fr/) is an interaction database focused on biomolecular interactions established by extracellular matrix (ECM) proteins and glycosaminoglycans (GAGs). It is an active member of the International Molecular Exchange (IMEx) consortium (https://www.imexconsortium.org/). It has adopted the HUPO Proteomics Standards Initiative standards for annotating and exchanging interaction data, either at the MIMIx (The Minimum Information about a Molecular Interaction eXperiment) or IMEx level. The following items related to GAGs have been added in the updated version of MatrixDB: (i) cross-references of GAG sequences to the GlyTouCan database, (ii) representation of GAG sequences in different formats (IUPAC and GlycoCT) and as SNFG (Symbol Nomenclature For Glycans) images and (iii) the GAG Builder online tool to build 3D models of GAG sequences from GlycoCT codes. The database schema has been improved to represent n-ary experiments. Gene expression data, imported from Expression Atlas (https://www.ebi.ac.uk/gxa/home), quantitative ECM proteomic datasets (http://matrisomeproject.mit.edu/ecm-atlas), and a new visualization tool of the 3D structures of biomolecules, based on the PDB Component Library and LiteMol, have also been added. A new advanced query interface now allows users to mine MatrixDB data using combinations of criteria, in order to build specific interaction networks related to diseases, biological processes, molecular functions or publications.

Quantitative proteomics identify Tenascin-C as a promoter of lung cancer progression and contributor to a signature prognostic of patient survival.

The extracellular microenvironment is an integral component of normal and diseased tissues that is poorly understood owing to its complexity. To investigate the contribution of the microenvironment to lung fibrosis and adenocarcinoma progression, two pathologies characterized by excessive stromal expansion, we used mouse models to characterize the extracellular matrix (ECM) composition of normal lung, fibrotic lung, lung tumors, and metastases. Using quantitative proteomics, we identified and assayed the abundance of 113 ECM proteins, which revealed robust ECM protein signatures unique to fibrosis, primary tumors, or metastases. These analyses indicated significantly increased abundance of several S100 proteins, including Fibronectin and Tenascin-C (Tnc), in primary lung tumors and associated lymph node metastases compared with normal tissue. We further showed that Tnc expression is repressed by the transcription factor Nkx2-1, a well-established suppressor of metastatic progression. We found that increasing the levels of Tnc, via CRISPR-mediated transcriptional activation of the endogenous gene, enhanced the metastatic dissemination of lung adenocarcinoma cells. Interrogation of human cancer gene expression data revealed that high TNC expression correlates with worse prognosis for lung adenocarcinoma, and that a three-gene expression signature comprising TNC, S100A10, and S100A11 is a robust predictor of patient survival independent of age, sex, smoking history, and mutational load. Our findings suggest that the poorly understood ECM composition of the fibrotic and tumor microenvironment is an underexplored source of diagnostic markers and potential therapeutic targets for cancer patients.

Characterization of the Extracellular Matrix of Normal and Diseased Tissues Using Proteomics.

The extracellular matrix (ECM) is a complex meshwork of insoluble fibrillar proteins and signaling factors interacting together to provide architectural and instructional cues to the surrounding cells. Alterations in ECM organization or composition and excessive ECM deposition have been observed in diseases such as fibrosis, cardiovascular diseases, and cancer. We provide here optimized protocols to solubilize ECM proteins from normal or tumor tissues, digest the proteins into peptides, analyze ECM peptides by mass spectrometry, and interpret the mass spectrometric data. In addition, we present here two novel R-script-based web tools allowing rapid annotation and relative quantification of ECM proteins, peptides, and intensity/abundance in mass spectrometric data output files. We illustrate this protocol with ECMs obtained from two pairs of tissues, which differ in ECM content and cellularity: triple-negative breast cancer and adjacent mammary tissue, and omental metastasis from high-grade serous ovarian cancer and normal omentum. The complete proteomics data set generated in this study has been deposited to the public repository ProteomeXchange with the data set identifier: PXD005554.

Extracellular matrix signatures of human primary metastatic colon cancers and their metastases to liver.

BACKGROUND: Colorectal cancer is the third most frequently diagnosed cancer and the third cause of cancer deaths in the United States. Despite the fact that tumor cell-intrinsic mechanisms controlling colorectal carcinogenesis have been identified, novel prognostic and diagnostic tools as well as novel therapeutic strategies are still needed to monitor and target colon cancer progression. We and others have previously shown, using mouse models, that the extracellular matrix (ECM), a major component of the tumor microenvironment, is an important contributor to tumor progression. In order to identify candidate biomarkers, we sought to define ECM signatures of metastatic colorectal cancers and their metastases to the liver. METHODS: We have used enrichment of extracellular matrix (ECM) from human patient samples and proteomics to define the ECM composition of primary colon carcinomas and their metastases to liver in comparison with normal colon and liver samples. RESULTS: We show that robust signatures of ECM proteins characteristic of each tissue, normal and malignant, can be defined using relatively small samples from small numbers of patients. Comparisons with gene expression data from larger cohorts of patients confirm the association of subsets of the proteins identified by proteomic analysis with tumor progression and metastasis. CONCLUSIONS: The ECM protein signatures of metastatic primary colon carcinomas and metastases to liver defined in this study, offer promise for development of diagnostic and prognostic signatures of metastatic potential of colon tumors. The ECM proteins defined here represent candidate serological or tissue biomarkers and potential targets for imaging of occult metastases and residual or recurrent tumors and conceivably for therapies. Furthermore, the methods described here can be applied to other tumor types and can be used to investigate other questions such as the role of ECM in resistance to therapy.

The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices.

The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins providing both biophysical and biochemical cues that are important regulators of cell proliferation, survival, differentiation, and migration. We present here a proteomic strategy developed to characterize the in vivo ECM composition of normal tissues and tumors using enrichment of protein extracts for ECM components and subsequent analysis by mass spectrometry. In parallel, we have developed a bioinformatic approach to predict the in silico "matrisome" defined as the ensemble of ECM proteins and associated factors. We report the characterization of the extracellular matrices of murine lung and colon, each comprising more than 100 ECM proteins and each presenting a characteristic signature. Moreover, using human tumor xenografts in mice, we show that both tumor cells and stromal cells contribute to the production of the tumor matrix and that tumors of differing metastatic potential differ in both the tumor- and the stroma-derived ECM components. The strategy we describe and illustrate here can be broadly applied and, to facilitate application of these methods by others, we provide resources including laboratory protocols, inventories of ECM domains and proteins, and instructions for bioinformatically deriving the human and mouse matrisome.

Extracellular matrix dynamics: A key regulator of cell migration across length-scales and systems.

The interactions between cells and their surrounding extracellular matrix (ECM) are dynamic and play critical roles in cell migration during development, health, and diseases. Recent advances have highlighted the complexity and diversity of ECM compositions, or "matrisomes", of tissues resulting in ECMs of different physical, mechanical, and biochemical properties. Investigating the effects of these properties on cell-ECM interactions in the context of cell migration have led to a better understanding of the principles underlying tissue morphogenesis, wound healing, immune response, or cancer metastasis. These new insights into the interplay between ECM dynamics and cell migration can lead to the identification of unique opportunities for therapeutic interventions.

Matrisome AnalyzeR: A suite of tools to annotate and quantify ECM molecules in big datasets across organisms.

The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays critical roles in all aspects of life: from orchestrating cell migration during development, to supporting tissue repair. It also plays critical roles in the etiology or progression of diseases. To study this compartment, we defined the compendium of all genes encoding ECM and ECM-associated proteins for multiple organisms. We termed this compendium the "matrisome" and further classified matrisome components into different structural or functional categories. This nomenclature is now largely adopted by the research community to annotate -omics datasets and has contributed to advance both fundamental and translational ECM research. Here, we report the development of Matrisome AnalyzeR, a suite of tools including a web-based application ( https://sites.google.com/uic.edu/matrisome/tools/matrisome-analyzer ) and an R package ( https://github.com/Matrisome/MatrisomeAnalyzeR ). The web application can be used by anyone interested in annotating, classifying, and tabulating matrisome molecules in large datasets without requiring programming knowledge. The companion R package is available to more experienced users, interested in processing larger datasets or in additional data visualization options. SUMMARY STATEMENT: Matrisome AnalyzeR is a suite of tools, including a web-based app and an R package, designed to facilitate the annotation and quantification of extracellular matrix components in big datasets.

Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Reduced SPAG17 Expression in Systemic Sclerosis Triggers Myofibroblast Transition and Drives Fibrosis.

Systemic sclerosis (SSc) is a clinically heterogeneous fibrotic disease with no effective treatment. Myofibroblasts are responsible for unresolving synchronous skin and internal organ fibrosis in SSc, but the drivers of sustained myofibroblast activation remain poorly understood. Using unbiased transcriptome analysis of skin biopsies, we identified the downregulation of SPAG17 in multiple independent cohorts of patients with SSc, and by orthogonal approaches, we observed a significant negative correlation between SPAG17 and fibrotic gene expression. Fibroblasts and endothelial cells explanted from SSc skin biopsies showed reduced chromatin accessibility at the SPAG17 locus. Remarkably, mice lacking Spag17 showed spontaneous skin fibrosis with increased dermal thickness, collagen deposition and stiffness, and altered collagen fiber alignment. Knockdown of SPAG17 in human and mouse fibroblasts and microvascular endothelial cells was accompanied by spontaneous myofibroblast transformation and markedly heightened sensitivity to profibrotic stimuli. These responses were accompanied by constitutive TGF-ß pathway activation. Thus, we discovered impaired expression of SPAG17 in SSc and identified, to our knowledge, a previously unreported cell-intrinsic role for SPAG17 in the negative regulation of fibrotic responses. These findings shed fresh light on the pathogenesis of SSc and may inform the search for innovative therapies for SSc and other fibrotic conditions through SPAG17 signaling.

Spatial recruitment and activation of the Fes kinase by ezrin promotes HGF-induced cell scattering.

The remodeling of epithelial monolayers induced by hepatocyte growth factor (HGF) results in the reorganization of actin cytoskeleton and cellular junctions. We previously showed that the membrane-cytoskeleton linker ezrin plays a major role in HGF-induced morphogenic effects. Here we identified a novel partner of phosphorylated ezrin, the Fes kinase, that acts downstream of ezrin in HGF-mediated cell scattering. We found that Fes interacts directly, through its SH2 domain, with ezrin phosphorylated at tyrosine 477. We show that in epithelial cells, activated Fes localizes either to focal adhesions or cell-cell contacts depending on cell confluency. The recruitment and the activation of Fes to the cell-cell contacts in confluent cells depend on its interaction with ezrin. When this interaction is impaired, Fes remains in focal adhesions and as a consequence the cells show defective spreading and scattering in response to HGF stimulation. Altogether, these results provide a novel mechanism whereby ezrin/Fes interaction at cell-cell contacts plays an essential role in HGF-induced cell scattering and implicates Fes in the cross-talk between cell-cell and cell-matrix adhesion.

Ezrin phosphorylation on tyrosine 477 regulates invasion and metastasis of breast cancer cells.

BACKGROUND: The membrane cytoskeletal crosslinker, ezrin, a member of the ERM family of proteins, is frequently over-expressed in human breast cancers, and is required for motility and invasion of epithelial cells. Our group previously showed that ezrin acts co-operatively with the non-receptor tyrosine kinase, Src, in deregulation of cell-cell contacts and scattering of epithelial cells. In particular, ezrin phosphorylation on Y477 by Src is specific to ezrin within the ERM family, and is required for HGF-induced scattering of epithelial cells. We therefore sought to examine the role of Y477 phosphorylation in ezrin on tumor progression. METHODS: Using a highly metastatic mouse mammary carcinoma cell line (AC2M2), we tested the effect of over-expressing a non-phosphorylatable form of ezrin (Y477F) on invasive colony growth in 3-dimensional Matrigel cultures, and on local invasion and metastasis in an orthotopic engraftment model. RESULTS: AC2M2 cells over-expressing Y477F ezrin exhibited delayed migration in vitro, and cohesive round colonies in 3-dimensional Matrigel cultures, compared to control cells that formed invasive colonies with branching chains of cells and numerous actin-rich protrusions. Moreover, over-expression of Y477F ezrin inhibits local tumor invasion in vivo. Whereas orthotopically injected wild type AC2M2 tumor cells were found to infiltrate into the abdominal wall and visceral organs within three weeks, tumors expressing Y477F ezrin remained circumscribed, with little invasion into the surrounding stroma and abdominal wall. Additionally, Y477F ezrin reduces the number of lung metastatic lesions. CONCLUSIONS: Our study implicates a role of Y477 ezrin, which is phosphorylated by Src, in regulating local invasion and metastasis of breast carcinoma cells, and provides a clinically relevant model for assessing the Src/ezrin pathway as a potential prognostic/predictive marker or treatment target for invasive human breast cancer.