Secretome Identifies Tenascin-X as a Potent Marker of Ovarian Cancer

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Profiling Phosphoproteome Landscape in Circulating Extracellular Vesicles from Microliters of Biofluids through Functionally Tunable Paramagnetic Separation

Many biological processes are regulated through dynamic protein phosphorylation. Monitoring disease-relevant phosphorylation events in circulating biofluids is highly appealing but also technically challenging. We introduce here a functionally tunable material and a strategy, extracellular vesicles to phosphoproteins (EVTOP), which achieves one-pot extracellular vesicles (EVs) isolation, extraction, and digestion of EV proteins, and enrichment of phosphopeptides, with only a trace amount of starting biofluids. EVs are efficiently isolated by magnetic beads functionalized with TiIV ions and a membrane-penetrating peptide, octa-arginine R8 + , which also provides the hydrophilic surface to retain EV proteins during lysis. Subsequent on-bead digestion concurrently converts EVTOP to TiIV ion-only surface for efficient enrichment of phosphopeptides for phosphoproteomic analyses. The streamlined, ultra-sensitive platform enabled us to quantify 500 unique EV phosphopeptides with only a few μL of plasma and over 1200 phosphopeptides with 100 μL of cerebrospinal fluid (CSF). We explored its clinical application of monitoring the outcome of chemotherapy of primary central nervous system lymphoma (PCNSL) patients with a small volume of CSF, presenting a powerful tool for broad clinical applications.

Tenascin-X as a causal gene for classical-like Ehlers-Danlos syndrome

Tenascin-X (TNX) is an extracellular matrix glycoprotein for which a deficiency results in a recessive form of classical-like Ehlers-Danlos syndrome (clEDS), a heritable connective tissue disorder with hyperextensible skin without atrophic scarring, joint hypermobility, and easy bruising. Notably, patients with clEDS also suffer from not only chronic joint pain and chronic myalgia but also neurological abnormalities such as peripheral paresthesia and axonal polyneuropathy with high frequency. By using TNX-deficient (Tnxb−/−) mice, well-known as a model animal of clEDS, we recently showed that Tnxb−/− mice exhibit hypersensitivity to chemical stimuli and the development of mechanical allodynia due to the hypersensitization of myelinated A-fibers and activation of the spinal dorsal horn. Pain also occurs in other types of EDS. First, we review the underlying molecular mechanisms of pain in EDS, especially that in clEDS. In addition, the roles of TNX as a tumor suppressor protein in cancer progression have been reported. Recent in silico large-scale database analyses have shown that TNX is downregulated in various tumor tissues and that high expression of TNX in tumor cells has a good prognosis. We describe what is so far known about TNX as a tumor suppressor protein. Furthermore, some patients with clEDS show delayed wound healing. Tnxb−/− mice also exhibit impairment of epithelial wound healing in corneas. TNX is also involved in liver fibrosis. We address the molecular mechanism for the induction of COL1A1 by the expression of both a peptide derived from the fibrinogen-related domain of TNX and integrin α11.

Revisiting the Tenascins: Exploitable as Cancer Targets?

For their full manifestation, tumors require support from the surrounding tumor microenvironment (TME), which includes a specific extracellular matrix (ECM), vasculature, and a variety of non-malignant host cells. Together, these components form a tumor-permissive niche that significantly differs from physiological conditions. While the TME helps to promote tumor progression, its special composition also provides potential targets for anti-cancer therapy. Targeting tumor-specific ECM molecules and stromal cells or disrupting aberrant mesenchyme-cancer communications might normalize the TME and improve cancer treatment outcome. The tenascins are a family of large, multifunctional extracellular glycoproteins consisting of four members. Although each have been described to be expressed in the ECM surrounding cancer cells, tenascin-C and tenascin-W are currently the most promising candidates for exploitability and clinical use as they are highly expressed in various tumor stroma with relatively low abundance in healthy tissues. Here, we review what is known about expression of all four tenascin family members in tumors, followed by a more thorough discussion on tenascin-C and tenascin-W focusing on their oncogenic functions and their potential as diagnostic and/or targetable molecules for anti-cancer treatment purposes.

Modeling the Tumor Microenvironment of Ovarian Cancer: The Application of Self-Assembling Biomaterials

Ovarian cancer (OvCa) is one of the leading causes of gynecologic malignancies. Despite treatment with surgery and chemotherapy, OvCa disseminates and recurs frequently, reducing the survival rate for patients. There is an urgent need to develop more effective treatment options for women diagnosed with OvCa. The tumor microenvironment (TME) is a key driver of disease progression, metastasis and resistance to treatment. For this reason, 3D models have been designed to represent this specific niche and allow more realistic cell behaviors compared to conventional 2D approaches. In particular, self-assembling peptides represent a promising biomaterial platform to study tumor biology. They form nanofiber networks that resemble the architecture of the extracellular matrix and can be designed to display mechanical properties and biochemical motifs representative of the TME. In this review, we highlight the properties and benefits of emerging 3D platforms used to model the ovarian TME. We also outline the challenges associated with using these 3D systems and provide suggestions for future studies and developments. We conclude that our understanding of OvCa and advances in materials science will progress the engineering of novel 3D approaches, which will enable the development of more effective therapies.

The Roles of Tenascins in Cardiovascular, Inflammatory, and Heritable Connective Tissue Diseases

Tenascins are a family of multifunctional extracellular matrix (ECM) glycoproteins with time- and tissue specific expression patterns during development, tissue homeostasis, and diseases. There are four family members (tenascin-C, -R, -X, -W) in vertebrates. Among them, tenascin-X (TNX) and tenascin-C (TNC) play important roles in human pathologies. TNX is expressed widely in loose connective tissues. TNX contributes to the stability and maintenance of the collagen network, and its absence causes classical-like Ehlers-Danlos syndrome (clEDS), a heritable connective tissue disorder. In contrast, TNC is specifically and transiently expressed upon pathological conditions such as inflammation, fibrosis, and cancer. There is growing evidence that TNC is involved in inflammatory processes with proinflammatory or anti-inflammatory activity in a context-dependent manner. In this review, we summarize the roles of these two tenascins, TNX and TNC, in cardiovascular and inflammatory diseases and in clEDS, and we discuss the functional consequences of the expression of these tenascins for tissue homeostasis.

Loss of Tenascin-X expression during tumor progression: A new pan-cancer marker

Cancer is a systemic disease involving multiple components produced from both tumor cells themselves and surrounding stromal cells. The pro- or anti-tumoral role of the stroma is still under debate. Indeed, it has long been considered the main physical barrier to the diffusion of chemotherapy by its dense and fibrous nature and its poor vascularization. However, in murine models, the depletion of fibroblasts, the main ExtraCellular Matrix (ECM)-producing cells, led to more aggressive tumors even though they were more susceptible to anti-angiogenic and immuno-modulators. Tenascin-C (TNC) is a multifunctional matricellular glycoprotein (i.e. an ECM protein also able to induce signaling pathway) and is considered as a marker of tumor expansion and metastasis. However, the status of other tenascin (TN) family members and particularly Tenascin-X (TNX) has been far less studied during this pathological process and is still controversial. Herein, through (1) in silico analyses of the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and (2) immunohistochemistry staining of Tissue MicroArrays (TMA), we performed a large and extensive study of TNX expression at both mRNA and protein levels (1) in the 6 cancers with the highest incidence and mortality in the world (i.e. lung, breast, colorectal, prostate, stomach and liver) and (2) in the cancers for which sparse data regarding TNX expression already exist in the literature. We thus demonstrated that, in most cancers, TNX expression is significantly downregulated during cancer progression and we also highlighted, when data were available, that high TNXB mRNA expression in cancer is correlated with a good survival prognosis.

Expression of small leucine-rich extracellular matrix proteoglycans biglycan and lumican reveals oral lichen planus malignant potential

OBJECTIVES

The aim of this study was to examine molecular alterations on the protein level in lesions of oral lichen planus (OLP), oral squamous cell carcinoma (OSCC) and healthy mucosa.

MATERIALS AND METHODS

Global protein profiling methods based on liquid chromatography coupled to mass spectrometry (LC-MS) were used, with a special emphasis on evaluation of deregulated extracellular matrix molecules expression, as well as on analyses of IG2F and IGFR2 expression in healthy mucosa, OLP and OSCC tissues by comparative semi-quantitative immunohistochemistry.

RESULTS

Mass spectrometry-based proteomics profiling of healthy mucosa, OLP and OSCC tissues (and accompanied histologically unaltered tissues, respectively) identified 55 extracellular matrix proteins. Twenty among identified proteins were common to all groups of samples. Expression of small leucine-rich extracellular matrix proteoglycans lumican and biglycan was found both in OSCC and OLP and they were validated by Western blot analysis as putative biomarkers. A significant increase (p < 0.05) of biglycan expression in OLP-AT group was determined in comparison with OLP-T group, while lumican showed significant up-regulation (p < 0.05) in OLP-T and OSCC-T groups vs. adjacent and control tissue groups. Biglycan expression was only determined in OSCC-AT group. Immunohistochemical analysis of IGF2 and IG2FR expression revealed no significant difference among groups of samples.

CONCLUSION/CLINICAL RELEVANCE

Biglycan and lumican were identified as important pathogenesis biomarkers of OLP that point to its malignant potential.

The Extracellular Matrix in Epithelial Ovarian Cancer - A Piece of a Puzzle

Epithelial ovarian cancer is the fifth leading cause of cancer-related deaths in women and the most lethal gynecological malignancy. Extracellular matrix (ECM) is an integral component of both the normal and tumor microenvironment. ECM composition varies between tissues and is crucial for maintaining normal function and homeostasis. Dysregulation and aberrant deposition or loss of ECM components is implicated in ovarian cancer progression. The mechanisms by which tumor cells induce ECM remodeling to promote a malignant phenotype are yet to be elucidated. A thorough understanding of the role of the ECM in ovarian cancer is needed for the development of effective biomarkers and new therapies.