Analysis of extracellular matrix network dynamics in cancer using the MatriNet database.

The extracellular matrix (ECM) is a three-dimensional network of proteins of diverse nature, whose interactions are essential to provide tissues with the correct mechanical and biochemical cues they need for proper development and homeostasis. Changes in the quantity of extracellular matrix (ECM) components and their balance within the tumor microenvironment (TME) accompany and fuel all steps of tumor development, growth and metastasis, and a deeper and more systematic understanding of these processes is fundamental for the development of future therapeutic approaches. The wealth of "big data" from numerous sources has enabled gigantic steps forward in the comprehension of the oncogenic process, also impacting on our understanding of ECM changes in the TME. Most of the available studies, however, have not considered the network nature of ECM and the possibility that changes in the quantity of components might be regulated (co-occur) in cancer and significantly "rebound" on the whole network through its connections, fundamentally altering the matrix interactome. To facilitate the exploration of these network-scale effects we have implemented MatriNet (www.matrinet.org), a database enabling the study of structural changes in ECM network architectures as a function of their protein-protein interaction strengths across 20 different tumor types. The use of MatriNet is intuitive and offers new insights into tumor-specific as well as pan-cancer features of ECM networks, facilitating the identification of similarities and differences between cancers as well as the visualization of single-tumor events and the prioritization of ECM targets for further experimental investigations.

Chondrocytes From Osteoarthritic and Chondrocalcinosis Cartilage Represent Different Phenotypes.

Basic calcium phosphate (BCP)-based calcification of cartilage is a common finding during osteoarthritis (OA) and is directly linked to the severity of the disease and hypertrophic differentiation of chondrocytes. Chondrocalcinosis (CC) is associated with calcium pyrophosphate dihydrate (CPPD) deposition disease in the joint inducing OA-like symptoms. There is only little knowledge about the effect of CPPD crystals on chondrocytes and the signaling pathways involved in their generation. The aim of this study was to investigate the chondrocyte phenotype in CC cartilage and the effect of CPPD crystals on chondrocytes. Cartilage samples of patients with CC, patients with severe OA, and healthy donors were included in this study. The presence of CC was evaluated using standard X-ray pictures, as well as von Kossa staining of cartilage sections. OA severity was evaluated using the Chambers Score on cartilage sections, as well as the radiological Kellgren-Lawrence Score. Patients with radiologically detectable CC presented calcification mainly on the cartilage surface, whereas OA patients showed calcification mainly in the pericellular matrix of hypertrophic chondrocytes. OA cartilage exhibited increased levels of collagen X and matrix metalloproteinase 13 (MMP13) compared with CC and healthy cartilage. This observation was confirmed by qRT-PCR using cartilage samples. No relevant influence of CPPD crystals on hypertrophic marker genes was observed in vitro, whereas BCP crystals significantly induced hypertrophic differentiation of chondrocytes. Interestingly, we observed an increased expression of p16 and p21 in cartilage samples of CC patients compared with OA patients and healthy controls, indicating cellular senescence. To investigate whether CPPD crystals were sufficient to induce senescence, we incubated chondrocytes with BCP and CPPD crystals and quantified senescence using ß-gal staining. No significant difference was observed for the staining, but an increase of p16 expression was observed after 10 days of culture. Primary chondrocytes from CC patients produced CPPD crystals in culture. This phenotype was stabilized by mitomycin C-induced senescence. Healthy and OA chondrocytes did not exhibit this phenotype. BCP and CPPD crystals seem to be associated with two different chondrocyte phenotypes. Whereas BCP deposition is associated with chondrocyte hypertrophy, CPPD deposition is associated with cellular senescence.

The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome.

BACKGROUND: To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome. METHODS: This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM. RESULTS: PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions. CONCLUSIONS: Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.

The expression and regulation of matrisome genes-the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors-is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of "landmark" matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.