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PubMed2026

Intratumoral collagen correlates with histological grade and patient prognosis in breast cancer.

Histological grading, using the Nottingham Grading System (NGS), is a major prognostic indicator for breast cancer. NGS involves the scoring of cancer cell-related morphological features, yet it overlooks tumor microenvironment (TME) components such as collagen. Collagen proteins, integral to the extracellular matrix (ECM), influence tumor architecture and progression but their relationship with histological grade is not fully characterized. Here, we assessed intratumoral collagen deposition using Masson Trichrome staining of whole slides (n = 166), proteomic profiling (n = 2) and transcriptomic analyses of the METABRIC (n = 1827) and TCGA-BRCA (n = 753) cohorts. We showed that low-grade tumors display significantly higher intratumoral collagen deposition compared to high-grade tumors. Moreover, we demonstrated that collagen expression at the transcript and protein levels (Masson Trichrome) could discriminate Grade II carcinomas into distinct prognostic groups, in which patients with Grade II carcinomas with elevated levels of collagen expression were associated with lower pTNM stage and better survival outcomes. Our results support the inclusion of TME features, such as collagen deposition, to enhance prognostic accuracy in breast cancer.

Humans↗

PubMed2002

Gene expression data analysis with a dynamically extended self-organized map that exploits class information.

MOTIVATION: Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling. RESULTS: The paper adapts a novel Self-Organizing map called supervised Network Self-Organized Map (sNet-SOM) to the peculiarities of multi-labeled gene expression data. The sNet-SOM determines adaptively the number of clusters with a dynamic extension process. This process is driven by an inhomogeneous measure that tries to balance unsupervised, supervised and model complexity criteria. Nodes within a rectangular grid are grown at the boundary nodes, weights rippled from the internal nodes towards the outer nodes of the grid, and whole columns inserted within the map The appropriate level of expansion is determined automatically. Multiple sNet-SOM models are constructed dynamically each for a different unsupervised/supervised balance and model selection criteria are used to select the one optimum one. The results indicate that sNet-SOM yields competitive performance to other recently proposed approaches for supervised classification at a significantly reduced computational cost and it provides extensive exploratory analysis potentiality within the analysis framework. Furthermore, it explores simple design decisions that are easier to comprehend and computationally efficient.

Algorithms↗