Collagen, the main non-cellular component of the extracellular matrix (ECM), is profoundly reorganized during tumorigenesis and has a strong impact on tumor behavior. The main source of collagen in tumors is cancer-associated fibroblasts. Cancer cells can also participate in the synthesis of ECM; however, the contribution of both types of cells to collagen rearrangements during the tumor progression is far from being clear. Here, we investigated the processes of collagen biosynthesis and remodeling in parallel with the transcriptome changes during cancer cells and fibroblasts interactions. Combining immunofluorescence, RNA sequencing, and second harmonic generation microscopy, we have explored the relationships between the ratio of epithelial (E) and mesenchymal (M) components of hybrid E/M cancer cells, their ability to activate fibroblasts, and the contributions of both cell types to collagen remodeling. To this end, we studied (i) co-cultures of colorectal cancer cells and normal fibroblasts in a collagen matrix, (ii) patient-derived cancer-associated fibroblasts, and (iii) mouse xenograft models. We found that the activation of normal fibroblasts that form dense collagen networks consisting of large, highly oriented fibers depends on the difference in E/M ratio in the cancer cells. The more-epithelial cells activate the fibroblasts more strongly, which correlates with a dense and highly ordered collagen structure in tumors in vivo. The more-mesenchymal cells activate the fibroblasts to a lesser degree; on the other hand, this cell line has a higher innate collagen remodeling capacity. Normal fibroblasts activated by cancer cells contribute to the organization of the extracellular matrix in a way that is favorable for migratory potency. At the same time, in co-culture with epithelial cancer cells, the contribution of fibroblasts to the reorganization of ECM is more pronounced. Therefore, one can expect that targeting the ability of epithelial cancer cells to activate normal fibroblasts may provide a new anticancer therapeutic strategy.

Nuclear proteins, like histone H2A, are promising non-viral carriers for gene delivery since they are biocompatible, biodegradable, bear intrinsic nuclear localization signal, and are easy to modify. The addition of surface-protein-binding ligand to histone H2A may increase its DNA delivery efficiency. Tumor microenvironment (TME) is a promising target for gene therapy since its surface protein repertoire is more stable than that of cancer cells. Cancer-associated fibroblasts (CAFs) are important components of TME, and one of their surface markers is beta-type platelet-derived growth factor receptor (PDGFRβ). In this study, we fused histone H2A with PDGFRβ-binding peptide, YG2, to create a novel non-viral fibroblast-targeting DNA carrier, H2A-YG2. The transfection efficiency of histone complexes with pDNA encoding a bicistronic reporter (enhanced green fluorescent protein, EGFP, and firefly luciferase) in PDGFRβ-positive and PDGFRβ-negative cells was estimated by luciferase assay and flow cytometry. The luciferase activity, percentage of transfected cells, and overall EGFP fluorescence were increased due to histone modification with YG2 only in PDGFRβ-positive cells. We also estimated the internalization efficiency of DNA-carrier complexes using tetramethyl-rhodamine-labeled pDNA. The ligand fusion increased DNA internalization only in the PDGFRβ-positive cells. In conclusion, we demonstrated that the H2A-YG2 carrier targeted gene delivery to PDGFRβ-positive tumor stromal cells.

Intercellular interactions involving adhesion factors are key operators in cancer progression. In particular, these factors are responsible for facilitating cell migration and metastasis. Strengthening of adhesion between tumor cells and surrounding cells or extracellular matrix (ECM), may provide a way to inhibit tumor cell migration. Recently, we demonstrated that PDX1 ectopic expression results in the reduction of pancreatic cancer line PANC-1 cell motility in vitro and in vivo, and we now provide experimental data confirming the hypothesis that suppression of migration may be related to the effect of PDX1 on cell adhesion. Cell migration analyses demonstrated decreased motility of pancreatic Colo357 and PANC-1 cell lines expressing PDX1. We observed decreased expression levels of genes associated with promoting cell migration and increased expression of genes negatively affecting cell motility. Expression of the EMT regulator genes was only mildly induced in cells expressing PDX1 during the simulation of the epithelial-mesenchymal transition (EMT) by the addition of TGFβ1 to the medium. PDX1-expressing cancer cell lines showed increased cell adhesion to collagen type I, fibronectin, and poly-lysine. We conclude that ectopic expression of PDX1 reduces the migration potential of cancer cells, by increasing the adhesive properties of cells and reducing the sensitivity to TGFβ1-induced EMT.

Enhancers make up a huge class of genome regulatory elements that play an important role in the formation and maintenance of specific patterns of gene transcriptional activity in all types of cells....

Enhancers make up a huge class of genome regulatory elements that play an important role in the formation and maintenance of specific patterns of gene transcriptional activity in all types of cells. In recent years, high-throughput methods for the genome-wide epigenetic analysis of chromatin have made it possible to identify structural and functional features of enhancers and their role in the spatial and functional organization of the genome and in the formation and maintenance of cell identity, as well as in the pathogenesis of certain diseases. Special attention has been focused on genome regions called super-enhancers, or stretch enhancers, which consist of clusters of elements with properties of classic enhancers. This review considers current data on specific properties of super-enhancers and their role in the formation of interconnected autoregulatory circuits with positive feedback that regulates the most important genes, the activity of which underlies the formation and maintenance of specialized cellular functions.

Background/Objectives: Large language models (LLMs) have been proposed as a means of converting unstructured electronic medical records (EMRs) into structured datasets. However, concerns regarding the reliability of these models in non-English clinical text and their capacity to generate novel insights remain unresolved. We aimed to utilize an LLM to identify a hypothetical “Luminal B poor-prognosis” breast cancer subgroup (LPP) based on progesterone receptor (PR), the Ki-67 proliferation index, and grade characteristics, while concurrently validating the LLM’s accuracy. Methods: We retrospectively compiled the EMRs on 7756 female breast cancer patients from five Moscow oncology centers. An LLM with a domain-engineered prompt extracted eight clinicopathological variables (Ki-67, estrogen receptor (ER)/PR Allred status, HER2 status, grade, relapse dates, and multiple primaries). The accuracy of the model was validated in 366 randomly sampled cases against oncologist annotations using Intraclass Correlation Coefficient (ICC) and weighted κ. Following data post-processing, the complete-case cohort (n = 2347) and the HR+/HER2− stage I–III sub-cohort (n = 1419) were analyzed. Survival was estimated with Kaplan–Meier/log-rank and modeled with Cox regression (adjusted for age, stage, and treatment). Ki-67 was modeled continuously; prespecified LPP definitions were compared. Results: LLM–human agreement was high (Ki-67 ICC = 0.882; grade κ = 0.887; ER κ = 0.997; PR κ = 0.975; HER2 κ = 0.935). Date extraction was characterized by a high degree of missing data. In HR+/HER2− stage I–III disease, ER < 5 was non-prognostic; however, PR < 4 and Ki-67 ≥ 40% were indicative of inferior survival (HR 2.25 and 1.85). The most effective LPP definition (PR < 4 and Ki-67 ≥ 40%) identified a subgroup (~5.3%) of patients with markedly poorer outcomes (age, stage, and treatment adjusted HR 2.60, 95% CI 1.53–4.43) compared to the Luminal B (HER2−) subgroup. Conclusions: The developed LLM has demonstrated the ability to reliably structure non-English EMRs and enable discovery of clinically meaningful subgroups. The discovered LPP phenotype defines a small, high-risk subset warranting external validation. Given the retrospective, single-system design of the study, it is imperative to interpret the discovered phenotype features as hypothesis-generating, rather than as definitive evidence.