Computer-aided assessment of the extra-cellular matrix during pancreatic carcinogenesis: a pilot study

Matrix-Rigidity Cooperates With Biochemical Cues in M2 Macrophage Activation Through Increased Nuclear Deformation and Chromatin Accessibility

Macrophages encounter a myriad of biochemical and mechanical stimuli across various tissues and pathological contexts. Notably, matrix rigidity has emerged as a pivotal regulator of macrophage activation through mechanotransduction. However, the precise mechanisms underlying the interplay between mechanical and biochemical cues within the nuclear milieu remain elusive. Here We elucidate how the increased matrix rigidity drives macrophages to amplify alternatively-activated (M2 phenotype) signaling cooperatively with biochemical cues (e.g., IL4/13) through altered nuclear mechanics. We demonstrate that reconstructed podosome-like F-actins and contractility induce nucleus deformation, opening nuclear pores, which facilitates nuclear translocation of the key transcription factor STAT6. Furthermore, the altered nuclear mechanics increases chromatin accessibility induced by H3K9 methylation, particularly of M2-associated gene promoters. These cooperative events of the mechano-chemical signaling at the cytoskeletal-to-nuclear domains facilitate M2 transcriptional activation and cellular functions. We further evidence the rigidity-primed M2 macrophages are immunosuppressive and accumulated within stiffened tumors in patients. This study proposes a mechanism by which matrix mechanics crosstalks with biochemical signals to potentiate macrophage activation through nuclear mechanosensing and chromatin modifications, offering insights into macrophage mechanobiology and its therapeutic modulations.

Tissue Mechanics and Hedgehog Signaling Crosstalk as a Key Epithelial-Stromal Interplay in Cancer Development

Epithelial-stromal interplay through chemomechanical cues from cells and matrix propels cancer progression. Elevated tissue stiffness in potentially malignant tissues suggests a link between matrix stiffness and enhanced tumor growth. In this study, employing chronic oral/esophageal injury and cancer models, it is demonstrated that epithelial-stromal interplay through matrix stiffness and Hedgehog (Hh) signaling is key in compounding cancer development. Epithelial cells actively interact with fibroblasts, exchanging mechanoresponsive signals during the precancerous stage. Specifically, epithelial cells release Sonic Hh, activating fibroblasts to produce matrix proteins and remodeling enzymes, resulting in tissue stiffening. Subsequently, basal epithelial cells adjacent to the stiffened tissue become proliferative and undergo epithelial-to-mesenchymal transition, acquiring migratory and invasive properties, thereby promoting invasive tumor growth. Notably, transcriptomic programs of oncogenic GLI2, mechano-activated by actin cytoskeletal tension, govern this process, elucidating the crucial role of non-canonical GLI2 activation in orchestrating the proliferation and mesenchymal transition of epithelial cells. Furthermore, pharmacological intervention targeting tissue stiffening proves highly effective in slowing cancer progression. These findings underscore the impact of epithelial-stromal interplay through chemo-mechanical (Hh-stiffness) signaling in cancer development, and suggest that targeting tissue stiffness holds promise as a strategy to disrupt chemo-mechanical feedback, enabling effective cancer treatment.

Artificial Intelligence in Pancreatic Image Analysis: A Review

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Prostate Cancer Microvascular Routes: Exploration and Measurement Strategies

Angiogenesis is acknowledged as a pivotal feature in the pathology of human cancer. Despite the absence of universally accepted markers for gauging the comprehensive angiogenic activity in prostate cancer (PCa) that could steer the formulation of focused anti-angiogenic treatments, the scrutiny of diverse facets of tumoral blood vessel development may furnish significant understanding of angiogenic processes. Malignant neoplasms, encompassing PCa, deploy a myriad of strategies to secure an adequate blood supply. These modalities range from sprouting angiogenesis and vasculogenesis to intussusceptive angiogenesis, vascular co-option, the formation of mosaic vessels, vasculogenic mimicry, the conversion of cancer stem-like cells into tumor endothelial cells, and vascular pruning. Here we provide a thorough review of these angiogenic mechanisms as they relate to PCa, discuss their prospective relevance for predictive and prognostic evaluations, and outline the prevailing obstacles in quantitatively evaluating neovascularization via histopathological examinations.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.

Rapid acceleration of KRAS-mutant pancreatic carcinogenesis via remodeling of tumor immune microenvironment by PPARδ

Pancreatic intraepithelial neoplasia (PanIN) is a precursor of pancreatic ductal adenocarcinoma (PDAC), which commonly occurs in the general populations with aging. Although most PanIN lesions (PanINs) harbor oncogenic KRAS mutations that initiate pancreatic tumorigenesis; PanINs rarely progress to PDAC. Critical factors that promote this progression, especially targetable ones, remain poorly defined. We show that peroxisome proliferator-activated receptor-delta (PPARδ), a lipid nuclear receptor, is upregulated in PanINs in humans and mice. Furthermore, PPARδ ligand activation by a high-fat diet or GW501516 (a highly selective, synthetic PPARδ ligand) in mutant KRASG12D (KRASmu) pancreatic epithelial cells strongly accelerates PanIN progression to PDAC. This PPARδ activation induces KRASmu pancreatic epithelial cells to secrete CCL2, which recruits immunosuppressive macrophages and myeloid-derived suppressor cells into pancreas via the CCL2/CCR2 axis to orchestrate an immunosuppressive tumor microenvironment and subsequently drive PanIN progression to PDAC. Our data identify PPARδ signaling as a potential molecular target to prevent PDAC development in subjects harboring PanINs.

Perspective of nuclear fractal dimension in diagnosis and prognosis of oral squamous cell carcinoma

Background:

Owing to the restricted predictive value of conventional prognostic factors and the inconsistent treatment strategies, several oral squamous cell carcinoma (OSCC) patients are still over-treated or under-treated. In recent years, computer-assisted nuclear fractal dimension (nFD) has emerged as an objective approach to predict the outcome of OSCC.

Objective:

This study is an attempt to find out the differences in nFD values of epithelial cells of normal tissue, fibroepithelial hyperplasia, verrucous carcinoma, and OSCC. Further effort to evaluate the predictive potential of nFD of tumor cells for cervical lymph node metastasis (cLNM) was also assessed.

Methodology:

Formalin-fixed paraffin-embedded blocks of OSCC tissues of patients treated with neck dissection were collected. Photomicrographs of H-&E-stained sections were subjected to the image analysis by ImageJ and Python programming to calculate nFD. The association of categorical variables with nFD was studied using cross-tabulation procedure and the Fisher exact test. Receiver operating curve analysis was performed to find out cutoff value of nFD. A logistic regression model was developed to test the individual and combined predictive potential of grading and nFD for cLNM.

Results:

A significant difference between the mean nFD of healthy cells and malignant epithelial cells was observed (P = 0.01). nFD was not found to be an independent predictor of cLNM, although nFD and grading together demonstrated significant predictive potential (P = 0.004).

Conclusion:

nFD combined with grading can predict lymph node metastasis in OSCC. To the best of our knowledge, this is the first study of its kind.

Antagonizing the CX3CR1 Receptor Markedly Reduces Development of Cardiac Hypertrophy After Transverse Aortic Constriction in Mice

ABSTRACT

Left-ventricular hypertrophy, characterized by cardiomyocyte hypertrophy, interstitial cell proliferation, and immune cell infiltration, is a high risk factor for heart failure and death. Chemokines interacting with G protein-coupled chemokine receptors probably play a role in left-ventricular hypertrophy development by promoting recruitment of activated leukocytes and modulating left-ventricular remodeling. Using the minimally invasive model of transverse aortic constriction in mice, we demonstrated that a variety of chemokine and chemokine receptor messenger Ribonucleic Acid are overexpressed in the early and late phase of hypertrophy progression. Among the chemokine receptors, Cx3cr1 and Ccr2 were most strongly overexpressed and were significantly upregulated at 3, 7, and 14 days after transverse aortic constriction. Ligands of CX3CR1 (Cx3cl1) and CCR2 (Ccl2, Ccl7, Ccl12) were significantly overexpressed in the left ventricle at the early stages after mechanical pressure overload. Pharmacological inhibition of CX3CR1 signaling using the antagonist AZD8797 led to a significant reduction of hypertrophy, whereas inhibition of CCR2 with the RS504393 antagonist did not show any effect. Furthermore, AZD8797 treatment reduced the expression of the hypertrophic marker genes Nppa and Nppb as well as the profibrotic genes Tgfb1 and Col1a1 at 14 days after transverse aortic constriction. These findings strongly suggest the involvement of the CX3CR1/CX3CL1 pathway in the pathogenesis of left-ventricular hypertrophy.

Myopalladin knockout mice develop cardiac dilation and show a maladaptive response to mechanical pressure overload

Myopalladin (MYPN) is a striated muscle-specific immunoglobulin domain-containing protein located in the sarcomeric Z-line and I-band. MYPN gene mutations are causative for dilated (DCM), hypertrophic, and restrictive cardiomyopathy. In a yeast two-hybrid screening, MYPN was found to bind to titin in the Z-line, which was confirmed by microscale thermophoresis. Cardiac analyses of MYPN knockout (MKO) mice showed the development of mild cardiac dilation and systolic dysfunction, associated with decreased myofibrillar isometric tension generation and increased resting tension at longer sarcomere lengths. MKO mice exhibited a normal hypertrophic response to transaortic constriction (TAC), but rapidly developed severe cardiac dilation and systolic dysfunction, associated with fibrosis, increased fetal gene expression, higher intercalated disc fold amplitude, decreased calsequestrin-2 protein levels, and increased desmoplakin and SORBS2 protein levels. Cardiomyocyte analyses showed delayed Ca2+ release and reuptake in unstressed MKO mice as well as reduced Ca2+ spark amplitude post-TAC, suggesting that altered Ca2+ handling may contribute to the development of DCM in MKO mice.

Smad4 deficiency substitutes Cdkn2b but not Cdkn2a downregulation in pancreatic cancer following induction of genetic events in adult mice

BACKGROUND

Minor progress in pancreatic cancer treatment and prognosis implies that more reliable animal models are urgently needed to decipher its molecular mechanisms and preclinical research. We recently reported a genetically engineered adult mouse model where Cdkn2b downregulation was required together with Cdkn2a downregulation to inactivate the Rb pathway. Besides, the role of Smad4, which is mutated more frequently than Cdkn2b in human pancreatic cancer, was determined critical on the development of the pancreas tumor by some reports. However, the impact of Smad4 deficiency in combination with PDAC-relevant mutations, such as Cdkn2a when induced in adult pancreas has not been completely elucidated in mice.

METHODS

Lentiviral delivered oncogene/tumor suppressors in adult pancreas. The development of pancreatic cancer was monitored. Hematoxylin and eosin staining and immunofluorescence were performed for pathological identification of the pancreatic cancer. Real-time polymerase chain reaction, immunofluorescence and western blot were used to test gene expression.

RESULTS

Loss of Smad4 could cooperate with alterations of KRAS, Trp53, and Cdkn2a to induce pancreatic cancer in adult mice. The role of Smad4 was mainly in downregulating the expression of Cdkn2b and further inducing phosphorylation of the Rb1 protein.

CONCLUSIONS

These findings show an essential role of Smad4 deficiency in pancreatic ductal adenocarcinoma (PDAC) formation. This model better recapitulates the adult onset, clonal origin, and genetic alterations in human PDAC and can be simply generated on a large-scale.