Nuclear Morphology and the Biology of Cancer Cells

Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma

BACKGROUND

The heterogeneous and aggressive nature of diffuse large B-cell lymphoma (DLBCL) presents significant treatment challenges as up to 50% of patients experience recurrence of disease after chemotherapy. Upfront detection of recurring patients could offer alternative treatments. Deep learning has shown potential in predicting recurrence of various cancer types but suffers from lack of interpretability. Particularly in prediction of recurrence, an understanding of the model's decision could eventually result in novel treatments.

METHODS

We developed a deep learning-based pipeline to predict recurrence of DLBCL based on histological images of a publicly available cohort. We utilized attention-based classification to highlight areas within the images that were of high relevance for the model's classification. Subsequently, we segmented the nuclei within these areas, calculated morphological features, and statistically analyzed them to find differences between recurred and non-recurred patients.

RESULTS

We achieved an f1 score of 0.88 indicating that our model can distinguish non-recurred from recurred patients. Additionally, we found that features that are the most predictive of recurrence include large and irregularly shaped tumor cell nuclei.

DISCUSSION

Our work underlines the value of histological images in predicting treatment outcomes and enhances our understanding of complex biological processes in aggressive, heterogeneous cancers like DLBCL.

Major nuclear locales define nuclear genome organization and function beyond A and B compartments

Models of nuclear genome organization often propose a binary division into active versus inactive compartments yet typically overlook nuclear bodies. Here, we integrated analysis of sequencing and image-based data to compare genome organization in four human cell types relative to three different nuclear locales: the nuclear lamina, nuclear speckles, and nucleoli. Although gene expression correlates mostly with nuclear speckle proximity, DNA replication timing correlates with proximity to multiple nuclear locales. Speckle attachment regions emerge as DNA replication initiation zones whose replication timing and gene composition vary with their attachment frequency. Most facultative LADs retain a partially repressed state as iLADs, despite their positioning in the nuclear interior. Knock out of two lamina proteins, Lamin A and LBR, causes a shift of H3K9me3-enriched LADs from lamina to nucleolus, and a reciprocal relocation of H3K27me3-enriched partially repressed iLADs from nucleolus to lamina. Thus, these partially repressed iLADs appear to compete with LADs for nuclear lamina attachment with consequences for replication timing. The nuclear organization in adherent cells is polarized with nuclear bodies and genomic regions segregating both radially and relative to the equatorial plane. Together, our results underscore the importance of considering genome organization relative to nuclear locales for a more complete understanding of the spatial and functional organization of the human genome.

ErbB2/HER2-targeted CAR-NK cells eliminate breast cancer cells in an organoid model that recapitulates tumor progression

Chimeric antigen receptor-engineered NK cells hold promise for adoptive cancer immunotherapy. In one such approach, the ErbB2 (HER2)-specific CAR-NK cell line NK-92/5.28.z is under investigation as an off-the-shelf therapy in a phase I trial in glioblastoma patients. To evaluate activity of NK-92/5.28.z cells against ErbB2-positive breast cancer, here we developed an organoid model derived from CKP mice that allows conditional activation of oncogenic driver mutations. Expression of ErbB2 and Cre recombinase in CKP mammary epithelial cells induced malignant transformation, with the resulting EC-CKP cells characterized by neoplastic morphology, loss of p53, and constitutive activation of the MAP kinase pathway. NK-92/5.28.z cells demonstrated potent CAR-mediated cytotoxicity against EC-CKP organoids, with tumor cell lysis dependent on exposure time and organoid size. In vivo passaging of EC-CKP organoids revealed cellular plasticity and induced an EMT phenotype associated with increased resistance to standard therapies. Importantly, NK-92/5.28.z cells retained high and specific cytotoxicity against these breast cancer cells in vitro and in an aggressive organoid-based in vivo mouse model that reflects advanced-stage disease. Our data highlight the therapeutic potential of NK-92/5.28.z cells against ErbB2-positive breast cancer, supporting their further development toward clinical application.

Spatially Resolved Single-Cell Morphometry of Benign Breast Disease Biopsy Images Uncovers Quantitative Cytomorphometric Features Predictive of Subsequent Invasive Breast Cancer Risk

Currently, benign breast disease (BBD) pathologic classification and invasive breast cancer (BC) risk assessment are based on qualitative epithelial changes, with limited utility for BC risk stratification for women with lower-risk category BBD (ie, nonproliferative disease [NPD] and proliferative disease without atypia [PDWA]). Here, machine learning-based single-cell morphometry was used to characterize quantitative changes in epithelial nuclear morphology that reflect functional/structural decline (ie, increasing nuclear size, assessed as epithelial nuclear area and nuclear perimeter), altered DNA chromatin content (ie, increasing nuclear chromasia), and increased cellular crowding/proliferation (ie, increasing nuclear contour irregularity). Cytomorphologic changes reflecting chronic stromal inflammation were assessed using stromal cellular density. Data and pathology materials were obtained from a case-control study (n = 972) nested within a cohort of 15,395 women diagnosed with BBD at Kaiser Permanente Northwest (1971-2012). Odds ratios (ORs) and 95% confidence intervals (CIs) for associations of cytomorphometric features with risk of subsequent BC were assessed using multivariable logistic regression. More than 55 million epithelial and 37 million stromal cells were profiled across 972 BBD images. Cytomorphometric features were individually predictive of subsequent BC risk, independently of BBD histologic classification. However, cytomorphometric features of epithelial functional/structural decline were statistically significantly predictive of low-grade but not high-grade BC following PDWA (OR for low-grade BC per 1-SD increase in nuclear area and nuclear perimeter, 2.10; 95% CI, 1.26-3.49, and 2.22; 95% CI, 1.30-3.78, respectively), whereas stromal inflammation was predictive of high-grade but not low-grade BC following NPD (OR for high-grade BC per 1-SD increase in stromal cellular density, 1.53; 95% CI, 1.13-2.08). Associations of nuclear chromasia and nuclear contour irregularity with subsequent tumor grade were context specific, with both features predicting low-grade BC risk following PDWA (OR per 1-SD, 1.58; 95% CI, 1.06-2.35, and 2.21; 95% CI, 1.25-3.91, for nuclear chromasia and nuclear contour irregularity, respectively) and high-grade BC following NPD (OR per 1-SD, 1.47; 95% CI, 1.11-1.96, and 1.29; 95% CI, 1.00-1.70, for nuclear chromasia and nuclear contour irregularity, respectively). The results indicate that cytomorphometric features on BBD hematoxylin-eosin-stained images might help to refine BC risk estimation and potentially inform BC risk reduction strategies for BBD patients, particularly those currently designated as low risk.

GsMTx-4 venom toxin antagonizes biophysical modulation of metastatic traits in human osteosarcoma cells

Despite their genetic diversity, metastatic cells converge on similar physical constraints during tumor progression. At the nanoscale, these forces can induce substantial molecular deformations, altering the structure and behavior of cancer cells. To address the challenges of osteosarcoma (OS), a highly aggressive cancer, we explored the mechanobiology of OS cells, in vitro. Using uniaxial-stretching technology, we examined the biophysical modulation of metastatic traits in SAOS-2, U-2 OS, and non-tumorigenic hFOB cells. Changes in cell morphology were quantified using confocal and fluorescence microscopy. To elucidate the molecular mechanisms that translate biomechanical alterations into biochemical responses, we employed Western blotting, real-time quantitative RT-PCR, reactive oxygen species ROS assay, and the mechanosensitive channel blocker Grammostola MechanoToxin4 (GsMTx-4). Our study reveals that mechanical stimulation uniquely affects OS cells, increasing nuclear size and altering the N/C ratio. We found that mechanosensitive (MS) channels are activated, leading to ROS accumulation, Src protein modulation, and histone H3 acetylation. These changes influence OS cell motility and adhesion but not proliferation. Importantly, mechanical preconditioning differentially impacts doxorubicin resistance, correlating with the Src-H3 acetylation axis. This study underscores the critical role of MS channels in OS cells and highlights the importance of mechanobiology in identifying molecular pathways that traditional biochemical approaches may not reveal. Notably, the GsMTx-4 venom peptide effectively countered mechanically induced responses, particularly by inhibiting OS cell migration, without harming healthy cells. Thus, suggesting its potential as a promising therapeutic agent for targeting osteosarcoma metastasis.

Selenium nutritional status and thyroid dysfunction

Selenium(Se) is an essential micronutrient for several immune and regulatory functions in the body. In thyroid tissue, Se contributes to the antioxidant system and is a crucial component of deiodinases, which are selenoproteins that participate in thyroid hormone metabolism. Additionally, this micronutrient exerts a significant impact on thyroid pathophysiology, as low levels of Se lead to reduced activity of glutathione peroxidase, a selenoprotein involved in antioxidative processes, thereby resulting in increased oxidative stress and damage to thyroid tissue. Selenium deficiency (SeD) can cause growth retardation and reproductive failure; in women and children, it may result in Keshan's disease and Kashin-Beck's disease. Research has shown an inverse correlation between Se serum levels and autoimmune thyroiditis in areas with mild SeD. In Graves' disease, Se supplementation has been linked to faster achievement of euthyroidism as well as improvements in quality of life, lessened orbital involvement, and slower ocular progression of the disease. Furthermore, several studies suggest an association between serum SeD and the development of thyroid cancer. Maintaining physiological Se concentrations appears to be related to the prevention of thyroid disease, although current data are insufficient to conclusively support or refute the efficacy of supplementation. Through this narrative review, we aim to present the latest information on the role of selenium in thyroid pathophysiology. To identify relevant literature, specific search strategies were employed in the electronic databases PubMed, Lilacs, and SciELO.

Subcellular Organelle Targeting as a Novel Approach to Combat Tumor Metastasis

Tumor metastasis, the spread of cancer cells from the primary site to distant organs, remains a formidable challenge in oncology. Central to this process is the involvement of subcellular organelles, which undergo significant functional and structural changes during metastasis. Targeting these specific organelles offers a promising avenue for enhanced drug delivery and metastasis therapeutic efficacy. This precision increases the potency and reduces potential off-target effects. Moreover, by understanding the role of each organelle in metastasis, treatments can be designed to disrupt the metastatic process at multiple stages, from cell migration to the establishment of secondary tumors. This review delves deeply into tumor metastasis processes and their connection with subcellular organelles. In order to target these organelles, biomembranes, cell-penetrating peptides, localization signal peptides, aptamers, specific small molecules, and various other strategies have been developed. In this review, we will elucidate targeting delivery strategies for each subcellular organelle and look forward to prospects in this domain.

Detection of Oral Submucous Fibrosis at the Crossroads of Altering Nucleogeometry, Nuclear Spatial Distribution and Nuclear Molecular Abundance

The manuscript concentrates on spatial distribution and various nucleomorphometric parameters of epithelium to diagnose Oral Submucous Fibrosis (OSF). Histologically confirmed OSF and normal submucosa tissue samples were procured and stained with diamidino phenylindole (DAPI) to visualize nuclei. E-cadherins and p63 were also immunohistochemically stained. Microphotographs were analyzed to quantify the spatial distance among the nuclei of the stained tissue samples. Intensity of the DAPI stained nuclei and p63 was quantified. In addition, morphometric analysis of nuclei was done with the help of ImageJ software to quantify the geometric alterations in OSF tissue. Spatial distances among the nuclei of OSF tissue samples were found to be significantly higher than that of normal tissue. We also observed a significant decrease in the mean intensity of DAPI and p63 in OSF tissue samples. In addition, we have found statistically significant alterations of various morphometric quantifications in OSF tissue nuclei. There was a considerable change in the spatial distribution of nuclei, as well as some distinct changes in the nucleogeometry of OSF tissue, which corresponds to histological abnormalities. Decreased intensity of DAPI and p63 advocate disease progression. The biomarkers in this study are the accountable role-players for early detection of oral carcinoma.

Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry

Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here, we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate that the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof-of-concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science.

At the nucleus of cancer: how the nuclear envelope controls tumor progression

Historically considered downstream effects of tumorigenesis-arising from changes in DNA content or chromatin organization-nuclear alterations have long been seen as mere prognostic markers within a genome-centric model of cancer. However, recent findings have placed the nuclear envelope (NE) at the forefront of tumor progression, highlighting its active role in mediating cellular responses to mechanical forces. Despite significant progress, the precise interplay between NE components and cancer progression remains under debate. In this review, we provide a comprehensive and up-to-date overview of how changes in NE composition affect nuclear mechanics and facilitate malignant transformation, grounded in the latest molecular and functional studies. We also review recent research that uses advanced technologies, including artificial intelligence, to predict malignancy risk and treatment outcomes by analyzing nuclear morphology. Finally, we discuss how progress in understanding nuclear mechanics has paved the way for mechanotherapy-a promising cancer treatment approach that exploits the mechanical differences between cancerous and healthy cells. Shifting the perspective on NE alterations from mere diagnostic markers to potential therapeutic targets, this review calls for further investigation into the evolving role of the NE in cancer, highlighting the potential for innovative strategies to transform conventional cancer therapies.

Nuclear morphological atypia in biopsy accurately reflects the prognosis of myxoid liposarcoma

Currently, it is difficult to predict the prognosis of myxoid liposarcoma (MLS) in biopsy specimens. In this study, we determined whether nuclear morphology may be used to predict the prognosis of MLS in primary biopsy specimens. Two pathologists evaluated nuclear morphology using the modified WHO/ISUP and Fuhrman grades. Survival analyses were performed by grouping nuclear high- and low-grades. We examined 53 MLS cases, which included 29 (54.7%) male and 24 (45.3%) female patients with a median age of 46 years (interquartile range, 37 - 60). In total, 7 (13.2%) and 16 (30.2%) cases were assigned to the high nuclear grade group based on the modified WHO/ISUP and Fuhrman gradings, respectively. Survival analyses revealed a significantly worse disease-free survival in the high-grade group (hazard ratio (HR), 7.51; 95% confidence interval (CI), 2.67-21.1, p < 0.001 by the modified WHO/ISUP grading; HR, 4.45; 95% CI, 1.63-12.1, p = 0.001 by the modified Fuhrman grading). Moreover, the modified WHO/ISUP grade showed a significantly worse overall survival in the high-grade group (HR, 4.39; 95% CI, 1.04-18.6, p = 0.028), and the modified Fuhrman grade exhibited a similar, but not significant, trend. Our results indicate that nuclear morphology grading is a good predictor of patient prognosis at the time of biopsy in MLS. Even when cell density is sparse, treatment strategies should be carefully considered when individual tumor cells exhibit atypical nuclei.

The role of ABI2 in modulating nuclear proteins: Therapeutic implications for NUP54 and NUP153 in TNBC

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that lacks hormone receptors, which makes it more likely to metastasize and have a poor prognosis. Despite some effectiveness of chemotherapy, TNBC remains challenging to manage, with high relapse and mortality rates. Recent findings have highlighted the role of the ubiquitin-protease system in TNBC, with ABI2 identified as a significant regulator. Reduced ABI2 expression is associated with aggressive disease and poor outcomes, whereas ABI2 overexpression (OE-ABI2) inhibits TNBC cell proliferation by modulating the PI3K/Akt signaling pathway. Although ABI2 is not a nuclear protein, it influences critical nuclear functions such as DNA repair and gene expression. Nuclear proteins, particularly those in the nuclear pore complex and nuclear matrix, are essential for cellular functions and have been linked to various diseases, including cancer. This study used RNA sequencing (RNA-seq) to examine the gene expression in MDA-MB-231 cell line and ABI2-overexpressing cells. Differentially expressed genes were annotated, and a protein-protein interaction network was constructed. Network and enrichment analysis identified the nucleoporins NUP54 and NUP153 as potential novel targets for TNBC. This study emphasizes the impact of ABI2 on nuclear proteins and suggests that targeting NUP54 and NUP153 could offer new therapeutic options for TNBC.

Deep learning for predicting prognostic consensus molecular subtypes in cervical cancer from histology images

Cervical cancer remains the fourth most common cancer among women worldwide. This study proposes an end-to-end deep learning framework to predict consensus molecular subtypes (CMS) in HPV-positive cervical squamous cell carcinoma (CSCC) from H&E-stained histology slides. Analysing three CSCC cohorts (n = 545), we show our Digital-CMS scores significantly stratify patients by both disease-specific (TCGA p = 0.0022, Oslo p = 0.0495) and disease-free (TCGA p = 0.0495, Oslo p = 0.0282) survival. In addition, our extensive tumour microenvironment analysis reveals differences between the two CMS subtypes, with CMS-C1 tumours exhibit increased lymphocyte presence, while CMS-C2 tumours show high nuclear pleomorphism, elevated neutrophil-to-lymphocyte ratio, and higher malignancy, correlating with poor prognosis. This study introduces a potentially clinically advantageous Digital-CMS score derived from digitised WSIs of routine H&E-stained tissue sections, offers new insights into TME differences impacting patient prognosis and potential therapeutic targets, and identifies histological patterns serving as potential surrogate markers of the CMS subtypes for clinical application.

Nuclear NAD+ synthase nicotinamide mononucleotide adenylyltransferase 1 contributes to nuclear atypia and promotes glioma growth

Background

Glioma is a malignant primary brain tumor with a poor prognosis and short survival. NAD+ is critical for cancer growth; however, clinical trials targeting NAD+ biosynthesis had limited success, indicating the need for mechanistic characterization. Nuclear atypia, aberrations in the size and shape of the nucleus, is widely observed in cancer and is often considered a distinctive feature in diagnosis; however, the molecular underpinnings are unclear.

Methods

We carried out high-resolution immunohistochemical analyses on glioma tissue samples from 19 patients to analyze the expression of NAD+ synthase nicotinamide mononucleotide adenylyltransferase 1 (NMNAT1), and its correlation with nuclear atypia in gliomas. Utilizing a Drosophila model of glial neoplasia, we investigated the genetic role of nuclear NMNAT in glioma growth in vivo, elucidating the cellular mechanisms of NMNAT1 in promoting nuclear atypia and glioma growth.

Results

In low-grade glioma and glioblastoma, a higher transcription level of NMNAT1 is correlated with poorer disease-free survival. Samples of high-grade gliomas contained a higher percentage of glial cells enriched with NMNAT1 protein. We identified a specific correlation between nuclear NMNAT1 protein level with nuclear atypia. Mechanistic studies in human glioma cell lines and in vivo Drosophila model suggest that NMNAT1 disrupts the integrity of the nuclear lamina by altering the distribution of lamin A/C and promotes glioma growth.

Conclusions

Our study uncovers a novel functional connection between the NAD+ metabolic pathway and glioma growth, reveals the contribution of the NAD+ biosynthetic enzyme NMNAT1 to nuclear atypia, and underscores the role of nuclear NMNAT1 in exacerbating glioma pathology.

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed.

Extreme wrinkling of the nuclear lamina is a morphological marker of cancer

Nuclear atypia is a hallmark of cancer. A recent model posits that excess surface area, visible as folds/wrinkles in the lamina of a rounded nucleus, allows the nucleus to take on diverse shapes with little mechanical resistance. Whether this model is applicable to normal and cancer nuclei in human tissues is unclear. We image nuclear lamins in patient tissues and find: (a) nuclear laminar wrinkles are present in control and cancer tissue but are obscured in hematoxylin and eosin (H&E) images, (b) nuclei rarely have a smooth lamina, and (c) wrinkled nuclei assume diverse shapes. Deep learning reveals the presence of extreme nuclear laminar wrinkling in cancer tissues, which is confirmed by Fourier analysis. These data support a model in which excess surface area in the nuclear lamina enables nuclear shape diversity in vivo. Extreme laminar wrinkling is a marker of cancer, and imaging the lamina may benefit cancer diagnosis.

Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

Homozygous slc25a20 zebrafish mutant reveals insights into carnitine-acylcarnitine translocase deficiency pathogenesis

The SLC25A20 gene encodes carnitine-acylcarnitine translocase (CACT), facilitating the transport of long-chain acylcarnitine required for energy production via β-oxidation into the mitochondria. Loss-of-function mutations in this gene lead to CACT deficiency, a rare autosomal recessive disorder of fatty acid metabolism characterized by severe symptoms including cardiomyopathy, hepatic dysfunction, rhabdomyolysis, hypoketotic hypoglycemia, and hyperammonemia, often resulting in neonatal mortality. Here, we utilized CRISPR/Cas9 gene editing to isolate slc25a20 mutant zebrafish. Homozygous mutants displayed significant lethality, with the majority succumbing before reaching maturity. However, we identified a notably rare homozygous individual that survived into adulthood, prompting a histological examination. Firstly, we observed adipose tissue accumulation at various sites in the homozygous mutant. The mutant heart exhibited hypertrophy, along with degenerated myocardial and muscle cells containing numerous eosinophilic nuclei. Additionally, we found no large oil droplet vacuoles in the mutant liver; however, the hepatocytes displayed numerous small vacuoles resembling lipid droplets. Iron deposition was evident in the spleen and parts of the liver. Overall, our slc25a20 zebrafish mutant displayed tissue pathologies analogous to human CACT deficiency, suggesting its potential as a pathological model contributing to the elucidation of pathogenesis and the improvement/development of therapies for CACT deficiency.

Molecular interplay between the upregulated levels of Sad1 and UNC84 Domain Containing 2 (SUN2) and gene expression in medulloblastoma cells

BACKGROUND

SUN2 is a nuclear envelope protein associated with the nuclear lamina and with proteins linked to nuclear export, splicing, and nucleo-cytoskeleton communication. Studies of SUN2 in cancer have been limited but have suggested that it has tumor-suppressive activity in some carcinomas. Medulloblastoma is a pediatric tumor that develops in the cerebellum and is currently classified into four molecular groups: WNT (Wingless), SHH (Sonic Hedgehog), 3, and 4. SUN2 expression profiles appear to be altered in brain cancer but have not been previously evaluated in medulloblastoma.

METHODS AND RESULTS

The University of Alabama at Birmingham Cancer (UALCAN) data analysis portal, Gene Expression Profiling Interactive Analysis (GEPIA), the Oncopression gene expression compendium, and the R2 genomics analysis and visualization platform were used to analyze SUN2 expression in cancer, which was found to vary by cancer type; in particular, SUN2 expression was found to be upregulated in medulloblastoma. We also explored the effects of reduced SUN2 protein levels (by RNA interference) on gene expression profiles using a cDNA microarray in DAOY medulloblastoma-derived cells. We found that SUN2 protein is upregulated in medulloblastoma, mainly in the SHH group, which correlates with poor survival. Furthermore, the reduced SUN2 expression in medulloblastoma cells is associated with the downregulation of the expression of other genes, including members of the bitter taste-sensing type 2 receptor (TAS2R) family.

CONCLUSIONS

This study shows that SUN2 is upregulated in medulloblastoma-with molecular interplay in gene expression-which has group-dependent implications for medulloblastoma development. In particular, the upregulation of SUN2 is associated with a progression of the SHH group of medulloblastoma.

Clinical values of nuclear morphometric analysis in fibroepithelial lesions

BACKGROUND

Fibroepithelial lesions (FELs) of the breast encompass a broad spectrum of lesions, ranging from commonly encountered fibroadenomas (FAs) to rare phyllodes tumors (PTs). Accurately diagnosing and grading these lesions is crucial for making management decisions, but it can be challenging due to their overlapping features and the subjective nature of histological assessment. Here, we evaluated the role of digital nuclear morphometric analysis in FEL diagnosis and prognosis.

METHODS

A digital nuclear morphometric analysis was conducted on 241 PTs and 59 FAs. Immunohistochemical staining for cytokeratin and Leukocyte common antigen (LCA) was used to exclude non-stromal components, and nuclear area, perimeters, calipers, circularity, and eccentricity in the stromal cells were quantified with QuPath software. The correlations of these features with FEL diagnosis and prognosis was assessed.

RESULTS

All nuclear features, including area, perimeter, circularity, maximum caliper, minimum caliper and eccentricity, showed significant differences between FAs and benign PTs (p ≤ 0.002). Only nuclear area, perimeter, minimum caliper and eccentricity correlated significantly with PT grading (p ≤ 0.022). For differentiation of FAs from benign PTs, the model integrating all differential nuclear features demonstrated a specificity of 90% and sensitivity of 70%. For PT grading, the nuclear morphometric score showed a specificity of 78% and sensitivity of 96% for distinguishing benign/borderline from malignant PTs. In addition, a relationship of nuclear circularity was found with PT recurrence. The Kaplan-meier analysis, using the best cutoff determined by ROC curve, showed shorter event free survival in benign PTs with high circularity (chi-square = 4.650, p = 0.031).

CONCLUSIONS

Our data suggested the digital nuclear morphometric analysis could have potentials to objectively differentiate different FELs and predict PT outcome. These findings could provide the evidence-based data to support the development of deep-learning based algorithm on nuclear morphometrics in FEL diagnosis.

Constructing polymorphonuclear cells: chromatin folding shapes nuclear morphology

Immune cell fate decisions are regulated, at least in part, by nuclear architecture. Here, we outline how nuclear architecture instructs mammalian polymorphonuclear cell differentiation. We discuss how in neutrophils loop extrusion mechanisms regulate the expression of genes involved in phagocytosis and shape nuclear morphology. We propose that diminished loop extrusion programs also orchestrate eosinophil and basophil differentiation. We portray a new model in which competitive physical forces, loop extrusion, and phase separation, instruct mononuclear versus polymorphonuclear cell fate decisions. We posit that loop extrusion programs instruct the spatial organization of cytoplasmic organelles, including neutrophil granules, mitochondria, and endoplasmic reticulum. Finally, we suggest that changing loop extrusion programs might allow the engineering of new nuclear shapes and artificial cytoplasmic architectures.

Carbon quantum dots in breast cancer modulate cellular migration via cytoskeletal and nuclear structure

Carbon nanomaterials have been widely applied for cutting edge therapeutic applications as they offer tunable physio-chemical properties with economic scale-up options. Nuclear delivery of cancer drugs has been of prime focus since it controls important cellular signaling functions leading to greater anti-cancer drug efficacies. Better cellular drug uptake per unit drug injection drastically reduces severe side-effects of cancer therapies. Similarly, carbon dots (CDs) uptaken by the nucleus can also be used to set-up cutting edge nano delivery systems. In an earlier paper, we showed the cellular uptake and plasma membrane impact of combustion generated yellow luminescing CDs produced by our group from fuel rich combustion reactors in a one-step tunable production. In this paper, we aim to specifically study the nucleus by establishing the uptake kinetics of these combustion-generated yellow luminescing CDs. At sub-lethal doses, after crossing the plasma membrane, they impact the actin and microtubule mesh, affecting cell adhesion and migration; enter nucleus by diffusion processes; modify the overall appearance of the nucleus in terms of morphology; and alter chromatin condensation. We thus establish how this one-step produced, cost and bulk production friendly carbon dots from fuel rich combustion flames can be innovatively repurposed as potential nano delivery agents in cancer cells.

Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN

Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detection without the need for additional post-processing. The results were analyzed using the Kumar dataset, a public dataset with over 20,000 nuclei annotations from various organs. The dice score of the baseline Mask R-CNN improved from 76% to 83% after integration with an FPN. This was comparable with the 82.6% dice score achieved by modern semantic-segmentation-based networks. Thus, evidence is provided that an end-to-end trainable detection-based instance segmentation algorithm with minimal post-processing steps can reliably be used for the detection and analysis of individual nuclear properties. This represents a relevant task for research and diagnosis in digital pathology, which can improve the automated analysis of histopathological images.

Altered expression and localization of nuclear envelope proteins in a prostate cancer cell system

BACKGROUND

The nuclear envelope (NE), which is composed of the outer and inner nuclear membranes, the nuclear pore complex and the nuclear lamina, regulates a plethora of cellular processes, including those that restrict cancer development (genomic stability, cell cycle regulation, and cell migration). Thus, impaired NE is functionally related to tumorigenesis, and monitoring of NE alterations is used to diagnose cancer. However, the chronology of NE changes occurring during cancer evolution and the connection between them remained to be precisely defined, due to the lack of appropriate cell models.

METHODS

The expression and subcellular localization of NE proteins (lamins A/C and B1 and the inner nuclear membrane proteins emerin and β-dystroglycan [β-DG]) during prostate cancer progression were analyzed, using confocal microscopy and western blot assays, and a prostate cancer cell system comprising RWPE-1 epithelial prostate cells and several prostate cancer cell lines with different invasiveness.

RESULTS

Deformed nuclei and the mislocalization and low expression of lamin A/C, lamin B1, and emerin became more prominent as the invasiveness of the prostate cancer lines increased. Suppression of lamin A/C expression was an early event during prostate cancer evolution, while a more extensive deregulation of NE proteins, including β-DG, occurred in metastatic prostate cells.

CONCLUSIONS

The RWPE-1 cell line-based system was found to be suitable for the correlation of NE impairment with prostate cancer invasiveness and determination of the chronology of NE alterations during prostate carcinogenesis. Further study of this cell system would help to identify biomarkers for prostate cancer prognosis and diagnosis.

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning

BACKGROUND

The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.

METHODS

We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.

RESULTS

The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.

CONCLUSIONS

We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.

In vivo assessment of bladder cancer with diffuse reflectance and fluorescence spectroscopy: A comparative study

OBJECTIVES

The aim of this work is to assess the performance of multimodal spectroscopic approach combined with single core optical fiber for detection of bladder cancer during surgery in vivo.

METHODS

Multimodal approach combines diffuse reflectance spectroscopy (DRS), fluorescence spectroscopy in the visible (405 nm excitation) and near-infrared (NIR) (690 nm excitation) ranges, and high-wavenumber Raman spectroscopy. All four spectroscopic methods were combined in a single setup. For 21 patients with suspected bladder cancer or during control cystoscopy optical spectra of bladder cancer, healthy bladder wall tissue and/or scars were measured. Classification of cancerous and healthy bladder tissue was performed using machine learning methods.

RESULTS

Statistically significant differences in relative total haemoglobin content, oxygenation, scattering, and visible fluorescence intensity were found between tumor and normal tissues. The combination of DRS and visible fluorescence spectroscopy allowed detecting cancerous tissue with sensitivity and specificity of 78% and 91%, respectively. The addition of features extracted from NIR fluorescence and Raman spectra did not improve the quality of classification.

CONCLUSIONS

This study demonstrates that multimodal spectroscopic approach allows increasing sensitivity and specificity of bladder cancer detection in vivo. The developed approach does not require special probes and can be used with single-core optical fibers applied for laser surgery.

U-Shape Pillar Strip for 3D Cell-Lumped Organoid Model (3D-COM) Mimicking Lung Cancer Hypoxia Conditions in High-Throughput Screening (HTS)

Hypoxia is a representative tumor characteristic associated with malignant progression in clinical patients. Engineered in vitro models have led to significant advances in cancer research, allowing for the investigation of cells in physiological environments and the study of disease mechanisms and processes with enhanced relevance. In this study, we propose a U-shape pillar strip for a 3D cell-lumped organoid model (3D-COM) to study the effects of hypoxia on lung cancer in a high-throughput manner. We developed a U-pillar strip that facilitates the aggregation of PDCs mixed with an extracellular matrix to make the 3D-COM in 384-plate array form. The response to three hypoxia-activated prodrugs was higher in the 3D-COM than in the 2D culture model. The protein expression of hypoxia-inducible factor 1 alpha (HIF-1α) and HIF-2α, which are markers of hypoxia, was also higher in the 3D-COM than in the 2D culture. The results show that 3D-COM better recapitulated the hypoxic conditions of lung cancer tumors than the 2D culture. Therefore, the U-shape pillar strip for 3D-COM is a good tool to study the effects of hypoxia on lung cancer in a high-throughput manner, which can efficiently develop new drugs targeting hypoxic tumors.

Role of lamins in cellular physiology and cancer

Lamins, which are crucial type V intermediate filament proteins found in the nuclear lamina, are essential for maintaining the stability and function of the nucleus in higher vertebrates. They are classified into A- and B-types, and their distinct expression patterns contribute to cellular survival, development, and functionality. Lamins emerged during the transition from open to closed mitosis, with their complexity increasing alongside organism evolution. Derived from the LMNA, LMNB1, and LMNB2 genes, lamins undergo alternative splicing to produce seven variants, influencing cellular processes such as stiffness, chromatin condensation, and cell cycle regulation. The lamin network interacts with the cytoskeleton via Linkers of the nucleoskeleton to the cytoskeleton (LINC) complexes, playing a critical role in cellular stability and mechanotransduction. Lamins also regulate active transport into and out of the nucleus, affecting nuclear integrity, positioning, DNA maintenance, and gene expression. Genetic mutations in lamin genes lead to laminopathies, highlighting their functional significance and organizational roles. Changes in lamin subtype composition within the nuclear lamina have significant implications for cancer development, impacting cellular stiffness, mobility, and the Epithelial-to-Mesenchymal Transition (EMT). Lamin A/C, in particular, plays multifaceted roles in cancer biology, influencing progression, metastasis, and therapy response through interactions with various proteins and pathways. Dysregulated lamin expression is commonly observed in cancers, suggesting their potential as diagnostic and prognostic markers. This chapter underscores the pivotal roles of lamins in nuclear architecture and cancer biology, emphasizing their impact on cellular functions and disease pathology. Understanding lamin behavior and regulation mechanisms holds promise for developing novel diagnostic tools and targeted therapies in cancer treatment.

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes

The shape of a cell as defined by its membrane can be closely associated with its physiological state. For example, the irregular shapes of cancerous cells and elongated shapes of neuron cells often reflect specific functions, such as cell motility and cell communication. However, it remains unclear whether and which cell shape descriptors can characterize different cellular physiological states. In this study, 12 geometric shape descriptors for a three-dimensional (3D) object were collected from the previous literature and tested with a public dataset of ~400,000 independent 3D cell regions segmented based on fluorescent labeling of the cell membranes in Caenorhabditis elegans embryos. It is revealed that those shape descriptors can faithfully characterize cellular physiological states, including (1) cell division (cytokinesis), along with an abrupt increase in the elongation ratio; (2) a negative correlation of cell migration speed with cell sphericity; (3) cell lineage specification with symmetrically patterned cell shape changes; and (4) cell fate specification with differential gene expression and differential cell shapes. The descriptors established may be used to identify and predict the diverse physiological states in numerous cells, which could be used for not only studying developmental morphogenesis but also diagnosing human disease (e.g., the rapid detection of abnormal cells).

Multidimensional screening of pancreatic cancer spheroids reveals vulnerabilities in mitotic and cell-matrix adhesion signaling that associate with metastatic progression and decreased patient survival

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, with a median survival of less than 12 months and a 5-year survival of less than 10 %. Here, we have established an image-based screening pipeline for quantifying single PDAC spheroid dynamics in genetically and phenotypically diverse PDAC cell models. Wild-type KRas PDAC cells formed tight/compact spheroids - compaction of these structures was completely blocked by cytoplasmic dynein and focal adhesion kinase (FAK) inhibitors. In contrast, PDAC cells containing mutant KRas formed loosely aggregated spheroids that grew significantly slower following inhibition of polo-like kinase 1 (PLK1) or focal adhesion kinase (FAK). Independent of genetic background, multicellular PDAC-mesenchymal stromal cell (MSC) spheroids self-organized into structures with an MSC-dominant core. The inclusion of MSCs into wild-type KRas PDAC spheroids modestly affected their compaction; however, MSCs significantly increased the compaction and growth of mutant KRas PDAC spheroids. Notably, exogenous collagen 1 potentiated PANC1 spheroid compaction while ITGA1 knockdown in PANC1 cells blocked MSC-induced PANC1 spheroid compaction. In agreement with a role for collagen-based integrin adhesion complexes in stromal cell-induced PDAC phenotypes, we also discovered that MSC-induced PANC1 spheroid growth was completely blocked by the ITGB1 immunoneutralizing antibody mAb13. Finally, multiplexed single-cell immunohistochemical analysis of a 25 patient PDAC tissue microarray revealed a relationship between decreased variance in Spearman r correlation for ITGA1 and PLK1 expression within the tumor cell compartment of PDAC in patients with advanced disease stage, and elevated expression of both ITGA1 and PLK1 in PDAC was found to be associated with decreased patient survival. Taken together, this work uncovers new therapeutic vulnerabilities in PDAC that are relevant to the progression of this stromal cell-rich malignancy and which may reveal strategies for improving patient outcomes.

Alterations in the chromatin packaging, driven by transcriptional activity, revealed by AFM

BACKGROUND

The gene expression differs in the nuclei of normal and malignant mammalian cells, and transcription is a critical initial step, which defines the difference. The mechanical properties of transcriptionally active chromatin are still poorly understood. Recently we have probed transcriptionally active chromatin of the nuclei subjected to mechanical stress, by Atomic Force Microscopy (AFM) [1]. Nonetheless, a systematic study of the phenomenon is needed.

METHODS

Nuclei were deformed and studied by AFM. Non-deformed nuclei were studied by fluorescence confocal microscopy. Their transcriptional activity was studied by RNA electrophoresis.

RESULTS

The malignant nuclei under the study were stable to deformation and assembled of 100-300 nm beads-like units, while normal cell nuclei were prone to deformation. The difference in stability to deformation of the nuclei correlated with DNA supercoiling, and transcription-depended units were responsive to supercoils breakage. The inhibitors of the topoisomerases I and II disrupted supercoiling and made the malignant nucleus prone to deformation. Cell nuclei treatment with histone deacetylase inhibitors (HDACIs) preserved the mechanical stability of deformed malignant nuclei and, at the same time, made it possible to observe chromatin decondensation up to 20-60 nm units. The AFM results were supplemented with confocal microscopy and RNA electrophoresis data.

CONCLUSIONS

Self-assembly of transcriptionally active chromatin and its decondensation, driven by DNA supercoiling-dependent rigidity, was visualized by AFM in the mechanically deformed nuclei.

GENERAL SIGNIFICANCE

We demonstrated that supercoiled DNA defines the transcription mechanics, and hypothesized the nuclear mechanics in vivo should depend on the chromatin architecture.

Automatic detection of cell-cycle stages using recurrent neural networks

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer

PURPOSE

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness.

METHODS

Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE = 47, 75, 100 ms and b-values of 0, 750, 1500 s/mm2 , resulting in a 3 × 3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively.

RESULTS

Cancers have a higher PQ4 (22.50% ± 21.27%) and lower PQ2 (69.86% ± 28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4 = 0.13% ± 0.56%, 5.73% ± 15.07%, 2.66% ± 4.05%, and PQ2 = 98.51% ± 3.05%, 86.18% ± 21.75%, 93.38% ± 9.88%, respectively). Cancers have a higher vector angle (206.5 ± 41.8°) and amplitude (0.017 ± 0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ| = 0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p < 0.001) for the differentiation of prostate cancer from benign prostatic tissue.

CONCLUSIONS

Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Disulfide Cross-Linked Polymeric Redox-Responsive Nanocarrier Based on Heparin, Chitosan and Lipoic Acid Improved Drug Accumulation, Increased Cytotoxicity and Selectivity to Leukemia Cells by Tumor Targeting via "Aikido" Principle

We have developed a micellar formulation of anticancer drugs based on chitosan and heparin grafted with lipoic and oleic acids that can release the cytotoxic cargo (doxorubicin) in response to external stimuli, such as increased glutathione concentration-a hallmark of cancer. Natural polysaccharides (heparin and chitosan) provide the pH sensitivity of the nanocarrier: the release of doxorubicin (Dox) is enhanced in a slightly acidic environment (tumor microenvironment). Fatty acid residues are necessary for the formation of nanoparticles (micelles) and solubilization of cytostatics in a hydrophobic core. Lipoic acid residues provide the formation of a labile S-S cross-linking between polymer chains (the first variant) or covalently attached doxorubicin molecules through glutathione-sensitive S-S bridges (the second variant)-both determine Redox sensitivity of the anticancer drugs carriers stable in blood circulation and disintegrate after intracellular uptake in the tumor cells. The release of doxorubicin from micelles occurs slowly (20%/6 h) in an environment with a pH of 7.4 and the absence of glutathione, while in a slightly acidic environment and in the presence of 10 mM glutathione, the rate increases up to 6 times, with an increase in the effective concentration up to 5 times after 7 h. The permeability of doxorubicin in micellar formulations (covalent S-S cross-linked and not) into Raji, K562, and A875 cancer cells was studied using FTIR, fluorescence spectroscopy and confocal laser scanning microscopy (CLSM). We have shown dramatically improved accumulation, decreased efflux, and increased cytotoxicity compared to doxorubicin control with three tumor cell lines: Raji, K562, and A875. At the same time, cytotoxicity and permeability for non-tumor cells (HEK293T) are significantly lower, increasing the selectivity index against tumor cells by several times.

Beyond the surface: Investigation of tumorsphere morphology using volume electron microscopy

The advent of volume electron microscopy (vEM) has provided unprecedented insights into cellular and subcellular organization, revolutionizing our understanding of cancer biology. This study presents a previously unexplored comparative analysis of the ultrastructural disparities between cancer cells cultured as monolayers and tumorspheres. By integrating a robust workflow that incorporates high-pressure freezing followed by freeze substitution (HPF/FS), serial block face scanning electron microscopy (SBF-SEM), manual and deep learning-based segmentation, and statistical analysis, we have successfully generated three-dimensional (3D) reconstructions of monolayer and tumorsphere cells, including their subcellular organelles. Our findings reveal a significant degree of variation in cellular morphology in tumorspheres. We observed the increased prevalence of nuclear envelope invaginations in tumorsphere cells compared to monolayers. Furthermore, we detected a diverse range of mitochondrial morphologies exclusively in tumorsphere cells, as well as intricate cellular interconnectivity within the tumorsphere architecture. These remarkable ultrastructural differences emphasize the use of tumorspheres as a superior model for cancer research due to their relevance to in vivo conditions. Our results strongly advocate for the utilization of tumorsphere cells in cancer research studies, enhancing the precision and relevance of experimental outcomes, and ultimately accelerating therapeutic advancements.

Assessment of breast cytoarchitecture and its associated axillary lymph node status under normal and pathological conditions in Egyptian women

OBJECTIVE

Herein, we compare the features of neoplastic cancer cells in invasive ductal carcinoma (IDC) grade II and III patients to their corresponding normal cells both in breast and axillary lymph node (ALN) tissues.

METHODS

A retrospective cohort of 70 female breast cancer patients enrolled between 2018 and 2020 at Medical Research Institute, Alexandria University, Egypt, was analyzed for clinicopathological features presentation. Fresh tiny pieces of breast tissue and its associated ALN tissues were then processed to investigate the morphological appearance by scanning electron microscopy. Moreover, the histological architecture of tissue sections stained with hematoxylin and eosin was studied by light microscope, while the characterization of the ultrastructure features of breast and ALN tissues was analyzed by transmission electron microscopy.

RESULTS

Clinicopathological presentation of patients revealed that the Egyptian female breast cancer population adhered to the global trends of breast cancer disease with elevated incidence rate among postmenopausal women (61.3%), high frequency of IDC (95.7%), and increased ALN metastasis (65.7%). The percentage of estrogen receptor alpha (ERα) and human epidermal growth factor receptor 2 (HER2) expression, as key indicators for carcinogenesis and disease progression was 87.1% and 55.8%, respectively. The present study points to the observed discrepancies among the investigated variables in the diagnostic separation between IDC grade II and grade III. Ductal epithelial cells organization, nuclei size and irregularity, chromatin amount and uniformity, mitochondrial abundance and dysfunction were differentially manifested in IDC grades. Moreover, aberrations in the cellular organelles like lysosomes, endoplasmic reticulum, and lipid droplets vary according to the grade of IDC and the aggressiveness of the invasive breast cancer.

CONCLUSIONS

To sum up, this study emphasizes the importance of accurate specimen evaluation for treatment choice and decision.

Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC)

BACKGROUND

Recently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC50 value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient's groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test.

METHODS

On the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.

RESULTS

The conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.

CONCLUSIONS

Therefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

The Ultraviolet Irradiation of Keratinocytes Induces Ectopic Expression of LINE-1 Retrotransposon Machinery and Leads to Cellular Senescence

Retrotransposons have played an important role in evolution through their transposable activity. The largest and the only currently active human group of mobile DNAs are the LINE-1 retrotransposons. The ectopic expression of LINE-1 has been correlated with genomic instability. Narrow-band ultraviolet B (NB-UVB) and broad-band ultraviolet B (BB-UVB) phototherapy is commonly used for the treatment of dermatological diseases. UVB exposure is carcinogenic and can lead, in keratinocytes, to genomic instability. We hypothesize that LINE-1 reactivation occurs at a high rate in response to UVB exposure on the skin, which significantly contributes to genomic instability and DNA damage leading to cellular senescence and photoaging. Immortalized N/TERT1 and HaCaT human keratinocyte cell lines were irradiated in vitro with either NB-UVB or BB-UVB. Using immunofluorescence and Western blotting, we confirmed UVB-induced protein expression of LINE-1. Using RT-qPCR, we measured the mRNA expression of LINE-1 and senescence markers that were upregulated after several NB-UVB exposures. Selected miRNAs that are known to bind LINE-1 mRNA were measured using RT-qPCR, and the expression of miR-16 was downregulated with UVB exposure. Our findings demonstrate that UVB irradiation induces LINE-1 reactivation and DNA damage in normal keratinocytes along with the associated upregulation of cellular senescence markers and change in miR-16 expression.

Comparison of Clinico-Demographic and Histological Parameters Between Young and Old Patients With Oral Squamous Cell Carcinoma

INTRODUCTION

Among the epithelial malignancies of the head and neck region, oral squamous cell carcinoma (OSCC) arising from the oral mucosa is the commonest type. OSCC is common in the older population; however, recent epidemiological data indicate an increase in the incidence in the younger age group. The present study was designed to compare the clinicopathological characteristics of OSCC between young and old South Indian patients.

METHODS

All the histopathologically confirmed cases of OSCC were retrieved from the department archives. Patients aged more than 40 years were considered Group I, and patients aged less than or equal to 40 were considered Group II. Age, gender, laterality, site, degree of keratinization, nuclear pleomorphism, pattern of invasion, lymphoplasmacytic infiltration, grade, tumor budding (TB), and tumor stroma ratio (TSR) were assessed.

RESULTS

Among 510 patients reported with OSCC, 442 were aged above 40 years, and 68 were aged 40 years or younger. Nuclear pleomorphism, TB, and stroma-rich ratio were statistically higher in younger OSCC patients (p=0.00).

CONCLUSION

The results of our study support the fact that OSCC in younger individuals is more aggressive. Targeting TB and tumor stroma could provide new strategies for the management of OSCC.

Chromatin-modifying enzymes as modulators of nuclear size during lineage differentiation

The mechanism of nuclear size determination and alteration during normal lineage development and cancer pathologies which is not fully understood. As recently reported, chromatin modification can change nuclear morphology. Therefore, we screened a range of pharmacological chemical compounds that impact the activity of chromatin-modifying enzymes, in order to get a clue of the specific types of chromatin-modifying enzymes that remarkably effect nuclear size and shape. We found that interrupted activity of chromatin-modifying enzymes is associated with nuclear shape abnormalities. Furthermore, the activity of chromatin-modifying enzymes perturbs cell fate determination in cellular maintenance and lineage commitment. Our results indicated that chromatin-modifying enzyme regulates cell fate decision during lineage differentiation and is associate with nuclear size alteration.

Changes in expression of mesothelial BBS genes in 2D and 3D after lithium chloride and ammonium sulphate induction of primary cilium disturbance: a pilot study

BACKGROUND

Malignant pleural mesothelioma (MPM), a rare and aggressive pleural tumor, has significant histological and molecular heterogeneity. Primary Cilium (PC), an organelle of emerging importance in malignancies, has been scarcely investigated in MPM. A critical molecular complex for the PC function is the BBSome and here we aimed at assessing its expression patterns in ordinary 2D and spheroid 3D cell cultures.

METHODS

A human benign mesothelial cell line (MeT-5A), MPM cell lines (M14K, epithelioid MPM; MSTO, biphasic MPM), and primary MPM cells (pMPM) were used. Primers specific for the human BBS1, 2, 4, 5, 7, 9, 18 transcripts were designed, and quantitative real-time PCR (qRT-PCR) was done with β-actin as the gene of reference. The relative gene expression across 2D and 3D cultures was analyzed by the expression factor (mean of 1/ΔCt values). With the 2-∆∆Ct method the gene expression fold changes were assessed from qRT-PCR data. Molecular changes using the PC-modulating drugs ammonium sulfate (AS) and lithium chloride (LC) were also determined.

RESULTS

PC was present in all cells used in the study at approximately 15% of the observed area. BBSome transcripts were differentially expressed in different dimensions of cell culture (2D vs. 3D) in all cell lines and pMPM. Treatment with AS and LC affected the expression of the ciliary BBS2 and BBS18 genes in the benign as well as in the MPM cells.

CONCLUSIONS

These data indicate distinct BBSome molecular profiles in human benign and MPM cells cultured in 2D and 3D dimensions and support the notion that PC genes should be investigated as potential MPM therapeutic targets.

Micronuclei detection in oral cytologic smear: does it add diagnostic value?

BACKGROUND

Screening and early diagnosis of oral squamous cell carcinoma (OSCC) are directly associated with increased survival rate and improved prognosis. Noninvasive diagnostic tools have been implemented in the early detection as toluidine blue staining, optical imaging, and oral cytology. This study aimed to assess and compare the presence of micronuclei (MN) in oral exfoliative cytology of healthy controls, subjects exposed to high-risk factors for oral cancer, subjects with oral potentially malignant lesions (OPMLs), and those with malignant oral lesions.

SUBJECTS AND METHODS

A total number of 92 subjects were divided into 46 healthy controls with no oral mucosal lesions (23 with no evidence of cancer risk factors and 23 with cancer risk factors), 23 with OPMLs and 23 with oral malignant lesions. All the 92 participants were subjected to cytological sampling for detection of MN. The final diagnosis of the oral lesions was confirmed by the histopathological picture and compared to the cytological results.

RESULTS

The results showed that the diagnostic accuracy of MN was higher in OPMLs group (95.2%). The sensitivity of MN test in malignant group was much lower (52.2%); however, all the cytological criteria of malignancy were markedly detected as compared to the OPMLs group.

CONCLUSIONS

Conventional oral cytology supported by MN is highly beneficial as adjunctive tool in the screening for early detection of dysplastic oral lesions.

Machine learning in computational histopathology: Challenges and opportunities

Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

Consequences of gaining an extra chromosome

Mistakes in chromosome segregation leading to aneuploidy are the primary cause of miscarriages in humans. Excluding sex chromosomes, viable aneuploidies in humans include trisomies of chromosomes 21, 18, or 13, which cause Down, Edwards, or Patau syndromes, respectively. While individuals with trisomy 18 or 13 die soon after birth, people with Down syndrome live to adulthood but have intellectual disabilities and are prone to multiple diseases. At the cellular level, mistakes in the segregation of a single chromosome leading to a cell losing a chromosome are lethal. In contrast, the cell that gains a chromosome can survive. Several studies support the hypothesis that gaining an extra copy of a chromosome causes gene-specific phenotypes and phenotypes independent of the identity of the genes encoded within that chromosome. The latter, referred to as aneuploidy-associated phenotypes, are the focus of this review. Among the conserved aneuploidy-associated phenotypes observed in yeast and human cells are lower viability, increased gene expression, increased protein synthesis and turnover, abnormal nuclear morphology, and altered metabolism. Notably, abnormal nuclear morphology of aneuploid cells is associated with increased metabolic demand for de novo synthesis of sphingolipids. These findings reveal important insights into the possible pathological role of aneuploidy in Down syndrome. Despite the adverse effects on cell physiology, aneuploidy is a hallmark of cancer cells. Understanding how aneuploidy affects cell physiology can reveal insights into the selective pressure that aneuploid cancer cells must overcome to support unlimited proliferation.

Kinetic Effects of Transferrin-Conjugated Gold Nanoparticles on the Antioxidant Glutathione-Thioredoxin Pathway

Nanoparticle-based therapeutics are being clinically translated for treating cancer. Even when thought to be biocompatible, nanoparticles are being increasingly identified as altering cell regulation and homeostasis. Antioxidant pathways are important for maintaining cell redox homeostasis and play important roles by maintaining ROS levels within tolerable ranges. Here, we sought to understand how a model of a relatively inert nanoparticle without any therapeutic agent itself could antagonize a cancer cell lines' antioxidant mechanism. A label-free protein expression approach was used to assess the glutathione-thioredoxin antioxidative pathway in a prostate cancer cell line (PC-3) after exposure to gold nanoparticles conjugated with a targeting moiety (transferrin). The impact of the nanoparticles was also corroborated through morphological analysis with TEM and classification of pro-apoptotic cells by way of the sub-G0/G1 population via the cell cycle and annexin V apoptosis assay. After a two-hour exposure to nanoparticles, major proteins associated with the glutathione-thioredoxin antioxidant pathway were downregulated. However, this response was acute, and in terms of protein expression, cells quickly recovered within 24 h once nanoparticle exposure ceased. The impact on PRDX-family proteins appears as the most influential factor in how these nanoparticles induced an oxidative stress response in the PC-3 cells. An apparent adaptive response was observed if exposure to nanoparticles continued. Acute exposure was observed to have a detrimental effect on cell viability compared to continuously exposed cells. Nanoparticle effects on cell regulation likely provide a compounding therapeutic advantage under some circumstances, in addition to the action of any cytotoxic agents; however, any therapeutic advantage offered by nanoparticles themselves with regard to vulnerabilities specific to the glutathione-thioredoxin antioxidative pathway is highly temporal.

Epigenetic instability caused by absence of CIZ1 drives transformation during quiescence cycles

BACKGROUND

Cip1-interacting zinc finger protein 1 (CIZ1) forms RNA-dependent protein assemblies that stabilise epigenetic state, notable at the inactive X chromosome in females. CIZ1 has been linked with a range of human cancers and in mice genetic deletion of CIZ1 manifests as hyperproliferative lymphoid lineages in females. This suggests that its role in maintenance of epigenetic stability is linked with disease.

RESULTS

Here, we show that male and female CIZ1-null primary murine fibroblasts have reduced H4K20me1 and that this compromises nuclear condensation on entry to quiescence. Global transcriptional repression remains intact in condensation-deficient CIZ1-null cells; however, a subset of genes linked with chromatin condensation and homology-directed DNA repair are perturbed. Failure to condense is phenotypically mimicked by manipulation of the H4K20me1 methyltransferase, SET8, in WT cells and partially reverted in CIZ1-null cells upon re-expression of CIZ1. Crucially, during exit from quiescence, nuclear decondensation remains active, so that repeated entry and exit cycles give rise to expanded nuclei susceptible to mechanical stress, DNA damage checkpoint activation, and downstream emergence of transformed proliferative colonies.

CONCLUSIONS

Our results demonstrate a role for CIZ1 in chromatin condensation on entry to quiescence and explore the consequences of this defect in CIZ1-null cells. Together, the data show that CIZ1's protection of the epigenome guards against genome instability during quiescence cycles. This identifies loss of CIZ1 as a potentially devastating vulnerability in cells that undergo cycles of quiescence entry and exit.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

A study to evaluate association of nuclear grooving in benign thyroid lesions with RET/PTC1 and RET/PTC3 gene translocation

INTRODUCTION

Papillary thyroid carcinoma (PTC) is the most common malignant lesion of the thyroid characterized by unique histological features like nuclear grooving, nuclear clearing, and intra-nuclear inclusions. However, nuclear grooves are observed even in benign thyroid lesions (BTL) like nodular goiter (NG), Hashimoto's thyroiditis (HT), and follicular adenoma (FA) resulting in diagnostic dilemma of the presence of PTC in such BTL. RET/PTC gene translocation is one of the most common oncogenic rearrangements seen in PTC, known to be associated with nuclear grooving. Among different types of RET/PTC translocations, RET/PTC1 and RET/PTC3 gene translocations are the most common types. These translocations have also been identified in many BTL like hyperplastic nodules and HT. Our study aimed to determine the frequency of nuclear grooving in BTL and evaluate their association with RET/PTC1 and RET/PTC3 gene translocation.

METHODS

Formalin-fixed, paraffin-embedded (FFPE) tissue blocks of NG, HT, and FA were included in the study. The hematoxylin and eosin (H&E) stained sections were evaluated for the presence of nuclear grooving/high power field (hpf) and a scoring of 0 to 3 was used for the number of grooves. Sections of 10 μ thickness were cut and the cells containing the nuclear grooves were picked using Laser-Capture microdissection. About 20 to 50 such cells were microdissected in each of the cases followed by RNA extraction, cDNA conversion, realtime-polymerase chain reaction (RQ-PCR) for RET/PTC1 and RET/PTC3 gene translocation, and the findings were analyzed for statistical significance.

RESULTS

Out of 87 BTL included in the study, 67 (77.0%) were NG, 12 (13.7%) were HT, and 8 (9.2%) were FA. Thirty-two cases (36.8%) had nuclear grooving with 18 out of 67 NG, 6 out of 12 HT, and all 8 cases of FA showing a varying number of nuclear grooves. A significant association between the number of nuclear grooves with RET/PTC gene translocation (p-value of 0.001) was obtained. A significant association of HT with RET/PTC gene translocation (p-value of 0.038) was observed. RET/PTC1 and RET/PTC3 translocation were seen in 5 out of 87 cases, with HT showing positivity in 2 and FA in 1 case for RET/PTC1 and HT in 1 and FA in 2 cases for RET/PTC3 gene translocation with 1 case of FA being positive for both RET/PTC1 and RET/PTC3 gene translocation.

CONCLUSIONS

The frequency of nuclear grooving among BTLs in our study was 36.8%. Our study shows, that when BTLs, show nuclear grooves, with an increase in the nuclear size, oval and elongated shape, favors the possibility of an underlying genetic aberration like RET/PTC gene translocation, which in turn supports the reporting pathologist to suggest a close follow up of the patients on seeing such nuclear features on cytology or histopathology sample, particularly in HT.

The Structural Profile of HPV 18 in Terms of Chromosomal and Nuclear Degenerative Changes and the Ratio of Nucleus/Cytoplasm on Liquid based Cervical Cells

Background

HPV 18 is one of the important oncogenic types. HPV 18 is generally evaluated together with HPV 16 and/or high-risk HPV types in light microscopic studies. The purpose of this study was to evaluate the impact of only HPV 18 on the nucleus/cytoplasm ratio, and chromosomal and nuclear degenerative changes in liquid-based samples.

Materials and Methods

Eighty liquid-based cervical samples were used in this retrospective study. These smears were prepared by HPV Deoxyribonucleic Acid (DNA) detection and genotyping with the Cobas 4800 HPV system. Forty HPV 18 infected and forty smears with no infection agent were evaluated for chromosomal (nuclear budding, micronuclei), nuclear degenerative changes (membrane irregularity, nuclear enlargement, hyperchromasia, abnormal chromatin distribution, binucleation (BN), karyorrhexis (KR), karyolysis (KL), karyopyknosis (KP)), and cytologic findings (koilocyte (KC), cells with perinuclear PR) using light microscopy. Cellular diameters were evaluated using image analysis software. Statistical analysis was performed with Statistical Package for Social Sciences (SPSS) 19.0. p values < .05 were considered significant.

Results

The statistically significant difference between the presence of HPV 18 and karyorrectic cell, KC, nuclear membrane irregularity, enlargement, the mean nuclear width and height (p < 0.05). No cellular changes other than those mentioned were observed.

Conclusions

The present study is significant in that, it reveals the relationship between only and particularly HPV 18 and nucleus/cytoplasm ratio, and chromosomal and nuclear degenerative changes in liquid-based cytology. HPV 18 affects KR, koilocytosis, nuclear membrane irregularity, enlargement, and nuclear diameters. Light microscopic analysis of these abnormalities increases the sensitivity and specificity of cytology in the evaluation of cellular pictures due to HPV 18.