Cell-stiffness and morphological architectural patterns in clinical samples of high grade serous ovarian cancers

Intracellular remodeling associated with endoplasmic reticulum stress modifies biomechanical compliance of bladder cells

Bladder cells face a challenging biophysical environment: mechanical cues originating from urine flow and regular contraction to enable the filling voiding of the organ. To ensure functional adaption, bladder cells rely on high biomechanical compliance, nevertheless aging or chronic pathological conditions can modify this plasticity. Obviously the cytoskeletal network plays an essential role, however the contribution of other, closely entangled, intracellular organelles is currently underappreciated. The endoplasmic reticulum (ER) lies at a crucial crossroads, connected to both nucleus and cytoskeleton. Yet, its role in the maintenance of cell mechanical stability is less investigated. To start exploring these aspects, T24 bladder cancer cells were treated with the ER stress inducers brefeldin A (10-40nM BFA, 24 h) and thapsigargin (0.1-100nM TG, 24 h). Without impairment of cell motility and viability, BFA and TG triggered a significant subcellular redistribution of the ER; this was associated with a rearrangement of actin cytoskeleton. Additional inhibition of actin polymerization with cytochalasin D (100nM CytD) contributed to the spread of the ER toward cell periphery, and was accompanied by an increase of cellular stiffness (Young´s modulus) in the cytoplasmic compartment. Shrinking of the ER toward the nucleus (100nM TG, 2 h) was related to an increased stiffness in the nuclear and perinuclear areas. A similar short-term response profile was observed also in normal human primary bladder fibroblasts. In sum, the ER and its subcellular rearrangement seem to contribute to the mechanical properties of bladder cells opening new perspectives in the study of the related stress signaling cascades. Video Abstract.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

Nanomechanical Characterization of Ovarian Cancer Cell Lines as a Marker of Response to 2c Treatment

Epithelial ovarian cancers (EOCs) are a heterogeneous group of tumors with different molecular and clinical features. In past decades, few improvements have been achieved in terms of EOC management and treatment efficacy, such that the 5-year survival rate of patients remained almost unchanged. A better characterization of EOCs' heterogeneity is needed to identify cancer vulnerabilities, stratify patients and adopt proper therapies. The mechanical features of malignant cells are emerging as new biomarkers of cancer invasiveness and drug resistance that can further improve our knowledge of EOC biology and allow the identification of new molecular targets. In this study, we determined the inter and intra-mechanical heterogeneity of eight ovarian cancer cell lines and their association with tumor invasiveness and resistance to an anti-tumoral drug with cytoskeleton depolymerization activity (2c).

Nanoscale Prognosis of Colorectal Cancer Metastasis from AFM Image Processing of Histological Sections

Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement, and, most importantly, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement to metastatic cancer detection with 97.7 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. This methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds based on relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low metastatic potential to those of high metastatic potential. This metastatic differentiation, which is also identified in higher moments of variograms, sets different hierarchical levels for metastatic progression dynamics.

Universal Markers Unveil Metastatic Cancerous Cross-Sections at Nanoscale

Simple Summary

We propose the use of two universal morphometric indices whose synergetic potency leads to the classification of a cancerous tissue of a few nanometers in size as metastatic or non-metastatic. The method is label-free, operates on conventional histological cross-sections, recording surface height–height roughness by AFM, and detects nanoscale changes associated with the progress of carcinogenesis which are the output of combined statistical approaches, namely multifractal analysis and the generalized moments method. The benefit of this approach is at least two-fold. On the one hand, its application in the context of early diagnosis can increase the life expectancy of patients, and on the other hand, differentiation between metastatic and non-metastatic tissues at the singular cell level can lead to new methodologies to treat cancer biology and therapies.

Abstract

The characterization of cancer histological sections as metastatic, M, or not-metastatic, NM, at the cellular size level is important for early diagnosis and treatment. We present timely warning markers of metastasis, not identified by existing protocols and used methods. Digitized atomic force microscopy images of human histological cross-sections of M and NM colorectal cancer cells were analyzed by multifractal detrended fluctuation analysis and the generalized moments method analysis. Findings emphasize the multifractal character of all samples and accentuate room for the differentiation of M from NM cross-sections. Two universal markers emphatically achieve this goal performing very well: (a) the ratio of the singularity parameters (left/right), which are defined relative to weak/strong fluctuations in the multifractal spectrum, is always greater than 0.8 for NM tissues; and (b) the index of multifractality, used to classify universal multifractals, points to log-normal distribution for NM and to log-Cauchy for M tissues. An immediate large-scale screening of cancerous sections is doable based on these findings.

AKT Isoforms Interplay in High-Grade Serous Ovarian Cancer Prognosis and Characterization

Simple Summary

New therapeutical strategies are needed to improve survival in high-grade serous ovarian cancer (HGSOC) patients. AKT inhibitors are promising agents able to act in synergy with PARP inhibitors and platinum-based therapies, but the subset of patients who could benefit from this approach is still unclear. We analyzed AKT isoforms expression in a retrospective cohort and we identified four AKT expression groups related to patients’ survival, tumor morphology and the BRCA status that could help in stratifying patients for future clinical trials.

Abstract

High-grade serous ovarian cancer (HGSOC) is among the deadliest gynecological malignancies. The acquired resistance to platinum-based therapies and the intrinsic heterogeneity of the disease contribute to the low survival rate. To improve patients’ outcomes, new combinatorial approaches able to target different tumor vulnerabilities and enhance the efficacy of the current therapies are required. AKT inhibitors are promising antineoplastic agents able to act in synergy with PARP inhibitors, but the spectrum of patients who can benefit from this combination is unclear, since the role of the three different isoforms of AKT is still unknown. Here, we study the expression of AKT isoforms on a retrospective cohort of archive tissue by RT-droplet digital PCR (ddPCR) analyzing their association with the clinicopathological features of patients. Based on AKT1/AKT2 and AKT1/AKT3 ratios, we define four AKT classes which were related to patients’ survival, tumor morphology and BRCA1 expression. Moreover, our results show that high AKT3 expression levels were frequently associated with tumors having classic features, a low number of mitoses and the presence of psammoma bodies. Overall, our study obtains new insights on AKT isoforms and their associations with the clinicopathological features of HGSOC patients. These evidences could help to better define the subsets of patients who can benefit from AKT and PARP inhibitors therapy in future clinical trials.

Histological patterns and intra-tumor heterogeneity as prognostication tools in high grade serous ovarian cancers

OBJECTIVE

High grade serous ovarian carcinoma (HGSOC) is the most common type of malignant ovarian neoplasm and the main cause of ovarian cancer related deaths worldwide. Although novel biomarkers such as homologous recombination deficiency testing have been implemented into the clinical decision-making algorithm since diagnosis, morphological classification and immunohistochemistry analysis are essential for diagnostic purpose. This study aims at identifying histologic and clinical features that can be predictive of patients' prognosis.

METHODS

Morphological and architectural characterization including SET (Solid-Endometroid-Transitional)/Classic features was carried out in a cohort of 234 patients analyzing 695 slides. From each slide tumor infiltrating lymphocyte (TILs), the presence of necrosis, the number of mitoses, the presence of psammoma bodies, giant cells and atypical mitoses were recorded. Morphological heterogeneity was quantified by the Shannon's diversity index (SDI) considering the percentage of each architectural pattern per patient's slide.

RESULTS

The frequency of architectural patterns and morphological variables varied with respect of the surgical strategy (primary debulking surgery vs interval surgery after neoadjuvant chemotherapy). HGSOCs exhibiting SET features had a longer overall as well as progression free survival. Among SET features, pseudo-endometrioid and transitional like patterns had the best outcome, while it was heterogenous for solid pattern, that had better outcome for BRCA 1 negative and less heterogeneous tumors. In patients submitted to neoadjuvant chemotherapy a higher intratumor heterogeneity as defined by SDI was a negative independent prognostic factor.

CONCLUSIONS

A comprehensive histological examination considering architectural patterns and their heterogeneity can help in prognostication of HGSOCs.