Stefania Maneta-Stavrakaki

Endometrial hyperplasia (EH) comprises a spectrum of abnormal proliferative changes in the endometrium, ranging from benign glandular overgrowth to lesions with substantial malignant potential. The importance of risk stratification and early identification is highlighted by the growing recognition of EH as a precursor to endometrial cancer. The main causes of EH, according to epidemiological research, include obesity, polycystic ovarian syndrome (PCOS), metabolic dysfunction, and extended exposure to unopposed estrogen. Emerging molecular markers, histological analysis, and imaging are all necessary for a proper diagnosis of EH because it might appear with vague clinical symptoms such as irregular uterine bleeding. Surgical intervention or progestin therapy are two possible management techniques for EH, depending on the lesion’s intricacy and the patient’s medical history, including fertility issues. Personalized therapy techniques and recent developments in molecular profiling have the potential to enhance patient outcomes by matching treatment to tumor biology and individual risk profiles. This review highlights the translational potential of molecular insights while synthesizing the most recent data on the epidemiology, risk factors, diagnostic techniques, and therapy of EH. A deeper comprehension of these elements is necessary to maximize treatment results and stop the development of endometrial cancer.

Understanding the dynamic cellular metabolism is essential for gaining deeper insights into inter- and intracellular functions. In recent years, mass spectrometry (MS) has become the technology of choice for the biochemical characterization and profiling of cell lines, particularly when coupled with separation techniques such as liquid chromatography (LC-MS). However, these methods typically involve extensive sample preparation with potent organic solvents, which is labor-intensive, time-consuming, and incompatible with direct analysis of intact, live cells. Here, we propose the use of the ambient ionization technique Laser Desorption-Rapid Evaporative Ionization Mass Spectrometry (LD-REIMS) incorporated in an automated platform, for the high-throughput profiling of live or frozen cell monolayers, with minimal pretreatment. Validation experiments using 10 breast and colorectal cancer cell lines confirmed high accuracy, repeatability, and molecular coverage of the method, with over 400 metabolites and lipids detected and identified, including saccharides, amino acids, fatty acids, and glycerophospholipids. Of these, 144 were further confirmed and quantified with LC-MS/MS and standard compounds. We also applied the method to establish lipidomic differences across the isogenic MCF10A cells harboring either WT or MUT PIK3CA. Finally, we conducted time-series experiments on hypoxic cells, which revealed significant dynamic changes in metabolism, including lactate accumulation due to anaerobic glycolysis.

Background: The widespread use of the human papillomavirus (HPV) vaccine and the rise in digital technologies like artificial intelligence (AI) are causing significant changes in the paradigms surrounding cervical cancer screening. Objective: This review addresses potential future paths toward risk-based, customized screening and prevention while summarizing the available data on how vaccination and digital innovation are changing cervical cancer screening methods. Results: A shift from cytology-based screening to HPV-based primary testing with longer intervals has been supported by the notable decrease in high-grade cervical lesions brought about by HPV vaccination. However, AI and digital health tools, such as digital colposcopy, automated cytology, telemedicine, and self-sampling, have the potential to increase diagnostic access, accuracy, and efficiency, especially in low-resource environments. Implementation hurdles, data security, and algorithmic bias are major obstacles. Conclusions: Digital platforms, molecular diagnostics, and vaccination histories must all be incorporated into screening methods in order to keep up with the decline in HPV-related illness. The elimination of cervical cancer could be accelerated and equality and efficiency increased with customized, risk-based algorithms.

Background/Objectives: Preeclampsia (PE), characterized by its complex and multisystemic nature, significantly compromises the health outcomes of both mothers and their newborns. According to recent research, its underlying pathophysiological mechanisms may be influenced by abnormalities in lipid metabolism. The purpose of this study is to assess the association between unfavorable pregnancy outcomes and increased lipoprotein(a) levels in the first trimester and the subsequent risk of PE. Methods: A prospective cohort study comprising 150 pregnant women with a gestational age of less than 12 weeks and no history of PE was carried out at the University Hospital of Ioannina. In the first trimester, lipoprotein(a) levels were assessed, and individuals were monitored for the emergence of preeclampsia, preterm birth, gestational hypertension without proteinuria, and fetal growth limitation. Selection bias was minimized through the use of sequential sampling and rigorous inclusion and exclusion criteria. Associations were assessed using logistic regression analysis. Results: Women with elevated lipoprotein(a) levels had a considerably greater risk of PE than those with normal levels (64.7% vs. 15.5%, p < 0.001). Additionally, elevated lipoprotein(a) was linked to higher odds of fetal growth restriction (p < 0.001), gestational hypertension without proteinuria (p = 0.024), and premature delivery (p = 0.009). These results imply that early lipoprotein(a) screening during pregnancy may help identify women who are at high risk for PE and its associated negative consequences. Conclusions: The association between PE and elevated first-trimester lipoprotein(a) levels highlights the necessity for a deeper understanding of the underlying pathophysiological mechanisms, which could ultimately improve outcomes for both mothers and newborns.

Laser desorption ionization (LDI) is generally considered to be an inferior ionization modality to matrix assisted LDI (MALDI), providing information solely on lipids with low sensitivity. The current study demonstrates that the combination of ambient LDI with in-source surface-induced declustering provides sensitivity and chemical coverage comparable to MALDI. The setup was characterised for infrared laser desorption using two different laser systems and was successfully used for ambient mass spectrometric imaging. 5 µm spatial resolution was achieved enabling single-cell resolution imaging, while metabolites and lipids ranging from amino acids through carbohydrates and nuclear bases to complex glycolipids were successfully detected. The technique was also tested as a platform for MS-guided surgery, raising the possibility of using a single technique for generating histological and in-vivo data. The results suggest that the method can be an important step forward in histological classification for surgery and pathology environments, potentially offering a versatile platform for generating both histological and in vivo data.

Extracellular vesicles (EVs), including exosomes and microvesicles, mediate intercellular communication in cancer, from development to metastasis. EV‐based liquid biopsy is a promising strategy for cancer diagnosis as EVs can be found in cancer patients’ body fluids. In this study, the lipid composition of breast cancer‐derived EVs was studied as well as the potential of blood plasma EVs for the identification of lipid biomarkers for breast cancer detection. Initially, an untargeted lipidomic analysis was carried out for a panel of cancerous and non‐cancerous mammary epithelial cells and their secreted EVs. We found that breast cancer‐derived EVs are enriched in sphingolipids and glycerophospholipids compared to their parental cells. The initial in vitro study showed that EVs and their parental cells can be correctly classified (100% accuracy) between cancerous and non‐cancerous, as well as into their respective breast cancer subtypes, based on their lipid composition. Subsequently, an untargeted lipidomic analysis was carried out for blood plasma EVs from women diagnosed with breast cancer (primary or progressive metastatic breast cancer) as well as healthy women. Correspondingly, when blood plasma EVs were analysed, breast cancer patients and healthy women were correctly classified with an overall accuracy of 93.1%, based on the EVs’ lipid composition. Similarly, the analysis of patients with primary breast cancer and healthy women showed an overall accuracy of 95% for their correct classification. Furthermore, primary and metastatic breast cancers were correctly classified with an overall accuracy of 89.5%. This reveals that the blood plasma EVs’ lipids may be a promising source of biomarkers for detection of breast cancer. Additionally, this study demonstrates the usefulness of untargeted lipidomics in the study of EV lipid composition and EV‐associated biomarker discovery studies. This is a proof‐of‐concept study and a starting point for further analysis on the identification of EV‐based biomarkers for breast cancer.

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case–control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC , and oncogenic mutations such as mutant KRAS . Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras . Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in Apc ^Min/+ mice indicating its potential as a metabolic drug target in CRC.

Background: Tumor-derived Extracellular Vesicles (EVs) are found in body fluids of cancer patients. These EVs include exosomes and microvesicles, which play an important role in tumor development and metastasis. Although EVs have great potential as liquid biopsies for early detection of breast cancer, the lipid composition of breast cancer-derived EVs is unknown. Here, we studied the lipid composition of EVs and its potential for the identification of body fluid-based biomarkers for breast cancer diagnosis. Methods: The lipid composition of EVs and their parental cells were evaluated for nine breast cancer and two non-cancerous mammary cell lines. EVs were isolated by size-exclusion chromatography (SEC). Furthermore, ten blood plasma samples were studied from women diagnosed with primary breast cancer before surgery, nine samples from patients with progressive metastatic breast cancer and ten samples from healthy women who neither had pre-existing medical conditions nor infections at the time the samples were collected or in the previous weeks. EVs were obtained from blood plasma samples by a combination of density gradient ultracentrifugation and SEC. Lipids were extracted from EVs and analyzed by reverse-phase liquid chromatography mass-spectrometry (LC-MS). Results: We found that EVs produced by breast cancer cell lines were enriched in sphingomyelins and ceramides when compared to their parental cells. Furthermore, phospholipids such as phosphatidylcholine and phosphatidylethanolamine were found to be abundant in these EVs. Multivariate analyses showed that EVs released by breast cancer cells can be distinguished from those released by non-cancerous cells based on their phospholipid and sphingolipid composition (AUCROC=0.99, p<0.001). A key finding of this study is that we also found significant differences in the phospholipid and sphingolipid composition between EVs found in blood plasma from breast cancer patients and healthy volunteers (AUCROC=0.99, p<0.001). Importantly with respect to the practical applications of this study for early diagnosis of breast cancer, these differences in EV's lipid content were also significant between EVs from primary breast cancer patients and healthy volunteers (AUCROC=0.99, p<0.001). Annotation of specific lipid species are currently being carried out by MS/MS analysis. Conclusion: Our findings demonstrate the potential of the lipid content of cancer-derived EVs for the identification of lipid biomarkers for breast cancer diagnosis and its use for the development of non-invasive (or minimally invasive) diagnostic methods based on blood plasma analysis. Citation Format: Erika Dorado, M Luisa Doria, Anika Nagelkerke, James S McKenzie, Stefania Maneta-Stavrakaki, Charlotte Ion, Thomas Whittaker, Jeremy Nicholson, Molly M Stevens, R Charles Coombes, Zoltan Takats. Lipidomic analysis of extracellular vesicles and its potential for the identification of body fluid-based biomarkers for breast cancer diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 459.

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption – Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April – June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.