The application of metabolomics in ovarian cancer management: a systematic review.

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.

Is ovarian cancer surgery stuck in the dark ages?: a commentary piece reviewing surgical technologies.

Ovarian cancer surgery endeavours to remove all visible tumour deposits, and surgical technologies could potentially facilitate this aim. However, there appear to be barriers around the adoption of new technologies, and we hope this article provokes discussion within the specialty to encourage a forward-thinking approach to new-age surgical gynaecological oncology.

The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS).

BACKGROUND: Survival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection. METHODS: Aerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed. RESULTS: A total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001). CONCLUSIONS: The REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.

Methylation of MYLK3 gene promoter region: a biomarker to stratify surgical care in ovarian cancer in a multicentre study.

BACKGROUND: Survival benefit from surgical debulking of ovarian cancer (OC) is well established, but some women, despite total macroscopic clearance of disease, still have poor prognosis. We aimed to identify biomarkers to predict benefit from conventional surgery. METHODS: Clinical data from women debulked for high-stage OC were analysed (Hammersmith Hospital, London, UK; 2001-2014). Infinium's HumanMethylation27 array interrogated tumour DNA for differentially methylated CpG sites, correlated to survival, in patients with the least residual disease (RD; Hammersmith Array). Validation was performed using bisulphite pyrosequencing (Charité Hospital, Berlin, Germany cohort) and The Cancer Genome Atlas' (TCGA) methylation data set. Kaplan-Meier curves and Cox models tested survival. RESULTS: Altogether 803 women with serous OC were studied. No RD was associated with significantly improved overall survival (OS; hazard ratio (HR) 1.25, 95% CI 1.06-1.47; P=0.0076) and progression-free survival (PFS; HR 1.23, 95% CI 1.05-1.43; P=0.012; Hammersmith database n=430). Differentially methylated loci within FGF4, FGF21, MYLK2, MYLK3, MYL7, and ITGAE associated with survival. Patients with the least RD had significantly better OS with higher methylation of MYLK3 (Hammersmith (HR 0.51, 95% CI 0.31-0.84; P=0.01), Charité (HR 0.46, 95% CI 0.21-1.01; P=0.05), and TCGA (HR 0.64, 95% CI 0.44-0.93; P=0.02)). CONCLUSIONS: MYLK3 methylation is associated with improved OS in patients with the least RD, which could potentially be used to determine response to surgery.

Clinicopathological characteristics and survival outcomes of patients with large cell neuroendocrine carcinoma of the uterine cervix: A systematic review and meta-analysis.

PURPOSE: Large cell neuroendocrine carcinoma (LCNEC) of the cervix represents a rare tumour entity associated with poor prognosis. Knowledge about carcinogenesis and therapeutic options is scarce, while novel therapeutic targeted approaches are limited. METHODS: We performed a systematic review of four electronic databases from inception to June 2020. Eligible studies included all reports that addressed survival outcomes of women with LCNEC. RESULTS: A total of 31 case studies including 87 LCNEC patients were identified. Median patients' age was 41 years (range: 21-81). Most women (76.3%) had FIGO stage I-II disease. Overall, 72.0% had surgery, 70.1% received chemotherapy and 50.7% received radiotherapy. Of 13 patients with known HPV-status, 15% were HPV negative. Median overall survival (OS) was 24 months (range: 0.5-151), with 3- and 5-year OS of 42% and 29%, respectively. In multivariate analyses, only surgery and lymphadenectomy significantly associated with survival (Surgery OS: HR 0.14; 95% C.I:0.03-0.71, p = 0.018 / Surgery PFS: HR 0.23, 95% C.I. 0.06, 0.92, p = 0.037 / Lymphadenectomy OS: HR 0.26, 95% C.I. 0.07-0.98, p = 0.046 / Lymphadenectomy PFS: HR 0.30, 95% C.I. 0.09-0.98, p = 0.046). Age, chemotherapy or radiotherapy did not significantly impact survival, but lower stage was associated with improved survival. CONCLUSION: Cervical LCNECs overall have a poor prognosis, despite their relatively early-stage initial presentation. Surgery and lymphadenectomy appear to significantly affect survival in contrast to chemotherapy and radiotherapy, which appear to have no significant effect on prognosis. Prospective multicentre cancer registries are warranted to improve treatment options for this rare disease.

Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)-A Prospective Pilot Study of Diagnostic Accuracy.

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

Analysis of worldwide surgical outcomes in COVID-19-infected patients: a gynecological oncology perspective.

Coronavirus Disease 2019 (COVID-19) guidance limits all but the most urgent surgery in the United Kingdom. We review the literature and our experience in gynecology to assess perioperative outcomes. PubMed was searched with (surg*[Title])AND(COVID[Title]), (surg*[Title])AND(2019-nCoV[Title]), and (surg*[Title])AND(SARS-CoV-2[Title]), and 67 COVID-19-positive surgical patients across ten hospitals in four countries are included. Median mortality was 33%. Cardiac and pulmonary co-morbidities associated with higher risk of COVID-19-positive postoperative death. Mortality was high in neurosurgery (80%) and the lowest in gynecological oncology surgery (none). This analysis provides an evidence base on which to consider surgical risk assessment for different specialties. Risk of perioperative death needs to be assessed in the context of patients' co-morbidities and surgical specialty. An individualized approach toward surgical decision making is imperative.

Clinical Use of Programmed Cell Death-1 and Its Ligand Expression as Discriminatory and Predictive Markers in Ovarian Cancer.

Purpose: We aimed to establish whether programmed cell death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) expression, in ovarian cancer tumor tissue and blood, could be used as biomarkers for discrimination of tumor histology and prognosis of ovarian cancer.Experimental Design: Immune cells were separated from blood, ascites, and tumor tissue obtained from women with suspected ovarian cancer and studied for the differential expression of possible immune biomarkers using flow cytometry. PD-L1 expression on tumor-associated inflammatory cells was assessed by immunohistochemistry and tissue microarray. Plasma soluble PD-L1 was measured using sandwich ELISA. The relationships among immune markers were explored using hierarchical cluster analyses.Results: Biomarkers from the discovery cohort that associated with PD-L1+ cells were found. PD-L1+ CD14+ cells and PD-L1+ CD11c+ cells in the monocyte gate showed a distinct expression pattern when comparing benign tumors and epithelial ovarian cancers (EOCs)-confirmed in the validation cohort. Receiver operating characteristic curves showed PD-L1+ and PD-L1+ CD14+ cells in the monocyte gate performed better than the well-established tumor marker CA-125 alone. Plasma soluble PD-L1 was elevated in patients with EOC compared with healthy women and patients with benign ovarian tumors. Low total PD-1+ expression on lymphocytes was associated with improved survival.Conclusions: Differential expression of immunological markers relating to the PD-1/PD-L1 pathway in blood can be used as potential diagnostic and prognostic markers in EOC. These data have implications for the development and trial of anti-PD-1/PD-L1 therapy in ovarian cancer. Clin Cancer Res; 23(13); 3453-60. ©2016 AACR.

Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging.

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.