Lipidomics provides comprehensive study of all lipid components in cells, serum, plasma or tissues of organisms, aiming to the discovery of diagnostic, prognostic and predictive biomarkers of diseases such as malignancies. This systematic review evaluates studies, where lipidomics applies to the diagnosis, prognosis, prediction of ovarian cancer (OC) and the discrimination between malignant and benign ovarian masses. Literature search was conducted in PubMed, Science Direct and SciFinder. Only publications written in English language after 2012 were included. From the reference lists of primary included studies, relevant citations were identified and were also added in our list. All studies included referred to the application of lipidomics in serum/plasma samples of human cases with OC, whereby few of them included also tumor tissue samples. In some of the included studies metabolomic analysis was also involved where other metabolites besides lipids were identified. Qualitative data were assessed and risk of bias was determined using the ROBINS-I tool. In total, 29 studies were included, 15 of which applied non-targeted, 7 targeted lipidomics and 7 were reviews relevant to our aim. Most studies concerned the possible application of lipidomics in OC diagnosis, exhibiting phospholipids and sphingolipids as the most significantly altered during development of the disease. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

(1) Background: BRCA mutation and homologous recombination deficiency (HRD) are used to administrate PARP inhibitors (PARPi) maintenance therapy. It is known that PARPi efficacy is related to platinum sensitivity and that the latter can be demonstrated from the CA-125 elimination rate constant (KELIM). This study aims to investigate if KELIM can be another tool in the identification of patients that could be benefit from PARPi therapy.; (2) Methods: Retrospective analysis of patients with high-grade serous advanced ovarian cancer that underwent cytoreduction and was further tested for HRD status. The HRD status was tested either by myChoice HRD CDx assay or by RediScore assay. KELIM score was measured in both neoadjuvant and adjuvant settings with the online tool biomarker-kinetics.org.; (3) Results: Thirty-nine patients had available data for estimating both HRD status and KELIM score. When assuming KELIM as a binary index test with the value 1 as the cut-off point, the sensitivity was 0.86, 95% CI (0.64-0.97), and the specificity 0.83, 95% CI (0.59-0.96). On the other hand, when assuming KELIM as a continuous index test, the AUC was 81% and the optimal threshold, using the Youden index, was identified as 1.03 with a sensitivity of 85.7% and a specificity of 83.3%; (4) Conclusions: KELIM score seems to be a new, cheaper and faster tool to identify patients that can benefit from PARPi maintenance therapy.

Several tumor types have been efficiently treated with PARPis, which are now approved for the treatment of ovarian, breast, prostate, and pancreatic cancer. The BRCA1/2 genes and mutations in many additional genes involved in the HR pathway may be responsible for the HRD phenomenon. In the present study, an NGS assay was used to analyze 513 genes associated with targeted and immuno-oncology therapies in 406 samples. In addition, the %gLOH values of 24 samples were also calculated using the Affymetrix technology to compare the results obtained by the two methodologies. HR variations occurred in 20.93% of the malignancies, while BRCA1/2 gene alterations in 5.17%. %LOH was highly correlated with alterations in the BRCA1/2 genes since 76.19% (16/21) of the BRCA1/2 positive tumors had a high %LOH value (p=0.007). Moreover, the LOH status was highly correlated with the TP53 and KRAS statuses, but there was no association with the TMB value. Lin's Concordance Correlation Coefficient for the 24 evaluable samples simultaneously examined by both assays was 0.87, indicating a nearly perfect agreement. In conclusion, the addition of gLOH analysis could assist the detection of additional patients eligible for PARPis