Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Metabolomics profiling and chemoresistance mechanisms in ovarian cancer cell lines: Implications for targeting glutathione pathway

Ovarian cancer presents a significant challenge due to its high rate of chemoresistance, which complicates the effectiveness of drug-response therapy. This study provides a comprehensive metabolomic analysis of ovarian cancer cell lines OVCAR-3 and SK-OV-3, characterizing their distinct metabolic landscapes. Metabolomics coupled with chemometric analysis enabled us to discriminate between the metabolic profiles of these two cell lines. The OVCAR-3 cells, which are sensitive to doxorubicin (DOX), exhibited a preference for biosynthetic pathways associated with cell proliferation. Conversely, DOX-resistant SK-OV-3 cells favored fatty acid oxidation for energy maintenance. Notably, a marked difference in glutathione (GSH) metabolism was observed between these cell lines. Our investigations further revealed that GSH depletion led to a profound change in drug sensitivity, inducing a shift from a cytostatic to a cytotoxic response. The results derived from this comprehensive metabolomic analysis offer potential targets for novel therapeutic strategies to overcome drug resistance. Our study suggests that targeting the GSH pathway could potentially enhance chemotherapy's efficacy in treating ovarian cancer.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management

BACKGROUND

In the United States, ovarian cancer remains the deadliest gynecologic cancer because most women are diagnosed with advanced disease. Although early-stage ovarian tumors are considered asymptomatic, women experience symptoms throughout disease.

OBJECTIVES

This review identifies ovarian cancer symptom clusters and explores the applicability of the National Institutes of Health Symptom Science Model (NIH-SSM) for prompt symptom recognition and clinical intervention.

METHODS

A focused CINAHL® and PubMed® database search was conducted for studies published from January 2000 to May 2022 using combinations of key terms.

FINDINGS

The NIH-SSM can guide the delivery of precision-focused interventions that address racial disparities and foster equity in symptom- focused care. Enhanced understanding of symptom biology can support clinical oncology nurses in ambulatory and inpatient settings.

The Impact of Donor and Recipient Genetic Variation on Outcomes After Solid Organ Transplantation: A Scoping Review and Future Perspectives

At the outset of solid organ transplantation, genetic variation between donors and recipients was recognized as a major player in mechanisms such as allograft tolerance and rejection. Genome-wide association studies have been very successful in identifying novel variant-trait associations, but have been difficult to perform in the field of solid organ transplantation due to complex covariates, era effects, and poor statistical power for detecting donor-recipient interactions. To overcome a lack of statistical power, consortia such as the International Genetics and Translational Research in Transplantation Network have been established. Studies have focused on the consequences of genetic dissimilarities between donors and recipients and have reported associations between polymorphisms in candidate genes or their regulatory regions with transplantation outcomes. However, knowledge on the exact influence of genetic variation is limited due to a lack of comprehensive characterization and harmonization of recipients' or donors' phenotypes and validation using an experimental approach. Causal research in genetics has evolved from agnostic discovery in genome-wide association studies to functional annotation and clarification of underlying molecular mechanisms in translational studies. In this overview, we summarize how the recent advances and progresses in the field of genetics and genomics have improved the understanding of outcomes after solid organ transplantation.

Identification and Validation of Autophagy-Related Genes in Primary Ovarian Insufficiency by Gene Expression Profile and Bioinformatic Analysis

Background

To investigate the relationship between primary ovarian insufficiency and autophagy, we detected and got the expression profile of human granulosa cell line SVOG, which was with or without LPS induced. The expression profile was analyzed with the focus on the autophagy genes, among which hub genes were identified.

Results

Totally, 6 genes were selected as candidate hub genes which might correlate with the process of primary ovarian insufficiency. The expression of hub genes was then validated by quantitative real-time PCR and two of them had significant expression change. Bioinformatics analysis was performed to observe the features of hub genes, including hub gene-RBP/TF/miRNA/drug network construction, functional analysis, and protein-protein interaction network. Pearson's correlation analysis was also performed to identify the correlation between hub genes and autophagy genes, among which there were four autophagy genes significantly correlated with hub genes, including ATG4B, ATG3, ATG13, and ULK1.

Conclusion

The results indicated that autophagy might play an essential role in the process and underlying molecular mechanism of primary ovarian insufficiency, which was revealed for the first time and may help to provide a molecular foundation for the development of diagnostic and therapeutic approaches for primary ovarian insufficiency.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

Background

Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.

Methods

Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).

Results

The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.

Conclusions

CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.