Treatment of Recurrent Epithelial Ovarian Cancer

Cytoreductive surgery plus chemotherapy versus chemotherapy alone for recurrent epithelial ovarian cancer

OBJECTIVES

This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To evaluate the benefits and harms of secondary CRS and chemotherapy in comparison to chemotherapy alone for women with platinum-sensitive recurrent epithelial ovarian cancer.

A Survival Analysis of Patients with Recurrent Epithelial Ovarian Cancer Based on Relapse Type: A Multi-Institutional Retrospective Study in Armenia

BACKGROUND

Annually, approximately 200 new ovarian cancer cases are diagnosed in Armenia, which is considered an upper-middle-income country. This study aimed to summarize the survival outcomes of patients with relapsed ovarian cancer in Armenia based on the type of recurrence, risk factors, and choice of systemic treatment.

METHODS

This retrospective case-control study included 228 patients with relapsed ovarian cancer from three different institutions.

RESULTS

The median age of the patients was 55. The median follow-up times from relapse and primary diagnosis were 21 and 48 months, respectively. The incidence of platinum-sensitive relapse was 81.6% (186), while platinum-resistant relapse was observed in only 18.4% (42) of patients. The median post-progression survival of the platinum-sensitive group compared to the platinum-resistant group was 54 vs. 25 months (p < 0.001), respectively, while the median survival after relapse was 25 vs. 13 months, respectively; three- and five-year post-progression survival rates in these groups were 31.2% vs. 23.8%, and 15.1% vs. 9.5%, respectively (p = 0.113).

CONCLUSIONS

Overall, despite new therapeutic approaches, ovarian cancer continues to be one of the deadly malignant diseases affecting women, especially in developing countries with a lack of resources, where chemotherapy remains the primary available systemic treatment for the majority of patients. Low survival rates demonstrate the urgent need for more research focused on this group of patients with poor outcomes.

Combinational use of trabectedin and pegylated liposomal doxorubicin for recurrent ovarian cancer: a meta-analysis of phase III randomized controlled trials

BACKGROUND

In recent years, pegylated liposomal doxorubicin (PLD) has been widely used to improve the survival of patients with ovarian cancer; however, it is unclear whether the combinational use of PLD with other drugs is more effective. Therefore, this meta-analysis aimed to confirm the efficacy and safety of trabectedin, combined with PLD, in the treatment of recurrent ovarian cancer.

METHODS

Data corresponding to all eligible clinical trials as of May 15, 2022, was retrieved using several electronic retrieval databases including PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), ClinicalTrials.gov, Excerpta Medica Database (EMBASE) and Cochrane Library clinical controlled trials (CENTRAL). Comprehensive hazard ratios (HRs), risk ratios (RRs), and 95% confidence intervals (CIs) were calculated using the Review Manager software 5.4 (RevMan 5.4).

RESULTS

From two phase III randomized controlled trials, 1248 patients with recurrent ovarian cancer were included in this meta-analysis. Results of meta-analysis revealed that trabectedin, combined with PLD chemotherapy, significantly improved overall survival (OS) in patients with BReast CAncer gene (BRCA)-associated recurrence (HR, 0.49; 95% CI, [0.33-0.73]; P = 0.0004) and platinum-sensitive recurrence whose platinum-free interval (PFI) was 6-12 months (HR, 0.66; 95% CI, [0.52-0.84]; P = 0.0005). In addition, compared with PLD alone, combination therapy significantly improved the progression-free survival (PFS) in patients with recurrent ovarian cancer (HR, 0.86; 95% CI, [0.74-0.99]; P = 0.03). Combination therapy also significantly improved PFS in patients with BRCA-associated recurrence (HR, 0.58; 95% CI, [0.40-0.58]; P = 0.004), and platinum-sensitive recurrence (HR, 0.73; 95% CI, [0.56-0.95]; P = 0.02). Trabectedin combined with PLD was more prone to grade 3-4 toxic side effects than PLD alone (P < 0.05); however, fatal adverse events related to non-toxic side effects occurred.

CONCLUSION

Trabectedin combined with PLD significantly improves OS and PFS in patients with BRCA-associated and platinum-sensitive recurrent ovarian cancers. The potential use of trabectedin combined with PLD should be selected according to the PFI and BRCA mutation status of patients.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

Exploration and validation of metastasis-associated genes for skin cutaneous melanoma

Skin cutaneous melanoma is a malignant and highly metastatic skin tumor, and its morbidity and mortality are still rising worldwide. However, the molecular mechanisms that promote melanoma metastasis are unclear. Two datasets (GSE15605 and GSE46517) were retrieved to identify the differentially expressed genes (DEGs), including 23 normal skin tissues (N), 77 primary melanoma tissues (T) and 85 metastatic melanoma tissues (M). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to explore the functions of the DEGs. We constructed protein–protein interaction network using the STRING database and Cytoscape software. Using the cytoHubba plugin of Cytoscape, we identified the most significant hub genes by five analytical methods (Degree, Bottleneck, MCC, MNC, and EPC). Hub gene expression was validated using the UALCAN website. Clinical relevance was investigated using The Cancer Genome Atlas resources. Finally, we explored the association between metastasis-associated genes and immune infiltrates through the Tumor Immune Estimation Resource (TIMER) database and performed drug–gene interaction analysis using the Drug-Gene Interaction database. A total of 294 specific genes were related to melanoma metastasis and were mainly involved in the positive regulation of locomotion, mitotic cell cycle process, and epithelial cell differentiation. Four hub genes (CDK1, FOXM1, KIF11, and RFC4) were identified from the cytoHubba plugin of Cytoscape. CDK1 was significantly upregulated in metastatic melanoma compared with primary melanoma, and high CDK1 expression was positively correlated with worse overall survival. Immune infiltration analysis revealed that CDK1 expression negatively correlated with macrophage infiltration (Rho = − 0.164, P = 2.02e−03) and positively correlated with neutrophil cells (Rho = 0.269, P = 2.72e−07) in SKCM metastasis. In addition, we identified that CDK1 had a close interaction with 10 antitumor drugs. CDK1 was identified as a hub gene involved in the progression of melanoma metastasis and may be regarded as a therapeutic target for melanoma patients to improve prognosis and prevent metastasis in the future.

Ovarian cancer recurrence: is the definition of platinum sensitivity modified by PARPi, bevacizumab or other intervening treatments? : a clinical perspective

In view of the high risk of recurrent disease in stage III and IV ovarian cancer following primary first-line chemotherapy, a variety of maintenance and consolidation treatment strategies have been developed. These have included: radiation, intravenous or intraperitoneal chemotherapy, targeted therapies, and immunotherapy. Popular at this time is the use of Poly-adenosine ribose polymerase (PARP) inhibitors and bevacizumab as maintenance therapy. What effect these maintenance or consolidation therapies have on subsequent response to therapy, specifically platinum-based chemotherapy, is only beginning to be studied. In this manuscript, we review the impact of PARP inhibitors and bevacizumab as well as radiation and maintenance chemotherapy on subsequent response to treatment. Prior use of bevacizumab does not appear to adversely affect subsequent response to platinum-based chemotherapy or platinum-based chemotherapy with bevacizumab. Prior therapy with PARP inhibitors induces platinum resistance to subsequent platinum-based therapy and negates classic predictors of response such as platinum-free interval and breast cancer susceptibility gene (BRCA) mutational status.

Select Per- and Polyfluoroalkyl Substances (PFAS) Induce Resistance to Carboplatin in Ovarian Cancer Cell Lines

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants associated with adverse reproductive outcomes including reproductive cancers in women. PFAS can alter normal ovarian function, but the effects of PFAS on ovarian cancer progression and therapy response remain understudied. Ovarian cancer is the most lethal gynecologic malignancy, and a major barrier to effective treatment is resistance to platinum-based chemotherapy. Platinum resistance may arise from exposure to external stimuli such as environmental contaminants. This study evaluated PFAS and PFAS mixture exposures to two human ovarian cancer cell lines to evaluate the ability of PFAS exposure to affect survival fraction following treatment with carboplatin. This is the first study to demonstrate that, at sub-cytotoxic concentrations, select PFAS and PFAS mixtures increased survival fraction in ovarian cancer cells following carboplatin treatment, indicative of platinum resistance. A concomitant increase in mitochondrial membrane potential, measured by the JC-1 fluorescent probe, was observed in PFAS-exposed and PFAS + carboplatin-treated cells, suggesting a potential role for altered mitochondrial function that requires further investigation.

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).

Recent Progress of the research on the benzimidazole PARP-1 inhibitors

Poly (ADP-ribose) polymerase-1 (PARP-1) is a multifunctional protein that plays an important role in DNA repair and genome integrity. PARP-1 inhibitors can be used as effective drugs not only to treat BRCA-1/2 deficient cancers because of the effect of synthetically lethal, but also to treat non-BRCA1/2 deficient tumours because of the effect of PARP capture. Therefore, the PARP inhibitors have become a focus of compelling research. Among these inhibitors, substituted benzimidazole derivatives were mainly concerned lead compounds. However, the commercial available benzimidazole PARP-1 inhibitors have some shortcomings such as serious toxicity in combination with chemotherapy drugs, in vivo cardiovascular side effects such as anemia. Therefore it's crucial for scientists to explore more structure-activity relationships of the benzimidazole PARP-1 inhibitors and access safer and more effective PARP inhibitors. As the binding region of PARP-1 and the substrates is usually characterized as NI site and AD site, the modification of benzimidazoles mainly occurs on the benzimidazole skeleton (NI site), and the side chain of benzimidazole on 2-C position (AD site). Herein, the recent progresses of the researches of benzamides PARP inhibitors were introduced. We noticed that even though many efforts were taken to the modification of NI sites, there were still lacks of optimistic and impressive results. However, the structure-activity relationships of the modification of AD sites have not thoroughly discovered yet. We hope that enlightened by the previous researches, more researches of AD site should be occurred and more effective benzimidazole PARP-1 inhibitors could be designed, synthesized, and applied to clinics.

CTCFL regulates the PI3K-Akt pathway and it is a target for personalized ovarian cancer therapy

High-grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy due to the lack of reliable biomarkers, effective treatment, and chemoresistance. Improving the diagnosis and the development of targeted therapies is still needed. The molecular pathomechanisms driving HGSC progression are not fully understood though crucial for effective diagnosis and identification of novel targeted therapy options. The oncogene CTCFL (BORIS), the paralog of CTCF, is a transcriptional factor highly expressed in ovarian cancer (but in rarely any other tissue in females) with cancer-specific characteristics and therapeutic potential. In this work, we seek to understand the regulatory functions of CTCFL to unravel new target genes with clinical relevance. We used in vitro models to evaluate the transcriptional changes due to the presence of CTCFL, followed by a selection of gene candidates using de novo network enrichment analysis. The resulting mechanistic candidates were further assessed regarding their prognostic potential and druggability. We show that CTCFL-driven genes are involved in cytoplasmic membrane functions; in particular, the PI3K-Akt initiators EGFR1 and VEGFA, as well as ITGB3 and ITGB6 are potential drug targets. Finally, we identified the CTCFL targets ACTBL2, MALT1 and PCDH7 as mechanistic biomarkers to predict survival in HGSC. Finally, we elucidated the value of CTCFL in combination with its targets as a prognostic marker profile for HGSC progression and as putative drug targets.

The Pretreatment Controlling Nutritional Status Score in Ovarian Cancer: Influence on Prognosis, Surgical Outcome, and Postoperative Complication Rate

Introduction The Controlling Nutritional (CONUT) Status score is an established predictor of impaired prognosis in patients with solid tumors. The aim of this study was to investigate the prognostic value of the CONUT score for overall survival and perioperative complication rates in patients with epithelial ovarian cancer.

Patients In this retrospective study we assessed the data of 337 consecutive patients with ovarian cancer. The CONUT score was associated with surgical outcome, postoperative complications and clinicopathological parameters. We used univariate log-rank test and multivariable Cox regression models to evaluate the association between pretreatment CONUT scores and survival.

Results A low CONUT score (0 – 2) was associated with an early FIGO stage (p = 0.004), complete tumor resection (p < 0.001), less neoadjuvant chemotherapy (p = 0.017) and other histologies than serous cystadenocarcinoma (p = 0.006). Postoperative complications were observed in 51.4% and 60.5% of patients with a CONUT score of 0 – 2 and a score > 2, respectively (p = 0.161). A shorter overall survival was observed in patients with a CONUT score > 2 compared to patients with a low CONUT score, with 5-year overall survival rates of 31.5% and 58.7%, respectively (p < 0.001). In multivariable analysis, both advanced age (p < 0.001) and FIGO stage (p < 0.001), residual disease (p < 0.001) and a high CONUT score (p = 0.048) were independently associated with unfavorable overall survival.

Conclusion Pretreatment CONUT score is an independent prognostic marker for overall survival and associated with successful surgery. Patients with a high CONUT score might benefit from pretreatment nutritional intervention.

The role of a ceRNA regulatory network based on lncRNA MALAT1 site in cancer progression

As a type of non-coding RNA of more than 200 nucleotides, long non-coding RNAs(lncRNAs) lack protein coding ability and can regulate gene expression. MicroRNAs(miRNAs), which are also non-coding RNAs, are short single-stranded RNAs, usually composed of 18-23 nucleotides. MiRNAs inhibits gene expression by specifically binding to the 3'-UTR of downstream target mRNAs and can function as oncogenes or suppressor oncogenes to regulate the occurrence and development of cancer. LncRNAs can function as competitive endogenous RNAs that bind to miRNAs, resulting in the recovery of downstream mRNA expression and activity. The regulatory network existing between lncRNAs, miRNAs and mRNAs regulates a variety of biological processes, including cell proliferation, apoptosis, migration and invasion as well as cell-cycle arrest. Disruption of the ceRNA network affects cell growth and development and often leads to various diseases, especially cancer. The lncRNA MALAT1, which is located on chromosome 11q13, contains more than 8000 nucleotides and is implicated in the occurrence and development of many cancers. Here, we review the impact of the ceRNA network and the lncRNA MALAT1 in cancer.