Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Evaluating Alkaline Phosphatase-Instructed Self-Assembly of d-Peptides for Selectively Inhibiting Ovarian Cancer Cells

Cancer is a major public health concern requiring novel treatment approaches. Enzyme-instructed self-assembly (EISA) provides a unique approach for selectively inhibiting cancer cells. However, the structure and activity correlation of EISA remains to be explored. This study investigates new EISA substrates of alkaline phosphatase (ALP) to hinder ovarian cancer cells. Analogues 2-8 were synthesized by modifying the amino acid residues of a potent EISA substrate 1 that effectively inhibits the growth of OVSAHO, a high-grade serous ovarian cancer (HGSOC) cell line. The efficacy of 2-8 against OVSAHO was assessed, along with the combination of substrate 1 with clinically used drugs. The results reveal that substrate 1 displays the highest cytotoxicity against OVSAHO cells, with an IC50 of around 8 μM. However, there was limited synergism observed between substrate 1 and the tested clinically used drugs. These findings indicate that EISA likely operates through a distinct mechanism that necessitates further elucidation.

"DEPHENCE" system-a novel regimen of therapy that is urgently needed in the high-grade serous ovarian cancer-a focus on anti-cancer stem cell and anti-tumor microenvironment targeted therapies

Ovarian cancer, especially high-grade serous type, is the most lethal gynecological malignancy. The lack of screening programs and the scarcity of symptomatology result in the late diagnosis in about 75% of affected women. Despite very demanding and aggressive surgical treatment, multiple-line chemotherapy regimens and both approved and clinically tested targeted therapies, the overall survival of patients is still unsatisfactory and disappointing. Research studies have recently brought some more understanding of the molecular diversity of the ovarian cancer, its unique intraperitoneal biology, the role of cancer stem cells, and the complexity of tumor microenvironment. There is a growing body of evidence that individualization of the treatment adjusted to the molecular and biochemical signature of the tumor as well as to the medical status of the patient should replace or supplement the foregoing therapy. In this review, we have proposed the principles of the novel regimen of the therapy that we called the "DEPHENCE" system, and we have extensively discussed the results of the studies focused on the ovarian cancer stem cells, other components of cancer metastatic niche, and, finally, clinical trials targeting these two environments. Through this, we have tried to present the evolving landscape of treatment options and put flesh on the experimental approach to attack the high-grade serous ovarian cancer multidirectionally, corresponding to the "DEPHENCE" system postulates.

Mucin (MUC) Family Influence on Acute Lymphoblastic Leukemia in Cancer and Non-Cancer Native American Populations from the Brazilian Amazon

The mucin (MUC) family includes several genes aberrantly expressed in multiple carcinomas and mediates diverse pathways essentials for oncogenesis, in both solid and hematological malignancies. Acute Lymphoblastic Leukemia (ALL) can have its course influenced by genetic variants, and it seems more frequent in the Amerindian population, which has been understudied. Therefore, the present work aimed to investigate the MUC family exome in Amerindian individuals from the Brazilian Amazon, in a sample containing healthy Native Americans (NAMs) and indigenous subjects with ALL, comparing the frequency of polymorphisms between these two groups. The population was composed of 64 Amerindians from the Brazilian Amazon, from 12 different isolated tribes, five of whom were diagnosed with ALL. We analyzed 16 genes from the MUC family and found a total of 1858 variants. We compared the frequency of each variant in the ALL vs. NAM group, which led to 77 variants with a significant difference and, among these, we excluded those with a low impact, resulting in 63 variants, which were distributed in nine genes, concentrated especially in MUC 19 (n = 30) and MUC 3A (n = 18). Finally, 11 new variants were found in the NAM population. This is the first work with a sample of native Americans with cancer, a population which is susceptible to ALL, but remains understudied. The MUC family seems to have an influence on the development of ALL in the Amerindian population and especially MUC19 and MUC3A are shown as possible hotspots. In addition, the 11 new variants found point to the need to have their clinical impact analyzed.

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer

Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Trichodermin Induces G0/G1 Cell Cycle Arrest by Inhibiting c-Myc in Ovarian Cancer Cells and Tumor Xenograft-Bearing Mice

Ovarian cancer is a fatal gynecological cancer because of a lack of early diagnosis, which often relapses as chemoresistant. Trichodermin, a trichothecene first isolated from Trichoderma viride, is an inhibitor of eukaryotic protein synthesis. However, whether trichodermin is able to suppress ovarian cancer or not was unclear. In this study, trichodermin (0.5 µM or greater) significantly decreased the proliferation of two ovarian cancer cell lines A2780/CP70 and OVCAR-3. Normal ovarian IOSE 346 cells were much less susceptible to trichodermin than the cancer cell lines. Trichodermin predominantly inhibited ovarian cancer cells by inducing G0/G1 cell cycle arrest rather than apoptosis. Trichodermin decreased the expression of cyclin D1, CDK4, CDK2, retinoblastoma protein, Cdc25A, and c-Myc but showed little effect on the expression of p21^Waf1/Cip1, p27^Kip1, or p16^Ink4a. c-Myc was a key target of trichodermin. Trichodermin regulated the expression of Cdc25A and its downstream proteins via c-Myc. Overexpression of c-Myc attenuated trichodermin's anti-ovarian cancer activity. In addition, trichodermin decelerated tumor growth in BALB/c nude mice, proving its effectiveness in vivo. These findings suggested that trichodermin has the potential to contribute to the treatment of ovarian cancer.

Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data

Some of the patients with epithelial ovarian cancer will not respond to initial therapy. These patients have a poor prognosis. Our aim was to identify patients with a worse prognosis by integrating clinical, pathologic, and genomic data. Using publicly available genomic data and integrating it with clinical data, we significantly improved the prediction of patients with worse surgical outcomes and those who do not respond to initial chemotherapy. We further improved these models with more precise data collection and better understanding of the genetic background of the studied population. Better prediction will lead to better patient classification and opportunities for individualized treatment.

Role of microRNAs as Clinical Cancer Biomarkers for Ovarian Cancer: A Short Overview

Ovarian cancer has the highest mortality rate among gynecological cancers. Early clinical signs are missing and there is an urgent need to establish early diagnosis biomarkers. MicroRNAs are promising biomarkers in this respect. In this paper, we review the most recent advances regarding the alterations of microRNAs in ovarian cancer. We have briefly described the contribution of miRNAs in the mechanisms of ovarian cancer invasion, metastasis, and chemotherapy sensitivity. We have also summarized the alterations underwent by microRNAs in solid ovarian tumors, in animal models for ovarian cancer, and in various ovarian cancer cell lines as compared to previous reviews that were only focused the circulating microRNAs as biomarkers. In this context, we consider that the biomarker screening should not be limited to circulating microRNAs per se, but rather to the simultaneous detection of the same microRNA alteration in solid tumors, in order to understand the differences between the detection of nucleic acids in early vs. late stages of cancer. Moreover, in vitro and in vivo models should also validate these microRNAs, which could be very helpful as preclinical testing platforms for pharmacological and/or molecular genetic approaches targeting microRNAs. The enormous quantity of data produced by preclinical and clinical studies regarding the role of microRNAs that act synergistically in tumorigenesis mechanisms that are associated with ovarian cancer subtypes, should be gathered, integrated, and compared by adequate methods, including molecular clustering. In this respect, molecular clustering analysis should contribute to the discovery of best biomarkers-based microRNAs assays that will enable rapid, efficient, and cost-effective detection of ovarian cancer in early stages. In conclusion, identifying the appropriate microRNAs as clinical biomarkers in ovarian cancer might improve the life quality of patients.