Morphological and molecular heterogeneity of epithelial ovarian cancer: Therapeutic implications

Is there a link between talcum powder, oxidative stress, and ovarian cancer risk?

INTRODUCTION

The link between talcum powder use and cancer, particularly ovarian cancer, has been a topic of scientific research and legal debate for several years. Studies have suggested a potential association between long-term talcum powder use in the genital area and an increased risk of ovarian cancer.

AREAS COVERED

The following report includes up-to-date evidence to support the potential link between talcum powder use and the risk of developing ovarian cancer. The International Agency for Research on Cancer, which is part of the World Health Organization, classified talc-based body powder as possibly carcinogenic to humans when used in the female genital area. However, other studies have not consistently supported this association, and thus more research is needed to establish a clear and definitive link between talcum powder use and cancer. Despite this, recent molecular-level data have linked talc to alterations in redox balance, gene mutations, and inflammatory responses. Specifically, we have identified a role for talc to induce the pro-oxidant state, inhibit apoptosis, and more importantly induced cellular transformation in normal ovarian cells.

EXPERT OPINION

We presented unequivocal evidence to support our opinion that talc is not biologically inert and induces molecular changes that mimic the hallmarks of cancer.

Molecular Pathology of Ovarian Epithelial Neoplasms: Predictive, Prognostic, and Emerging Biomarkers

This review focuses on the diagnostic, prognostic, and predictive molecular biomarkers in ovarian epithelial neoplasms in the context of their morphologic classifications. Currently, most clinically actionable molecular findings are reported in high-grade serous carcinomas; however, the data on less common tumor types are rapidly accelerating. Overall, the advances in genomic knowledge over the last decade highlight the significance of integrating molecular findings with morphology in ovarian epithelial tumors for a wide-range of clinical applications, from assistance in diagnosis to predicting response to therapy.

Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics

Ovarian cancer, a highly fatal gynecological cancer, warrants the need for understanding its heterogeneity. The disease's prevalence and impact are underscored with statistics on mortality rates. Ovarian cancer is categorized into distinct morphological groups, each with its characteristics and prognosis. Despite standard treatments, survival rates remain low due to relapses and chemoresistance. Immune system involvement is evident in ovarian cancer's progression, although the tumor employs immune evasion mechanisms. Immunotherapy, particularly immune checkpoint blockade therapy, is promising, but ovarian cancer's heterogeneity limits its efficacy. Single-cell sequencing technology could be explored as a solution to dissect the heterogeneity within tumor-associated immune cell populations and tumor microenvironments. This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research. The present review focuses on recent advancements and the challenges in applying single-cell transcriptomics to ovarian cancer.

Endometriosis-Related Ovarian Cancer: Where Are We Now? A Narrative Review towards a Pragmatic Approach

BACKGROUND

Endometriosis affects more than 10% of reproductive-aged women, causing pelvic pain and infertility. Despite the benign nature of endometriosis, ovarian endometriomas carry a higher risk of developing endometrioid carcinomas (EnOCs) and clear cell ovarian carcinomas (CCCs). Atypical endometriosis, defined as cytological atypia resembling intraepithelial cancer, is considered the precursor of endometriosis-associated ovarian cancer (EAOC). This narrative review aims to provide an overview of EAOC, proposing a practical approach to clinical and therapeutic decision making.

METHODS

An electronic literature search was conducted from inception up to January 2023, using the MEDLINE database via PubMed to evaluate the existing literature on EAOC, including its pathogenesis, the diagnostic process, and the therapeutic possibilities, with articles not relevant to the topic or lacking scientific merit being excluded.

RESULTS

Eighty-one articles were included in the review to present the current state of the art regarding EAOC. A pragmatic clinical flowchart is proposed to guide therapeutic decisions and improve patient outcomes.

CONCLUSIONS

Endometriosis patients may have an increased risk of developing EAOC (either EnOC or CCC). Despite not being fully accepted, the concept of AE may reshape the endometriosis-ovarian cancer relationship. Further research is needed to understand the unaddressed issues.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer

BACKGROUND

Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence.

METHODS

We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance.

RESULTS

Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients.

CONCLUSION

Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Angio-Long Noncoding RNA MALAT1 (rs3200401) and MIAT (rs1061540) Gene Variants in Ovarian Cancer

The genotyping of long non-coding RNA (lncRNA)-related single-nucleotide polymorphisms (SNPs) could be associated with cancer risk and/or progression. This study aimed to analyze the angiogenesis-related lncRNAs MALAT1 (rs3200401) and MIAT (rs1061540) variants in patients with ovarian cancer (OC) using "Real-Time allelic discrimination polymerase chain reaction" in 182 formalin-fixed paraffin-embedded (FFPE) samples of benign, borderline, and primary malignant ovarian tissues. Differences in the genotype frequencies between low-grade ovarian epithelial tumors (benign/borderline) and malignant tumors and between high-grade malignant epithelial tumors and malignant epithelial tumors other than high-grade serous carcinomas were compared. Odds ratios (ORs)/95% confidence intervals were calculated as measures of the association strength. Additionally, associations of the genotypes with the available pathological data were analyzed. The heterozygosity of MALAT1 rs3200401 was the most common genotype (47.8%), followed by C/C (36.3%). Comparing the study groups, no significant differences were observed regarding this variant. In contrast, the malignant epithelial tumors had a higher frequency of the MIAT rs1061540 C/C genotype compared to the low-grade epithelial tumor cohorts (56.7% vs. 37.6, p = 0.031). The same genotype was significantly higher in high-grade serous carcinoma than its counterparts (69.4% vs. 43.8%, p = 0.038). Multivariate Cox regression analysis showed that the age at diagnosis was significantly associated with the risk of OC development. In contrast, the MIAT T/T genotype was associated with a low risk of malignant epithelial tumors under the homozygote comparison model (OR = 0.37 (0.16-0.83), p = 0.017). Also, MIAT T allele carriers were less likely to develop high-grade serous carcinoma under heterozygote (CT vs. CC; OR = 0.33 (0.12-0.88), p = 0.027) and homozygote (TT vs. CC; OR = 0.26 (0.07-0.90), p = 0.034) comparison models. In conclusion, our data provide novel evidence for a potential association between the lncRNA MIAT rs1061540 and the malignant condition of ovarian cancer, suggesting the involvement of such lncRNAs in OC development.

Comprehensive Analysis of DNA Methyltransferases Expression in Primary and Relapsed Ovarian Carcinoma

BACKGROUND

Despite recent advances in epithelial ovarian carcinoma (EOC) treatment, its recurrence and mortality rates have not improved significantly. DNA hypermethylation has generally been associated with an ominous prognosis and chemotherapy resistance, but the role of DNA methyltransferases (DNMTs) in EOC remains to be investigated.

METHODS

In the current study, we systematically retrieved gene expression data from patients with EOC and studied the immunohistochemical expression of DNMTs in 108 primary and 26 relapsed tumors.

RESULTS

Our results showed that the DNMT1, DNMT3A, DNMT3B and DNMT3L RNA levels were higher and the DNMT2 level was lower in tumors compared to non-neoplastic tissue, and DNMT3A and DNMT2 expression decreased from Stage-II to Stage-IV carcinomas. The proteomic data also suggested that the DNMT1 and DNMT3A levels were increased in the tumors. Similarly, the DNMT1, DNMT3A and DNMT3L protein levels were overexpressed and DNMT2 expression was reduced in high-grade carcinomas compared to non-neoplastic tissue and low-grade tumors. Moreover, DNMT1 and DNMT3L were increased in relapsed tumors compared to their primaries. The DNMT3A, DNMT1 and DNMT3B mRNA levels were correlated with overall survival.

CONCLUSIONS

Our study demonstrates that DNMT1 and DNMT3L are upregulated in primary high-grade EOC and further increase in relapses, whereas DNMT3A is upregulated only in the earlier stages of cancer progression. DNMT2 downregulation highlights the presumed tumor-suppressor activity of this gene in ovarian carcinoma.

The Prognostic Significance of Tumor-Infiltrating Lymphocytes, PD-L1, BRCA Mutation Status and Tumor Mutational Burden in Early-Stage High-Grade Serous Ovarian Carcinoma-A Study by the Spanish Group for Ovarian Cancer Research (GEICO)

Early stages are under-represented in studies on the molecular and immune features of high-grade serous ovarian carcinoma (HGSOC), and specific studies focused on early-stage HGSOC are required for a better prognostic stratification and to personalize chemotherapy. The aim of this study was to determine the prognostic significance of CD8+ and CD4+ tumor-infiltrating lymphocytes (TILs), tumoral cell PD-L1 expression, BRCA mutational status and tumor mutation burden (TMB) in early-stage HGSOC. A retrospective study was performed on stage I and II HGSOC from the Molecular Reclassification of Early Stages of Ovarian Cancer (RECLAMO) cohort from the Spanish Group of Ovarian Cancer Research (GEICO). Centralized histological typing was performed based on morphological and immunohistochemical features. Intraepithelial (i) and stromal (s) CD8+ and CD4+ T cells and PD-L1 were evaluated on tissue microarrays by immunohistochemistry. BRCA1 and BRCA2 mutation status and TMB were analyzed in tumor DNA using next-generation sequencing. The study included 124 tumors. High iCD8+ (>20 TILs/core), low/intermediate CD4+ (<20 TILs/core) and high CD8+/CD4+ ratio (>35/core) were associated with favorable outcomes. Tumor cell PD-L1 expression (TPS ≥ 1) was present in only 8% of tumors. In total, 11 (16%) and 6 (9%) out of 69 HGSOC tested carried pathogenic or likely pathogenic BRCA1 or BRCA2 mutations, respectively. Median TMB of 40 tumors analyzed was 5.04 mutations/Mb and only 6 tumors had 10 or more mutations/Mb. BRCA status and TMB were not associated with TILs or prognosis. When compared with studies on advanced HGSOC, our results suggested that prognostic variables differed according to stage and that more studies focused on early stages of HGSOC are needed to better stratify these tumors.

The potential of RAS/RAF/MEK/ERK (MAPK) signaling pathway inhibitors in ovarian cancer: A systematic review and meta-analysis

BACKGROUND

The RAS/RAF/MEK/ERK (MAPK) pathway plays a role in ovarian carcinogenesis. Low-grade serous ovarian carcinoma (LGSOC) frequently harbors activating MAPK mutations. MAPK inhibitors have been used in small subsets of ovarian carcinoma (OC) patients to control tumor growth. Therefore, we performed a meta-analysis to evaluate the effectiveness of MAPK inhibitors in OC patients. We aimed to determine the clinical benefit rate (CBR), the subgroup of MAPK inhibitors with the best CBR and overall response rate (ORR), and the most common adverse events.

METHODS

We conducted a search in PubMed, Embase via Ovid, the Cochrane library and clinicaltrials.gov on studies evaluating the efficacy of single MAPK pathway inhibition with MAPK pathway inhibitors in OC patients. Our primary outcome included the CBR, defined by the proportion of patients with stable disease (SD), complete (CR) and partial response (PR). Secondary outcomes included the ORR (including PR and CR) and grade 3 and 4 adverse events. Meta-analysis was performed using a random-effects model.

RESULTS

We included nine studies with a total of 319 OC patients, for which we determined a pooled CBR of 63% (95%-CI 39-84%, I² = 92%). Combined treatment with Raf- and MEK inhibitors in in BRAF^v600 mutated LGSOC (n = 6) had the greatest efficacy with a CBR of 100% and ORR of 83%. MEK inhibitors had the best efficacy as a single agent. Subgroup analysis by tumor histology demonstrated a significantly higher CBR and ORR in patients with LGSOC, with a pooled CBR and ORR of 87% (95%-CI 81-92%, I² = 0%) and 27% (95%-CI 10-48%, I² = 77%) respectively. Adverse events of grade 3 or higher were reported frequently: 123 in 167 patients.

CONCLUSIONS

MEK inhibitors are the most promising single agents in (LGS)OC. However, dual MAPK pathway inhibition should be considered in patients with a BRAF^v600 mutation, or non-mutated OC with depleted treatment options due indications of higher efficacy and tolerable toxicity profiles.

Ovarian Cancer in a Northern Italian Province and the Multidisciplinary Team

Ovarian cancer represents one of the most aggressive female cancers in the world, remaining a tumor with high lethality. This study aims to present how a multidisciplinary team (MDT) approach can improve the prognosis in terms of recurrence and death of patients. In total, 448 ovarian cancer cases registered in an Italian Cancer Registry between 2012 and 2020 were included. Information on age, morphology, stage, and treatment was collected. Recurrence and death rates were reported 1 and 2 years after diagnosis, comparing MDT vs. non-MDT approaches. Ninety-three percent had microscopic confirmation, and most showed cystic-mucinous morphology. In total, 50% were older than 65 years old. The distribution by stage was 17.6%, 4%, 44.9%, and 32.6% for stages I, II, III, and IV, respectively. The women followed by the MDT were 24.1%. Disease-free survival 1-year post-diagnosis, recurrences, recurrences-deaths, and deaths were 67.5%, 14.5%, 8.4%, and 9.6%, respectively, better than the non-MDT group (46.2%, 13.2%, 20.8 %, and 19.8%, respectively) (p < 0.01). The same positive results were confirmed two years after diagnosis, particularly for stages III and IV. Albeit small numbers, the study confirms a better prognosis for women managed by MDT with fewer recurrences and deaths, especially within the first 24 months of diagnosis.

Incidence of lymph node metastasis in early-stage low-grade serous ovarian cancer: a systematic review

OBJECTIVE

The objective of this systematic review was to assess the incidence of lymph node metastasis in patients with clinically presumed early-stage low-grade serous ovarian cancer that underwent primary surgical treatment.

METHODS

This study was registered in PROSPERO (CRD42022308923). A systematic literature review was conducted following the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) checklist. PubMed/MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Ovid, and Scopus databases were searched since inception and up to March 2022. The inclusion criteria were: pathological confirmation of low-grade serous ovarian cancer (clinically presumed FIGO 2014 stages I-IIA at time of surgery) that underwent primary surgical treatment, including pelvic and/or para-aortic lymph node dissection.

RESULTS

The search identified 3763 articles; 59 were considered potentially eligible after removing duplicates, and eight studies finally met the selection criteria. In total, 35 of 277 (12.6%) patients had lymph node metastasis, and only four studies reported upstaging due to lymph node metastasis in 16 of 153 (10.5%) patients. None of the eight studies included reported the rate of complications or complications specifically for the subgroup of patients with early-stage low-grade tumors.

CONCLUSION

In patients with early-stage low-grade serous ovarian cancer, lymph node assessment should be discussed when counseling for primary surgical staging.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

Modular self-assembly system for development of oligomeric, highly internalizing and potent cytotoxic conjugates targeting fibroblast growth factor receptors

BACKGROUND

Overexpression of FGFR1 is observed in numerous tumors and therefore this receptor constitutes an attractive molecular target for selective cancer treatment with cytotoxic conjugates. The success of cancer therapy with cytotoxic conjugates largely relies on the precise recognition of a cancer-specific marker by a targeting molecule within the conjugate and its subsequent cellular internalization by receptor mediated endocytosis. We have recently demonstrated that efficiency and mechanism of FGFR1 internalization are governed by spatial distribution of the receptor in the plasma membrane, where clustering of FGFR1 into larger oligomers stimulated fast and highly efficient uptake of the receptor by simultaneous engagement of multiple endocytic routes. Based on these findings we aimed to develop a modular, self-assembly system for generation of oligomeric cytotoxic conjugates, capable of FGFR1 clustering, for targeting FGFR1-overproducing cancer cells.

METHODS

Engineered FGF1 was used as FGFR1-recognition molecule and tailored for enhanced stability and site-specific attachment of the cytotoxic drug. Modified streptavidin, allowing for controlled oligomerization of FGF1 variant was used for self-assembly of well-defined FGF1 oligomers of different valency and oligomeric cytotoxic conjugate. Protein biochemistry methods were applied to obtain highly pure FGF1 oligomers and the oligomeric cytotoxic conjugate. Diverse biophysical, biochemical and cell biology tests were used to evaluate FGFR1 binding, internalization and the cytotoxicity of obtained oligomers.

RESULTS

Developed multivalent FGF1 complexes are characterized by well-defined architecture, enhanced FGFR1 binding and improved cellular uptake. This successful strategy was applied to construct tetrameric cytotoxic conjugate targeting FGFR1-producing cancer cells. We have shown that enhanced affinity for the receptor and improved internalization result in a superior cytotoxicity of the tetrameric conjugate compared to the monomeric one.

CONCLUSIONS

Our data implicate that oligomerization of the targeting molecules constitutes an attractive strategy for improvement of the cytotoxicity of conjugates recognizing cancer-specific biomarkers. Importantly, the presented approach can be easily adapted for other tumor markers.

Molecular Pathology of Ovarian Epithelial Neoplasms: Predictive, Prognostic, and Emerging Biomarkers

This review focuses on the diagnostic, prognostic, and predictive molecular biomarkers in ovarian epithelial neoplasms in the context of their morphologic classifications. Currently, most clinically actionable molecular findings are reported in high-grade serous carcinomas; however, the data on less common tumor types are rapidly accelerating. Overall, the advances in genomic knowledge over the last decade highlight the significance of integrating molecular findings with morphology in ovarian epithelial tumors for a wide-range of clinical applications, from assistance in diagnosis to predicting response to therapy.

Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer

OBJECTIVE

This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC).

METHODS

Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set.

RESULTS

The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001).

CONCLUSIONS

These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

A Review of the Clinical Characteristics and Novel Molecular Subtypes of Endometrioid Ovarian Cancer

Ovarian cancer is one of the most common gynecologic cancers that has the highest mortality rate. Endometrioid ovarian cancer, a distinct subtype of epithelial ovarian cancer, is associated with endometriosis and Lynch syndrome, and is often accompanied by synchronous endometrial carcinoma. In recent years, dysbiosis of the microbiota within the female reproductive tract has been suggested to be involved in the pathogenesis of endometrial cancer and ovarian cancer, with some specific pathogens exhibiting oncogenic having been found to contribute to cancer development. It has been shown that dysregulation of the microenvironment and accumulation of mutations are stimulatory factors in the progression of endometrioid ovarian carcinoma. This would be a potential therapeutic target in the future. Simultaneously, multiple studies have demonstrated the role of four molecular subtypes of endometrioid ovarian cancer, which are of particular importance in the prediction of prognosis. This literature review aims to compile the potential mechanisms of endometrioid ovarian cancer, molecular characteristics, and molecular pathological types that could potentially play a role in the prediction of prognosis, and the novel therapeutic strategies, providing some guidance for the stratified management of ovarian cancer.

Glycomic-Based Biomarkers for Ovarian Cancer: Advances and Challenges

Ovarian cancer remains one of the most common causes of death among gynecological malignancies afflicting women worldwide. Among the gynecological cancers, cervical and endometrial cancers confer the greatest burden to the developing and the developed world, respectively; however, the overall survival rates for patients with ovarian cancer are worse than the two aforementioned. The majority of patients with ovarian cancer are diagnosed at an advanced stage when cancer has metastasized to different body sites and the cure rates, including the five-year survival, are significantly diminished. The delay in diagnosis is due to the absence of or unspecific symptoms at the initial stages of cancer as well as a lack of effective screening and diagnostic biomarkers that can detect cancer at the early stages. This, therefore, provides an imperative to prospect for new biomarkers that will provide early diagnostic strategies allowing timely mitigative interventions. Glycosylation is a protein post-translational modification that is modified in cancer patients. In the current review, we document the state-of-the-art of blood-based glycomic biomarkers for early diagnosis of ovarian cancer and the technologies currently used in this endeavor.