Early Detection Of Ovarian Cancer Research Articles

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool.

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

Abstract LB381: Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection

Abstract Background: Ovarian cancer accounts for 2% of all female cancers in the United States yet it is the 5th leading cancer death among women. Advanced epithelial ovarian cancer (EOC) is associated with an overall survival rate of 30% but can be cured in up to 90% of cases if diagnosed early when the disease is still limited to the ovaries and fallopian tubes. While there is no effective screening method for EOC, developing novel and minimally invasive clinical tests is paramount to identify women with early disease as well as those who are at increased risk. Plasma-based microbiota signatures specific to EOC could become leading biomarkers of early disease in the field that complement existing clinical tools. Methods: We utilized an established institutional biobank to access 180 biobanked plasma samples from: (1) women with EOC; (2) women with non-EOC solid tumors; (3) women undergoing gynecologic-related procedures and non-EOC surgeries; and (4) healthy controls. Batch 1 included 96 plasma samples (n=24 per group) and Batch 2 included 84 samples (n=19 per group and 8 repeat EOC samples). Microbial profiles of plasma specimens were assessed using an amplicon-based sequencing method that targets the 16S rRNA gene in the bacterial genome to study bacterial phylogeny and taxonomy in diseased and healthy women. A stringent bioinformatic pipeline was applied to filter out suspicious bacterial contaminants at the genus level. Bacterial decontamination procedures in the pipeline included evaluating repeat plasma samples of EOC cases and sampling nucleic acid isolation kit reagents to serve as negative controls (only in Batch 2) . Differential abundances of the plasma microbiota among the groups were assessed using analysis of compositions of microbiomes (ANCOM) as implemented in R software package ANCOM-BC. Results: The 16S rRNA gene sequencing identified, classified, and quantified 564 bacterial genera in the circulation of women diagnosed with EOC, non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Bioinformatic decontamination analysis removed bacteria: (1) with differing bacterial abundances between batches; (2) with higher bacteria in negative controls; (3) with differing bacterial abundances in repeat samples; and (4) known as contaminants in the literature that resulted in the removal of 225 high-risk bacterial contaminants. A total of 339 high quality bacteria remained for evaluation. Because we did not collect negative controls in Batch 1, only Batch 2 plasma samples (n=84; 27 in EOC group; 19 in each other group) were included in the final analysis. Women with EOC, which were primarily of the high-grade serous ovarian carcinoma histologic subtype had significantly distinct differential abundances of genera Brevibacterium (p <0.001), Chloronema (p <0.001), Facklamia (p <0.001), Zymomonas (p <0.001), and Sutterella (p <0.001) when compared to women with non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Conclusions: These findings suggest that plasma EOC-associated bacteria could be exploited in the development of a microbial-based blood test for the diagnosis and early detection of EOC in routine practice settings. Citation Format: Diane E. Mahoney, Prabhakar Chalise, Dong Pei, Rachel Griffard, Trisha Home, Harsh Pathak, Shahid Umar, Andrew K. Godwin. Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB381.

Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.

Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

Nanopore Testing: A Paradigm Shift in Ovarian Cancer Screening

Abstract 1065: Two-step method for early detection of ovarian cancer with high specificity via platelet RNA

Abstract Owing to lack of disease-specific symptoms and screening methods, most of patients with ovarian cancer are diagnosed at an advanced stage. Recent technology based on platelet RNA aids in detection of tumors at the early stages. Also, for population screening of low-prevalence diseases like cancer, accurate detection with high specificity is essential to improve diagnostic methods. Thus, we proposed platelet RNA-based early ovarian cancer detection with great specificity. It is a two-step method for detecting ovarian tumors with more than 99% specificity using exon-exon junction features and sampling invariant normalization and finding their malignancy with more than 99% negative predictive value using exon-exon junction and hematology parameters. First, we downloaded public platelet transcriptome data of gynecological cancer including ovarian cancer (n=151), benign tumor (n=30), and healthy counterparts (n=218) from GEO (GSE158508, 89843, and 183635). Second, we prospectively enrolled patients with gynecological cancer (n=31), benign tumors (n=20), and healthy women (n=18) at Seoul National University Hospital (SNUH) and Boaz Medical Center and obtained platelet transcriptome data (FMH data). Feature selection and model development were conducted using public training set (n=243), public validation set (n=156), and FMH training/validation set (n=47). The batch-invariant normalization was performed, and 198 splice junctions were selected as features from training/validation set for the bootstrap aggregation of the SVM model diagnosing the existence of an ovarian tumor. The model performance was assessed from a separate test set only composed of FMH data (n=22). Our model that demonstrated 96.8% sensitivity, 100.0% specificity, with a predetermined cut-off value. Next, FMH training/validation set was used for model development of following malignancy test. 10 splice junctions and 19 hematology parameters were selected and SVM models were separately developed for splice junctions and hematology parameters using 5-fold cross validation. The predictions were combined using a logical OR operation to obtain the final prediction result. The result showed its diagnostic performance with 100% sensitivity or NPV, and 50.0% specificity in the test data set with a predetermined cut-off value. We proposed a two-step method to operationally improve the existing protocol for ovarian cancer screening. Our model initially excludes most healthy individuals who do not have ovarian tumors and excludes patients with benign ovarian tumors who do not necessitate medical testing. To conclude, our model allows physicians to identify a subset of ovarian tumor patients with a higher risk of malignancy even at early stages; it exhibits potential in enhancing early diagnosis of ovarian cancer, leading to increase of treatment effectiveness and ultimately augmenting the survival of patients with ovarian cancer. Citation Format: Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Yeochan Kim, Eunchong Huang, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, TaeJin Ahn, Yong-Sang Song. Two-step method for early detection of ovarian cancer with high specificity via platelet RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1065.

Abstract 7003: Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer

Abstract We present a microfluidic platform for molecule-by-molecule detection of heterogeneous epigenetic patterns of rare tumor-derived DNA by highly parallelized digital melt. The platform digitizes DNA molecules in a 4-module, 40,000 1-nL array microfluidic device and observes sequence-dependent fluorescence changes during temperature ramping. The platform is applied to quantify DNA methylation heterogeneity of a biomarker panel within Pap specimens in a small cohort of healthy and ovarian cancer patients, and demonstrates an accuracy of 0.9. DNA hypermethylation of tumor-suppressor genes is an epigenetic phenomena that occurs early in tumorigenesis. Variable methylation patterns are observed in ovarian cancer precursor tissue, and trace amounts of ovarian-tumor-derived DNA can be found in Pap specimens, albeit at low frequencies (<0.01%). This suggests that methylation profiles of DNA extracted from routinely-obtained Pap specimens could contain early cancer biomarkers. However, due to the scarcity of these molecules, current techniques, such as sequencing and digital PCR (dPCR), have technical limitations precluding their ability to profile methylation patterns reliably. Sequencing, although comprehensive, has limited sensitivity, undermining its utility for routine detection of fractions below 0.1%. dPCR achieves high sensitivity, but is limited to known sequences, thus cannot provide comprehensive analysis of molecular heterogeneity. To overcome these limitations, we developed a technique called digital high resolution melt (dHRM), a thermodynamics-based approach to detecting molecular variability by observing the sequence-specific release of a DNA-intercalating dye under a thermal ramp. dHRM can discriminate methylation patterns on CpG-by-CpG basis and detect methylated epialleles at frequencies as low as 0.00005%. Here, DNA from Pap specimens was loaded on a microfluidic chip that digitizes rare target molecules into individual reaction chambers. A thermal-optical platform was developed to perform parallelized dPCR and dHRM. Methylation patterns of a biomarker panel were analyzed to produce a probability score that the Pap specimen contained tumor-derived DNA. 12 healthy and 12 ovarian cancer Pap specimens were assessed for methylation heterogeneity of 9 genes. Optimal methylation density thresholds were determined, and data above each threshold was combined into a single score for each possible combination of biomarkers. In this small cohort, methylation heterogeneity analysis of just 2 loci produced an area under the receiver-operator characteristics curve (AUC) of 0.9. This highly sensitive methylation profiling technology demonstrates promising utility towards early cancer detection. Future work aims to expand the testing cohort for ovarian cancer detection and permit further studies on the impact of methylation heterogeneity on early cancer development. Citation Format: Chrissy O'Hersey, Yang Zhao, Thomas R. Pisanic, Tian-Li Wang, Ie-Ming Shih, Tza-Huei Jeff Wang. Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7003.

Abstract 1070: Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease

Abstract Introduction: High-grade serous ovarian cancer (HGSOC) accounts for over 75% of all epithelial ovarian cancer and has a high mortality rate due to a lack of early detection methods. Many biomarkers have been proposed; however, none have been shown to improve detection at an early stage in this patient population. The purpose of this study is to assess whether there are unique extracellular vesicle (EV) protein signatures of early-stage HGSOC that could serve as early detection biomarkers. Methods: 250 serum samples were obtained from a multi-institutional retrospective cohort of patients with all stages of HGSOC and healthy controls. LC-MS/MS and PEA of serum samples from patients with early-stage HGSOC identified differentially expressed EVs proteins in HGSOC versus controls. Tob candidate proteins are validated by ELISA. Results: We have identified the top 10 EVs candidate proteins based on their fold change and statistical significance. When comparing early-stage HGSOC and controls using ELISA, CFH, PCP, and CCNE1 exhibited the highest Area Under the Curve (AUC) values of 0.94, 0.91, and 0.83, respectively. In contrast, MUC16 (also known as CA-125) exhibited an AUC of only 0.42 in whole serum. Furthermore, MUC16 distinguished between controls and both early and advanced-stage HGSOC in EVs, while in whole serum, it was only differentially expressed between controls and advanced-stage disease. Our results demonstrate that the predictive performance of the combined biomarkers surpasses that of any individual biomarker. Further, we integrated the ten aforementioned biomarkers, deriving the optimal combination biomarker by maximizing the corresponding Sum of Harmonic Means (SHUM) function by utilizing data from all 70 patients, results showed the optimal coefficients of the biomarkers, which maximize the diagnostic accuracy for early-stage HGSOC. The overall true positive rate (TPR) of 0.943, false positive rate (FPR) of 0.000, and Mathew's correlation coefficient (MCC) were presented for both the combined biomarker approach and individual biomarkers. The results demonstrated that the predictive performance of the combined biomarkers surpassed that of any individual biomarker. Conclusion: Expression of EV-based protein biomarkers is significantly different in early-stage HGSOC compared to both control and late-stage HGSOC. This work can be readily translated to clinical use and potentially improve the early detection of HGSOC substantially. Citation Format: Michelle Lightfoot, Kalpana Deepa Priya Dorayappan, Lianbo Yu, Colin Hisey, Takahiko Sakaue, Muralidharan Anbalagan, Casey M. Cosgrove, Larry G. Maxwell, Premal Thaker, Beth Y. Karlan, David O’Malley, Raphael E. Pollock, David E. Cohn, Rajan Gogna, Selvendiran Karuppaiyah. Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1070.

Ovarian Cancer Risk-Reduction and Screening in BRCA1/2 Mutation Carriers.

Objective: To determine the utilization of risk-reducing strategies and screening protocols for ovarian cancer in female BRCA1/2 carriers. Methods: This study was a sub-analysis of female participants from a larger multicenter, cross-sectional survey of BRCA1/2 mutation carriers unaffected by cancer. The questionnaire was administered electronically via email at four institutions located in the northeast United States. Data were analyzed with Fisher's exact test. Results: The survey was completed by 104 female BRCA mutation carriers. BRCA subtypes included 54.3% BRCA2, 41.0% BRCA1, and 2.9% both. The age at which patients underwent genetic testing varied 21.2% were 18-24 years, 25.0% were 25-34 years, 29.8% were 35-44 years, and 24.0% were 45 years or older. Nearly, all respondents (97.1%) reported that a provider had discussed risk-reducing surgeries. Of the 79 females who underwent genetic testing before 45 years of age, 53.2% reported that a health care provider recommended taking combined oral contraceptive pills (COCs) to reduce their risk of ovarian cancer, and, of these women, 88.1% chose to use them. COCs were offered at higher rates among women who were younger at the age of genetic testing (18-24: 86%, 25-34: 62%, 35-44: 23%; p < 0.0001). Approximately half (55.8%) of the respondents reported having been offered increased screening for possible early detection of ovarian cancer, of which 81.0% chose to undergo screening. The majority utilized a combination of transvaginal ultrasound and serum CA125 measurements. There were no differences observed in screening utilization based on BRCA mutation type. Conclusion: In our cohort of female BRCA mutation carriers, risk-reducing surgery was offered to almost all women, whereas only half were offered risk-reducing medication and/or increased screening. Further investigation is needed to identify barriers to the utilization of risk-reducing strategies among this high-risk population.

Abstract B048: Evaluating the impact of pulmonary diseases on the use of microRNAs as biomarkers for early detection of ovarian cancer

Abstract B048: Evaluating the impact of pulmonary diseases on the use of microRNAs as biomarkers for early detection of ovarian cancer

Abstract B106: A minimal driver pair for a spontaneous high-grade serous ovarian cancer mouse model: Myc and p53-R270H

Abstract Genetically engineered mouse models (GEMM) have fundamentally changed how ovarian cancer etiology, early detection, and treatment can be understood. One challenge of the last decade has been to provide evidence for which cells are responsible for the origin of epithelial ovarian cancer. Different promoters, including Pax8, Lgr5, Ogvp1, and others have allowed for expression of transgenes in specific epithelial compartments in mice. Ovgp1 has been shown to be specific for fallopian tube epithelium (FTE), which most HGSOC is now thought to originate from. Pairing Ovgp1 with multiple homozygous deletions of powerful tumor suppressors like Brca1, Trp53, Rb1, and Nf1 yields murine disease after a year of age. However, human tumors do not contain homozygous deletions of these many tumor suppressors. Others have utilized Pax8 to knock out Trp53 and Pten to yield tumors at 3-8 months of age, but human tumors rarely have PTEN knockouts and Pax8 drives kidney and thyroid expression as well. We postulated that it is possible to strictly mimic human HGSOC genetics in a mouse model and nonetheless drive oncogenesis within the lifespan of a mouse. Here, we describe the OvTrpMyc model, Trp53em1Jdel_Tg(Ovgp1-Trp53*R270H-Myc), in which p53-R270H and Myc expression are driven by the Ovgp1 promoter. The transgene was inserted at the endogenous Trp53 locus, predicting that this design would allow for loss of the opposing wild-type chromosome 11 (syntenic to chromosome 17 in humans) to better mimic human HGSOC genetics. This model generated lethal disease at a median of 15.6 months of age. Dissected metastatic tumors were peritoneal and expressed Pax8, consistent with human disease. Immunohistochemical staining of the fallopian tube, ovary, and uterus indicated clear nuclear staining of p53 and Myc on the FTE. Notably, pockets of intraepithelial metastases expressing p53 and Myc were also found on the uterine epithelium. Cells grown ex vivo from uterus or fallopian tube dissections were analyzed for transcriptomes. RNA-seq revealed a clustering of these extracted cancer cells with human HGSOC and p53 mutant serous endometrial cancers. Loss of heterozygosity at the p53 locus was universal in these ex vivo grown cancer cells, as was extensive aneuploidy. The OvTrpMyc model requires only a single transgene for its breeding strategy, better enabling cancer detection, prevention, and treatment experiments. Taken together, the OvTrpMyc model of p53-R270H mutation and Myc expression driven by Ovgp1 is sufficient to recapitulate cancer hallmarks of HGSOC and, surprisingly, p53 mutant serous uterine cancer as well. Citation Format: Joe R Delaney. A minimal driver pair for a spontaneous high-grade serous ovarian cancer mouse model: Myc and p53-R270H [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B106.

Abstract A045: Improving the diagnostic accuracy of an ovarian cancer triage test using a joint miRNA-protein model

Abstract Background: Multianalyte protein assays that combine several protein biomarkers with an individual’s menopausal status can aid in the preoperative prediction of ovarian cancer among women presenting with an adnexal mass. Serum microRNAs (miRNAs) are another class of biomarkers which have also shown potential to discriminate ovarian cancer cases from benign lesions or healthy controls. Here we investigate the complementarity of miRNA and protein-based approaches. Methods: The population consisted of serum samples from n=678 total study subjects. The training set was provided by Aspira Women’s Health, comprising n=568 women undergoing preoperative evaluation for an adnexal mass (n=333 benign and n=235 malignant). An independent, external validation set (n=110) comprised study subjects enrolled in prospective collection protocols at Brigham and Women’s Hospital, including n=59 subjects with benign masses or healthy controls and n=51 ovarian cancer cases. Among the 51 cases in the validation set, n=20 were FIGO Stage I/II and n=31 were FIGO Stage III/IV. Histologies included n=34 serous and n=17 non-serous. All samples underwent miRNA profiling using a custom 179-miRNA panel optimized for serum analysis using the Fireplex® circulating miRNA assay (Abcam, Cambridge, MA). Proteins were measured using ELISA kits by Aspira. Seven proteins were provided for each patient, including CA-125, as well as age and menopausal status. To reduce the dimensionality of the miRNA data, we employed a forward regression algorithm, which selects a subset of miRNA to optimize linear separation between cases and controls. The miRNA panel was combined with the protein and metadata to train a neural network to diagnose ovarian cancer. We used receiver operating characteristic curves to compare the effectiveness of models trained on miRNA + protein + metadata (joint) vs miRNA alone vs protein + metadata as assessed by the area under the curve (AUC). Results: The joint model produced an AUC 0.95 on the validation set. The miRNA and protein + metadata models offered an AUC of 0.84 and 0.9, respectively. By histology, the joint model offered the highest sensitivity among cancers which were serous and early-stage (89%) and among early-stage cancers overall (90%) when compared to miRNA alone (56% and 80%) or protein+metadata (67% and 65%). Conclusions: These results suggest that miRNA, protein, and metadata are complimentary tools, and the proposed model, which uses all data types simultaneously, is shown to offer the optimal AUC on the external validation set, when compared to the same model trained on miRNA alone or proteins and metadata. The joint model appears to be particularly effective for early-stage, high-risk histologies. This data will be useful to inform ovarian cancer screening and early detection efforts. Citation Format: James W. Webber, Laura Wollborn, Sudhanshu Mishra, Allison Vitonis, Daniel W. Cramer, Ryan Phan, Todd Pappas, Dipanjan Chowdhury, Kevin M. Elias. Improving the diagnostic accuracy of an ovarian cancer triage test using a joint miRNA-protein model [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A045.

Abstract B045: In vivo ovarian cancer detection using folate receptor-α targeted iron oxide nanoparticles

Abstract Ovarian cancer is the second most common and most deadly gynecological cancer in women. Early and accurate detection is crucial in improving survival rate and quality of life of patients. Superparamagnetic iron oxide nanoparticles (SPIONs) have been used in a variety of cancer detection/imaging applications, including Magnetic Resonant Imaging (MRI), Magnetic Particle Imaging (MPI) as well as Superparamagnetic Relaxometry (SPMR) detection currently being developed in-house. SPMR is a highly sensitive detection technology that can differentiate the magnetic signature of nanoparticles bound to tumor cells from unbound nanoparticles (NP). Folate receptor alpha (FRα) is a folate transporter overexpressed in approximately 90% of ovarian cancer, which makes it a suitable molecular target to use in developing tumor imaging methods. Our earlier study using xenograft model with KB (FRα +ve) and A549 (FRα -ve) cells implanted subcutaneously on the flank region of female athymic nude mice demonstrated higher level tumor accumulation of folate-NPs in KB tumor compared to A549 tumor, detectable by SPMR and MRI. To translate such results into a potential clinical application, we recently generated two orthotopic mice ovarian cancer models by implanting KB cells surgically into ovary as well intraperitoneally (IP) into mice abdominal cavity (metastasis model). Implanted tumor cells were allowed to grow for about 2 weeks. Folate-NPs (20mg Fe/kg) were delivered via tail vein or IP injections. PEG-NPs were also delivered as controls. Mice were euthanized 24-hour post-dosing. Mesenteric tumor tissue and reproductive organs with tumors were resected and measured on SPMR. Same tissues from mice without NP injection or with PEG-NP injection were also measured. Our results demonstrated that Folate-NPs reached the tumor sites in both orthotopic models and accumulated specifically at high level detectable by SPMR ex vivo. The amount of NP accumulation in mice with orthotopically implanted tumor was 3x-10x more than those of the xenograft subcutaneous flank implanted tumor model, indicating efficient NP delivery and binding of NP with tumor cells in its natural tissues/organs. The repeat studies of these orthotopic mice models used in the SPMR experiment are currently being conducted using MRI as an alternative in vivo detection method with images taken pre- and post- dosing of the NPs. We anticipate that folate-NPs presence in the tumors will be easily observable on MRI images as it produces T2 contrast along with additional anatomical information. Favorable orthotopic mice model results presented here will lay the groundwork for IND-enabling development of these targeted NP as T2 contrast agent for early ovarian cancer detection using MRI. This type of targeted imaging technology will improve the accuracy of cancer detection in vivo without the use of radioisotopes or radiation, therefore, minimize the needs of invasive biopsies/surgery, with further potential of monitoring therapy response or recurrence. Citation Format: Marie Zhang, Yamitha Perera, Kathirvel Kandasamy, Dan Inglese. In vivo ovarian cancer detection using folate receptor-α targeted iron oxide nanoparticles [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B045.

Paper based molecularly imprinted SERS substrate for early detection of lysophosphatidic acid in ovarian cancer

The current study is related to fabrication of point-of-care molecularly imprinted paper-based analytical devices, specifically related to ovarian cancer early stage detection. The task involves creating a paper coated with silver nanoparticles (AgNPs) that can be used as a transducer via surface enhanced Raman spectroscopy (SERS) to detect the threshold level of the biomarker lysophosphatidic acid (LPA) as a template molecule to diagnose ovarian cancer. MIPs are synthetic receptors designed for a targeted molecule which are prepared from polymers in the presence of target such as small molecules, proteins or cells etc. Removal of the template results in cavities which are structurally and electrostatically complementary to the template molecule. The molecularly imprinted polymer (MIP) layer was anchored on vinyl trimethoxy silane modified-paper, which served as a detector by hydrolytically modifying the paper surface with vinyl trimethoxy silane. To detect LPA in serum samples for ovarian cancer, MIP-paper based on SERS was developed. Patients with benign ovarian tumors had an LPA level of 7.73 μM whereas those with ovarian cancer have a level of 16.99 μM. A healthy person's LPA level is 2.92 μM. Consequently, the rise in LPA levels may aid in the early detection of ovarian cancer. The study refers to the use of a point of care device to diagnose ovarian cancer early, which should be simple, effective, and cost-effective. The analytical tool in the study is a sensor with high selectivity and sensitivity, quick assay times, and inexpensive costs. For LPA in serum, the binding constant of modified paper-based MIP ranges from 11.00 μM to 0.08 μM. The amalgamation of nanoparticles on 2D-paper with MIP have lead to develop sensor which can effectively select LPA in untreated real samples like serum.

Proteomic insight towards key modulating proteins regulated by the aryl hydrocarbon receptor involved in ovarian carcinogenesis and chemoresistance

Gynecological malignancies pose a severe threat to female lives. Ovarian cancer (OC), the most lethal gynecological malignancy, is clinically presented with chemoresistance and a higher relapse rate. Several studies have highly correlated the incidence of OC to exposure to environmental pollutants, such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a process mainly mediated through activating the aryl hydrocarbon receptor (AhR). We have previously reported that exposure of OC cells to TCDD, an AhR activator, significantly modulated the expression of several genes that play roles in stemness and chemoresistance. However, the effect of AhR activation on the whole OC cell proteome aiming at identifying novel druggable targets for both prevention and treatment intervention purposes remains unrevealed. For this purpose, we conducted a comparative proteomic analysis of OC cells A2780 untreated/treated with TCDD for 24 h using a mass spectrometry-based label-free shotgun proteomics approach. The most significantly dysregulated proteins were validated by Western blot analysis. Our results showed that upon AhR activation by TCDD, out of 2598 proteins identified, 795 proteins were upregulated, and 611 were downregulated. STRING interaction analysis and KEGG-Reactome pathway analysis approaches identified several significantly dysregulated proteins that were categorized to be involved in chemoresistance, cancer progression, invasion and metastasis, apoptosis, survival, and prognosis in OC. Importantly, selected dysregulated genes identified by the proteomic study were validated at the protein expression levels by Western blot analysis. In conclusion, this study provides a better understanding of the the cross-talk between AhR and several other molecular signaling pathways and the role and involvement of AhR in ovarian carcinogenesis and chemoresistance. Moreover, the study suggests that AhR is a potential therapeutic target for OC prevention and maintenance. SignificanceTo our knowledge, this is the first study that investigates the role and involvement of AhR and its regulated genes in OC by performing a comparative proteomic analysis to identify the critical proteins with a modulated expression upon AhR activation. We found AhR activation to play a tumor-promoting and chemoresistance-inducing role in the pathogenesis of OC. The results of our study help to devise novel therapeutics for better management and prevention and open the doors to finding novel biomarkers for the early detection and prognosis of OC.

Uterine washings as a novel method for early detection of ovarian cancer: Trials and tribulations

Given the tubal origin of high-grade serous ovarian cancer (HGSC), we sought to investigate intrauterine lavage (IUL) as a novel method of biomarker detection. IUL and serum samples were collected from patients with HGSC or benign pathology. Although CA-125 and HE4 concentrations were significantly higher in IUL samples compared to serum, they were similar between IUL samples from patients with HGSC vs benign conditions. In contrast, CA-125 and HE4 serum concentrations differed between HGSC and benign pathology (P =.002 for both). IUL and tumor samples from patients with HGSC were subjected to targeted panel sequencing and droplet digital PCR (ddPCR). Tumor mutations were found in 75 % of matched IUL samples. Serum CA-125 and HE4 biomarker levels allowed for better differentiation of HGSC and benign pathology compared to IUL samples. We believe using IUL for early detection of HGSC requires optimization, and current strategies should focus on prevention until early detection strategies improve.

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network (ANN) and adaptive-network-based fuzzy inference system (ANFIS) for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA-125), and carcinoembryonic antigen (CEA), are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9% in predicting ovarian cancer. With an accuracy rate of at least 89%, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

A Review on Emerging Biomarkers for Early Detection of Ovarian Cancer

A Review on Emerging Biomarkers for Early Detection of Ovarian Cancer

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BackgroundMultiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.MethodsTwenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.ResultsEight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.ConclusionA four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Biomarkers for Early Detection of Ovarian Cancer: A Review

Ovarian cancer, with its asymptomatic early stages, poses a significant challenge for early diagnosis and survival improvement. The utilization of tumor markers, including CA -125 and HE4 has become crucial in detecting these silent killers. Additionally, gene-based markers like BRCA1 and BRCA2 mutations are increasingly acknowledged for indicating ovarian cancer susceptibility. Promising emerging biomarkers, such as microRNAs and circulating tumor cells, offer new research avenues and diagnostic possibilities for the early detection of ovarian cancer. This article provides a comprehensive review of current specific markers, gene-based indicators, emerging markers, and other notable approaches in the early identification of ovarian cancer.