Mathematical modeling of the evolution of resistance and aggressiveness of high-grade serous ovarian cancer from patient CA-125 time series.

A time-series analysis of serum Cancer Antigen 125 (CA-125) levels was performed in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to evaluate the development of chemoresistance and response to therapy. To investigate chemoresistance and better predict the treatment effectiveness, we examined two traits: resistance (defined as the rate of CA-125 change when patients were treated with therapy) and aggressiveness (defined as the rate of CA-125 change when patients were not treated). We found that as the number of treatment lines increases, the data-based resistance increases (a decreased rate of CA-125 decay). We use mathematical models of two distinct cancer cell types, treatment-sensitive cells and treatment-resistant cells, to estimate the values and evolution of the two traits in individual patients. By fitting to individual patient HGSOC data, our models successfully capture the dynamics of the CA-125 level. The parameters estimated from the mathematical models show that patients with inferred low growth rates of treatment-sensitive cells and treatment-resistant cells (low model-estimated aggressiveness) and a high death rate of treatment-resistant cells (low model-estimated resistance) have longer survival time after completing their second-line of therapy. These findings show that mathematical models can characterize the degree of resistance and aggressiveness in individual patients, which improves our understanding of chemoresistance development and could predict treatment effectiveness in HGSOC patients.

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer.

OBJECTIVE: The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy. METHODS: Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients. RESULTS: Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively. CONCLUSIONS: Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

New immune phenotypes for treatment response in high-grade serous ovarian carcinoma patients.

Despite advances in surgical and therapeutic approaches, high-grade serous ovarian carcinoma (HGSOC) prognosis remains poor. Surgery is an indispensable component of therapeutic protocols, as removal of all visible tumor lesions (cytoreduction) profoundly improves the overall survival. Enhanced predictive tools for assessing cytoreduction are essential to optimize therapeutic precision. Patients' immune status broadly reflects the tumor cell biological behavior and the patient responses to disease and treatment. Serum cytokine profiling is a sensitive measure of immune adaption and deviation, yet its integration into treatment paradigms is underexplored. This study is part of the IMPACT trial (NCT03378297) and aimed to characterize immune responses before and during primary treatment for HGSOC to identify biomarkers for treatment selection and prognosis. Longitudinal serum samples from 22 patients were collected from diagnosis until response evaluation. Patients underwent primary cytoreductive surgery or neoadjuvant chemotherapy (NACT) based on laparoscopy scoring. Twenty-seven serum cytokines analyzed by Bio-Plex 200, revealed two immune phenotypes at diagnosis: Immune High with marked higher serum cytokine levels than Immune Low. The immune phenotypes reflected the laparoscopy scoring and allocation to surgical treatment. The five Immune High patients undergoing primary cytoreductive surgery exhibited immune mobilization and extended progression-free survival, compared to the Immune Low patients undergoing the same treatment. Both laparoscopy and cytoreductive surgery induced substantial and transient changes in serum cytokines, with upregulation of the inflammatory cytokine IL-6 and downregulation of the multifunctional cytokines IP-10, Eotaxin, IL-4, and IL-7. Over the study period, cytokine levels uniformly decreased in all patients, leading to the elimination of the initial immune phenotypes regardless of treatment choice. This study reveals distinct pre-treatment immune phenotypes in HGSOC patients that might be informative for treatment stratification and prognosis. This potential novel biomarker holds promise as a foundation for improved assessment of treatment responses in patients with HGSOC. ClinicalTrials.gov Identifier: NCT03378297.

Evaluation of serum CA125, HE4 and CA724 and the risk of ovarian malignancy algorithm score in the diagnosis of high-grade serous ovarian cancer.

AIM: To develop a new algorithm for the detection of high-grade serous ovarian cancer (HGSOC). METHODS: Patients diagnosed with HGSOC, borderline ovarian tumours (BOTs) or benign ovarian masses (BOMs) were enrolled between February 2019 and December 2020. Patients with BOTs or BOMs were grouped as non-HGSOC. The cases were divided randomly into a training cohort (two-thirds of cases) and a validation cohort (one-third of cases). Logistic regression was used to find risk factors for HGSOC and to create a new algorithm in the training cohort. Receiver operating characteristic curves were used to compare the diagnostic value of tumour biomarkers. Sensitivity and specificity of tumour markers and the new algorithm were calculated in the training cohort and validation cohort. RESULTS: This study found significant differences in age; BRCA1/2 mutation status; CA125, CA724 and HE4 levels; and Risk of Ovarian Malignancy Algorithm score between the two groups.Logistic regression analysis showed that CA125 and BRCA1/2 were risk factors for HGSOC. A new algorithm combining CA125 and BRCA1/2 increased the specificity of CA125 for diagnosis of HGSOC. The new algorithm had sensitivity of 81.08% and specificity of 93.10% in the training cohort. CONCLUSION: The new algorithm using CA125 and BRCA1/2 helped to distinguish between patients with HGSOC and patients with non-HGSOC.

An increase of serum CA-125 to two times of nadir level strongly predicts the image-identified relapse of serous ovarian cancer.

Using 70 U/ml or 35 U/ml as CA125 routine abnormal threshold may result in omissions in the relapse detection of Ovarian cancer (OvCa). This study aimed to clarify the association between a biochemical relapse (only the elevation of CA125) and an image-identified relapse to predict the relapsed lesions better. 162 patients who achieved complete clinical response were enrolled from women diagnosed with stage I-IV serous ovarian, tubal, and peritoneal cancers from January 2013 to June 2019 at our center. The CA125 level of 2 × nadir was defined as the indicator of image-identified relapse (P < 0.001). Compared to CA125 level exceeding 35 U/ml, the 2 × nadir of CA125 improve the sensitivity of image-identified relapse (84.9% vs 67.4%, P < 0.001); the 2 × nadir value can act as an earlier warning relapse signal with a longer median time to image-identified relapse (2.7 vs. 0 months, P < 0.001). Of the relapsed population, there was no difference of CA125 changing trend between the neoadjuvant chemotherapy (NACT) and primary debulking surgery (PDS) group after initial treatment. Compared with 35 U/ml, CA125 reaching 2 × nadir during the follow-up process might be a more sensitive and early relapse signal in patients with serous OvCa. This criterion may help guide patients to be recommended for imaging examination to detect potential relapse in time.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer.

BACKGROUND: The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery. RESULTS: We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC. CONCLUSIONS: This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

The Serum CA-125 Concentration Data Assists in Evaluating CT Imaging Information When Used to Differentiate Borderline Ovarian Tumor from Malignant Epithelial Ovarian Tumors

OBJECTIVE: We wanted to evaluate the diagnostic value of serum CA-125 concentration, when used in combination with the preoperative contrast-enhanced CT results, to differentiate borderline ovarian tumors (BOTs) from stage I malignant epithelial ovarian tumors (MEOTs). MATERIALS AND METHODS: Ninety-eight masses (46 BOTs and 52 stage I MEOTs) from 87 consecutive patients (49 with BOTs and 38 with stage I MEOTs) who had undergone preoperative contrast-enhanced computed tomography (CT) and surgical staging were evaluated retrospectively and independently by two radiologists. The preoperative serum CA-125 concentration was measured in all patients. The utility of analyzing serum CA-125 concentration in combination with the CT results was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: An irregular tumor surface and lymphadenopathy were predictive of a MEOT. ROC analysis showed that the combination of CT data and the serum CA-125 level resulted in a higher diagnostic performance than did using the CT alone for differentiating BOTs from MEOTs. The areas under the curves (AUCs) without and with the use of the serum CA-125 level data were 0.67 (95% confidence interval [CI]: 0.57-0.77) and 0.78 (95% CI: 0.68-0.85), respectively, for reader 1 (p = 0.029) and 0.71 (95% CI: 0.61-0.80) and 0.81 (95% CI: 0.72-0.89), respectively, for reader 2 (p = 0.009). CONCLUSION: The serum CA-125 concentration is of additional diagnostic value when used in conjunction with the CT imaging results for differentiating BOTs from MEOTs.