Urine CA125 and HE4 for the Triage of Symptomatic Women with Suspected Endometrial Cancer

Diagnostic role of urine human epididymis protein 4 in ovarian cancer

Ovarian cancer is the 8th most common malignancy in women and the deadliest gynecological cancer. Due to the non-specific symptoms and the lack of effective diagnostic methods, late diagnosis remains the main barrier for improving the poor prognosis. Human epididymis protein 4 (HE4) is a protein overexpressed in ovarian cancer, but not in healthy individuals or benign conditions. The aim of this review article is to evaluate the laboratory aspect and potential clinical application of urine HE4. The methodology is presented, together with discussion on preanalytical, analytical and postanalytical phase of HE4 detection using urine. Moreover, we present the diagnostic role of urine HE4 in differential diagnosis, chemotherapy response, detection of recurrence and detection of low-malignant potential tumors. It has been found that urine HE4 presents as a promising, non-invasive tumor marker for detection and monitoring of ovarian cancer. However, standardization of the HE4 detection process is needed prior to implementation in clinical diagnostics.

Non-Invasive Endometrial Cancer Screening through Urinary Fluorescent Metabolome Profile Monitoring and Machine Learning Algorithms

Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods.

Screening Strategies to Improve Early Diagnosis in Endometrial Cancer

Endometrial cancer is the most common gynecological malignancy in high-income countries and the sixth most common cancer in women. Overall incidence has risen in the last few decades as a consequence of the increase in the prevalence of its risk factors, mainly obesity and the aging of the population, and although diagnoses have increased across all age groups, the incidence rates have doubled in women under the age of 40 years. The survival rates of endometrial cancer are highly dependent on its stage at diagnosis, bringing to the fore the importance of early diagnosis. The aim of a screening strategy in this type of tumor should be to detect the disease in the pre-invasive or early stage (before developing myometrial invasion), which would improve cure rates, reduce the morbidity associated with aggressive treatment and offer uterus-sparing management options for younger women. The ideal screening tool in this scenario would be a minimally invasive, inexpensive and easy-to-perform test or auto-test, which could be implemented in a routine gynecologic checkup of patients at-risk or in the general adult population. In this comprehensive review, we aim to define the populations at higher risk of developing endometrial cancer, to assess the performance of current diagnostic tools when used in a screening setting and to discuss the accuracy of new molecular screening strategies.

Preoperative serum CA125 level and age at diagnosis: An effective prognosis prediction tool for patients with early-stage endometrial cancer

OBJECTIVE

To investigate the combined predictive value of the preoperative serum cancer antigen 125 (CA125) level and age at diagnosis among patients with early-stage endometrial cancer (EC) after initial treatment.

METHODS

We retrospectively analyzed data from patients with early-stage EC from 1999 to 2015 in multiple institutions in China. All 447 patients received postoperative adjuvant radiotherapy for FIGO 2009 stage I and II EC with complete data on preoperative serum CA125 levels. All patients were divided into four groups according to the ESMO-ESGO-ESTRO risk classification. The predictive probability of 5-year overall survival (OS) and the sensitivity and specificity of CA125 and age were calculated.

RESULTS

The median follow-up time was 59 months (3-201 months). The 5-year OS and disease-free survival rates were 94.4% and 89.1%. Multivariate analysis showed that the preoperative CA125 level and age at diagnosis were independent prognostic factors for 5-year OS. The area under the curve for CA125 combined with age at diagnosis for 5-year OS was .692, and the corresponding sensitivity and specificity were 68.2% and 68.2% (p < .002), which were significantly better than the corresponding values for CA125 or age alone. After all 447 patients were divided into four groups according to CA125 combined with age, the 5-year OS of the elderly and higher CA125 group was only 73.7%.

CONCLUSIONS

Although preoperative CA125 had limited sensitivity in predicting the prognosis for early-stage EC after initial treatment, it remains a useful serum marker for risk assessment of early-stage EC. Combining CA125 with age may increase its predictive sensitivity.

Evaluation of Somatic Mutations in Urine Samples as a Noninvasive Method for the Detection and Molecular Classification of Endometrial Cancer

PURPOSE

Current diagnostic methods for endometrial cancer lack specificity, leading to many women undergoing invasive procedures. The aim of this study was to evaluate somatic mutations in urine to accurately discriminate patients with endometrial cancer from controls.

EXPERIMENTAL DESIGN

Overall, 72 samples were analyzed using next-generation sequencing (NGS) with molecular identifiers targeting 47 genes. We evaluated urine supernatant samples from women with endometrial cancer (n = 19) and age-matched controls (n = 20). Cell pellets from urine and plasma samples from seven cases were sequenced; further, we also evaluated paired tumor samples from all cases. Finally, immunohistochemical markers for molecular profiling were evaluated in all tumor samples.

RESULTS

Overall, we were able to identify mutations in DNA from urine supernatant samples in 100% of endometrial cancers. In contrast, only one control (5%) showed variants at a variant allele frequency (VAF) ≥ 2% in the urine supernatant samples. The molecular classification obtained by using tumor samples and urine samples showed good agreement. Analyses in paired samples revealed a higher number of mutations and VAF in urine supernatants than in urine cell pellets and blood samples.

CONCLUSIONS

Evaluation of somatic mutations using urine samples may offer a user-friendly and reliable tool for endometrial cancer detection and molecular classification. The diagnostic performance for endometrial cancer detection was very high, and cases could be molecularly classified using these noninvasive and self-collected samples. Additional multicenter evaluations using larger sample sizes are needed to validate the results and understand the potential of urine samples for the early detection and prognosis of endometrial cancer.

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BACKGROUND

A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

METHODS

This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

RESULTS

The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).

CONCLUSION

A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)-A Prospective Pilot Study of Diagnostic Accuracy

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

Serum CA125 and HE4 as Biomarkers for the Detection of Endometrial Cancer and Associated High-Risk Features

Early detection of endometrial cancer improves survival. Non-invasive diagnostic biomarkers would improve triage of symptomatic women for investigations. This study aimed to determine the diagnostic accuracy of serum Cancer Antigen 125 (CA125) and Human Epididymis 4 (HE4) for endometrial cancer and associated high-risk features. Serum samples from women investigated for gynaecological symptoms or diagnosed with endometrial cancer were analysed for CA125 and HE4. Conventional diagnostic metrics were calculated. In total, 755 women were included; 397 had endometrial cancer. Serum CA125 and HE4 were significantly elevated in cases compared with controls (both p < 0.001), and with pathological markers of disease severity (p < 0.05). A combination of CA125 and HE4 detected endometrial cancer with an area under the curve (AUC) of 0.77 (95% CI: 0.74−0.81). In a model with body mass index (BMI) and parity, HE4 predicted endometrial cancer in pre-menopausal women with an AUC of 0.91 [sensitivity = 84.5%, specificity = 80.9% (p < 0.001)]. In women with abnormal ultrasound, HE4 ≥ 77 pmol/L improved specificity compared with imaging alone [68.6% (95% CI: 75.0−83.6) vs. 34.4% (95% CI: 27.1−42.3), respectively], but at a cost to sensitivity. HE4 ≥ 77 pmol/L improved the detection of myometrial invasion ≥50% in women with stage I disease compared with magnetic resonance imaging (MRI) alone [sensitivity = 100% (95% CI: 54.1−100)]. CA125 ≥ 35 U/mL did not add to imaging. HE4 is a good predictor of poor prognostic features which could assist staging investigations.

HE4 Tissue Expression as A Putative Prognostic Marker in Low-Risk/Low-Grade Endometrioid Endometrial Cancer: A Review

Low-grade stage I endometrioid endometrial carcinomas should have an excellent prognosis, but a small subset of these cancers can relapse. The search for putative immunohistochemical prognostic markers for relapse in low-risk/low-grade endometrioid endometrial cancers remains open. Among the candidate molecules that may implicate the roles of immunohistochemical risk markers, we focused our attention on human epididymis protein 4 (HE4) after a review of the literature. Few authors have devoted themselves to this topic, and none have found a correlation between the tissue expression of HE4 and the molecular classification of endometrial cancer. Five different variants of HE4 mRNA and multiple protein isoforms of HE4 were identified many years ago, but current HE4 assays only measure the total HE4 expression and do not distinguish the different proteins encoded by different mRNA variants. It is important to have an approach to distinguish specific variants in the future.

Advances on Prevention and Screening of Gynecologic Tumors: Are We Stepping Forward?

According to 2020 comprehensive global cancer statistics published by the International Agency for Research on Cancer, gynecologic malignancies accounted overall for 16 [...].