Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid

Exploring the protein signature of endometrial cancer: A comprehensive review through diverse samples and mass spectrometry-based proteomics

Endometrial cancer (EC) is increasing incidence among women, and it constitutes a health problem for women globally. An important aspect of EC management involves the use of protein biomarkers for early detection and monitoring. Protein biomarkers allow the identification of high-risk patients, the detection of the disease in its early stages, and the assessment of treatment responses. Mass spectrometry (MS)-based proteomics offers robust analytical techniques and a comprehensive understanding of proteins. Proteomics methods allow scientists to investigate both the quantities and functions of proteins. Thus, it provides valuable insights into how proteins are altered under different conditions. This review summarizes recent advances in MS-based proteomic biomarker discovery for EC, focusing on different sample types and MS-based techniques used in clinical studies. The review emphasized in detail the most commonly used key sources such as blood, urine, vaginal fluids and tissue. Furthermore, MS-based proteomics techniques such as untargeted, targeted, sequential window acquisition of all theoretical mass spectra (SWATH-MS) and mass spectrometry imaging used in the discovery and validation/validation phases were evaluated. This review highlights the importance of biomarker discovery and clinical translation to improve diagnostic and therapeutic outcomes in EC. It aims to provide a comprehensive overview of MS-based proteomics in EC, guiding future research and clinical applications.

Vaginal Sensors

The development and market emergence of vaginal sensors have begun to demonstrate their impact on women's healthcare. Until recently, in limited cases, these sensors have exhibited their capabilities in diagnosing and monitoring disorders of the vaginal tract during different stages of women's lives. This Perspective is a compilation of what has been accomplished so far in the landscape of vaginal sensors. The text explores the diverse types of vaginal sensor technologies, their applications, and their potential impact on women's healthcare. The review introduces the anatomy of the vagina and cervix and categorizes vaginal sensors that have been developed, highlighting the technologies and potential applications. The paper covers biomarkers of the vaginal tract and discusses their importance in maintaining the overall characteristics of the vaginal system. The text also explores the clinical implications of vaginal sensors in pregnancy monitoring, disease detection, and sexual health management. In the final step, the manuscript provides future perspectives and possibilities that can be incorporated in the emerging field of vaginal sensors.

Proteomic Profile of Endometrial Cancer: A Scoping Review

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomics and machine learning for biomarker discovery

BACKGROUND

The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.

METHODS

Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.

FINDINGS

A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.

INTERPRETATION

Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.

FUNDING

Cancer Research UK, Blood Cancer UK, National Institute for Health Research.

Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

BACKGROUND

The incidence of endometrial cancer is increasing worldwide. While delays in diagnosis reduce survival, case molecular misclassification might be associated with under- and over-treatment. The objective of this study was to evaluate genetic alterations to detect and molecularly classify cases of endometrial cancer using non-invasive samples.

METHODS

Consecutive patients with incident endometrial cancer (N = 139) and controls (N = 107) from a recent Spanish case-control study were included in this analysis. Overall, 339 cervicovaginal samples (out of which 228 were clinician-collected and 111 were self-collected) were analysed using a test based on next-generation sequencing (NGS), which targets 47 genes. Immunohistochemical markers were evaluated in 133 tumour samples. A total of 159 samples were used to train the detection algorithm and 180 samples were used for validation.

FINDINGS

Overall, 73% (N = 94 out of 129 clinician-collected samples, and N = 66 out of 90 self-collected samples) of endometrial cancer cases had detectable mutations in clinician-collected and self-collected samples, while the specificity was 80% (79/99) for clinician-collected samples and 90% (19/21) for self-collected samples. The molecular classifications obtained using tumour samples and non-invasive gynaecologic samples in our study showed moderate-to-good agreement. The molecular classification of cases of endometrial cancer into four groups using NGS of both clinician-collected and self-collected cervicovaginal samples yielded significant differences in disease-free survival. The cases with mutations in POLE had an excellent prognosis, whereas the cases with TP53 mutations had the poorest clinical outcome, which is consistent with the data on tumour samples.

INTERPRETATION

This study classified endometrial cancer cases into four molecular groups based on the analysis of cervicovaginal samples that showed significant differences in disease-free survival. The molecular classification of endometrial cancer in non-invasive samples may improve patient care and survival by indicating the early need for aggressive surgery, as well as reducing referrals to highly specialized hospitals in cancers with good prognosis. Validation in independent sets will confirm the potential for molecular classification in non-invasive samples.

FUNDING

This study was funded by a competitive grant from Instituto de Salud Carlos III through the projects PI19/01835, PI23/00790, and FI20/00031, CIBERESP CB06/02/0073 and CIBERONC CB16/12/00231, CB16/12/00234 (Co-funded by European Regional Development Fund. ERDF: A way to build Europe). Samples and data were provided by Biobank HUB-ICO-IDIBELL, integrated into the Spanish Biobank Network, and funded by the Instituto de Salud Carlos III (PT20/00171) and by Xarxa de Bancs de Tumors de Catalunya (XBTC) sponsored by Pla Director d'Oncologia de Catalunya. This work was supported in part by the AECC, Grupos estables (GCTRA18014MATI). It also counts with the support of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya, and grants to support the activities of research groups 2021SGR01354 and 2021SGR1112.

Identification of Endometrial Cancer-Specific microRNA Biomarkers in Endometrial Fluid

Abnormal uterine bleeding is a common benign gynecological complaint and is also the most common symptom of endometrial cancer (EC). Although many microRNAs have been reported in endometrial carcinoma, most of them were identified from tumor tissues obtained at surgery or from cell lines cultured in laboratories. The objective of this study was to develop a method to detect EC-specific microRNA biomarkers from liquid biopsy samples to improve the early diagnosis of EC in women. Endometrial fluid samples were collected during patient-scheduled in-office visits or in the operating room prior to surgery using the same technique performed for saline infusion sonohysterography (SIS). The total RNA was extracted from the endometrial fluid specimens, followed by quantification, reverse transcription, and real-time PCR arrays. The study was conducted in two phases: exploratory phase I and validation phase II. In total, endometrial fluid samples from 82 patients were collected and processed, with 60 matched non-cancer versus endometrial carcinoma patients used in phase I and 22 in phase II. The 14 microRNA biomarkers, out of 84 miRNA candidates, with the greatest variation in expression from phase I, were selected to enter phase II validation and statistical analysis. Among them, three microRNAs had a consistent and substantial fold-change in upregulation (miR-429, miR-183-5p, and miR-146a-5p). Furthermore, four miRNAs (miR-378c, miR-4705, miR-1321, and miR-362-3p) were uniquely detected. This research elucidated the feasibility of the collection, quantification, and detection of miRNA from endometrial fluid with a minimally invasive procedure performed during a patient in-office visit. The screening of a larger set of clinical samples was necessary to validate these early detection biomarkers for 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.

Minimally invasive approaches for the early detection of endometrial cancer

Endometrial cancer (EC) is one of the most common gynecologic cancers and its incidence is rising globally. Although advanced EC has a poor prognosis; diagnosing EC at an earlier stage could improve long-term patient outcomes. However, there is no consensus on the early detection strategies for EC and the current diagnostic practices such as transvaginal ultrasound, hysteroscopy and endometrial biopsy are invasive, costly and low in specificity. Thus, accurate and less invasive screening tests that detect EC in women with early stages of the disease are needed. Current research has revolutionized novel EC early detection methodologies in many aspects. This review aims to comprehensively characterizes minimally invasive screening techniques that can be applied to EC in the future, and fully demonstrate their potential in the early detection of EC.

DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment

Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.

Cervical Fluids Are a Source of Protein Biomarkers for Early, Non-Invasive Endometrial Cancer Diagnosis

BACKGROUND

Abnormal uterine bleeding is the main symptom of endometrial cancer (EC), but it is highly nonspecific. This represents a huge burden for women's health since all women presenting with bleeding will undergo sequential invasive tests, which are avoidable for 90-95% of those women who do not have EC.

METHODS

This study aimed to evaluate the potential of cervical samples collected with five different devices as a source of protein biomarkers to diagnose EC. We evaluated the protein quantity and the proteome composition of five cervical sampling methods.

RESULTS

Samples collected with a Rovers Cervex Brush^® and the HC2 DNA collection device, Digene, were the most suitable samples for EC proteomic studies. Most proteins found in uterine fluids were also detected in both cervical samples. We then conducted a clinical retrospective study to assess the expression of 52 EC-related proteins in 41 patients (22 EC; 19 non-EC), using targeted proteomics. We identified SERPINH1, VIM, TAGLN, PPIA, CSE1L, and CTNNB1 as potential protein biomarkers to discriminate between EC and symptomatic non-EC women with abnormal uterine bleeding in cervical fluids (AUC > 0.8).

CONCLUSIONS

This study opens an avenue for developing non-invasive protein-based EC diagnostic tests, which will improve the standard of care for gynecological patients.

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

Simple Summary

Prostate cancer is the third most frequent cancer in men worldwide, with a notable increase in prevalence over the past two decades. The PSA is the only well-established protein biomarker for prostate cancer diagnosis, staging, and surveillance. It frequently leads to inaccurate diagnosis and overtreatment since it is an organ-specific biomarker rather than a tumour-specific biomarker. As a result, one of the primary goals of prostate cancer proteome research is to identify novel biomarkers that can be used with or instead of PSA, particularly in non-invasive blood samples. Thousands of peptides or assays were detected in blood samples from patients with low- to high-grade prostate cancer and healthy individuals, allowing data processing of sequential window acquisition of all theoretical mass spectra (SWATH-MS). By assisting in the detection of prostate cancer biomarkers in blood samples, this useful resource will improve our understanding of the role of proteomics in prostate cancer diagnosis and risk assessment.

Abstract

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort’s pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651.

HE4 as a Biomarker for Endometrial Cancer

Simple Summary

There are currently no blood biomarkers approved for routine clinical use in endometrial cancer. Serum human epididymis protein 4 (HE4) is significantly higher in patients with endometrial cancer compared to patients without endometrial cancer and is associated with a poorer prognosis. This makes HE4 an attractive candidate for clinical use in endometrial cancer. The aim of this review is to summarise the evidence for the use of serum HE4 in the detection, prognosis, prediction of therapy response and recurrence monitoring in endometrial cancer. The utility of combining HE4 with other biomarkers or imaging and clinical variables, and its detection in other biofluids is also discussed, as well as potential challenges for clinical use and recommended areas for future research.

Abstract

There are currently no blood biomarkers in routine clinical use in endometrial carcinoma (EC). Human epididymis protein 4 (HE4) is a glycoprotein that is overexpressed in the serum of patients with EC, making it a good candidate for use as a diagnostic and/or prognostic biomarker. HE4 is correlated with poor prognostic factors, including stage, myometrial invasion and lymph node metastases, which means it could be used to guide decisions regarding the extent of surgery and need for adjuvant therapy. Serum HE4 has also shown promise for predicting responses to progestin therapy in early-stage EC. The use of algorithms and indices incorporating serum HE4 and other biomarkers, including clinical and imaging variables, is an area of increasing interest. Serum HE4 levels rise with age and renal dysfunction, which may affect the interpretation of results. This review covers the evidence supporting the use of HE4 as an EC biomarker for diagnosis, prognosis, recurrence monitoring, and prediction of therapy response. The evidence for combining serum HE4 with other biomarkers, including clinical and imaging variables, its value as a biomarker in other biofluids and potential challenges of its clinical use are also discussed.