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

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.

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.

Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis

Endometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article's findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.

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.

Unlocking the potential of tumor-derived DNA in urine for cancer detection: methodological challenges and opportunities

High cancer mortality rates and the rising cancer burden worldwide drive the development of innovative methods in order to advance cancer diagnostics. Urine contains a viable source of tumor material and allows for self-collection from home. Biomarker testing in this liquid biopsy represents a novel approach that is convenient for patients and can be effective in detecting cancer at a curable stage. Here, we set out to provide a detailed overview of the rationale behind urine-based cancer detection, with a focus on non-urological cancers, and its potential for cancer diagnostics. Moreover, evolving methodological challenges and untapped opportunities for urine biomarker testing are discussed, particularly emphasizing DNA methylation of tumor-derived cell-free DNA. We also provide future recommendations for technical advancements in urine-based cancer detection and elaborate on potential mechanisms involved in the transrenal transport of cell-free DNA.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Deciphering the potential of proteomic-based biomarkers in women's reproductive diseases: empowering precision medicine in gynecology

CONTEXT

Gynecological disorders represent a complex set of malignancies that result from a diverse array of molecular changes affecting the lives of over a million women worldwide. Ovarian, Endometrial, and Cervical cancers, Endometriosis, PCOS are the most prevalent ones that pose a grave threat to women's health. Proteomics has emerged as an invaluable tool for developing novel biomarkers, screening methods, and targeted therapeutic agents for gynecological disorders. Some of these biomarkers have been approved by the FDA, but regrettably, they have a constrained diagnostic accuracy in early-stage diagnosis as all of these biomarkers lack sensitivity and specificity. Lately, high-throughput proteomics technologies have made significant strides, allowing for identification of potential biomarkers with improved sensitivity and specificity. However, limited successes have been shown with translation of these discoveries into clinical practice.

OBJECTIVE

This review aims to provide a comprehensive overview of the current and potential protein biomarkers for gynecological cancers, endometriosis and PCOS, discusses recent advances and challenges, and highlights future directions for the field.

CONCLUSION

We propose that proteomics holds great promise as a powerful tool to revolutionize the fight against female reproductive diseases and can ultimately improve personalized patient outcomes in women's biomedicine.

Rank aggregation of independent genetic screen results highlights new strategies for adoptive cellular transfer therapy of cancer

Efficient intratumoral infiltration of adoptively transferred cells is a significant barrier to effectively treating solid tumors with adoptive cellular transfer (ACT) therapies. Our recent forward genetic, whole-genome screen identified T cell-intrinsic gene candidates that may improve tumor infiltration of T cells. Here, results are combined with five independent genetic screens using rank aggregation to improve rigor. This resulted in a combined total of 1,523 candidate genes - including 1,464 genes not currently being evaluated as therapeutic targets - that may improve tumor infiltration of T cells. Gene set enrichment analysis of a published human dataset shows that these gene candidates are differentially expressed in tumor infiltrating compared to circulating T cells, supporting translational potential. Importantly, adoptive transfer of T cells overexpressing gain-of-function candidates (AAK1ΔN125, SPRR1B, and EHHADH) into tumor-bearing mice resulted in increased T cell infiltration into tumors. These novel gene candidates may be considered as potential therapeutic candidates that can aid adoptive cellular therapy in improving T cell infiltration into solid tumors.

Micro-histology combined with cytology improves the diagnostic accuracy of endometrial lesions

BACKGROUND

In the study, we aimed to evaluate the ability of micro-histology combined with cytology to improve the quality of slides and diagnose endometrial lesions.

METHODS

Endometrial specimens were collected from Li Brushes. Every specimen was prepared for micro-histological and cytological slides, using cell block (CB) and liquid-based cytology (LBC) technologies. Semi-quantitative scoring system was used to evaluate the qualities of slides. CB slides were assessed by 5-category scoring system. Diagnostic accuracy was calculated in LBC, CB, and LBC + CB groups based on the histological gold standard. Endometrial atypical hyperplasia, and endometrial cancer were considered positive, whereas others were considered negative.

RESULTS

A total of 167 patients were enrolled. CB slides were inferior to LBC slides only in cellularity (p < 0.001), but superior in the other six parameters (all p < 0.001). The satisfaction rate of micro-histology accounted for 92.3%. The accuracy index in the CB group was higher than in the LBC group in terms of sensitivity (85.5% vs. 82.7%) and specificity (98.9% vs. 95.7%). The sensitivity and specificity in the LBC + CB group were increased to 94.2% and 99.0%, respectively.

CONCLUSIONS

The quality of micro-histological slides was higher than that of cytological slides. By combining micro-histology with cytology, higher accuracy was achieved for endometrial lesions diagnosis.

The Latest Developments in Using Proteomic Biomarkers from Urine and Serum for Non-Invasive Disease Diagnosis and Prognosis

Due to diagnostic improvements, medical diagnostics is demanding non-invasive or minimally invasive methods. Non-invasively obtained body fluids (eg., Urine, serum) can replace cerebral fluid, amniotic fluid, synovial fluid, bronchoalveolar lavage fluid, and others for diagnostic reasons. Many illnesses are induced by perturbations of cellular signaling pathways and associated pathway networks as a result of genetic abnormalities. These disturbances are represented by a shift in the protein composition of the fluids surrounding the tissues and organs that is, tissue interstitial fluid (TIF). These variant proteins may serve as diagnostic "signatures" for a variety of disorders. This review provides a concise summary of urine and serum biomarkers that may be used for the diagnosis and prognosis of a variety of disorders, including cancer, brain diseases, kidney diseases, and other system diseases. The studies reviewed in this article suggest that serum and urine biomarkers of various illnesses may be therapeutically useful for future diagnostics. Correct illness management is crucial for disease prognosis, hence non-invasive serum and urine biomarkers have been extensively studied for diagnosis, subclassification, monitoring disease activity, and predicting treatment results and consequences.