Alcohol-induced extracellular vesicle signatures: Exploring liquid biopsy biomarkers for early cancer detection and prognosis

Despite major improvements in cancer treatment, detection, and health promotion, the mortality rates of late-stage cancer remain high. This is a critical issue because a large proportion of cancer mortality is experienced by patients who have late-stage disease at diagnosis. As survival is substantially higher for almost all cancers when diagnosed at an early stage, effective early cancer detection strategies could drastically reduce overall cancer mortality. Advances in various technologies have culminated in the development of liquid biopsies. The tumour biomarkers applied for non- or minimally-invasive cancer detection include tumour cells and their components in bodily fluids, especially peripheral blood for circulating tumour biomarkers. The most well-studied circulating tumour biomarkers in recent years for the early detection of cancer are circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA), with research into other classes rapidly expanding. CTCs and ctDNA have been detected at an early stage in several types of cancer with high specificity, aiding risk stratification and, in some cases, identifying clinically actionable molecular features. Therefore, these circulating biomarkers offer several advantages over the traditional cancer detection methods. Although their limitations are considerable, the evolving evidence suggests they have tremendous potential as tools for early cancer detection. In this review, we evaluate the development and applications of circulating biomarkers for early cancer detection, with a focus on CTCs and ctDNA. We also briefly explore the emerging evidence on extracellular vesicles, circulating proteins and synthetic biomarkers, discuss the limitations of current approaches and provide suggestions to achieve further progress in this setting.

Background and ObjectivesParaneoplastic neurologic syndromes (PNS) are cancer-related neurologic disorders caused by an autoimmune response targeting both the tumor and the nervous system. Identifying new biomarkers for early cancer detection could improve treatment outcomes. Tumor-derived cells release extracellular vesicles (EVs) carrying tumor-specific molecular signatures, which can help distinguish patients with cancer even in early stages. The aim of this study was to assess the potential of EVs as biomarkers to enhance cancer detection in patients with PNS.MethodsThis observational and multicenter study included 27 patients with tumor-associated PNS, 26 with suspected PNS without a tumor, 35 with cancer, and 32 healthy controls. Subsequently, among the patients with PNS, individuals were subclassified according to the PNS-Care Score as definite, probable, possible, and non-PNS. Total EVs were isolated from blood by precipitation and from B cells, T cells, and neurons by immunoisolation. To identify a biomarker for diagnostic refinement and clinical stratification, EV levels, size, and protein content were compared across study groups. To find a tumor biomarker in intermediate-risk cases, the possible association between EV protein content and cancer detection in intermediate-risk syndromes was analyzed.ResultsPatients with tumor-associated PNS showed significantly higher circulating EV levels compared with those with suspected PNS without evidence of a tumor (p = 0.028). Moreover, total EV levels, along with B cell–derived EVs, effectively differentiated patients with definite PNS from those with probable (p = 0.05) and possible (p = 0.006) PNS. A cutoff value of 2.10 × 1010 particles/mL EVs was identified, above which diagnosis of PNS was definite, with 86% sensitivity and 81% specificity. Proteomic analysis identified specific proteins, including ACADM, HPT, ACTBL, and CCAR2, as markers of definite PNS, differentiating such patients from those with probable and possible PNS, contributing to diagnostic refinement for clinical stratification. It is important to note that increased levels of EVs and CD44 distinguished intermediate-risk cases with tumors from those without.DiscussionEVs may act as tumor biomarkers in patients with PNS, even in intermediate-risk cases.Classification of EvidenceThis study provides Class IV evidence that higher circulating blood levels of EVs can distinguish between tumor-associated PNS from suspected PNS without tumor.

Blood-based early detection of breast cancer has recently gained novel momentum, as liquid biopsy diagnostics is a fast emerging field. In this study, we aimed to identify secreted proteins which are up-regulated both in tumour tissue and serum samples of breast cancer patients compared to normal tissue and sera. Based on two independent tissue cohorts (n = 75 and n = 229) and one serum cohort (n = 80) of human breast cancer and healthy serum samples, we characterised AGR3 as a novel potential biomarker both for breast cancer prognosis and early breast cancer detection from blood. AGR3 expression in breast tumours is significantly associated with oestrogen receptor α (P<0.001) and lower tumour grade (P<0.01). Interestingly, AGR3 protein expression correlates with unfavourable outcome in low (G1) and intermediate (G2) grade breast tumours (multivariate hazard ratio: 2.186, 95% CI: 1.008-4.740, P<0.05) indicating an independent prognostic impact. In sera analysed by ELISA technique, AGR3 protein concentration was significantly (P<0.001) elevated in samples from breast cancer patients (n = 40, mainly low stage tumours) compared to healthy controls (n = 40). To develop a suitable biomarker panel for early breast cancer detection, we measured AGR2 protein in human serum samples in parallel. The combined AGR3/AGR2 biomarker panel achieved a sensitivity of 64.5% and a specificity of 89.5% as shown by receiver operating characteristic (ROC) curve statistics. Thus our data clearly show the potential usability of AGR3 and AGR2 as biomarkers for blood-based early detection of human breast cancer.

Excessive telomere shortening is observed in breast cancer lesions when compared to adjacent non-cancerous tissues, suggesting that telomere length may represent a key biomarker for early cancer detection. Because tumor-derived, cell-free DNA (cfDNA) is often released from cancer cells and circulates in the bloodstream, we hypothesized that breast cancer development is associated with changes in the amount of telomeric cfDNA that can be detected in the plasma. To test this hypothesis, we devised a novel, highly sensitive and specific quantitative PCR (qPCR) assay, termed telomeric cfDNA qPCR, to quantify plasma telomeric cfDNA levels. Indeed, the internal reference primers of our design correctly reflected input cfDNA amount (R2 = 0.910, P = 7.82 × 10−52), implying accuracy of this assay. We found that plasma telomeric cfDNA levels decreased with age in healthy individuals (n = 42, R2 = 0.094, P = 0.048), suggesting that cfDNA is likely derived from somatic cells in which telomere length shortens with increasing age. Our results also showed a significant decrease in telomeric cfDNA level from breast cancer patients with no prior treatment (n = 47), compared to control individuals (n = 42) (P = 4.06 × 10−8). The sensitivity and specificity for the telomeric cfDNA qPCR assay was 91.49% and 76.19%, respectively. Furthermore, the telomeric cfDNA level distinguished even the Ductal Carcinoma In Situ (DCIS) group (n = 7) from the healthy group (n = 42) (P = 1.51 × 10−3). Taken together, decreasing plasma telomeric cfDNA levels could be an informative genetic biomarker for early breast cancer detection.

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

Introduction: Sarcomas are a diverse group of tumours encompassing over 100 different subtypes occurring in bone, muscle, and cartilage. The sarcoma tumour microenvironment (TME) contains various immune modulators and is influenced by the specific sarcoma subtype. The sarcoma TME is modulated by secretion of cytokines, chemokines, and extracellular vesicles (EVs). EVs play a role in inflammatory and immune responses in the TME. EVs effects are attributed to their cargo, consisting of mRNAs, proteins, miRNAs, chemokines, and cytokines. Sarcoma cells release tumour-derived EVs, facilitating communication, tumour progression, and inflammation. Methods: We conducted a literature search for keywords and concepts associated with EVs and inflammatory responses in sarcomas on the PubMed database. Keyword combinations included relevance to EVs, sarcomas and inflammation. Only studies from peer reviewed journals, written in English, and published between 2013-2024 were considered. Sarcoma subtypes viewed included: rhabdomyosarcoma, Ewing sarcoma, chondrosarcoma, liposarcoma, and osteosarcoma. Results: Ewing sarcoma EVs carry miRNA and mRNAs including EWS/FLI-1 mRNA, and surface proteins causing upregulation of inflammatory pathways, and contributing to the release of pro-inflammatory cytokines. Rhabdomyosarcoma EVs carry miRNAs and PAX3/7-FOXO1 mRNA stimulating inflammatory pathways to release cytokines and chemokines. Chondrosarcoma’s hypoxic TME causes increased EV secretion, favouring M2 polarisation, and the production of immunosuppressive markers. Liposarcoma EVs enriched with miRNAs and MDM2 induced an inflammatory response in macrophages. Osteosarcoma EVs contained TGF-β and miRNA cargo which influenced macrophage polarisation. Discussion: Sarcoma-derived EVs contain a wide range of mRNAs and miRNAs, with Ewing sarcoma and Rhabdomyosarcoma also containing the fusion oncogenes EWS/FLI-1 and PAX3/7-FOXO1, respectively. Surface proteins on Ewing sarcoma and osteosarcoma EVs were better understood compared to other sarcomas. Various sarcoma-derived EV cargo demonstrated potential to influence inflammatory pathways within macrophages. Macrophage polarisation is fundamental to immune function, determining pro-inflammatory and anti-inflammatory responses. Conclusion: By understanding the role of EVs in the inflammatory process, a deeper insight in tumour progression and tumour cell-cell communication can be achieved. The uniqueness of EV cargo to certain sarcoma subtypes could potentially indicate a future as biomarkers for early cancer detection. Further research into inflammatory effects by EVs can provide potential as novel therapeutics.

Background: Cancer is the second leading cause of death in the world. Unfortunately, survival rates for cancer patients have stayed poor for many decades. Thus, there is an unmet need to identify novel strategies for early cancer detection and prevention. Recent studies suggest that the gut microbiome including bacteria may have a role in cancer initiation and progression. These bacteria play a vital role in maintaining homeostasis in the body. An imbalance in the bacterial composition may cause diseases including cancer. This evidence led us to hypothesize that the changes in the composition of the gut microbiome can be used as a biomarker for early cancer detection. To test this hypothesis, we focused on analyzing the microbial community of colorectal and blood cancers, which have poor survival rates. Methods and Results: To analyze the association of cancer with the microbiome, we used a published microbiome dataset of leukemic and healthy individuals that were collected by sequencing stool samples (Oliver et al., 2022). We identified that specific bacteria are either abundant, such as Faecalibacterium (1.17 fold), or reduced, such as Clostridium (0.32 fold, p value=0.0029), in leukemia patients compared to healthy controls. We observed similar correlations by analyzing the published colorectal cancer data (Flemer et al., 2017) in which Prevotella increased by 3.75 fold and Escherichia/Shigella increased by 4.0 fold in colorectal cancer while Blautia is reduced compared to healthy controls. Faecalibacterium, which is abundant in leukemia, is reduced in colorectal cancer. Further, we found that treatment of leukemia patients normalized the abundance of these bacteria to the level of healthy patients. To improve analysis efficiency, we applied machine learning algorithms to predict the presence of cancer in patients based on the composition of bacteria, age, gender, and symptoms. Among the tested algorithms, the decision tree and Random Forest both had very high false-negative rates, with 45.5% and 40% respectively, as well as having low sensitivities, with 50% and 60% respectively. The support Vector Machine had a suboptimal false-negative rate of 28.6% and good sensitivity of 80%. Finally, the Neural network had a good false-negative rate of 16.7% and great sensitivity of 90%. Overall, our results suggest that different cancers have an abundance of specific bacteria that can be used as biomarkers for cancer detection. Conclusion: We demonstrate that the increased abundance of certain bacteria in the microbiome can be used as a biomarker for cancer detection. Our data set was small for this project, but the machine learning approaches can be used to further predict cancer These are clinically relevant findings as a microbiome test would be non-invasive and easily accessible that can be performed at a routine checkup. Citation Format: Ekansh Mittal, Andrew Oliver, Kenza El Alaoui, Carolyn Haunschild, Julio Avelar-Barragan, Laura F. Mendez Luque, Katrine Whiteson, Angela G. Fleischman. Specific gut microbial community is associated with specific cancer types: A strategy for cancer detection and prevention [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5908.

Early cancer detection is crucial for improving patient outcomes. Molecular imaging biomarkers offer the potential for non-invasive, early-stage cancer diagnosis. To evaluate the effectiveness and accuracy of molecular imaging biomarkers for early cancer detection across various imaging modalities and cancer types. A comprehensive search of PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus was performed, covering the period from January 2010 to December 2023. Eligibility criteria included original research articles published in English on molecular imaging biomarkers for early cancer detection in humans. The risk of bias for included studies was evaluated using the QUADAS-2 tool. The findings were synthesized through narrative synthesis, with quantitative analysis conducted where applicable. In total, 50 studies were included. Positron emission tomography (PET)-based biomarkers showed the highest sensitivity (mean: 89.5%, range: 82-96%) and specificity (mean: 91.2%, range: 85-100%). Novel tracers such as [68Ga]-PSMA for prostate cancer and [18F]-FES for breast cancer demonstrated promising outcomes. Optical imaging techniques showed high specificity in intraoperative settings. Molecular imaging biomarkers show significant potential for improving early cancer detection. Integration into clinical practice could lead to earlier interventions and improved outcomes. Further research is needed to address standardization and cost-effectiveness.

Circulating tumor-derived extracellular vesicles (EVs) have emerged as a promising source for identifying cancer biomarkers for early cancer detection. However, the clinical utility of EVs has thus far been limited by the fact that most EV isolation methods are tedious, nonstandardized, and require bulky instrumentation such as ultracentrifugation (UC). Here, we report a size-based EV isolation tool called ExoTIC (exosome total isolation chip), which is simple, easy-to-use, modular, and facilitates high-yield and high-purity EV isolation from biofluids. ExoTIC achieves an EV yield ∼4-1000-fold higher than that with UC, and EV-derived protein and microRNA levels are well-correlated between the two methods. Moreover, we demonstrate that ExoTIC is a modular platform that can sort a heterogeneous population of cancer cell line EVs based on size. Further, we utilize ExoTIC to isolate EVs from cancer patient clinical samples, including plasma, urine, and lavage, demonstrating the device's broad applicability to cancers and other diseases. Finally, the ability of ExoTIC to efficiently isolate EVs from small sample volumes opens up avenues for preclinical studies in small animal tumor models and for point-of-care EV-based clinical testing from fingerprick quantities (10-100 μL) of blood.

Salivary extracellular vesicles (EVs) have been recognized as one of the most promising noninvasive sources of biomarkers for early cancer detection. However, the lack of efficient isolation and accurate identification methods for salivary EVs limits their clinical utility in early cancer diagnosis. Here, we report the rapid and precise identification of esophageal cancer using the Saliva Extracellular vesicle-based Early Diagnostic system (SEEDx). In this system, salivary EVs were isolated, purified, amplified, and analyzed from human saliva by integrating the ultrafast-isolation platform EXODUS with tandem mass tag (TMT)-based proteomics analysis (EXODUS-TMT proteomics). Using TCGA esophageal cancer (EC) and GTEx healthy population cohorts, we developed a diagnostic model consisting of 9 markers (HIST2H2BE, TP53BP2, TPM4, CALR, HDGF, LMNA, GUSB, NADSYN1, and AGRN), which demonstrated high diagnostic accuracy with AUC values of 0.996 for EC and 0.991 for early-stage EC. We further established a cost-effective model using only two markers (HDGF and CALR) to diagnose both EC and early-stage EC, achieving AUC values of 0.950 and 0.935, respectively. This model was validated in an independent, prospectively collected cohort of EC patients, yielding AUC values of 0.873 and 0.854, respectively. Our streamlined EV isolation and identification workflow facilitates noninvasive biomarker discovery for early-stage esophageal cancer detection. This study extends the EXODUS platform to salivary EVs for EC detection, documents matrix-specific workflow optimizations, and identifies saliva-derived biomarker signatures integrated into a multimarker diagnostic model.

Breast cancer is the most common cancer amongst women worldwide. Recently, owing to screening programs and new technologies, the survival rate has increased significantly. Breast cancer can potentially develop metastases, and, despite them, lung metastases generally occur within five years of breast cancer diagnosis. In this study, the objective was to analyze the effect of breast cancer-derived EVs on a lung epithelial cell line. BEAS-2B cells were treated with extracellular vesicles (EVs) derived from triple-negative breast cancer cells (TNBCs), e.g., MDA-MB-231 and HS578T, separated using differential ultracentrifugation. We observed an increased growth, migration, and invasiveness of normal epithelial lung cells over time in the presence of TNBC EVs compared to the control. Therefore, these data suggest that EVs released by tumor cells contain biological molecules capable of influencing the pro-tumorigenic activity of normal cells. Exploring the role of EVs in oncology research and their potential cargo may be novel biomarkers for early cancer detection and further diagnosis.

Circulating extracellular vesicles and particles (EVPs) are being investigated as potential biomarkers for early cancer detection, prognosis, and disease monitoring. However, the suboptimal purity of EVPs isolated from peripheral blood plasma has posed a challenge of in‐depth analysis of the EVP proteome. Here, we compared the effectiveness of different methods for isolating EVPs from healthy donor plasma, including ultracentrifugation (UC)‐based protocols, phosphatidylserine‐Tim4 interaction‐based affinity capture (referred to as “PS”), and several commercial kits. Modified UC methods with an additional UC washing or size exclusion chromatography step substantially improved EVP purity and enabled the detection of additional proteins via proteomic mass spectrometry, including many plasma membrane and cytoplasmic proteins involved in vesicular regulation pathways. This improved performance was reproduced in cancer patient plasma specimens, resulting in the identification of a greater number of differentially expressed EVP proteins, thus expanding the range of potential biomarker candidates. However, PS and other commercial kits did not outperform UC‐based methods in improving plasma EVP purity. PS yielded abundant contaminating proteins and a biased enrichment for specific EVP subsets, thus unsuitable for proteomic profiling of plasma EVPs. Therefore, we have optimized UC‐based protocols for circulating EVP isolation, which enable further in‐depth proteomic analysis for biomarker discovery.

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Simple SummaryCKAP4, originally identified as an endoplasmic reticulum protein, has been found to localize on the plasma membrane and function as a receptor for various ligands, notably Dickkopf-1 (DKK1). The interaction between DKK1 and CKAP4 activates the PI3K/AKT signaling pathway, promoting cancer cell proliferation and metastasis in various GI malignancies, including esophageal, gastric, pancreatic, and colorectal cancers. The review highlights new findings on CKAP4′s involvement in tumor progression, its potential as a diagnostic biomarker, and its promise as a therapeutic target. These findings could influence future studies by directing research towards developing CKAP4-targeted therapies, exploring its potential as a serum biomarker for early cancer detection, and investigating combination therapies that include CKAP4 inhibition.Cytoskeleton-associated protein 4 (CKAP4) has emerged as a critical player in gastrointestinal (GI) cancer progression, diagnosis, and therapy. This comprehensive review synthesizes current knowledge on CKAP4′s multifaceted roles across GI malignancies, providing novel insights into its mechanisms of action and clinical potential. Its interaction with DKK1 and subsequent activation of the PI3K/AKT pathway underscores its role in promoting tumor growth. This review also highlights novel insights into CKAP4′s mechanisms of action beyond the well-established DKK1-CKAP4 axis, including its interaction with integrin β1 and involvement in angiogenesis through the FMNL2/EGFL6/CKAP4/ERK pathway. CKAP4′s impact on tumor microenvironment and immune evasion is elucidated, offering a new perspective on its contribution to cancer progression. In addition, CKAP4 arises as a promising serum biomarker for early detection and prognosis across multiple GI cancers, emphasizing its potential superiority over traditional markers. The therapeutic potential of targeting CKAP4 is extensively explored, including novel approaches like anti-CKAP4 antibodies and aptamers, and their synergistic effects with existing treatments. By integrating findings from esophageal, gastric, pancreatic, and colorectal cancers, this review provides a unique, comprehensive overview of CKAP4 in GI oncology, underscoring CKAP4′s potential to revolutionize GI cancer diagnosis and treatment and paving the way for future translational research.

One of the cornerstones of effective cancer treatment is early diagnosis. In this context, the identification of proteins that can serve as cancer biomarkers in bodily fluids ("liquid biopsies") has gained attention over the last decade. Plasma and serum fractions of blood are the most commonly investigated sources of potential cancer liquid biopsy biomarkers. However, the high complexity and dynamic range typical of these fluids hinders the sensitivity of protein detection by the most commonly used mass spectrometry technology (data-dependent acquisition mass spectrometry (DDA-MS)). Recently, data-independent acquisition mass spectrometry (DIA-MS) techniques have overcome the limitations of DDA-MS, increasing sensitivity and proteome coverage. In addition to DIA-MS, isolating extracellular vesicles (EVs) can help to increase the depth of serum/plasma proteome coverage by improving the identification of low-abundance proteins which are a potential treasure trove of diagnostic molecules. EVs, the nano-sized membrane-enclosed vesicles present in most bodily fluids, contain proteins which may serve as potential biomarkers for various cancers. Here, we describe a detailed protocol that combines DIA-MS and EV methodologies for discovering and validating early cancer biomarkers using blood serum. The pipeline includes size exclusion chromatography methods to isolate serum-derived extracellular vesicles and subsequent EV sample preparation for liquid chromatography and mass spectrometry analysis. Procedures for spectral library generation by DDA-MS incorporate methods for off-line peptide separation by microflow HPLC with automated fraction concatenation. Analysis of the samples by DIA-MS includes recommended protocols for data processing and statistical methods. This pipeline will provide a guide to discovering and validating EV-associated proteins that can serve as sensitive and specific biomarkers for early cancer detection and other diseases.