Pancreatic ductal adenocarcinoma represents one of the solid tumors showing the worst prognosis worldwide, with a high recurrence rate after adjuvant or neoadjuvant therapy. Circulating tumor DNA analysis raised as a promising non-invasive tool to characterize tumor genomics and to assess treatment response. In this study, surgical tumor tissue and sequential blood samples were analyzed by next-generation sequencing and were correlated with clinical and pathological characteristics. Thirty resectable/borderline pancreatic ductal adenocarcinoma patients treated at the Hospital Universitario de Navarra were included. Circulating tumoral DNA sequencing identified pathogenic variants in KRAS and TP53, and in other cancer-associated genes. Pathogenic variants at diagnosis were detected in patients with a poorer outcome, and were correlated with response to neoadjuvant therapy in borderline pancreatic ductal adneocarcinoma patients. Higher variant allele frequency at diagnosis was associated with worse prognosis, and thesum of variant allele frequency was greater in samples at progression. Our results build on the potential value of circulating tumor DNA for non-metastatic pancreatic ductal adenocarcinoma patients, by complementing tissue genetic information and as a non-invasive tool for treatment decision. Confirmatory studies are needed to corroborate these findings.

BACKGROUND: Optimizing functional outcomes and securing long-term remissions are key goals in managing patients with locally advanced rectal cancer. In this proof-of-concept study, we set out to further optimize neoadjuvant therapy by integrating the radiosensitizer trifluridine/tipiracil and explore the potential of cell free tumor DNA (ctDNA) to monitor residual disease.
METHODS: About 10 patients were enrolled in the phase I dose finding part which followed a 3 + 3 dose escalation design. Tipiracil/trifluridine was administered concomitantly to radiotherapy. ctDNA monitoring was performed before and after chemoradiation with patient-individualized digital droplet PCRs.
RESULTS: No dose-limiting toxicities were observed at the maximum tolerated dose level of 2 × 35 mg/m² trifluridine/tipiracil. There were 9 grade 3 adverse events, of which 8 were hematologic with anemia and leukopenia. Chemoradiation yielded a pathological complete response in 1 out of 8 assessable patients, downstaging in nearly all patients, and 1 clinical complete response referred for watchful waiting. Three of 4 assessable patients with residual tumor cells at pathological assessment remained liquid biopsy positive after chemoradiation, but 1 turned negative.
CONCLUSIONS: In this exploratory phase I trial, the novel combination of neoadjuvant trifluridine/tipiracil and radiotherapy proved to be feasible, tolerable, and effective. However, the application of liquid biopsy as a potential marker for therapeutic de-escalation in the neoadjuvant setting requires additional research and prospective validation. The trial was registered at ClinicalTrials.gov: NCT04177602.

Extracellular vesicles (EVs) have emerged as a promising tool for clinical liquid biopsy. However, the identification of EVs derived from blood samples is hindered by the presence of abundant plasma proteins, which impairs the downstream biochemical analysis of EV-associated proteins and nucleic acids. Here, we employed optimized asymmetric flow field-flow fractionation (AF4) combined with density cushion ultracentrifugation (UC) to obtain high-purity and intact EVs with very low lipoprotein contamination from human plasma and serum. Further proteomic analysis revealed more than 1000 EV-associated proteins, a large proportion of which has not been previously reported. Specifically, we found that cell-line-derived EV markers are incompatible with the identification of plasma-EVs and proposed that the proteins MYCT1, TSPAN14, MPIG6B and MYADM, as well as the traditional EV markers CD63 and CD147, are plasma-EV markers. Benefiting from the high-purity of EVs, we conducted comprehensive miRNA profiling of plasma EVs and nanosized particles (NPs), as well as compared plasma- and serum-derived EVs, which provides a valuable resource for the EV research community. Overall, our findings provide a comprehensive assessment of human blood EVs as a basis for clinical biopsy applications.

Early cancer detection and accurate monitoring are crucial to ensure increased patient survival. Recent research has focused on developing non-invasive biomarkers to diagnose cancer early and monitor disease progression at low cost and risk. Extracellular vesicles (EVs), nanosized particles secreted into extracellular spaces by most cell types, are gaining immense popularity as novel biomarker candidates for liquid cancer biopsy, as they can transport bioactive cargo to distant sites and facilitate intercellular communications. A literature search was conducted to discuss the current approaches for EV isolation and the advances in using EV-associated proteins, miRNA, mRNA, DNA, and lipids as liquid biopsies. We discussed the advantages and challenges of using these vesicles in clinical applications. Moreover, recent advancements in machine learning as a novel tool for tumor marker discovery are also highlighted.

Serum prostate-specific antigen (PSA), its derivatives, and magnetic resonance tomography (MRI) lack sufficient specificity and sensitivity for the prediction of risk reclassification of prostate cancer (PCa) patients on active surveillance (AS). We investigated selected transcripts in urinary extracellular vesicles (uEV) from PCa patients on AS to predict PCa risk reclassification (defined by ISUP 1 with PSA > 10 ng/mL or ISUP 2-5 with any PSA level) in control biopsy. Before the control biopsy, urine samples were prospectively collected from 72 patients, of whom 43% were reclassified during AS. Following RNA isolation from uEV, multiplexed reverse transcription, and pre-amplification, 29 PCa-associated transcripts were quantified by quantitative PCR. The predictive ability of the transcripts to indicate PCa risk reclassification was assessed by receiver operating characteristic (ROC) curve analyses via calculation of the area under the curve (AUC) and was then compared to clinical parameters followed by multivariate regression analysis. ROC curve analyses revealed a predictive potential for AMACR, HPN, MALAT1, PCA3, and PCAT29 (AUC = 0.614-0.655, p < 0.1). PSA, PSA density, PSA velocity, and MRI maxPI-RADS showed AUC values of 0.681-0.747 (p < 0.05), with accuracies for indicating a PCa risk reclassification of 64-68%. A model including AMACR, MALAT1, PCAT29, PSA density, and MRI maxPI-RADS resulted in an AUC of 0.867 (p < 0.001) with a sensitivity, specificity, and accuracy of 87%, 83%, and 85%, respectively, thus surpassing the predictive power of the individual markers. These findings highlight the potential of uEV transcripts in combination with clinical parameters as monitoring markers during the AS of PCa.

Immune checkpoint inhibitors have promising outcomes in patients with hepatocellular carcinoma (HCC); however, there is no reliable biomarker for predicting disease progression. Circulating tumor cells (CTCs) derived from peripheral blood have attracted attention in monitoring therapeutic efficacy. In this study, CTCs were serially collected from HCC patients undergoing atezolizumab plus bevacizumab (Atezo+Bev), and changes in molecular expression and CTC numbers were analyzed to identify effective biomarkers. Changes in CTC numbers during Atezo+Bev reflected the tumor volume. Targeted RNA sequencing with next-generation sequencing (NGS) revealed that patients with elevated transforming growth factor (TGF)-β signaling molecules had a poorer response, whereas those with elevated apoptosis signaling molecules had a favorable response. In addition, compared with changes in CTC counts, changes in TGF-β signaling molecule expression in CTCs accurately and promptly predicted treatment response. Overall, NGS analysis of CTC-derived RNA showed that changes in TGF-β signaling molecules predict treatment response earlier than changes in CTC counts. These findings suggest that changes in the expression of TGF-β molecules in CTCs could serve as novel biomarkers for the early prediction of therapeutic response in patients with unresectable HCC undergoing Atezo+Bev.

Ovarian cancer (OC) is the deadliest gynaecological malignancy. Identifying new prognostic biomarkers is an important research field. Haemostatic components together with leukocytes can drive cancer progression while increasing the susceptibility to venous thromboembolism (VTE) through immunothrombosis. Unravelling the underlying complex interactions offers the prospect of uncovering relevant OC prognostic biomarkers, predictors of cancer-associated thrombosis (CAT), and even potential targets for cancer therapy. Thus, this study evaluated the expression of F3, F5, F8, F13A1, TFPI1, and THBD in peripheral blood cells (PBCs) of 52 OC patients. Those with VTE after tumour diagnosis had a worse overall survival (OS) compared to their counterparts (mean OS of 13.8 ± 4.1 months and 47.9 ± 5.7 months, respectively; log-rank test, p = 0.001). Low pre-chemotherapy F3 and F8 expression levels were associated with a higher susceptibility for OC-related VTE after tumour diagnosis (χ2, p < 0.05). Regardless of thrombogenesis, patients with low baseline F8 expression had a shorter progression-free survival (PFS) than their counterparts (adjusted hazard ratio (aHR) = 2.54; p = 0.021). Among those who were not under platelet anti-aggregation therapy, low F8 levels were also associated with a shorter OS (aHR = 6.16; p = 0.006). Moving forward, efforts should focus on external validation in larger cohorts.

GBM WHO CNS Grade 4 represents a major challenge for oncology due to its aggressive behavior. Conventional imaging has restrictions in detecting tumor recurrence. This prospective study aims to identify gene-based biomarkers in whole blood instead of isolating exosomes for the early detection of tumor recurrence. Blood samples (n = 33) were collected from seven GBM patients at time points before and after surgery as well as upon tumor recurrence. Four tumor tissue samples were assessed in parallel. Next-generation sequencing (NGS), including mRNA-seq and small RNA-seq, was used to analyze gene expression profiles in blood samples and tumor tissues. A novel filtering pipeline was invented to narrow down potential candidate genes. In total, between 6-93 mRNA and 1-19 small RNA candidates could be identified among the seven patients. The overlap of genes between the patients was minimal, indicating significant inter-individual variance among GBM patients. In summary, this prospective study supports the applicability of gene expression measurements in whole blood for the detection of tumor recurrence. It might provide an alternative to the challenging workflow of liquid biopsy after laborious exosome isolation from whole blood.

Gastric cancer is the fifth most common disease in the world and the fourth most common cause of death. It is diagnosed through esophagogastroduodenoscopy with biopsy; however, there are limitations in finding lesions in the early stages. Recently, research has been actively conducted to use liquid biopsy to diagnose various cancers, including gastric cancer. Various substances derived from cancer are reflected in the blood. By analyzing these substances, it was expected that not only the presence or absence of cancer but also the type of cancer can be diagnosed. However, the amount of these substances is extremely small, and even these have various variables depending on the characteristics of the individual or the characteristics of the cancer. To overcome these, we collected methylated DNA fragments using MeDIP and compared them with normal plasma to characterize gastric cancer tissue or patients\' plasma. We attempted to diagnose gastric cancer using the characteristics of cancer reflected in the blood through the cancer tissue and patients\' plasma. As a result, we confirmed that the consistency of common methylated fragments between tissue and plasma was approximately 41.2% and we found the possibility of diagnosing and characterizing cancer using the characteristics of the fragments through SFR and 5\'end-motif analysis.

Exosomes, as a class of small extracellular vesicles closely related to the biological behavior of various types of tumors, are currently attracting research attention in cancer diagnosis and treatment. Regarding cancer diagnosis, the stability of their membrane structure and their wide distribution in body fluids render exosomes promising biomarkers. It is expected that exosome-based liquid biopsy will become an important tool for tumor diagnosis in the future. For cancer treatment, exosomes, as the \"golden communicators\" between cells, can be designed to deliver different drugs, aiming to achieve low-toxicity and low-immunogenicity targeted delivery. Signaling pathways related to exosome contents can also be used for safer and more effective immunotherapy against tumors. Exosomes are derived from a wide range of sources, and exhibit different biological characteristics as well as clinical application advantages in different cancer therapies. In this review, we analyzed the main sources of exosomes that have great potential and broad prospects in cancer diagnosis and therapy. Moreover, we compared their therapeutic advantages, providing new ideas for the clinical application of exosomes.