Gastrointestinal (GI) cancers are among the most prevalent and lethal malignancies worldwide. Early, non-invasive detection is essential for timely intervention and improved survival. To address this clinical need, we developed GutSeer, a blood-based assay combining DNA methylation and fragmentomics for multi-GI cancer detection.

Genome-wide methylome profiling identified 1,656 markers specific to five major GI cancers and their tissue origins. Based on these findings, we designed GutSeer, a targeted bisulfite sequencing panel, which was trained and validated using plasma samples from 1,057 cancer patients and 1,415 non-cancer controls. The locked model was blindly tested in an independent cohort of 846 participants, encompassing both inpatient and outpatient settings across five hospitals.

In the validation cohort, GutSeer achieved an area under the curve (AUC) of 0.950 [95% Confidence Interval (CI): 0.937-0.962] for cancer detection, with 82.8% sensitivity (95% CI: 79.5-86.0) and 95.8% specificity (95% CI: 94.3-97.2). It detected 92.2% of colorectal, 75.5% of esophageal, 65.3% of gastric, 92.9% of liver, and 88.6% of pancreatic cancers. The independent test cohort included 198 early-stage cancers (stage I/II, 66.4%) and 63 advanced precancerous lesions. GutSeer maintained robust performance, with 81.5% sensitivity (95% CI: 77.1-85.9) for GI cancers and 94.4% specificity (95% CI: 92.4-96.5). It also demonstrated the ability to detect advanced precancerous lesions in the colorectum, esophagus, and stomach as a single, non-invasive blood test.

By integrating DNA methylation and fragmentomics into a compact panel, GutSeer outperformed genome-wide sequencing in both accuracy and clinical applicability. Its high sensitivity for early-stage GI cancers and practicality as a non-invasive assay highlights its potential to revolutionize early cancer detection and improve patient outcomes.

ClinicalTrials.gov identifier: NCT05431621.

Treatment response biomarkers are needed in the care of patients hospitalised with HIV-associated tuberculosis (TB).

We describe the changes in bacillary load during early treatment using quantitative and semi-quantitative measures of Mycobacterium tuberculosis in blood and urine.

We collected serial blood and urine samples at multiple timepoints in consenting adult patients with HIV and positive urine lipoarabinomannan (LAM), admitted to Mitchells Plain Hospital, Cape Town. Blood and urine Xpert Ultra, mycobacterial blood culture and urine LAM were performed. Survival analysis and mixed-effects modelling were used to determine time to a negative test, and to give the predicted probability of a positive test at the different timepoints.

Sixteen participants, predominantly male (63%), with median age 39 years (interquartile range [IQR] 36-43), and CD4 count 27 cells/mm3 (IQR 8-83) were included. At day 14, urine LAM, urine Xpert Ultra and blood Xpert Ultra remained positive in between 75% and 86% of the participants. A mixed-effects model predicted a decline in ordinal values of urine Xpert Ultra (cycle threshold), blood Xpert Ultra (cycle threshold) and blood culture (time-to-positivity) in response to anti-TB treatment. Conversely, urine LAM grade intensity increased over the 14 days.

M. tuberculosis DNA was detectable in urine and blood in decreasing quantity up to 14 days of standard treatment in patients with HIV-associated TB. Urine Alere LAM showed an increasing grade intensity during this period. Further research in larger groups and extended periods are needed to assess relation to clinical outcomes.

Liquid biopsies, which analyze circulating tumor cells or cell-free circulating tumor DNA (ctDNA) from blood, have emerged as promising cancer detection and monitoring tools. Specifically, human papillomavirus (HPV) cell-free (cf) DNA is gaining recognition as a prognostic marker in high-risk HPV-related cancers. However, detecting circulating markers for cervical cancer (CC) requires highly sensitive techniques to quantify circulating HPV DNA. This study aimed to evaluate the use of droplet digital PCR (ddPCR), a highly sensitive technique, for detecting and quantifying circulating HPV DNA in cervical cancer patients, both at baseline (before chemo- or radiotherapy) and during follow-up, to assess its utility as a prognostic marker. Blood samples were collected from 60 cervical cancer patients (Stages I-IV) at AIIMS, New Delhi, at baseline and three months post-treatment. Samples from 10 healthy controls were also included. Plasma was separated and stored at - 80 °C, and cfDNA was extracted from 1 ml of plasma. The presence of high-risk HPV types, HPV16 and HPV18, in cfDNA from 35 patients was assessed using ddPCR. The median concentration of cfDNA in cervical cancer patients was 9.35 ng/µL at baseline, which decreased to 7 ng/µL after three months of treatment. In healthy controls, the median cfDNA concentration was 6.95 ng/µL. ddPCR screening showed that detection rates for HPV18 and HPV16 detection were 45.71% and 82.86%, respectively. A significant correlation was observed between cf HPV16 DNA levels and tumor size, suggesting its potential as biomarker for disease burden.

In the practice of colorectal cancer (CRC), traditional tumor tissue analysis is limited by intratumoral and intertumoral heterogeneity and its invasive nature. Circulating tumor DNA (ctDNA) analysis, a promising liquid biopsy approach, has been increasingly explored in clinical studies. Biologically, ctDNA is characterized by tumor-specific diversity and rapid clearance from circulation, enabling real-time, dynamic, and repeatable assessments. Technologically, PCR- and NGS-based downstream analysis methods have been developed and validated. However, variables in pre-analytical and analytical procedures underscores the need for standardized protocols. Compared with clinicopathology-based risk stratification, ctDNA-based molecular residual disease detection has demonstrated significant potential in guiding treatment decisions. Qualitative and quantitative changes in ctDNA have also shown predictive and prognostic value during neoadjuvant or adjuvant treatment, as well as in later-line treatment for metastatic CRC. Specific molecular aberrations in ctDNA can not only assist in identifying candidates for targeted therapies but also reveal resistance mechanisms. Additionally, emerging research is exploring the potential of ctDNA in early cancer detection. Overall, as a novel biomarker, ctDNA holds substantial promise in advancing clinical practice. This review focuses on the biological characteristics, pre-analytical variables, and downstream analysis methods of ctDNA and summarizes its role across various clinical scenarios in CRC.

Lymphoma, a malignant tumor derived from lymphocytes and lymphoid tissues, presents with complex and heterogeneous clinical manifestations, requiring accurate patient classification for appropriate treatment. While invasive pathological examination of lymph nodes or lymphoid tissue remains the gold standard for lymphoma diagnosis, its utility is limited in cases of deep-seated tumors such as intraperitoneal and central nervous system lymphomas. In addition, biopsy procedures carry an inherent risk of complications. Computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) imaging are essential for treatment assessment and monitoring, but lack the ability to detect early clonal evolution and minimal residual disease (MRD). Liquid biopsy-based analysis of circulating tumor DNA (ctDNA) offers a non-invasive alternative that allows for repeated sampling and overcomes the limitations of spatial heterogeneity and invasive biopsies. ctDNA provides genetic and epigenetic insights into lymphoma and serves as a dynamic, quantifiable biomarker for diagnosis, risk stratification, and treatment response. This review comprehensively summarizes common genetic variations in lymphoma and systematically evaluates ctDNA detection technologies, including PCR-based assays and next-generation sequencing (NGS). Applications of ctDNA detection in noninvasive genotyping, risk stratification, therapeutic response monitoring, and MRD detection are discussed across various lymphoma subtypes, including diffuse large B-cell lymphoma, Hodgkin lymphoma, follicular lymphoma, and T-cell lymphoma. By integrating recent research findings, the review highlights the role of ctDNA profiling in advancing precision medicine, enabling personalized therapeutic strategies, and improving clinical outcomes in lymphoma.

Cell-free DNA (cfDNA) in plasma carries epigenetic signatures specific to tissue or cell of origin. Aberrant methylation patterns in circulating cfDNA have emerged as valuable tools for noninvasive cancer detection, prenatal diagnostics, and organ transplant assessment. Such epigenetic changes also hold significant promise for the diagnosis of neurodegenerative diseases, which often progresses slowly and has a lengthy asymptomatic period. However, genome-wide cfDNA methylation changes in neurodegenerative diseases remain poorly understood.

We used whole-genome bisulfite sequencing (WGBS) to profile age-dependent and ALS-associated methylation signatures in cfDNA from 30 individuals, including young and middle-aged controls, as well as ALS patients with matched controls. We identified 5,223 age-related differentially methylated loci (DMLs) (FDR < 0.05), with 51.6% showing hypomethylation in older individuals. Our results significantly overlapped with age-associated CpGs identified in a large blood-based epigenome-wide association study (EWAS). Comparing ALS patients to controls, we detected 1,045 differentially methylated regions (DMRs) in gene bodies, promoters, and intergenic regions. Notably, these DMRs were linked to key ALS-associated pathways, including endocytosis and cell adhesion. Integration with spinal cord transcriptomics revealed that 31% of DMR-associated genes exhibited differential expression in ALS patients compared to controls, with over 20 genes significantly correlating with disease duration. Furthermore, comparison with published single-nucleus RNA sequencing (snRNA-Seq) data of ALS demonstrated that cfDNA methylation changes reflects cell-type-specific gene dysregulation in the brain of ALS patients, particularly in excitatory neurons and astrocytes. Deconvolution of cfDNA methylation profiles suggested altered proportions of immune and liver-derived cfDNA in ALS patients.

cfDNA methylation is a powerful tool for assessing age-related changes and ALS-specific molecular dysregulation by revealing perturbed locus, genes, and the proportional contributions of different tissues/cells to the plasma. This technique holds promise for clinical application in biomarker discovery across a broad spectrum of neurodegenerative disorders.

Lung cancer (LC) remains the leading cause of cancer death globally. Recent reports have suggested that circulating microbial nucleic acids have potential as promising biomarkers for cancer liquid biopsies. However, circulating microbial profiles and their potential clinical value in LC patients remained unexplored. In this study, plasma samples from 76 LC patients, 9 liver cancer patients, 11 pancreatic cancer patients, and 53 healthy controls (HCs) were collected and underwent metagenomic analyses by whole genome sequencing. The composition and relative abundance of the microbial profiles were significantly different between the LC patients and HCs. A distinct plasma-based microbial profile was observed in LC patients. By differential analysis using MaAslin, 40 significant species between LC patients and HCs were identified. Five species were selected as optimal circulating microbial biomarkers for LC. The constructed classifier based on these five species showed an AUC of 0.9592, 0.9131, and 0.8077 in the discovery, validation, and additional validation cohorts, respectively. Furthermore, metagenomic profiles of 25 lung tumor tissue and plasma paired samples were analyzed and compared. The microbial diversity was significantly increased in plasma compared with the tumor tissue. Among the 13 shared core microbial species, 10 had no difference between the tumor tissue and paired plasma. In conclusion, circulating microbial nucleic acids in the plasma have potential as biomarkers for LC liquid biopsies. The microbiome in the tumor tissue was one of the possible sources of circulating microbial nucleic acids.

Non-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size).

We conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months.

Of the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37-54.42) and a TOO accuracy of 52.94% (95%CI 30.96-73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84-99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83-85.09) and a specificity of 99.71% (95% CI 99.58-99.80) for detecting various cancer types, including those without SOC screening options.

This study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited.

ClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional's skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Cancer detection remains a significant global healthcare challenge, and circulating tumor DNA (ctDNA) is a biomarker for noninvasive cancer screening.

This systematic review aimed to describe health economic evaluations of ctDNA for cancer screening.

A comprehensive literature search was performed (following PRISMA guidelines) across MEDLINE, Embase, APA PsycINFO, Cochrane Library, Web of Science, and the Center for Review and Dissemination. The review included full-scale health economic analyses such as cost-effectiveness, cost-utility, cost-benefit, and cost-consequence analyses. The quality of the included reports was assessed using CHEERS 2022 standards, and each report was categorized as excellent, very good, good, or insufficient.

Eighteen studies were selected, including four ctDNA tests (EBV-DNA, cf-DNA, mSEPT9, and mt-sDNA) for three types of cancer screening: nasopharyngeal carcinoma (NPC) (2; 11.11%), breast cancer (BC) (1; 5.56%), and colorectal cancer (CRC) (15; 83.33%). Five studies (27.78%) found ctDNA cost-effective for CRC screening (mt-sDNA (with higher uptake than conventional tests) versus fecal immunochemical testing (FIT) or colonoscopy (n = 4); mSEPT9 versus computed tomography colonoscopy (CTC) (n = 1)). Thirteen studies (72.22%) found ctDNA not cost-effective for NPC (EBV-DNA versus no screening (n = 2)); BC (cf-DNA versus conventional testing (n = 1)); CRC (mSEPT9 versus FIT or colonoscopy (n = 2)); mt-sDNA versus FIT or colonoscopy (n = 5); mSEPT9 or mt-sDNA versus conventional tests (n = 3)). The CHEERS assessment found all reports in the "very good" category.

All ctDNA tests were generally not cost-effective comparing to conventional screening methods, except when the mt-sDNA uptake was higher than the comparators or when mSEPT9 was compared with CTC.

CRD42023477732.

Electrochemical biosensors are gaining attention as powerful tools in cancer diagnosis, particularly in liquid biopsy, due to their high efficiency, rapid response, exceptional sensitivity, and specificity. However, the complexity of intra- and intertumor heterogeneity, with variations in genetic and protein expression profiles and epigenetic modifications, makes electrochemical biosensors susceptible to false-positive or false-negative diagnostic outcomes. To address this challenge, there is growing interest in simultaneously analyzing multiple biomarkers to reveal molecular characteristics of tumor heterogeneity for precise cancer diagnosis. In this Perspective, we highlight recent advancements in utilizing electrochemical biosensors for cancer diagnosis, with a specific emphasis on the multitarget analysis of cancer biomarkers including tumor-associated nucleic acids, tumor protein markers, extracellular vesicles, and tumor cells. These biosensors hold significant promise for improving precision in early cancer diagnosis and monitoring, as well as potentially offering new insights into personalized cancer management.

Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the tissue origin of cfDNA through cell type deconvolution, we can detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are many methylation-based methods for cfDNA cell type deconvolution, a comprehensive and systematic evaluation of these methods has yet to be conducted.

In this study, we benchmark five methods: MethAtlas, cfNOMe toolkit, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generate in silico cfDNA samples with ground truth cell type proportions to assess the deconvolution performance of the five methods under multiple scenarios. Our findings indicate that multiple factors, including reference marker selection, sequencing depth, and reference atlas completeness, jointly influence the deconvolution performance. Notably, an incomplete reference with missing markers or cell types leads to suboptimal results. We observe performance differences among methods under varying conditions, underscoring the importance of tailoring cfDNA deconvolution analyses. To increase the clinical relevance of our findings, we further evaluate each method's performance in potential clinical applications using real-world datasets.

Based on the benchmark results, we propose general guidelines to choose the suitable methods based on sequencing depth of the cfDNA data and completeness of the reference atlas to maximize the performance of methylation-based cfDNA cell type deconvolution.

Diagnosis is the most important step in different diseases, especially in cancers and blood malignancies. There are different methods in order to better diagnose of cancer, but many of them are invasive and also, some of them are not useful for immediate diagnosis. Cell-free DNA (cfDNA) or liquid biopsy easily accessible in peripheral blood is one of the non-invasive prognostic biomarkers in various areas of cancer management. In fact, amounts of cfDNA in serum or plasma can be used for diagnosis. In this review, we have considered some cancers such as hepatocellular carcinoma, lung cancer, breast cancer, and hematologic malignancies to compare the various methods of cfDNA diagnosis.

Introduction: Cancer is a genetic disease that affects people worldwide, and breast cancer is the most common cancer in women. Studies have been conducted on molecular parameters to predict tumor behavior and develop therapeutic strategies. Telomeres, which are at the end of chromosomes, have been studied for their relationship with breast cancer, but more research is needed to understand their role in the disease. Circulating-free DNA (cfDNA) is DNA that is free in the bloodstream and is considered a promising target for early cancer detection, treatment response monitoring, and prognosis assessment. This study is aimed at comparing cfDNA telomere length of breast cancer patients and healthy individuals and analyzing the impact of neoadjuvant chemotherapy on telomere length in cfDNA. Materials and Methods: Blood samples were collected from 33 breast cancer patients undergoing neoadjuvant chemotherapy before and after treatment. The quantitative PCR method is used to measure the average telomere lengths. Results: This study found that the telomere length of cfDNA in breast cancer patients before and after treatment is significantly shorter than in the control group. Neoadjuvant chemotherapy is found to shorten the cfDNA telomere length, especially in the treatment-responsive group. Conclusion: Our study suggests that telomere length in cfDNA may be a useful biomarker for predicting therapy response and possible reoccurrence of the disease in breast cancer patients.

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients' 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients' lives.

In the bloodstream or other physiological fluids, "circulating cells and sub-cellular bio-particles" include many microscopic biological elements such as circulating tumor cells (CTCs), cell-free DNA (cfDNA), exosomes, microRNAs, platelets, immune cells, and proteins are the most well-known and investigated. These structures are crucial biomarkers in healthcare and medical research for the early detection of cancer and other disorders, enabling treatment to commence before the onset of clinical symptoms and enhancing the efficacy of treatments. As the size of these biomarkers to be detected decreases and their numbers in body fluids diminishes, the detection materials, ranging from visual inspection to advanced microscopy techniques, begin to become smaller, more sensitive, faster, and more effective, thanks to developing nanotechnology. This review first defines the circulating cells and subcellular bio-particles with their biological, physical, and mechanical properties and second focuses on their diagnostic importance, including their most recent applications as biomarkers, the biosensors that are utilized to detect them, the present obstacles that must be surmounted, and prospective developments in the domain. As technology advances and biomolecular pathways are deepens, diagnostic tests will become more sensitive, specific, and thorough. Finally, integrating recent advances in the diagnostic use of circulating cells and bioparticles into clinical practice is promising for precision medicine and patient outcomes.

Demand for large-scale tumour profiling across cancer types has increased in recent years, driven by the emergence of targeted drug therapies. Analysing alternations in plasma circulating tumour DNA (ctDNA) for cancer detection can improve survival; ctDNA testing is recommended when tumour tissue is unavailable. An online survey of molecular pathology testing was circulated by six external quality assessment members of IQN Path to registered laboratories and all IQN Path collaborative corporate members. Data from 275 laboratories across 45 countries were collected; 245 (89%) perform molecular pathology testing, including 177 (64%) which perform plasma ctDNA diagnostic service testing. The most common tests were next-generation sequencing-based (n = 113). Genes with known stratified treatment options, including KRAS (n = 97), NRAS (n = 84), and EGFR (n = 130), were common targets. The uptake of ctDNA plasma testing and plans to implement further testing demonstrates the importance of support from a well-designed EQA scheme.

Surveillance approaches with high sensitivity and specificity for hepatocellular carcinoma (HCC) are still urgently needed. Previous studies have shown that methylation of GNB4 and Riplet can effectively diagnose HCC.

This study plan to analyze the performance of a blood test for detecting HCC using GNB4 and Riplet methylation.

This study mainly investigated the analytical performance of the dual-target HCC blood test (DT-HBT), including cut-off value, limit of detection (LOD), precision, analytical specificity, and coincidence rate. In addition, the detection performance for HCC was validated in 1030 clinical plasma samples (214 HCC and 816 non-HCC). Plasma samples from 25 HCC patients after hepatectomy were collected to assess the feasibility of the kit for postoperative recurrence monitoring. All analytical performance of the DT-HBT met prespecified requirements. The LOD for GNB4, Riplet, and β-actin was 1% methylation/100 copies/μL with cut-offs of 43, 43, and 35, respectively. The DT-HBT showed excellent precision, within 5% CV. It had a specificity of 91.5% for detecting other cancers, and 100% for breast, lung, and bladder cancer. No cross-reactions were observed with 9 potential interfering substances. The DT-HBT achieved a 100% coincidence rate in detecting reference and clinical samples. The clinical performance study found that the kit showed a sensitivity of 81.7% for stage I HCC, and an overall sensitivity and specificity of 87.4% and 92.3%, respectively. The detection sensitivity for postoperative recurrent patients was 95.8%, with a specificity of 100%.

The analytical performance of the DT-HBT met prespecified criteria. It provided HCC patients with a reliable and high-performing new blood test for the HCC diagnosis and surveillance.

ClinicalTrials.gov identifier: NCT05685524.

Colorectal cancer (CRC) is a major global health concern. This study aimed to investigate the role of ALU-based cell-free DNA (cfDNA) in the diagnosis and prognosis of CRC.

We selected relevant literature from PubMed, Scopus, Web of Science, EMBASE, and Science Direct databases based on strict inclusion and exclusion criteria. 17 eligible studies were included in the final analysis (13 studies for diagnostic and 4 studies for prognostic meta-analysis). The search covered relevant publications up to July 1, 2024.

The pooled sensitivity, specificity, and diagnostic odds ratios (DOR) of ALU-based cfDNA in CRC diagnosis were 0.81 (95% CI= [0.70, 0.89]), 0.90 (95% CI= [0.70, 0.96]), and 40.58 (95% CI= [17.87, 92.19]), respectively. The area under the ROC curve was 0.92 (95% CI= [0.89, 0.94]). Patients with higher concentrations of plasma/serum ALU-based cfDNA had poorer overall survival (OS) (pooled hazard ratio = 2.33 ([95% CI= [1.80, 3.03]).

The current evidence supports the utility of circulating ALU as a promising non-invasive diagnostic and prognostic tool for CRC. Furthermore, as a potential biomarker, ALU-based cfDNA could play a significant role in clinical application.

The evidence suggests that circulating ALU-based cell-free DNA (cfDNA) holds promise as a non-invasive diagnostic and prognostic tool for colorectal cancer, potentially enhancing clinical decision-making.

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO (CRD42023486369).

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.

Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.

The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.

This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Instruction-tuned large language models (LLMs) demonstrate exceptional ability to align with human intentions. We present an LLM-based model-instruction-tuned LLM for assessment of cancer (iLLMAC)-that can detect cancer using cell-free deoxyribonucleic acid (cfDNA) end-motif profiles. Developed on plasma cfDNA sequencing data from 1135 cancer patients and 1106 controls across three datasets, iLLMAC achieved area under the receiver operating curve (AUROC) of 0.866 [95% confidence interval (CI), 0.773-0.959] for cancer diagnosis and 0.924 (95% CI, 0.841-1.0) for hepatocellular carcinoma (HCC) detection using 16 end-motifs. Performance increased with more motifs, reaching 0.886 (95% CI, 0.794-0.977) and 0.956 (95% CI, 0.89-1.0) for cancer diagnosis and HCC detection, respectively, with 64 end-motifs. On an external-testing set, iLLMAC achieved AUROC of 0.912 (95% CI, 0.849-0.976) for cancer diagnosis and 0.938 (95% CI, 0.885-0.992) for HCC detection with 64 end-motifs, significantly outperforming benchmarked methods. Furthermore, iLLMAC achieved high classification performance on datasets with bisulfite and 5-hydroxymethylcytosine sequencing. Our study highlights the effectiveness of LLM-based instruction-tuning for cfDNA-based cancer detection.

Circulating tumor DNA (ctDNA), a fragment of tumor DNA found in the bloodstream, has emerged as a revolutionary tool in cancer management. This review delves into the biology of ctDNA, examining release mechanisms, including necrosis, apoptosis, and active secretion, all of which offer information about the state and nature of the tumor. Comprehensive DNA profiling has been enabled by methods such as whole genome sequencing and methylation analysis. The low abundance of the ctDNA fraction makes alternative techniques, such as digital PCR and targeted next-generation exome sequencing, more valuable and accurate for mutation profiling and detection. There are numerous clinical applications for ctDNA analysis, including non-invasive liquid biopsies for minimal residual disease monitoring to detect cancer recurrence, personalized medicine by mutation profiling for targeted therapy identification, early cancer detection, and real-time evaluation of therapeutic response. Integrating ctDNA analysis into routine clinical practice creates promising avenues for successful and personalized cancer care, from diagnosis to treatment and follow-up.

In accordance with the World Health Organization data, cancer remains at the forefront of fatal diseases. An upward trend in cancer incidence and mortality has been observed globally, emphasizing that efforts in developing detection and treatment methods should continue. The diagnostic path typically begins with learning the medical history of a patient; this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy. Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization. Thus, there is a need for novel cancer detection methods such as liquid biopsy, elastography, synthetic biosensors, fluorescence imaging, and reflectance confocal microscopy. Conventional therapeutic methods, although still common in clinical practice, pose many limitations and are unsatisfactory. Nowadays, there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy, exosome-based therapy, nanotechnology, dendritic cells, chimeric antigen receptors, immune checkpoint inhibitors, natural product-based therapy, tumor-treating fields, and photodynamic therapy. The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions. As evidenced, modern methods are not without drawbacks; there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity, specificity, safety, and efficacy. Nevertheless, an appropriate route has been taken, as confirmed by the approval of some modern methods by the Food and Drug Administration.

Circulating free tumor DNA (ctDNA) analysis is gaining popularity in precision oncology, particularly in metastatic breast cancer, as it provides non-invasive, real-time tumor information to complement tissue biopsies, allowing for tailored treatment strategies and improved patient selection in clinical trials. Its use in early breast cancer has been limited so far, due to the relatively low sensitivity of available techniques in a setting characterized by lower levels of ctDNA shedding. However, advances in sequencing and bioinformatics, as well as the use of methylome profiles, have led to an increasing interest in the application of ctDNA analysis in early breast cancer, from screening to curative treatment evaluation and minimal residual disease (MRD) detection. With multiple prospective clinical trials in this setting, ctDNA evaluation may become useful in clinical practice. This article reviews the data regarding the analytical validity of the currently available tests for ctDNA detection and the clinical potential of ctDNA analysis in early breast cancer.

Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.

One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance.

The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.

Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

The human telomerase reverse transcriptase (hTERT) enzyme, encoded by the hTERT gene, synthesizes protective telomeric sequences on chromosomes and plays a fundamental role in cancer formation. Methylation of the hTERT gene has an upregulatory effect, increasing hTERT enzyme synthesis and allowing continuous tumor cell division.

In a group of patients with breast cancer, we aimed to analyze the methylation status of hTERT in the tumor, surrounding tissue, and circulating free deoxyribonucleic acid (cfDNA) of blood collected on the day of mastectomy and then approximately one year later.

A prospective study was conducted at a university hospital in Rio de Janeiro, Brazil.

Samples were collected from 15 women with breast cancer on the day of mastectomy and approximately one year postoperatively. cfDNA was analyzed by sodium bisulfite conversion, followed by polymerase chain reaction, electrophoresis, and silver nitrate staining.

Methylation of hTERT was detected in the tumors and surrounding tissues of all 15 patients. Five patients displayed hTERT methylation in the cfDNA from the blood of the first collection. Of the ten patients who returned for the second collection, three showed methylation. Two patients with methylation in the first collection did not display methylation in the second collection. One patient with no methylation in the first collection displayed methylation in the second collection, and one patient had a diminished level of methylation in the second collection.

Only one-third of patients displayed methylation in their cfDNA, which may be related to the success of chemotherapy.

Oncogene testing is widely used to detect or direct cancer treatments. Compared to people without disabilities, people with disabilities in Korea have a lower cancer incidence rate but a fivefold higher cancer mortality rate, implying delayed detection.

We used an administrative database combining disability status and care utilization to analyze every case of cancer-related genetic testing paid for by the National Health Insurance Services of Korea between 2016 and 2019. We first compared percentages of individuals who had taken a registered genetic test by their disability statuses. We then compared the most frequently utilized tests between individuals with and without disabilities.

Korean citizens, 175,000 in total, underwent at least one of the 192 registered cancer-related genetic tests between 2016 and 2019. People with disabilities utilized these genetic tests at higher rates than those without disabilities, regardless of sex or age. Among people aged ≥40 years, lung and colorectal cancer-related tests were most frequently utilized, regardless of disability status.

Although the cancer-related genetic test uptake rate is higher among people with disabilities than among those without disabilities, it is still possible that information on these tests is not as readily available to people with disabilities. Therefore, it is imperative for the government to actively devise strategies to enhance national cancer screening rates among people with disabilities.

Liquid biopsy is emerging as a pivotal tool in precision oncology, offering a noninvasive and comprehensive approach to cancer diagnostics and management. By harnessing biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusions, this technique profiles key biomarkers including circulating tumor DNA, circulating tumor cells, microRNAs, and extracellular vesicles. This review discusses the extended scope of liquid biopsy, highlighting its indispensable role in enhancing patient outcomes through early detection, continuous monitoring, and tailored therapy. While the advantages are notable, we also address the challenges, emphasizing the necessity for precision, cost-effectiveness, and standardized methodologies in its broader application. The future trajectory of liquid biopsy is set to expand its reach in personalized medicine, fueled by technological advancements and collaborative research.

Biosimilars are biological drugs created from living organisms or that contain living components. They share an identical amino-acid sequence and immunogenicity. These drugs are considered to be cost-effective and are utilized in the treatment of cancer and other endocrine disorders. The primary aim of biosimilars is to predict biosimilarity, efficacy, and treatment costs; they are approved by the Food and Drug Administration (FDA) and have no clinical implications. They involve analytical studies to understand the similarities and dissimilarities. A biosimilar manufacturer sets up FDA-approved reference products to evaluate biosimilarity. The contribution of next-generation sequencing is evolving to study the organ tumor and its progression with its impactful therapeutic approach on cancer patients to showcase and target rare mutations. The study shall help to understand the future perspectives of biosimilars for use in gastro-entero-logic diseases, colorectal cancer, and thyroid cancer. They also help target specific organs with essential mutational categories and drug prototypes in clinical practices with blood and liquid biopsy, cell treatment, gene therapy, recombinant therapeutic proteins, and personalized medications. Biosimilar derivatives such as monoclonal antibodies like trastuzumab and rituximab are common drugs used in cancer therapy. Escherichia coli produces more than six antibodies or antibody-derived proteins to treat cancer such as filgrastim, epoetin alfa, and so on.

Alzheimer's disease (AD) and Alzheimer's disease-related disorders (ADRD) are progressive neurodegenerative diseases without cure. Alzheimer's disease occurs in 2 forms, early-onset familial AD and late-onset sporadic AD. Early-onset AD is a rare (~1%), autosomal dominant, caused by mutations in presenilin-1, presenilin-2, and amyloid precursor protein genes and the other is a late-onset, prevalent and is evolved due to age-associated complex interactions between environmental and genetic factors, in addition to apolipoprotein E4 polymorphism. Cellular senescence, promoting the impairment of physical and mental functions is constituted to be the main cause of aging, the primary risk factor for AD, which results in progressive loss of cognitive function, memory, and visual-spatial skills for an individual to live or act independently. Despite significant progress in the understanding of the biology and pathophysiology of AD, we continue to lack definitive early detectable biomarkers and/or drug targets that can be used to delay the development of AD and ADRD in elderly populations. However, recent developments in the studies of DNA double-strand breaks result in the release of fragmented DNA into the bloodstream and contribute to higher levels of cell-free DNA (cf-DNA). This fragmented cf-DNA can be released into the bloodstream from various cell types, including normal cells and cells undergoing apoptosis or necrosis and elevated levels of cf-DNA in the blood have the potential to serve as blood blood-based biomarker for early detection of AD and ADRD. The overall goal of our study is to discuss the latest developments in circulating cell-free DNA into the blood in the progression of AD and ADRD. Our article summarized the status of research on double-strand breaks and circulating cell-free DNA in both healthy and disease states and how these recent developments can be used to develop early detectable biomarkers for AD and ADRD. Our article also discussed the impact of lifestyle and epigenetic factors that are involved in DNA double-strand breaks and circulating cell-free DNA in AD and ADRD.

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.