Liquid Biopsy Research Articles

Circulating HPVDNA in patients undergoing transoral robotic surgery for oropharyngeal cancer: liquid biopsy could identify molecular residual disease.

we evaluated the hypothesis that level of ctHPVDNA on the first postoperative day (POD-1); and at 15 days (POD-15) could be associated with the need for adjuvant therapy and the presence of recurrence. this is a prospective observational study on biomarkers, focusing on the longitudinal monitoring of ctHPVDNA in a cohort of HPV-OPSCC patients undergoing TORS. Blood samples were collected according to the following schema: (1) pretreatment; (2) on first postoperative day (POD 1); and (3) at 15 days (POD 15). Plasma samples were analyzed with ddPCR assay comprising E6 of HPV16, HPV 33 and HPV 35. Present study was conducted on 44 OPSCC patients and revealed a ctHPVDNA sensitivity of 100% (95%CI: 89-100%) in blood samples at first diagnosis. Data demonstrated a significant different of ctHPVDNA levels at POD-1 among patients who received observation vs. adjuvant treatment and among patients who remained disease-free at the last follow-up, compared to those who experienced recurrence. In the next years, studies on larger patients' surgical cohorts focused on ctHPVDNA levels at POD-1 and continued improvements in assay methodology could allow the implementation of ctHPVDNA in routine clinical use. Liquid biopsy could identify residual molecular disease after surgery and guide clinicians choosing adjuvant treatment.

Liquid biopsy technologies: innovations and future directions in breast cancer biomarker detection.

Globally, breast cancer is the most frequent type of cancer, and its early diagnosis and screening can significantly improve the probability of survival and quality of life of those affected. Liquid biopsy-based targets such as circulating tumor cells, circulating tumor DNA, and exosomes have been instrumental in the early discovery of cancer, and have been found to be effective in stage therapy, recurrence monitoring, and drug selection. Biosensors based on these target related biomarkers convert the tested substances into quantifiable signals such as electrical and optical signals through signal transduction, which has the advantages of high sensitivity, simple operation, and low invasiveness. This review provides an overview of the latest progress of liquid biopsy biomarkers in the diagnosis, prognosis and treatment of breast cancer, compares the application and advantages of different biosensors based on these biomarkers in the diagnosis of breast cancer, and analyzes the limitations and solutions of biosensor based methods.

Silver Microdisc Array Electrode Chip for Urea Detection in Saliva Samples from Patients with Chronic Nephritis.

Urea is an important biomarker for diagnosing various kidney and liver disorders. However, many existing methods rely on invasive blood sampling, which can potentially harm patients. Saliva has been recently recognized as a noninvasive and easily collectible alternative to blood for urea quantification. Electrochemical urea detection in saliva remains limited, with catalytic materials typically applied to the electrode surface via drop casting. This results in a random distribution of materials and potential aggregation on the electrode, which inevitably hinders the efficient mass transport of analytes, reducing both detection sensitivity and the utilization of catalytic materials. In this work, a silver nanoparticle (AgNP)-integrated microdisc array electrode chip was fabricated through the in situ growth of AgNPs on polydopamine (PDA) arrays, which were patterned using the microcontact printing (μCP) technique on an indium tin oxide (ITO) glassy substrate. The resulting AgNP microdisc array chip sensor exhibited much higher sensitivity toward urea sensing and greater material utilization as compared to traditional drop-cast electrodes, due to the enhanced mass transfer. Furthermore, the chip sensors demonstrated superior selectivity when challenged with potential interferences. More importantly, reliable urea quantification was achieved in clinical saliva samples from nephritis patients. These results indicate that the current sensor presents great opportunities for developing a noninvasive and sensitive liquid biopsy platform for urea determination in clinical diagnosis applications.

Enhancing early breast cancer detection with APE1-triggered oligonucleotide probes and graphene oxide: The impact of variable AP site modification on sensitivity and specificity.

There is a critical need for inclusive diagnostic platforms to enhance the accuracy of early breast cancer detection. Dysregulated microRNA-1246 (miR-1246), closely linked to the disease progression and recurrence, has emerged as a promising diagnostic and prognostic biomarker for BC. However, achieving simple, rapid, and ultrasensitive quantification of serum miRNAs remains significant challenge. In this study, we present an innovative detection platform triggered by endogenous DNA repair enzyme apurinic/apyrimidinic endonuclease 1 (APE1). This platform utilizes an oligonucleotide probe with variable modified AP sites (denoted as AOP) coupled with graphene oxide (GO) for quantifying miR-1246. Our in vitro experiments reveal that the proposed method employing the AOP2 probe with two AP sites exhibits exceptional selectivity and sensitivity. The method achieves a detection limit as low as 2.3 pM towards miR-1246, which is approximately 260-fold more sensitive than the enzyme-free system. RT-qPCR experiments further validate the accuracy and practicability of the AOP2-based platform. In clinical trials, our platform has successfully differentiated between BC patients and normal healthy controls. In conclusion, we have established an integrated biosensing technology for PCR-free, non-invasive liquid biopsies of miR-1246, offering a promising approach for BC diagnosis.

Targeting HER2 in Gastroesophageal Cancer: A New Appetite for an Old Plight.

The incidence of gastroesophageal cancers is rising, driven, in part, by an increasing burden of risk factors of obesity and gastroesophageal reflux. Despite efforts to address these risk factors, and a growing interest in methods of population screening, the bulk of these tumours are unresectable at diagnosis. In this setting, effective systemic treatments are paramount to improve survival and quality of life. Early and accurate identification of oncogenic drivers, such as human epidermal growth factor receptor 2 (HER2), present in 5-30% of gastroesophageal adenocarcinomas (GEAs), is integral to guide choice of therapies due to the clear predictive implications that arise from overexpression of this receptor. After trastuzumab, the first anti-HER2 agent with approved use in HER2-positive GEA, the addition of pembrolizumab to first-line trastuzumab-chemotherapy and trastuzumab deruxtecan in the refractory space have more recently changed practice. Yet, the response to these agents has been vastly different across patients with HER2-positive disease, underpinning the need for reliable biomarkers of response. Emergent data have suggested that levels of HER2 expression on tissue or liquid biopsies may predict response to first-generation HER2 therapies while HER2 heterogeneity, receptor changes, co-occurring molecular alterations and oncogenic genomic and metabolic reprogramming may be implicated in resistance. A robust knowledge of the mechanisms of resistance and response to HER2-directed therapies is necessary to inform novel strategies of HER2-targeting and guide choice combinations with other biomarker-directed therapies, to improve outcomes from a new generation of clinical trials in HER2-positive GEA. Understanding and close examination of previous failures in this space form an important part of this assessment, as does correlative biomarker and translational work pertaining to the role of HER2 and dynamic changes that result through treatment exposure. In this review, we aim to provide an overview of strategies for HER2 targeting, summarising both the successes and disappointments in this therapeutic landscape and discuss existing challenges and future perspectives on development in this highly morbid tumour type.

Leveraging Epigenetic Alterations in Pancreatic Ductal Adenocarcinoma for Clinical Applications.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by late detection and poor prognosis. Recent research highlights the pivotal role of epigenetic alter- ations in driving PDAC development and progression. These changes, in conjunction with genetic mutations, contribute to the intricate molecular landscape of the disease. Specific modifications in DNA methylation, histone marks, and non-coding RNAs are emerging as robust predictors of disease progression and patient survival, offering the potential for more precise prognostic tools compared to conventional clinical staging. Moreover, the detection of epigenetic alterations in blood and other non-invasive samples holds promise for earlier diagnosis and improved manage- ment of PDAC. This review comprehensively summarises current epigenetic research in PDAC and identifies persisting challenges. These include the complex nature of epigenetic profiles, tumour hetero- geneity, limited access to early-stage samples, and the need for highly sensitive liquid biopsy technologies. Addressing these challenges requires the standardisation of methodologies, integra- tion of multi-omics data, and leveraging advanced computational tools such as machine learning and artificial intelligence. While resource-intensive, these efforts are essential for unravelling the functional consequences of epigenetic changes and translating this knowledge into clinical appli- cations. By overcoming these hurdles, epigenetic research has the potential to revolutionise the management of PDAC and improve patient outcomes.

Epigenomics-guided precision oncology: Chromatin variants in prostate tumor evolution.

Prostate cancer is a common malignancy that in 5%-30% leads to treatment-resistant and highly aggressive disease. Metastasis-potential and treatment-resistance is thought to rely on increased plasticity of the cancer cells-a mechanism whereby cancer cells alter their identity to adapt to changing environments or therapeutic pressures to create cellular heterogeneity. To understand the molecular basis of this plasticity, genomic studies have uncovered genetic variants to capture clonal heterogeneity of primary tumors and metastases. As cellular plasticity is largely driven by non-genetic events, complementary studies in cancer epigenomics are now being conducted to identify chromatin variants. These variants, defined as genomic loci in cancer cells that show changes in chromatin state due to the loss or gain of epigenomic marks, inclusive of histone post-translational modifications, DNA methylation and histone variants, are considered the fundamental units of epigenomic heterogeneity. In prostate cancer chromatin variants hold the promise of guiding the new era of precision oncology. In this review, we explore the role of epigenomic heterogeneity in prostate cancer, focusing on how chromatin variants contribute to tumor evolution and therapy resistance. We therefore discuss their impact on cellular plasticity and stochastic events, highlighting the value of single-cell sequencing and liquid biopsy epigenomic assays to uncover new therapeutic targets and biomarkers. Ultimately, this review aims to support a new era of precision oncology, utilizing insights from epigenomics to improve prostate cancer patient outcomes.

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)

BackgroundAn increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers.MethodsWe retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building.ResultsAmong LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99).ConclusionsThese results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Clinical significance of stratifying prostate cancer patients through specific circulating genes.

Patient stratification remains a challenge for optimal treatment of prostate cancer (PCa). This clinical heterogeneity implies intra-tumoural heterogeneity, with different prostate epithelial cell subtypes not all targeted by current treatments. We reported that such cell subtypes are traceable in liquid biopsies through representative transcripts. Expanding on this concept, we included 57 genes representing cell subtypes, drug targets and relevant to resistance as non-invasive biomarkers for stratification. This panel was tested by RT-qPCR (quantitative reverse transcription polymerase chain reaction) in blood of controls and different categories of PCa patients. Overall, circulating transcripts showed predictive value throughout the disease. Those with aggressive pathological features such as intra-ductal carcinoma at diagnosis showed more genes over-expressed. In metastatic patients, signatures of subtypes or resistance were associated with treatments, progression-free survival and overall survival. Altogether, testing markers of cell diversity, an intrinsic feature of tumours, and drug targets via liquid biopsies represents a valuable means to stratify patients and predict responses to current or new therapeutic modalities. Over-expressed drug target genes suggest potential benefit from targeted treatments, justifying new clinical trials to offer patient-tailored strategies to eventually impact on PCa mortality.

Research Trends and Development Dynamics of qPCR-based Biomarkers: A Comprehensive Bibliometric Analysis.

Quantitative polymerase chain reaction (qPCR) is a vital molecular technique for biomarker detection; however, its clinical application is impeded by the scarcity of robust biomarkers and the inherent limitations of the technology. This study conducted a bibliometric analysis of 4063 qPCR-based biomarker studies sourced from the Web of Science (WOS) database, employing VOSviewer and CiteSpace to generate multi-dimensional structural insights into this field. The results reveal a growing trend in research within this domain, with gene expression analysis playing a central role in the identification of potential biomarkers. Among these, cancer-related biomarkers are the most prominent, while research on biomarkers for other diseases remains limited. Liquid biopsy biomarkers, including microRNA (miRNA), circulating free DNA (cfDNA), and circulating tumor DNA (ctDNA), are increasingly being explored. The integration of bioinformatics, omics analysis, and high-throughput technologies with qPCR is accelerating biomarker discovery. Furthermore, large-scale parallel sequencing is emerging as a potential alternative to relative quantification and microarray techniques. Nevertheless, qPCR remains essential for validating specific biomarkers, and further standardization of its protocols is necessary to enhance reliability. This study provides a systematic analysis of qPCR-based biomarker research and underscores the need for future technological integration and standardization to facilitate broader clinical applications.

Artificial Intelligence (AI) and Liquid Biopsy Transforming Early Detection of Liver Metastases in Gastrointestinal Cancers.

Liver metastases from Gastrointestinal (GI) cancers present significant challenges in oncology, often signaling poor prognosis. Traditional detection methods like imaging and tissue biopsies have limitations in sensitivity, specificity, and tumor heterogeneity represen-tation. The advent of artificial intelligence (AI) in healthcare, driven by advancements in ma-chine learning, algorithms, and data science, offers a promising frontier for early detection and management of liver metastases. This review explores the integration of AI and liquid biopsy technologies as transformative tools in the proactive detection of liver metastases aris-ing from GI malignancies. Liquid biopsy, a non-invasive method, analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA) in bodily fluids. It provides a compre-hensive overview of tumor heterogeneity and enables real-time monitoring of tumor evolu-tion and treatment response. Despite its advantages, liquid biopsy faces challenges such as low sensitivity for early-stage metastases, reduced detectability due to liver filtration, and technical limitations. AI enhances the potential of liquid biopsies by improving diagnostic accuracy through ad-vanced algorithms like Convolutional Neural Networks (CNNs) and Natural Language Pro-cessing (NLP). These AI models analyze complex biomedical data, offering higher sensitivity and specificity in cancer detection. The synergy between AI and liquid biopsies promises early detection, better disease monitoring, and personalized treatment strategies. This review underscores the significant advancements AI and liquid biopsy technologies bring to oncological precision medicine, particularly in improving overall survival (OS) and disease-free survival (DFS) for patients with GI cancer metastases. As we transition into the era of precision medicine, the integration of these technologies holds the potential to redefine cancer care and patient management.

Clinical Utility of Liquid Biopsy for the Early Diagnosis of EGFR-Mutant Advanced Lung Cancer Patients in a Real-Life Setting (CLEAR Study)

Clinical Utility of Liquid Biopsy for the Early Diagnosis of EGFR-Mutant Advanced Lung Cancer Patients in a Real-Life Setting (CLEAR Study)

Informatics strategies for early detection and risk mitigation in pancreatic cancer patients.

This review provides a comprehensive overview of the current landscape in pancreatic cancer (PC) screening, diagnosis, and early detection. This emphasizes the need for targeted screening in high-risk groups, particularly those with familial predispositions and genetic mutations, such as BRCA1, BRCA2, and PALB2. This review highlights the sporadic nature of most PC cases and significant risk factors, including smoking, alcohol consumption, obesity, and diabetes. Advanced imaging techniques, such as Endoscopic Ultrasound (EUS) and Contrast-Enhanced Harmonic Imaging (CEH-EUS), have been discussed for their superior sensitivity in early detection. This review also explores the potential of novel biomarkers, including those found in body fluids, such as serum, plasma, urine, and bile, as well as the emerging role of liquid biopsy technologies in analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes. AI-driven approaches, such as those employed in Project Felix and CancerSEEK, have been highlighted for their potential to enhance early detection through deep learning and biomarker discovery. This review underscores the importance of universal genetic testing and the integration of AI with traditional diagnostic methods to improve outcomes in high-risk individuals. Additionally, this review points to future directions in PC diagnostics, including next-generation imaging, molecular biomarkers, and personalized medicine, aiming to overcome current diagnostic challenges and improve survival rates. Ultimately, the review advocates the adoption of informatics and AI-driven strategies to enhance early detection, reduce morbidity, and save lives in the fight against pancreatic cancer.

Detection of Circulating Tumor DNA in Liquid Biopsy: Current Techniques and Potential Applications in Melanoma.

The treatment landscape for advanced melanoma has transformed significantly with the advent of BRAF and MEK inhibitors (BRAF/MEKi) targeting BRAFV600 mutations, as well as immune checkpoint inhibitors (ICI) like anti-PD-1 monotherapy or its combinations with anti-CTLA-4 or anti-LAG-3. Despite that, many patients still do not benefit from these treatments at all or develop resistance mechanisms. Therefore, prognostic and predictive biomarkers are needed to identify patients who should switch or escalate their treatment strategies or initiate an intensive follow-up. In melanoma, liquid biopsy has shown promising results, with a potential role in predicting relapse in resected high-risk patients or in disease monitoring during the treatment of advanced disease. Several components in peripheral blood have been analyzed, such as circulating tumor cells (CTCs), cell-free DNA (cfDNA), and circulant tumoral DNA (ctDNA), which have turned out to be particularly promising. To analyze ctDNA in blood, different techniques have proven to be useful, including digital droplet polymerase chain reaction (ddPCR) to detect specific mutations and, more recently, next-generation sequencing (NGS) techniques, which allow analyzing a broader repertoire of the mutation landscape of each patient. In this review, our goal is to update the current understanding of liquid biopsy, focusing on the use of ctDNA as a biological material in the daily clinical management of melanoma patients, in particular those with advanced disease treated with ICI.

Detection of Cancer Stem Cells from Patient Samples.

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

The Clinical Utility of a Next-Generation Sequencing-Based Approach to Detecting Circulating HPV DNA in Patients with Advanced Anal Cancer.

To extend the practicality of liquid biopsy beyond the historical HPV circulating tumor DNA (ctDNA) assays, we evaluated the clinical relevance of a novel next-generation sequencing HPV ctDNA assay in patients with locally advanced and metastatic squamous cell cancer of the anal canal (mSCCA). ctDNA isolated from the plasma of patients with mSCCA was sequenced using a 1.4 Mb hybrid-capture target-enrichment panel covering the whole genome sequences of all 193 HPV types. The HPV type, copy number (CN), and integration sites were determined using a bioinformatic pipeline. A total of 77 plasma samples from 28 patients with HPV-related SCCA were retrospectively analyzed. HPV ctDNA was detected in 26 cases (93%) (including uncommon subtypes). The median HPV CN was higher in metastatic versus locally recurrent/unresectable SCCA (p = 0.043). Changes in the HPV CN were concordant with the radiographic response (p = 0.027). An integration event was detected in 23 patients (82%), with presumed episomal HPV DNA present in the remaining patients. Higher HPV integration (a mean of ≥1 integration across samples) was associated with a worse overall survival from the start of immunotherapy (13.6 months versus 36.0 months; p = 0.003). Using HPV-informed next-generation sequencing of the ctDNA, we found changes in the HPV CN correlated with the treatment response and that HPV integration detected in the ctDNA is an unfavorable prognostic biomarker.

Molecular clustering on ctDNA improves the prognostic stratification of DLBCL patients compared to ctDNA levels.

Circulating tumor DNA (ctDNA) levels can help predict outcomes in diffuse large B-cell lymphoma (DLBCL), but its integration with DLBCL molecular clusters remains unexplored. Using the LymphGen tool in 77 DLBCL with both ctDNA and tissue biopsy, a 95.8% concordance rate in molecular cluster assignment was observed, showing the reproducibility of molecular clustering on ctDNA. A multicenter, prospective cohort of 166 newly diagnosed DLBCL was analyzed for ctDNA levels and molecular clusters using CAPP-seq. Patients with ctDNA levels < 2.5 log10hGE/mL had a 4-year progression-free survival (PFS) and overall survival (OS) of 71.7% and 85.7%, respectively, compared to 50.3% and 61.0% for those with higher ctDNA levels (p=0.0018 and p=0.0017). Recursive partitioning showed that patients with ctDNA levels > 2.5 log10hGE/mL were further stratified by clusters ST2/BN2. In this group, ST2/BN2 patients associated with a favorable outcome with a 4-year PFS and OS of 87.5% and 100%, respectively, compared to 38.0% and 47.1% for other clusters (p=0.003 and p=0.001). Combining ctDNA levels and ST2/BN2 clusters improved outcome prediction. Low-risk patients (N=51), characterized by ctDNA levels < 2.5 log10hGE and/or BN2/ST2 cluster, had a 4-year PFS and OS of 75.3% and 87.8%. High-risk patients (N=115), with ctDNA levels > 2.5 log10hGE and no BN2/ST2 cluster, had a 4-year PFS and OS of 38.0% and 47.1%. Adding cluster assignment to ctDNA levels improved the model's C statistics (0.63 vs. 0.59 for PFS and 0.68 vs. 0.63 for OS). Liquid biopsy thus provides a multi-layered approach for outcome prediction in DLBCL.

Detection and Characterization of Circulating Tumor Cells in Colorectal Cancer Patients via Epithelial-Mesenchymal Transition Markers.

Background/Objectives: Liquid biopsy methods have gained prominence as minimally invasive tools to improve cancer treatment outcomes. Circulating tumor cells (CTCs) offer valuable insights into both primary and metastatic lesions. However, validating the CTC test results requires confirmation that the detected cells originate from cancer tissue. While studies have identified CTCs in colorectal cancer (CRC) patients using molecular markers, simultaneous validation of their cancer tissue origin remains unexplored. Methods: This study introduces a simple approach to detect adenomatous polyposis coli (APC) gene abnormalities alongside established CTC markers using a molecular imaging flow cytometer (MI-FCM). Given that APC gene abnormalities occur in 60-70% of CRC patients, their detection serves as strong evidence of cancer origin. Results: Our method achieved 92% concordance with DNA sequence analysis of tumor-derived cells. In a proof-of-concept study using 5 mL of whole blood from CRC patients, we observed a high frequency of cells exhibiting APC abnormalities, cytokeratin (CK), and vimentin (Vim) expression. Extending the study to 80 CRC patients across pathological stages I-IV confirmed CK and Vim as valid CTC markers. Three distinct cell populations were identified in blood: CK+/Vim-, CK+/Vim+, and CK-/Vim+. CTC number and frequency increased progressively with cancer stage. Conclusions: This is the first report demonstrating CK and Vim as effective markers for direct CTC detection in CRC patients. Our findings provide evidence-based validation of CTC markers, offering new insights and advancing approaches for patient care.

Two-Step Acoustic Cell Separation Based on Cell Size and Acoustic Impedance─toward Isolation of Viable Circulating Tumor Cells.

Isolation and characterization of circulating tumor cells (CTCs) present a noninvasive alternative to monitor disease progression in individual patients. However, the heterogeneous lineage specificity of CTCs makes it difficult to isolate and identify possible CTCs by a liquid biopsy. Better label-free methods for the isolation of viable CTCs are needed. Our solution is a combined approach that is inherently epitope independent. Cells are separated by size-sensitive acoustophoresis using an ultrasonic standing wave field, followed by size-insensitive, acoustic barrier-medium focusing, which enables the enrichment of viable cancer cells in blood. With standard acoustophoresis in homogeneous medium, lymphocytes and monocytes were efficiently removed, while removal of granulocytes from the target MCF7 breast cancer cells was not possible due to overlapping acoustic migration velocities for viable cells. Remaining granulocytes were removed by a second separation step with an acoustic impedance barrier-medium selectively blocking the transport of MCF7 cells to generate a clean cancer cell fraction. For two series of 500 mL samples containing 5 × 105 white blood cells, spiked with 2 × 104 or 1 × 103 MCF7 cells, the recovery of MCF7 cells was 77.3% with a 99.9% depletion of white blood cells in the final cancer cell fraction. The most abundant contaminating cell type was granulocytes (85.9% of remaining cells). Nearly all lymphocytes (99.996%) and monocytes (99.995%) were depleted. A two-step acoustic cell separation based on cell size and acoustic impedance is well suited to generate a purified cancer cell fraction as a preparatory step for downstream single-cell analysis.