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Dubrovsky G, Ross A, Jalali P, Lotze M. Liquid Biopsy in Pancreatic Ductal Adenocarcinoma: A Review of Methods and Applications. Int J Mol Sci 2024; 25:11013. [PMID: 39456796 PMCID: PMC11507494 DOI: 10.3390/ijms252011013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 10/08/2024] [Accepted: 10/11/2024] [Indexed: 10/28/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains a malignancy with one of the highest mortality rates. One limitation in the diagnosis and treatment of PDAC is the lack of an early and universal biomarker. Extensive research performed recently to develop new assays which could fit this role is available. In this review, we will discuss the current landscape of liquid biopsy in patients with PDAC. Specifically, we will review the various methods of liquid biopsy, focusing on circulating tumor DNA (ctDNA) and exosomes and future opportunities for improvement using artificial intelligence or machine learning to analyze results from a multi-omic approach. We will also consider applications which have been evaluated, including the utility of liquid biopsy for screening and staging patients at diagnosis as well as before and after surgery. We will also examine the potential for liquid biopsy to monitor patient treatment response in the setting of clinical trial development.
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Affiliation(s)
- Genia Dubrovsky
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; (G.D.); (A.R.)
- Pittsburgh VA Medical Center, Pittsburgh, PA 15240, USA
| | - Alison Ross
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; (G.D.); (A.R.)
| | - Pooya Jalali
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran 1983969411, Iran
| | - Michael Lotze
- Departments of Surgery, Immunology, and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Chowdhury S, Kesling M, Collins M, Lopez V, Xue Y, Oliveira G, Friedl V, Bergamaschi A, Haan D, Volkmuth W, Levy S. Analytical Validation of an Early Detection Pancreatic Cancer Test Using 5-Hydroxymethylation Signatures. J Mol Diagn 2024; 26:888-896. [PMID: 39230538 DOI: 10.1016/j.jmoldx.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/13/2024] [Accepted: 06/21/2024] [Indexed: 09/05/2024] Open
Abstract
Early detection of pancreatic cancer has been shown to improve patient survival rates. However, effective early detection tools to detect pancreatic cancer do not currently exist. The Avantect Pancreatic Cancer Test, leveraging the 5-hydroxymethylation [5-hydroxymethylcytosine (5hmC)] signatures in cell-free DNA, was developed and analytically validated to address this unmet need. We report a comprehensive analytical validation study encompassing precision, sample stability, limit of detection, interfering substance studies, and a comparison with an alternative method. The assay performance on an independent case-control patient cohort was previously reported with a sensitivity for early-stage (stage I/II) pancreatic cancer of 68.3% (95% CI, 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). Precision studies showed a cancer classification of 100% concordance in biological replicates. The sample stability studies revealed stable assay performance for up to 7 days after blood collection. The limit of detection studies revealed equal results between early- and late-stage cancer samples, emphasizing strong early-stage performance characteristics. Comparisons of concordance of the Avantect assay with the enzymatic methyl sequencing (EM-Seq) method, which measures both methylation (5-methylcytosine) and 5hmC, were >95% for all samples tested. The Avantect Pancreatic Cancer Test showed strong analytical validation in multiple validation studies required for laboratory-developed test accreditation. The comparison of 5hmC versus EM-Seq further validated the 5hmC approach as a robust and reproducible assay.
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Affiliation(s)
| | | | | | | | - Yuan Xue
- ClearNote Health, San Mateo, California
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West-Szymanski DC, Zhang Z, Cui XL, Kowitwanich K, Gao L, Deng Z, Dougherty U, Williams C, Merkle S, He C, Zhang W, Bissonnette M. 5-Hydroxymethylated Biomarkers in Cell-Free DNA Predict Occult Colorectal Cancer up to 36 Months Before Diagnosis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. JCO Precis Oncol 2024; 8:e2400277. [PMID: 39393034 DOI: 10.1200/po.24.00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/25/2024] [Accepted: 08/28/2024] [Indexed: 10/13/2024] Open
Abstract
PURPOSE Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis. MATERIALS AND METHODS We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio. RESULTS We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]). CONCLUSION Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.
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Affiliation(s)
- Diana C West-Szymanski
- Department of Chemistry, The University of Chicago, Chicago, IL
- Department of Medicine, The University of Chicago, Chicago, IL
| | - Zhou Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Xiao-Long Cui
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | - Lu Gao
- Department of Chemistry, The University of Chicago, Chicago, IL
- Department of Medicine, The University of Chicago, Chicago, IL
| | - Zifeng Deng
- Department of Medicine, The University of Chicago, Chicago, IL
| | | | | | | | - Chuan He
- Department of Chemistry, The University of Chicago, Chicago, IL
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL
- The Howard Hughes Medical Institute, The University of Chicago, Chicago, IL
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
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Levy S, Bergamaschi A. Reply. Clin Gastroenterol Hepatol 2024; 22:673-674. [PMID: 37863405 DOI: 10.1016/j.cgh.2023.09.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023]
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Xu R, Wang C, Zhao Y. Early Detection of Pancreatic Cancer: Considerable Advances, but Still a Long Way to Go. Clin Gastroenterol Hepatol 2024; 22:672-673. [PMID: 37683881 DOI: 10.1016/j.cgh.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 09/10/2023]
Affiliation(s)
- Ruiyuan Xu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China; Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, PR China; National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, PR China; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China
| | - Chengcheng Wang
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, PR China; National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, PR China; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China; Medical Science Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China
| | - Yupei Zhao
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China; Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, PR China; National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, PR China; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China
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Guler GD, Ning Y, Coruh C, Mognol GP, Phillips T, Nabiyouni M, Hazen K, Scott A, Volkmuth W, Levy S. Plasma cell-free DNA hydroxymethylation profiling reveals anti-PD-1 treatment response and resistance biology in non-small cell lung cancer. J Immunother Cancer 2024; 12:e008028. [PMID: 38212123 PMCID: PMC10806554 DOI: 10.1136/jitc-2023-008028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable antitumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell-free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection. METHODS 151 blood samples were collected from 31 patients with non-small cell lung cancer (NSCLC) before therapy started and at multiple time points while on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively. RESULTS Comparison of on-treatment time point to matched pretreatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responding patients, as judged by Response evaluation criteria in solid tumors, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as inteferon (IFN)-γ and IFN-α response, inflammatory response and tumor necrosis factor (TNF)-α signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. Molecular response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early on, starting with the first cycle of treatment. Comparison of pretreatment plasma samples revealed that anti-PD-1 treatment response and resistance associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Furthermore, 5hmC profiling of pretreatment plasma samples was able to distinguish responders from non-responders using T cell-inflamed gene expression profile, which was previously identified by tissue RNA analysis. CONCLUSIONS These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma-derived cfDNA, offering a novel approach for non-invasive prediction and monitoring of immunotherapy response in NSCLC.
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Affiliation(s)
| | - Yuhong Ning
- ClearNote Health Inc, San Diego, California, USA
| | - Ceyda Coruh
- ClearNote Health Inc, San Diego, California, USA
| | | | | | | | - Kyle Hazen
- ClearNote Health Inc, San Diego, California, USA
| | - Aaron Scott
- ClearNote Health Inc, San Diego, California, USA
| | | | - Samuel Levy
- ClearNote Health Inc, San Diego, California, USA
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Dinesh MG, Bacanin N, Askar SS, Abouhawwash M. Diagnostic ability of deep learning in detection of pancreatic tumour. Sci Rep 2023; 13:9725. [PMID: 37322046 PMCID: PMC10272117 DOI: 10.1038/s41598-023-36886-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.
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Affiliation(s)
- M G Dinesh
- Department of Computer Science and Engineering, EASA College of Engineering and Technology, Coimbatore, India
| | | | - S S Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Computational Mathematics, Science and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
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