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Hasanzadeh Kafshgari M, Hayden O. Advances in analytical microfluidic workflows for differential cancer diagnosis. NANO SELECT 2023. [DOI: 10.1002/nano.202200158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- Morteza Hasanzadeh Kafshgari
- Heinz‐Nixdorf‐Chair of Biomedical Electronics Campus Klinikum München rechts der Isar TranslaTUM Technical University of Munich Munich Germany
| | - Oliver Hayden
- Heinz‐Nixdorf‐Chair of Biomedical Electronics Campus Klinikum München rechts der Isar TranslaTUM Technical University of Munich Munich Germany
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2
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Guo Z, Lin X, Hui Y, Wang J, Zhang Q, Kong F. Circulating Tumor Cell Identification Based on Deep Learning. Front Oncol 2022; 12:843879. [PMID: 35252012 PMCID: PMC8889528 DOI: 10.3389/fonc.2022.843879] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/21/2022] [Indexed: 12/18/2022] Open
Abstract
As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis.
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Affiliation(s)
- Zhifeng Guo
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
| | - Xiaoxi Lin
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
| | - Yan Hui
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
| | - Jingchun Wang
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
| | - Qiuli Zhang
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
| | - Fanlong Kong
- Department of Oncology, Chifeng Municipal Hospital, Chifeng, China
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Li F, Xu H, Zhao Y. Magnetic particles as promising circulating tumor cell catchers assisting liquid biopsy in cancer diagnosis: A review. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Cheng KS, Pan R, Pan H, Li B, Meena SS, Xing H, Ng YJ, Qin K, Liao X, Kosgei BK, Wang Z, Han RP. ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells. Am J Cancer Res 2020; 10:11026-11048. [PMID: 33042268 PMCID: PMC7532685 DOI: 10.7150/thno.44053] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 05/11/2020] [Indexed: 12/12/2022] Open
Abstract
A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Methods: Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological manipulations with modern statistical machine learning, we deployed a next generation software, ALICE (Automated Liquid Biopsy Cell Enumerator) to identify and enumerate minute amounts of tumor cell phenotypes bestrewed in massive populations of leukocytes. As a code designed for futurity, ALICE is armed with internet of things (IOT) connectivity to promote pedagogy and continuing education and also, an advanced cybersecurity system to safeguard against digital attacks from malicious data tampering. Results: By combining robust principal component analysis, random forest classifier and cubic support vector machine, ALICE was able to detect synthetic, anomalous and tampered input images with an average recall and precision of 0.840 and 0.752, respectively. In terms of phenotyping enumeration, ALICE was able to enumerate various circulating tumor cell (CTC) phenotypes with a reliability ranging from 0.725 (substantial agreement) to 0.961 (almost perfect) as compared to human analysts. Further, two subpopulations of circulating hybrid cells (CHCs) were serendipitously discovered and labeled as CHC-1 (DAPI+/CD45+/E-cadherin+/vimentin-) and CHC-2 (DAPI+ /CD45+/E-cadherin+/vimentin+) in the peripheral blood of pancreatic cancer patients. CHC-1 was found to correlate with nodal staging and was able to classify lymph node metastasis with a sensitivity of 0.615 (95% CI: 0.374-0.898) and specificity of 1.000 (95% CI: 1.000-1.000). Conclusion: This study presented a machine-learning-augmented rule-based hybrid AI algorithm with enhanced cybersecurity and connectivity for the automatic and flexibly-adapting enumeration of cellular liquid biopsies. ALICE has the potential to be used in a clinical setting for an accurate and reliable enumeration of CTC phenotypes.
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He B, Lu Q, Lang J, Yu H, Peng C, Bing P, Li S, Zhou Q, Liang Y, Tian G. A New Method for CTC Images Recognition Based on Machine Learning. Front Bioeng Biotechnol 2020; 8:897. [PMID: 32850745 PMCID: PMC7423836 DOI: 10.3389/fbioe.2020.00897] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/13/2020] [Indexed: 12/18/2022] Open
Abstract
Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Qingqing Lu
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jidong Lang
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Hai Yu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Chao Peng
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Shijun Li
- Department of Pathology, Chifeng Municipal Hospital, Chifeng, China
| | - Qiliang Zhou
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Yuebin Liang
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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Rafiee SD, Kocabey S, Mayer M, List J, Rüegg C. Detection of HER2 + Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification. ChemMedChem 2020; 15:661-666. [PMID: 31943804 PMCID: PMC7187270 DOI: 10.1002/cmdc.201900697] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 12/17/2019] [Indexed: 01/25/2023]
Abstract
Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, detection of CTCs remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuit. We applied this approach to detect HER2+ cancer cells using the anti‐HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4 °C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2− cells and peripheral blood leukocytes, which remained almost non‐fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.
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Affiliation(s)
- Sarah D Rafiee
- Department of Oncology, Microbiology and Immunology Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 18, PER17, 1700, Fribourg, Switzerland
| | - Samet Kocabey
- Department of Oncology, Microbiology and Immunology Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 18, PER17, 1700, Fribourg, Switzerland
| | - Michael Mayer
- BioPhysics, Adolphe Merkle Institute, University of Fribourg, Chemin des Verdiers 4, PER 18, 1700, Fribourg, Switzerland
| | - Jonathan List
- BioPhysics, Adolphe Merkle Institute, University of Fribourg, Chemin des Verdiers 4, PER 18, 1700, Fribourg, Switzerland.,Physics of Synthetic Biological Systems, Technical University Munich, Am Coulombwall 4a, 85748, Garching, Germany
| | - Curzio Rüegg
- Department of Oncology, Microbiology and Immunology Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 18, PER17, 1700, Fribourg, Switzerland
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Wang Y, Wu D, Wu G, Wu J, Lu S, Lo J, He Y, Zhao C, Zhao X, Zhang H, Wang S. Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy. Theranostics 2020; 10:300-311. [PMID: 31903121 PMCID: PMC6929630 DOI: 10.7150/thno.38736] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 08/29/2019] [Indexed: 12/21/2022] Open
Abstract
Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies. Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device. Results:Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells. Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.
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Affiliation(s)
- Yimin Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Di Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Guohua Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Jianguo Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Siming Lu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - James Lo
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, 94720, United States of America
| | - Yong He
- State Key Laboratory of Fluid Power and Mechatronic Systems, Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Chao Zhao
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute and Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0AH, United Kingdom
| | - Xin Zhao
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hongbo Zhang
- Department of Pharmaceutical Science, Åbo Akademic University, FI-20520, Turku, Finland
| | - ShuQi Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
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Liu Y, Li T, Xu M, Zhang W, Xiong Y, Nie L, Wang Q, Li H, Wang W. A high-throughput liquid biopsy for rapid rare cell separation from large-volume samples. LAB ON A CHIP 2018; 19:68-78. [PMID: 30516210 DOI: 10.1039/c8lc01048j] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Liquid biopsy techniques for rare tumor cell separation from body fluids have shown enormous promise in cancer detection and prognosis monitoring. This work established a high-throughput liquid biopsy platform with a high recovery rate and a high cell viability based on a previously reported 2.5D micropore-arrayed filtration membrane. Thanks to its high porosity (>40.2%, edge-to-edge space between the adjacent micropores <4 μm), the achieved filtration throughputs can reach >110 mL min-1 for aqueous samples and >17 mL min-1 for undiluted whole blood, only driven by gravity with no need for any extra pressure loading. The recoveries of rare lung tumor cells (A549s) spiked in PBS (10 mL), unprocessed BALF (10 mL) and whole blood (5 mL) show high recovery rates (88.0 ± 3.7%, 86.0 ± 5.3% and 83.2 ± 6.2%, respectively, n = 5 for every trial) and prove the high performance of this platform. Successful detection of circulating tumor cells (CTCs) from whole blood samples (5 mL) of lung cancer patients (n = 5) was demonstrated. In addition, it was both numerically and experimentally proved that a small edge-to-edge space was significant to improve the viability of the recovered cells and the purity of the target cell recovery, which was reported for the first time to the best of the authors' knowledge. This high-throughput technique will expand the detecting targets of liquid biopsy from the presently focused CTCs in whole blood to the exfoliated tumor cells (ETCs) in other large-volume clinical samples, such as BALF, urine and pleural fluid. Meanwhile, the technique is easy to operate and ready for integration with other separation and analysis tools to fulfill a powerful system for practical clinical applications of liquid biopsy.
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Affiliation(s)
- Yaoping Liu
- Institute of Microelectronics, Peking University, 100871, Beijing, China.
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