Detection of Disease-Specific Parent Cells Via Distinct Population of Nano-Vesicles by Machine Learning

Author(s): Abhimanyu Thakur*, Ambika Prasad Mishra, Bishnupriya Panda, Kumari Sweta, Babita Majhi

Journal Name: Current Pharmaceutical Design

Volume 26 , Issue 32 , 2020


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Abstract:

Background: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. AMD is primarily caused due to the dysfunction of retinal pigment epithelium (RPE), whereas endothelial cells (ECs) play one of the major roles in angiogenesis; an important process which occurs in malignant progression of cancer. Several reports suggested the augmented release of nano-vesicles under pathological conditions, including from RPE as well as cancer-associated ECs, which take part in various biological processes, including intercellular communication in disease progression. Importantly, these nano-vesicles are around 30-1000 nm and carry the fingerprint of their initiating parent cells (IPCs). Therefore, these nano-vesicles could be utilized as the diagnostic tool for AMD and cancer, respectively. However, the analysis of nano-vesicles for biomarker study is confounded by their extensive heterogeneous nature.

Methods: To confront this challenge, we utilized artificial intelligence (AI) based machine learning (ML) algorithms such as support vector machine (SVM) and decision tree model on the dataset of nano-vesicles from RPE and ECs cell lines with low dimensionality.

Results: Overall, Gaussian SVM demonstrated the highest prediction accuracy of the IPCs of nano-vesicles, among all the chosen SVM classifiers. Additionally, the bagged tree showed the highest prediction among the chosen decision tree-based classifiers.

Conclusion: Therefore, the overall bagged tree showed the best performance for the prediction of IPCs of nanovesicles, suggesting the applicability of AI-based prediction approach in diagnosis and prognosis of pathological conditions, including non-invasive liquid biopsy via various biofluids-derived nano-vesicles.

Keywords: Nano-vesicles, exosome, diagnosis, initiating parent cells, machine learning, artificial intelligence.

[1]
Bonilha VL. Age and disease-related structural changes in the retinal pigment epithelium. Clin Ophthalmol 2008; 2(2): 413-24.
[http://dx.doi.org/10.2147/OPTH.S2151] [PMID: 19668732]
[2]
Wang AL, Lukas TJ, Yuan M, Du N, Tso MO, Neufeld AH. Autophagy and exosomes in the aged retinal pigment epithelium: possible relevance to drusen formation and age-related macular degeneration. PLoS One 2009; 4(1)e4160
[http://dx.doi.org/10.1371/journal.pone.0004160] [PMID: 19129916]
[3]
Kang G-Y, Bang JY, Choi AJ, et al. Exosomal proteins in the aqueous humor as novel biomarkers in patients with neovascular age-related macular degeneration. J Proteome Res 2014; 13(2): 581-95.
[http://dx.doi.org/10.1021/pr400751k] [PMID: 24400796]
[4]
Thakur A, Roy A, Ghosh A, Chhabra M, Banerjee S. Abiraterone acetate in the treatment of prostate cancer. Biomed Pharmacother 2018; 101: 211-8.
[http://dx.doi.org/10.1016/j.biopha.2018.02.067] [PMID: 29494958]
[5]
Anand P, Kunnumakkara AB, Sundaram C, et al. Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 2008; 25(9): 2097-116.
[http://dx.doi.org/10.1007/s11095-008-9661-9] [PMID: 18626751]
[6]
Blumen H, Fitch K, Polkus V. Comparison of treatment costs for breast cancer, by tumor stage and type of serviceAm Heal drug benefits 2016. 9: 23-32.
[7]
Nishida N, Yano H, Nishida T, Kamura T, Kojiro M. Angiogenesis in cancer. Vasc Health Risk Manag 2006; 2(3): 213-9.
[http://dx.doi.org/10.2147/vhrm.2006.2.3.213] [PMID: 17326328]
[8]
Lamalice L, Le Boeuf F, Huot J. Endothelial cell migration during angiogenesis. Circ Res 2007; 100(6): 782-94.
[http://dx.doi.org/10.1161/01.RES.0000259593.07661.1e] [PMID: 17395884]
[9]
Ghosh A, Gao L, Thakur A, Siu PM, Lai CWK. Role of free fatty acids in endothelial dysfunction. J Biomed Sci 2017; 24(1): 50.
[http://dx.doi.org/10.1186/s12929-017-0357-5] [PMID: 28750629]
[10]
Zhang J, Chen C, Hu B, et al. Exosomes derived from human endothelial progenitor cells accelerate cutaneous wound healing by promoting angiogenesis through Erk1/2 signaling. Int J Biol Sci 2016; 12(12): 1472-87.
[http://dx.doi.org/10.7150/ijbs.15514] [PMID: 27994512]
[11]
Whiteside TL. Tumor-derived exosomes and their role in cancer progression. Adv Clin Chem 2016; 103-41.
[12]
de la Torre Gomez C, Goreham R V, Bech Serra J J, Nann T, Kussmann M. Exosomics”- A review of biophysics, biology and biochemistry of exosomes with a focus on human breast milk. Front Genet 2018. 27; 9:92.
[13]
McAndrews KM, Kalluri R. Mechanisms associated with biogenesis of exosomes in cancer. Mol Cancer 2019; 18(1): 52.
[http://dx.doi.org/10.1186/s12943-019-0963-9] [PMID: 30925917]
[14]
Sharma S, Gillespie BM, Palanisamy V, Gimzewski JK. Quantitative nanostructural and single-molecule force spectroscopy biomolecular analysis of human-saliva-derived exosomes. Langmuir 2011; 27(23): 14394-400.
[http://dx.doi.org/10.1021/la2038763] [PMID: 22017459]
[15]
Urbanelli L, Magini A, Buratta S, et al. Signaling pathways in exosomes biogenesis, secretion and fate. Genes (Basel) 2013; 4(2): 152-70.
[http://dx.doi.org/10.3390/genes4020152] [PMID: 24705158]
[16]
Gardiner C, Di Vizio D, Sahoo S, et al. Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell Vesicles 2016; 5: 32945.
[http://dx.doi.org/10.3402/jev.v5.32945] [PMID: 27802845]
[17]
Huang C, Clayton EA, Matyunina LV, et al. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci Rep 2018; 8(1): 16444.
[http://dx.doi.org/10.1038/s41598-018-34753-5] [PMID: 30401894]
[18]
Golkarnarenji G, Naebe M, Badii K, Milani AS, Jazar RN, Khayyam H. A machine learning case study with limited data for prediction of carbon fiber mechanical properties. Comput Ind 2019; 105: 123-32.
[http://dx.doi.org/10.1016/j.compind.2018.11.004]
[19]
Ko J, Bhagwat N, Yee SS, et al. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano 2017; 11(11): 11182-93.
[http://dx.doi.org/10.1021/acsnano.7b05503] [PMID: 29019651]
[20]
Gaur P, Chaturvedi A. Clustering and candidate motif detection in exosomal miRNAs by application of machine learning algorithms interdiscip. Sci Comput Life Sci 2017; 11(2): 206-14.
[21]
Ito K, Ogawa Y, Yokota K, et al. Host cell prediction of exosomes using morphological features on solid surfaces analyzed by machine learning. J Phys Chem B 2018; 122(23): 6224-35.
[http://dx.doi.org/10.1021/acs.jpcb.8b01646] [PMID: 29771528]
[22]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20: 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[23]
Schölkopf B, Smola AJ. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press 2001.
[24]
Quinlan JR. Induction of decision trees. Mach Learn 1986; 1: 81-106.
[http://dx.doi.org/10.1007/BF00116251]
[25]
Breiman L. Bagging predictors. Mach Learn 1996; 24: 123-40.
[http://dx.doi.org/10.1007/BF00058655]
[26]
Raposo G, Nijman HW, Stoorvogel W, et al. B lymphocytes secrete antigen-presenting vesicles. J Exp Med 1996; 183(3): 1161-72.
[http://dx.doi.org/10.1084/jem.183.3.1161] [PMID: 8642258]
[27]
Freund Y, Schapire RE. Experiments with a new boosting algorithm mach. Learn Proc Thirteen Int Conf. 148-56.
[28]
Montazeri M, Montazeri M, Montazeri M, Beigzadeh A. Machine learning models in breast cancer survival prediction. Technol Health Care 2016; 24(1): 31-42.
[http://dx.doi.org/10.3233/THC-151071] [PMID: 26409558]
[29]
Mansbridge N, Mitsch J, Bollard N, et al. Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in Sheep. Sensors 2018; 18: 3532.
[30]
Zhang Y, Liu Y, Liu H, Tang WH. Exosomes: biogenesis, biologic function and clinical potential. Cell Biosci 2019; 9: 19.
[http://dx.doi.org/10.1186/s13578-019-0282-2] [PMID: 30815248]
[31]
Rahbarghazi R, Jabbari N, Sani NA, et al. Tumor-derived extracellular vesicles: Reliable tools for cancer diagnosis and clinical applications. Cell Commun Signal 2019; 17(1): 73.
[http://dx.doi.org/10.1186/s12964-019-0390-y] [PMID: 31291956]
[32]
Willms E, Cabañas C, Mäger I, Wood MJA, Vader P. Extracellular vesicle heterogeneity: subpopulations, isolation techniques, and diverse functions in cancer progression. Front Immunol 2018; 9.
[33]
Szatanek R, Baj-Krzyworzeka M, Zimoch J, Lekka M, Siedlar M, Baran J. The methods of choice for Extracellular Vesicles (EVs) characterization. Int J Mol Sci 2017; 18(6): 1153.
[http://dx.doi.org/10.3390/ijms18061153] [PMID: 28555055]
[34]
Welch N G, Scoble J A, Muir B W, Pigram P J. 2017.
[35]
Anderson W, Lane R, Korbie D, Trau M. Observations of tunable resistive pulse sensing for exosome analysis: improving system sensitivity and stability. Langmuir 2015; 31(23): 6577-87.
[http://dx.doi.org/10.1021/acs.langmuir.5b01402] [PMID: 25970769]
[36]
Lim J, Choi M, Lee H, et al. Direct isolation and characterization of circulating exosomes from biological samples using magnetic nanowires. J Nanobiotechnology 2019; 17(1): 1.
[http://dx.doi.org/10.1186/s12951-018-0433-3] [PMID: 30612562]
[37]
Contreras-Naranjo JC, Wu H-J, Ugaz VM. Microfluidics for exosome isolation and analysis: Enabling liquid biopsy for personalized medicine. Lab Chip 2017; 17(21): 3558-77.
[http://dx.doi.org/10.1039/C7LC00592J] [PMID: 28832692]


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Article Details

VOLUME: 26
ISSUE: 32
Year: 2020
Published on: 23 September, 2020
Page: [3985 - 3996]
Pages: 12
DOI: 10.2174/1381612826666200422091753
Price: $65

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