NDLI: Deep neural network based tissue deconvolution of circulating tumor cell RNA

Content Provider	Springer Nature : BioMed Central
Author	Yan, Fengyao Jiang, Limin Ye, Fei Ping, Jie Bowley, Tetiana Y. Ness, Scott A. Li, Chung-I Marchetti, Dario Tang, Jijun Guo, Yan
Abstract	Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-023-04663-w.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-023-04663-w
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2023-11-04
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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