Search
Abstract
Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (9)
-
Anna Supernat dr
-
Anna J. Żaczek
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/s41598-024-61378-8
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Scientific Reports
no. 14,
ISSN: 2045-2322 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Pastuszak K., Sieczczyński M., Dzięgielewska M., Wolniak R., Drewnowska A., Korpal M., Zembrzuska L., Supernat A., Żaczek A. J.: Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing// Scientific Reports -,iss. 1 (2024),
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/s41598-024-61378-8
- Sources of funding:
-
- Spoza PG
- Verified by:
- Gdańsk University of Technology
seen 7 times
Recommended for you
Biochemical, Structural Analysis, and Docking Studies of Spiropyrazoline Derivatives
- A. Adamus-Grabicka,
- M. Daśko,
- P. Hikisz
- + 3 authors
The imidazoacridinone C-1311 induces p53-dependent senescence or p53-independent apoptosis and sensitizes cancer cells to radiation
- A. Skwarska,
- S. Ramachandran,
- G. Dobrynin
- + 2 authors