ArticleGenomics, proteomics & bioinformatics2017
eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood.
Article in Genomics, proteomics & bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- KRAS and BRAF mutations in circulating tumour DNA from locally advanced rectal cancer.Scientific reports · 2018Trial
- Article
- Genome-Wide Scan for Copy Number Variations in Chinese Merino Sheep Based on Ovine High-Density 600K SNP Arrays.Animals : an open access journal from MDPI · 2024Article
- MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.Briefings in bioinformatics · 2021Article
- Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.Journal of translational medicine · 2020Article
- Article
- Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.Journal of gynecologic oncology · 2019Article
- Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.Scientific reports · 2018Article
- Liquid Biopsy in Endometrial Cancer: New Opportunities for Personalized Oncology.International journal of molecular sciences · 2018Review
- ACGH detects distinct genomic alterations of primary intrahepatic cholangiocarcinomas and matched lymph node metastases and identifies a poor prognosis subclass.Scientific reports · 2018Article
- Clinical value of circulatingCancer management and research · 2018Review
- Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma.Scientific reports · 2017Article
- Landscape of somatic mutations in different subtypes of advanced breast cancer with circulating tumor DNA analysis.Scientific reports · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86-0.96) and high recall rates (0.79-0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78-0.92) and recall rates (0.58-0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.
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