Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer.
Breast neoplasms
Computer simulation
Metabolomics
Unsupervised machine learning
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
02
2020
revised:
15
05
2020
accepted:
16
05
2020
entrez:
9
7
2020
pubmed:
9
7
2020
medline:
9
7
2020
Statut:
epublish
Résumé
Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering.
Identifiants
pubmed: 32637048
doi: 10.1016/j.csbj.2020.05.021
pii: S2001-0370(20)30284-1
pmc: PMC7327012
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1509-1524Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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