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Genome Med. 2016 Mar 31;8(1):34. doi: 10.1186/s13073-016-0289-9.

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis.

Author information

1
Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
2
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
3
Department of Microbiology, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
4
Department of Pediatrics, University of California, San Diego, CA, 92093, USA.
5
Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego School of Medicine, San Diego, CA, 92093, USA.
6
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA. GXie@cc.hawaii.edu.
7
Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, 96822, USA. LGarmire@cc.hawaii.edu.
8
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA. LGarmire@cc.hawaii.edu.

Abstract

BACKGROUND:

More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies.

METHODS:

We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods.

RESULTS:

The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer.

CONCLUSIONS:

We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis.

PMID:
27036109
PMCID:
PMC4818393
DOI:
10.1186/s13073-016-0289-9
[Indexed for MEDLINE]
Free PMC Article

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