Oncology Central

Cancer subtypes could be distinguished using metabolomic analysis

New methods of using metabolomics as a tool for clinical cancer research and care have been presented at the 2nd Annual Biomarker Conference (6–7 February 2017, San Diego, CA, USA) by CureMatch (CA, USA), developer of a decision support platform for combination therapy in cancer.

The emerging field of metabolomics has the potential to contribute significantly to biomarker discovery and cancer. While other techniques, such as DNA sequencing, have led to significant advances in precision oncology, metabolomics has yet to make its mark on the field.

Metabolites from the four subtypes of lung cancer (adenocarcinoma, squamous cell carcinoma, large cell lung cancer, small cell lung cancer) and the four subtypes of breast cancer (basal-like, luminal A, luminal B, HER-2 positive) were analyzed to examine differentially expressed metabolic pathways in each type of cancer. Five metabolic pathways selective for adenocarcinoma and 14 for small cell lung cancer were discovered in the lung cancer study. In the breast cancer study, at least one metabolic pathway could be used to distinguish the four cancer subtypes.

“The results of the studies indicate that our metabolomic profiling technique can potentially be used as an accurate and inexpensive way to distinguish among different cancer subtypes,” stated lead author Valentina Kouznetsova (UC San Diego Supercomputer Center and Moores Cancer Center, CA, USA).

“Our CureMatch decision support platform already incorporates genomic, transcriptomic, and proteomic data to allow oncologists to determine the best treatment plan for their patients,” explained Stephane Richard, CureMatch, “We believe that this is the first step in adding metabolomics data into our platform, and will enable a broader range of genes to be analyzed and additional drug therapy options to be provided to the oncologist.”

“I need to note, that using metabolomics profiling and recognition strategies can help in elucidation of genes that are activated or deactivated based on the metabolic responses of the organism,” added Igor Tsigelny (CureMatch).

Sources: Kouznetsova V, Zhuo K, Bronars M, Richard S, Tsigelny I. Combined Metabolomics and Genomic Analyses Pave the Way to Machine-Learning Methods for Lung Cancer Diagnosis. Proceedings of the 2nd Annual Biomarker Conference. San Diego, CA, USA, 6–7 February 2017; http://www.digitaljournal.com/pr/3248042

Kouznetsova V, Zhuo K, Bronars M, Richard S, Tsigelny I. Metabolomic Profiling to Distinguish Between Subtypes of Breast Cancer. Proceedings of the 2nd Annual Biomarker Conference. San Diego, CA, USA, 6–7 February 2017.

Also featured on our sister site, Bioanalysis Zone.




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