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2016-2-11

The Main Points About the Current Research

  • A mathematical model*1 was developed to comprehensively understand cancer biology, allowing the development of novel cancer treatments.

  • Polyamine metabolism*2 was identified to specifically underlie the drug resistance in cancer stem cells.
  • Mathematical analysis can identify novel drug targets, and could lead to the eradication and cure of cancer.

20160211-2

Abstract

Cancer Stem Cells (CSCs) are what you would call “Masters of Cancer.” We will never get a complete cure unless the CSCs are uprooted, since the existence of the CSCs leads to the relapse of cancer after treatment. Therefore the CSCs are very important targets for antitumor therapy to neutralize cancers. The characteristic details, however, are not yet known.

J. Koseki (Assistant Professor) and H. Ishii (Professor) at the Graduate School of Medicine, Osaka University, in cooperation with M. Mori (Professor) and Y. Doki (Professor) of the Department of Gastroenterological Surgery, Osaka University, developed a novel Trans-omics System for innovative cancer treatment. Our novel trans-omics methodology is mathematical methods to analyse some significant correlation between big data for CSCs and non-CSCs. They reported that CSCs have a specific polyamine metabolome mechanism different from non-CSCs.

It has been generally reported that an increase in the total amount of polyamine leads to cell death. This time, we found that CSCs control the amount of polyamine for themselves to survive after exposure to antitumor agents. In the future, the development of inhibitors of this metabolic pathway used by cancer stem cells could lead to the complete cure of cancers resistant to current treatments, through targeting of the cancer stem cells.

This research was published in the electronic version of the British scientific journal "Scientific Reports" on February 11, 2016 (10 am UK time).

Background

Big data of genetic and metabolic information concerning cancer stem cells is increasingly used. In order to analyze for associations between these different types of large data (omics data*4), it is necessary to use mathematical and statistical methods to calculate the significance of the various measurements, and to scientifically predict associations. The highly precise predictions are expected to increase the efficiency of the subsequent validation experiments and allow scientists to achieve their goals quicker. In this research, we have built the basis of mathematical analysis to identify new treatment strategies against cancer stem cells.

Impact of this research for the wider society (the significance of this research)

This research project has successfully introduced mathematical and statistical methods for trans-omic research. The current research is expected to pave the way for the development of metabolic inhibitors of cancer stem cells, allowing the complete eradication of cancers intractable to the currently available treatments.

This is a collaborative research project with Kyushu University.

Glossary

*1 Mathematical model

The use of mathematical techniques, whilst taking into consideration the laws of chemistry/physics with the aim of furthering our understanding in various fields.

*2 Polyamine Metabolism

This includes a group of compounds that are metabolized from ornithine, which is most famous for being produced in freshwater clams, and plays an important role in cell division and growth, essential for maintaining cellular homeostasis.

*3 Trans-omics

This is the mass analysis for correlations or associations of data collected under the same conditions but from different viewpoints.

*4 Omics data

This is a general term for the analysis of large information (big data) generated from a variety of experiments concerning the molecules within a living organism.

To learn more about this research, please view the full research report entitled "A Trans-omics Mathematical Analysis Reveals Novel Functions of the Ornithine Metabolic Pathway in Cancer Stem Cells" at this page of the Scientific Reports website.


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