【For INTERNATIONAL COLLABORATIVE RESEARCH 2017】

SUMMARY of

DATE: Day 5 Month5Year 20182017 RESEARCH RESULTS REPORTFor International Collaborative Research with IPR, Osaka University
Research Title

Experimental and Theoretical Detection of Phase Transition in Endocrine
Resistance in Breast Cancer

Applicant

Name

Luonan Chen

Affiliation

Key Laboratory of Systems Biology, Shanghai Institutes for BiologicalSciences, Chinese Academy of Sciences

Present Title
Research Collaborator (Host PI)

Professor
Mariko Okada

Summary
Breast cancer is one of the most common diseases in modern society and overcoming this disease is achallenging task in worldwide. Seventy percent of breast cancer are categorized as estrogen receptor(ER)-dependent tumor and initially respond to endocrine therapy using such as tamoxifen. However, about
30-40 % of tamoxifen-responsive tumor eventually acquires endocrine resistance within 15 years after initialdiagnosis after long-term treatment of the drug. Due to this reason, molecular mechanisms of tamoxifen
resistance have been intensively studied both in vivo and in vitro. Nevertheless, a deterministic factor that canmodulate the endocrine resistance is not identified yet. Based on this fact, one can assume that drug resistanceof breast cancer might be caused by modification of the molecular networks associated with many genes
rather than by changes of a few molecules. Therefore, we hypothesis that drug resistance is a result of statechanges in the time-course cellular transition in which gene networks are rewired to adapt to new environment
rather than relaying on the old network. Specifically, we hypothesis that there is a tipping point just before thedrastic state change (or drastic network rewiring) resulting in the drug resistance.
However, it is a challenging task to determine the tipping point of endocrine resistance and further detect
the associated molecules. To meet this challenge, we previously developed a computational framework called
dynamic network biomarker (DNB) analysis, which is able to identify such tipping point theoretically byanalyzing: (1) increase of averaged correlation coefficients of molecular behaviors and (2) drastic increase of
the averaged standard deviations within the network (DNB) together with (3) drastic decrease in thecorrelation coefficients of molecular behaviors outside of the DNB (protein or gene expressions). In thisstudy, we applied the DNB analysis for time-course transcriptome data obtained from tamoxifen-treated breast
cancer cells to quantitatively identify the tipping point of a drastic system transition as well as the DNB genesthat play key roles in acquiring drug resistance.
Specifically, we analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen
receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its
leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which

【For INTERNATIONAL COLLABORATIVE RESEARCH 2017】

the accumulated mutations eventually affect the DNB genes that subsequently cause the change oftranscriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets
validated that the DNB genes were associated with the poor survival of breast cancer patients.

*Deadline: May 18, 2018
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