若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類

Name: Sayaka MizutaniTitle: Statistical analysis of breast cancer progression based on genomic, transcriptomic andphenotypic data
Institute: Bioinformatics Center, Institute for Chemical Research, Kyoto University
Partner institute of your choice：Centre for Computational Biology, Mines ParisTech
Duration of your choice: September 30, 2010 – December 24, 2010
Research Objectives
I will be dedicated to a cancer research under the supervision of Dr. Jean-Philippe Vert and Dr.
Yoshihiro Yamanishi at Centre for Computational Biology, Mines ParisTech.
Centre for Computational Biology is dedicated to epidemiology, bioinformatics and systems
biology of cancer. Their research objective is the development of probabilistic models andstatistical machine learning to process and analyze data produced by high-throughput technologies.
The laboratory has a joint program with Institut Curie, one of the leading medical, biological andbiophysical research centers in the world. The two institutes have established cooperative research
projects, in which the developed computational models are applied to experimental data obtained
from experimental laboratories in Institut Curie. Staying in Mines ParisTech would be a valuable
experience in working both with researchers in computational biology and experimental biology.
Therefore, I would like to apply for the ITP program for three-month stay in Mines ParisTech.
There has been an increasing demand for bioinformatics approaches in the analyses ofhigh-throughput data, which vary from genetic polymorphisms, to gene copy numbers, tomicroarray gene expression, and to data produced by next-generation sequencers. Accordingly,
several research laboratories including the Centre of Computational Biology have been dedicatedto developing computational methods to extract useful information from these data sets. One of thegoals of my collaborative research is to learn computational methods, which have been studied by
the laboratory and been applied to the analyses of several medical and biological data. Anotherimportant role of bioinformatics in medical research is to collect biologically relevant knowledge
in relation of clinical symptoms. Kanehisa laboratory has been developing the KEGG database, awidely accepted database in which molecular biological information regarding to several cancer
pathogenesis are accumulated. My participation to the three-month program will provide anopportunity to further develop a collaborative partnership between two laboratories by integrating
the resources of the two laboratories. Finally, I will anticipate in interacting with researchers inother clinical laboratories in the Institut Curie. The Centre for Computational Biology participatesa joint seminar with many laboratories in the institute. By participating discussions withresearchers from different clinical interests will provide me many opportunities to learn theessence of clinical research in a new viewpoint.

若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類

Research Plan
1. Background
Breast cancer is one of the most common types of non-skin cancer in women and the fifth mostcommon cause of cancer death. Recent advances in the analyses of high-throughput data haveenabled us to classify breast cancers into different subtypes. However, the mechanism of cancer
progression has not been well understood. Unlike the colon carcinomas, which are well explainedby the Volgenstein’s adenoma to more malignant carcinoma progression model, the complexities
in the pathological entities of breast cancer makes it more challenging to set up a good model tounderstand cancer progression. Recent genotypic-phenotypic correlation analyses have delineatedevolutional pathways, which lead to different subtypes of clinical behaviors. However, ourknowledge on molecular alterations associated with each step of progression is only fragmental. In
my research we will analyze recently published breast cancer data to identify stage-specificalterations of genes by applying a statistical method called the canonical correlation analysis(CCA).
2. Materials and methods
I will analyze a breast cancer data published by Chin et al. in 2006 [1]. It consists of three
types of data; CGH, gene expression, and clinical diagnostic data taken from the same set ofpatients. CGH covers gene copy number alterations, which have recently shown to be causative tothe malignancy of breast cancer. Gene expression data covers the aberrations of mRNAs. Clinicaldiagnostic data includes the histopathological grades of the tumors, the results of cancer markers
and other features of the patients.
I will apply a recently developed version of the canonical correlation analysis (CCA) to theChin et al. data. Given two sets of data over the same sets of samples, CCA finds elements thatmaximize the correlation between the two data sets. A useful algorithm has been provided by
Witten et al., 2009 in an implementation to the widely used software R [2]. Using this algorithm
we look for sets of genes that are altered both in the gene copy numbers and the transcription
levels. Then, we classify gene sets into different subtypes of breast cancer in the progressive stages
and clinical outcomes. This approach will help us predict genomic and transcriptomic alterationsof genes that may give rise to a specific stage or a specific clinical of the tumor.
3. References[1] Chin, K. et al., Genomic and transcriptional aberrations linked to breast cancer
pathophysiologies, Cancer Cell, 10:529–541, 2006.
[2] Witten, D.M., Tibshirani, R., and Hastie T., A penalized matrix decomposition, withapplications to sparse principal components and canonical correlation analysis,
Biostatistics, 10:515–534, 2009.