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: Ahmed Mohamed Mohamed
Title: Network-based analysis of cancer pathways
Institute: Bioinformatics Center (Mamitsuka Laboratory), Kyoto University Institute for Chemical
Research; Kyoto, Uji, Gokasho 611-0011, Japan
Partner institute of your choice：Bioinformatics Program, Boston University (Prof. Charles DeLisi)
Duration of your choice: January 24th – April 20th , 2013
Plan：

Background:
Aberration from normally functioning metabolic pathways has become one of ten hallmarks ofcancer (Hanahan and Weinberg, 2011). The Cancer Genome Atlas Network studies have enriched
the data repository with expression, genetic and epigenetic profiles for various cancer types (TCGANetwork). Integrating such data with a priori knowledge can help clarify causal and affecting
genes in the cancer cascade.
Since cancer utilizes abnormal pathways, reliance upon known normal pathways solely isinsufficient. Network inference from expression data have been employed to help constructcancer-specificpathways(TorkamaniandSchork,2009).

Otherapproaches

annexedliterature-extracted networks with protein interactions predicted by machine learning techniques(Ciriello, et al., 2012; Wu, et al., 2010) and used this network for analysis. However, such methods
reduce the current knowledge to undirected gene networks. Analysis using more detailed networks
may improve the construction of causality relationships between altered genes.
Another challenge for cancer pathway analysis is to identify not only the genes associated with thedisease, but also the ones causing it. Several network models have been developed to discriminate
between driver genes responsible for cancer initiation and metastasis, and genes downstream inthe cascade. Extension of driver pathway detection methods is clearly needed.

Goals:The purpose of the visit to Professor DeLisi’s laboratory is to discuss possible improvements tocurrent cancer pathway analysis methods. Examples of such improvements are given below:
1. Provide a network representation that enables integration of prior knowledge withpredicted networks. The representation should keep information extracted from pathways
databases, such as protein interaction type and direction. Also, since networks areconstructed from multiple-sources with varying reliabilities, confidence in interactions
should also be preserved.
2. Incorporation of novel network inference methods, such as these based on chemical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

similarity. This will also allow us to detect distinctive metabolomics signatures.
3. Incorporation of knowledge provided by ENCODE project (Gerstein,	   et	   al.,	   2012;	   Khatun	   et	   al.,	  2012).
4. Adding protein complex information to the network. Many studies (Bandyopadhyay, et al.,2010; Dixon, et al., 2008) in yeast indicated that under different contexts protein complexes
tend to preserve their composition while changing their functional relationship. Thus,rewired pathways are more expected to use the same complexes as normal known
pathways.
5. Genetic mutations that affect the same pathway tend to not co-occur in the same patient,
an observation called “mutual exclusivity”(Ciriello, et al., 2012). Analysis based on thisassumption, incorporating pathways topology methods(Draghici, et al., 2007) hasn’t been
done yet.
6. Linking mutations to genetic expression,(Vaske, et al., 2009), may also clarify causal
relationships.

References:
Bandyopadhyay,	  S.,	  et	  al.	  (2010)	  Rewiring	  of	  genetic	  networks	  in	  response	  to	  DNA	  damage.,	  Science,	  330,	  1385-­‐1389.	  
Ciriello,	   G.,	   et	   al.	   (2012)	   Mutual	   exclusivity	   analysis	   identifies	   oncogenic	   network	   modules,	   Genome	  Research,	  22,	  398-­‐406.	  
Dixon,	  S.J.,	  et	  al.	  (2008)	  Significant	  conservation	  of	  synthetic	  lethal	  genetic	  interaction	  networks	  between	  distantly	  related	  eukaryotes.,	  Proceedings	  of	  the	  National	  Academy	  of	  Sciences,	  105,	  16653-­‐16658.	  
Draghici,	  S.,	  et	  al.	  (2007)	  A	  systems	  biology	  approach	  for	  pathway	  level	  analysis.	  
Gerstein,	   M.	   B.,	   et	   al.	   "Architecture	   of	   the	   human	   regulatory	   network	   derived	   from	   ENCODE	  data."	  Nature	  489.7414	  (2012):	  91-­‐100.	  
Hanahan,	  D.	  and	  Weinberg,	  R.A.	  (2011)	  Hallmarks	  of	  cancer:	  the	  next	  generation,	  Cell,	  144,	  646-­‐674.	  
Khatun,	  J.	  "An	  Integrated	  Encyclopedia	  of	  DNA	  Elements	  in	  the	  Human	  Genome."	  Nature	  (2012).	  
TCGA	  Network	   	   https://tcga-­‐data.nci.nih.gov/tcga/.	  
Torkamani,	   A.	   and	   Schork,	   N.J.	   (2009)	   Identification	   of	   rare	   cancer	   driver	   mutations	   by	   network	  reconstruction,	  Genome	  Research,	  19,	  1570-­‐1578.	  
Vaske,	   C.J.,	   et	   al.	   (2009)	   A	   factor	   graph	   nested	   effects	   model	   to	   identify	   networks	   from	   genetic	  perturbations,	  PLoS	  Computational	  Biology,	  5,	  e1000274.	  
Wu,	   G.,	   Feng,	   X.	   and	   Stein,	   L.	   (2010)	   A	   human	   functional	   protein	   interaction	   network	   and	   its	   application	   to	  cancer	  data	  analysis.,	  Genome	  Biology,	  11,	  R53.