Functional modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage

MOTIVATION Childhood B-precursor lymphoblastic leukaemia (ALL) is the most common paediatric malignancy. Despite the fact that 80% of ALL patients respond to anti-cancer drugs, the patho-physiology of this disease is still not fully understood. mRNA expression-profiling studies that have been performed have not yet provided novel insights into the mechanisms behind cellular response to DNA damage. More powerful data analysis techniques may be required for identifying novel functional pathways involved in the cellular responses to DNA damage. RESULTS In order to explore the possibility that unforeseen biological processes may be involved in the response to DNA damage, we have developed and applied a novel procedure for the identification of functional modules in ALL cells. We have discovered that the overall activity of functional modules integrating protein degradation and mRNA processing is predictive of response to DNA damage. AVAILABILITY Supplementary material including R code, additional results, experimental datasets, as well as a detailed description of the methodology are available at http://www.bip.bham.ac.uk/vivo/fumo.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[2]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[3]  T. Stankovic,et al.  Microarray analysis reveals that TP53- and ATM-mutant B-CLLs share a defect in activating proapoptotic responses after DNA damage but are distinguished by major differences in activating prosurvival responses. , 2004, Blood.

[4]  Anastasios Bezerianos,et al.  Growing functional modules from a seed protein via integration of protein interaction and gene expression data , 2007, BMC Bioinformatics.

[5]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[6]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[7]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[8]  Francesco Falciani,et al.  GALGO: an R package for multivariate variable selection using genetic algorithms , 2006, Bioinform..

[9]  Benno Schwikowski,et al.  Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.

[10]  Brad T. Sherman,et al.  The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists , 2007, Genome Biology.

[11]  U. Moll,et al.  The Role of Ubiquitination in the Direct Mitochondrial Death Program of p53 , 2007, Cell cycle.

[12]  S. Rafii,et al.  Splitting vessels: Keeping lymph apart from blood , 2003, Nature Medicine.

[13]  Ron Shamir,et al.  Identification of functional modules using network topology and high-throughput data , 2007, BMC Systems Biology.

[14]  Trevor Hastie,et al.  Averaged gene expressions for regression. , 2007, Biostatistics.

[15]  David J. Reiss,et al.  BioNetBuilder: automatic integration of biological networks , 2006, Bioinform..

[16]  Cheng Cheng,et al.  Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment. , 2004, The New England journal of medicine.

[17]  Zelmina Lubovac,et al.  Combining functional and topological properties to identify core modules in protein interaction networks , 2006, Proteins.

[18]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[19]  Christopher C. Moser,et al.  Natural engineering principles of electron tunnelling in biological oxidation–reduction , 1999, Nature.

[20]  Marina Vannucci,et al.  Models and computational strategies linking physiological response to molecular networks from large-scale data , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Shan Wu,et al.  Decision tree modeling predicts effects of inhibiting contractility signaling on cell motility , 2007, BMC Systems Biology.