Detecting pathways transcriptionally correlated with clinical parameters.
The recent explosion in the number of clinical studies involving microarray data calls for novel computational methods for their dissection. Human protein interaction networks are rapidly growing and can assist in the extraction of functional modules from microarray data. We describe a novel methodology for extraction of connected network modules with coherent gene expression patterns that are correlated with a specific clinical parameter. Our approach suits both numerical (e.g., age or tumor size) and logical parameters (e.g., gender or mutation status). We demonstrate the method on a large breast cancer dataset, where we identify biologically-relevant modules related to nine clinical parameters including patient age, tumor size, and metastasis-free survival. Our method is capable of detecting disease-relevant pathways that could not be found using other methods. Our results support some previous hypotheses regarding the molecular pathways underlying diversity of breast tumors and suggest novel ones.
Ulitsky
Igor
I
School of Computer Science, Tel Aviv University, Tel Aviv, Israel. ulitskyi@post.tau.ac.il
Shamir
Ron
R
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis*; D015398:Signal Transduction; D014157:Transcription Factors / Q000378:metabolism*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D014157:Transcription Factors