title: Sharing information to reconstruct patient-specific pathways in heterogeneous diseases.
abstract: Advances in experimental techniques resulted in abundant genomic, transcriptomic, epigenomic, and proteomic data that have the potential to reveal critical drivers of human diseases. Complementary algorithmic developments enable researchers to map these data onto protein-protein interaction networks and infer which signaling pathways are perturbed by a disease. Despite this progress, integrating data across different biological samples or patients remains a substantial challenge because samples from the same disease can be extremely heterogeneous. Somatic mutations in cancer are an infamous example of this heterogeneity. Although the same signaling pathways may be disrupted in a cancer patient cohort, the distribution of mutations is long-tailed, and many driver mutations may only be detected in a small fraction of patients. We developed a computational approach to account for heterogeneous data when inferring signaling pathways by sharing information across the samples. Our technique builds upon the prize-collecting Steiner forest problem, a network optimization algorithm that extracts pathways from a protein-protein interaction network. We recover signaling pathways that are similar across all samples yet still reflect the unique characteristics of each biological sample. Leveraging data from related tumors improves our ability to recover the disrupted pathways and reveals patient-specific pathway perturbations in breast cancer.
authors.affiliation: Microsoft Research, Cambridge, MA, USA.
authors.forename: Anthony
authors.initials: A
authors.lastname: Gitter
authors.forename: Alfredo
authors.initials: A
authors.lastname: Braunstein
authors.forename: Andrea
authors.initials: A
authors.lastname: Pagnani
authors.forename: Carlo
authors.initials: C
authors.lastname: Baldassi
authors.forename: Christian
authors.initials: C
authors.lastname: Borgs
authors.forename: Jennifer
authors.initials: J
authors.lastname: Chayes
authors.forename: Riccardo
authors.initials: R
authors.lastname: Zecchina
authors.forename: Ernest
authors.initials: E
authors.lastname: Fraenkel
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology; D030541:Databases, Genetic; D066246:ErbB Receptors / Q000235:genetics / Q000378:metabolism; D005260:Female; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D060066:Protein Interaction Maps; D015398:Signal Transduction
chemical_list: C512478:EGFR protein, human; D066246:ErbB Receptors