Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

PIF3 is a phytochrome-interacting basic helix-loop-helix transcription factor that negatively regulates light responses, including hypocotyl elongation, cotyledon opening, and hypocotyl negative gravitropism. However, the role of PIF3 in chlorophyll biosynthesis has not been clearly defined. Here, we show that PIF3 also negatively regulates chlorophyll biosynthesis by repressing biosynthetic genes in the dark. Consistent with the gene expression patterns, the etiolated pif3 mutant accumulated a higher amount of protochlorophyllide and was bleached severely when transferred into light. The photobleaching phenotype of pif3 could be suppressed by the gun5 mutation and mimicked by overexpression of GUN5. When 4 negative phytochrome-interacting protein genes (PIF1, PIF3, PIF4, and PIF5) were mutated, the resulting quadruple mutant seedlings displayed constitutive photomorphogenic phenotypes, including short hypocotyls, open cotyledons, and disrupted hypocotyl gravitropism in the dark. Microarray analysis further confirmed that the dark-grown quadruple mutant has a gene expression pattern similar to that of red light-grown WT. Together, our data indicate that 4 phytochrome-interacting proteins are required for skotomorphogenesis and phytochromes activate photomorphogenesis by inhibiting these factors.

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.