Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.

Cancer chromosomal instability (CIN) results in an elevated rate of change of chromosome number and structure and generates intratumour heterogeneity1,2. CIN is observed in the majority of solid tumours and is associated with both poor prognosis and drug resistance3,4. Therefore, understanding a mechanistic basis for CIN is paramount. Here we find evidence for impaired replication fork progression and elevated DNA replication stress in CIN+ colorectal cancer (CRC) cells relative to CIN− CRC cells, with structural chromosome abnormalities precipitating chromosome missegregation in mitosis. We identify three novel CIN-suppressor genes (PIGN (MCD4), RKHD2 (MEX3C) and ZNF516 (KIAA0222)) encoded on chromosome 18q, which is subject to frequent copy number loss in CIN+ CRC. 18q loss was temporally associated with aneuploidy onset at the adenoma-carcinoma transition. CIN-suppressor gene silencing leads to DNA replication stress, structural chromosome abnormalities and chromosome missegregation. Supplementing cells with nucleosides, to alleviate replication-associated damage5, reduces the frequency of chromosome segregation errors following CIN-suppressor gene silencing and attenuates segregation errors and DNA damage in CIN+ cells. These data implicate a central role for replication stress in the generation of structural and numerical CIN, which may inform new therapeutic approaches to limit intratumour heterogeneity.

We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm's performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.computational biology | systems biology | oncogenomics | principal curve T he operation of many important pathways is altered during cancer initiation and progression. Identifying the involved pathways and quantifying their deregulation is a very important step toward understanding the malignancy process (1-5). Because advanced therapies target specific pathways, pathway-level understanding is a key step also for developing personalized cancer treatments. Indeed, many methods, such as those described in refs. 5-10, were developed for pathway analysis of high-throughput data. Nearly all methods characterize a pathway's activity for an entire sample set and do not provide information on its deregulation in a particular tumor. One prominent exception is Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) (11), a tool that deduces for each pathway and sample a score using the pathway's known connectivity and functional structure. Hence, it may not work well for many complex pathways that play significant roles in cancer, for which either the mechanism of pathway activity is not well known or essential relevant data (such as protein abundance and phosphorylation status) are unavailable.We introduce a method to calculate, independently for every pathway, a score that represents the extent to which the pathway is deregulated in every individual sample. We quantify the level of deregulation of a pathway in a sample by measuring the deviation of the sample from normal behavior. We do not need detailed reliable knowledge of the network or wiring diagram that underlies the pathway's activity. Hence, our estimates of pathway deregulation in a given sample are not restricted to only simple pathways. The method is knowledge-based, because we use generally well-known external information on the identity of the genes that belong to each pathway. Since the detailed interactions in each pathway are largel...

During disease progression the cells that comprise solid malignancies undergo significant changes in gene copy number and chromosome structure. Colorectal cancer provides an excellent model to study this process. To indentify and characterize chromosomal abnormalities in colorectal cancer, we performed a statistical analysis of 299 expression and 130 SNP arrays profiled at different stages of the disease, including normal tissue, adenoma, stages 1-4 adenocarcinoma, and metastasis. We identified broad (> 1/2 chromosomal arm) and focal (< 1/2 chromosomal arm) events. Broad amplifications were noted on chromosomes 7, 8q, 13q, 20, and X and broad deletions on chromosomes 4, 8p, 14q, 15q, 17p, 18, 20p, and 22q. Focal events (gains or losses) were identified in regions containing known cancer pathway genes, such as VEGFA, MYC, MET, FGF6, FGF23, LYN, MMP9, MYBL2, AURKA, UBE2C, and PTEN. Other focal events encompassed potential new candidate tumor suppressors (losses) and oncogenes (gains), including CCDC68, CSMD1, POLR1D, and PMEPA1. From the expression data, we identified genes whose expression levels reflected their copy number changes and used this relationship to impute copy number changes to samples without accompanying SNP data. This analysis provided the statistical power to show that deletions of 8p, 4p, and 15q are associated with survival and disease progression, and that samples with simultaneous deletions in 18q, 8p, 4p, and 15q have a particularly poor prognosis. Annotation analysis reveals that the oxidative phosphorylation pathway shows a strong tendency for decreased expression in the samples characterized by poor prognosis.colon cancer ͉ DNA copy number ͉ gene expression ͉ SNP arrays

Summary Naïve T cell stimulation activates anabolic metabolism to fuel the transition from quiescence to growth and proliferation. Here we show that naïve CD4+ T cell activation induces a unique program of mitochondrial biogenesis and remodeling. Using mass spectrometry, we quantified protein dynamics during T cell activation. We identified substantial remodeling of the mitochondrial proteome over the first 24 hr of T cell activation to generate mitochondria with a distinct metabolic signature, with one carbon metabolism as the most induced pathway. Salvage pathways and mitochondrial one carbon metabolism, fed by serine, contribute to purine and thymidine synthesis to enable T cell proliferation and survival. Genetic inhibition of the mitochondrial serine catabolic enzyme SHMT2 impaired T cell survival in culture, and antigen-specific T cell abundance in vivo. Thus, during T cell activation, mitochondrial proteome remodeling generates specialized mitochondria with enhanced one carbon metabolism that is critical for T cell activation and survival.

L1-CAM, a neuronal cell adhesion receptor, is also expressed in a variety of cancer cells. Recent studies identified L1-CAM as a target gene of B-catenin-T-cell factor (TCF) signaling expressed at the invasive front of human colon cancer tissue. We found that L1-CAM expression in colon cancer cells lacking L1-CAM confers metastatic capacity, and mice injected in their spleen with such cells form liver metastases. We identified ADAM10, a metalloproteinase that cleaves the L1-CAM extracellular domain, as a novel target gene of B-catenin-TCF signaling. ADAM10 overexpression in colon cancer cells displaying endogenous L1-CAM enhanced L1-CAM cleavage and induced liver metastasis, and ADAM10 also enhanced metastasis in colon cancer cells stably transfected with L1-CAM. DNA microarray analysis of genes induced by L1-CAM in colon cancer cells identified a cluster of genes also elevated in a large set of human colon carcinoma tissue samples. Expression of these genes in normal colon epithelium was low. These results indicate that there is a gene program induced by L1-CAM in colon cancer cells that is also present in colorectal cancer tissue and suggest that L1-CAM can serve as target for colon cancer therapy. [Cancer Res 2007;67(16):7703-12]

Specific HPV DNA sequences are associated with more than 90% of invasive carcinomas of the uterine cervix. Viral E6 and E7 oncogenes are key mediators in cell transformation by disrupting TP53 and RB pathways. To investigate molecular mechanisms involved in the progression of invasive cervical carcinoma, we performed a gene expression study on cases selected according to viral and clinical parameters. Using Coupled Two-Way Clustering and Sorting Points Into Neighbourhoods methods, we identified a 'cervical cancer proliferation cluster' composed of 163 highly correlated transcripts. Most of these transcripts corresponded to E2F pathway genes controlling cell division or proliferation, whereas none was known as TP53 primary target. The average expression level of the genes of this cluster was higher in tumours with an early relapse than in tumours with a favourable course (P ¼ 0.026). Moreover, we found that E6/E7 mRNA expression level was positively correlated with the expression level of the cluster genes and with viral DNA load. These findings suggest that HPV E6/E7 expression level plays a key role in the progression of invasive carcinoma of the uterine cervix via the deregulation of cellular genes controlling tumour cell proliferation. HPV expression level may thus provide a biological marker useful for prognosis assessment and specific therapy of the disease.

The microRNA miR-504 targets TP53 mRNA encoding the p53 tumor suppressor. miR-504 resides within the fibroblast growth factor 13 (FGF13) gene, which is overexpressed in various cancers. We report that the FGF13 locus, comprising FGF13 and miR-504, is transcriptionally repressed by p53, defining an additional negative feedback loop in the p53 network. Furthermore, we show that FGF13 1A is a nucleolar protein that represses ribosomal RNA transcription and attenuates protein synthesis. Importantly, in cancer cells expressing high levels of FGF13, the depletion of FGF13 elicits increased proteostasis stress, associated with the accumulation of reactive oxygen species and apoptosis. Notably, stepwise neoplastic transformation is accompanied by a gradual increase in FGF13 expression and increased dependence on FGF13 for survival ("nononcogene addiction"). Moreover, FGF13 overexpression enables cells to cope more effectively with the stress elicited by oncogenic Ras protein. We propose that, in cells in which activated oncogenes drive excessive protein synthesis, FGF13 may favor survival by maintaining translation rates at a level compatible with the protein qualitycontrol capacity of the cell. Thus, FGF13 may serve as an enabler, allowing cancer cells to evade proteostasis stress triggered by oncogene activation.proteostasis | p53 | miR-504 | FGF13 | ribosomal biogenesis