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      A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

      Nature medicine
      Antibodies, Monoclonal, Humanized, therapeutic use, Antineoplastic Agents, Colon, pathology, Colorectal Neoplasms, classification, diagnosis, therapy, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genetic Markers, genetics, Humans, Neoplasm Metastasis, Oligonucleotide Array Sequence Analysis, Phenotype, Signal Transduction, Treatment Outcome

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          Abstract

          Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            New driver mutations in non-small-cell lung cancer.

            Treatment decisions for patients with lung cancer have historically been based on tumour histology. Some understanding of the molecular composition of tumours has led to the development of targeted agents, for which initial findings are promising. Clearer understanding of mutations in relevant genes and their effects on cancer cell proliferation and survival, is, therefore, of substantial interest. We review current knowledge about molecular subsets in non-small-cell lung cancer that have been identified as potentially having clinical relevance to targeted therapies. Since mutations in EGFR and KRAS have been extensively reviewed elsewhere, here, we discuss subsets defined by so-called driver mutations in ALK, HER2 (also known as ERBB2), BRAF, PIK3CA, AKT1, MAP2K1, and MET. The adoption of treatment tailored according to the genetic make-up of individual tumours would involve a paradigm shift, but might lead to substantial therapeutic improvements. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Single-cell dissection of transcriptional heterogeneity in human colon tumors

              Cancer is often viewed as a caricature of normal developmental processes, but the extent by which its cellular heterogeneity truly recapitulates multi-lineage differentiation processes of normal tissues remains unknown. Here, we implement “single-cell PCR gene-expression analysis” (SINCE-PCR) to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single-cell (n = 1), we show that transcriptional diversity of cancer tissues is largely explained by in vivo multi-lineage differentiation, not only by clonal genetic heterogeneity. Finally, we show that perturbations in gene-expression programs linked to multi-lineage differentiation strongly associate with patient survival. Guided by SINCE-PCR data, we develop two-gene classifier systems (KRT20 vs CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard-ratios superior to pathological grade and comparable to microarray-derived multi-gene expression signatures.
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