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This Week in Cell: Nov 6, 2013

A large international team including members of the Cancer Genome Atlas Research Network describes the somatic alterations identified in more than 500 glioblastoma tumors. As part of their integrated analysis of the brain cancer, researchers brought together whole-exome sequence data for 291 glioblastomas, RNA sequence profiles for 164 of the tumors, and whole-genome sequence data for 42 more. They also considered copy number, microRNA, expression, and other profiles obtained by array-based approaches. The analysis uncovered genes that are recurrently mutated in glioblastoma, study authors note. But it also helped in defining glioblastoma subtypes with better or worse survival outcomes, apparent markers for such subtypes, and potential treatment targets.

The expression of genes in multigene complexes can be co-regulated through long-range physical contact between chromosomes, according to a study by researchers in South Africa, Portugal, and the US. The team used TALE nuclease enzymes, or TALENs, to interfere with gene loops involving members of an already-characterized multigene complex. When they tracked the consequences of altering such chromosomal interactions using fluorescence-based approaches, investigators saw transcriptional shifts that appeared to reflect hierarchical relationships amongst multigene complex members mediated, in part, by interactions within and between chromosomes.

Finally, Brigham and Women's Hospital researcher Stephen Elledge and colleagues present a computational method for digging up potential tumor suppressor genes and oncogenes in cancer genomes. Using this approach — called the Tumor Suppressor and Oncogene, or TUSON, Explorer — the group uncovered a previously unappreciated suite of apparent cancer driver genes using genomic data for more than 8,200 paired tumor and normal samples described in existing databases. Together with copy number profiles in the cancer, study authors say, patterns identified with TUSON suggest aneuploidy and copy changes could potentially be predictable based on the chromosomal distribution of such oncogenes in relation to tumor suppressor genes and genes generally required for cell growth.