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This Week in Genome Biology: Sep 10, 2014

A Washington University team used transcriptome sequence data to track down recurrent shifts in long intergenic non-coding RNA (lncRNA) profiles in lung cancer. Using existing RNA sequences produced using 567 lung adenocarcinoma or lung squamous cell carcinoma samples for The Cancer Genome Atlas and other projects, the researchers identified thousands of previously undescribed lncRNAs. These included 100 lncRNAs with expression patterns that differed in the lung cancers compared to matched normal controls. By folding in sequence data for hundreds more tumors from other cancer types, the study's authors began distinguishing between lncRNAs with altered expression in lung cancer specifically and those showing more general expression shifts in cancer.

Researchers from Weill Cornell Medical College did targeted deep DNA sequencing on samples from more than a dozen individuals with recurring diffuse large B-cell lymphoma as part of their effort to understand relapse and the role that immune-related VDJ rearrangements at the immunoglobulin heavy locus might play in this process. Using matched samples from 14 individuals at the time of diagnosis and matched samples taken from the same patients at DLBCL relapse, the team used deep sequencing to assess VDJ rearrangement junctions and exome sequencing on half of the tumor pairs. The study's authors found that clonal evolution events leading to relapse may involve relapse tumors that are genetically similar to diagnostic tumors or relapse tumors that diverged from the primary tumor early on.

Finally, a team from the UK, US, and Canada presents a breast cancer classification system called IntClust that it developed using copy number and gene expression profiles produced with genomic and transcriptomic data on almost 1,000 breast cancer samples. After identifying 10 sub-types with this method, the researchers went on to apply it to transcriptome data for another 7,544 breast cancer samples, parsing the samples into reproducible sub-type classifications that offered clues to features such as clinical outcomes or relapse-free survival.