Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer.

While mutations affecting protein-coding regions have been examined across many cancers, structural variants at the genome-wide level are still poorly defined. Through integrative deep whole-genome and -transcriptome analysis of 101 castration-resistant prostate cancer metastases (109X tumor/38X normal coverage), we identified structural variants altering critical regulators of tumorigenesis and progression not detectable by exome approaches. Notably, we observed amplification of an intergenic enhancer region 624 kb upstream of the androgen receptor (AR) in 81% of patients, correlating with increased AR expression. Tandem duplication hotspots also occur near MYC, in lncRNAs associated with post-translational MYC regulation. Classes of structural variations were linked to distinct DNA repair deficiencies, suggesting their etiology, including associations of CDK12 mutation with tandem duplications, TP53 inactivation with inverted rearrangements and chromothripsis, and BRCA2 inactivation with deletions. Together, these observations provide a comprehensive view of how structural variations affect critical regulators in metastatic prostate cancer.

SomaticSignatures: inferring mutational signatures from single-nucleotide variants.

UNLABELLED: Mutational signatures are patterns in the occurrence of somatic single-nucleotide variants that can reflect underlying mutational processes. The SomaticSignatures package provides flexible, interoperable and easy-to-use tools that identify such signatures in cancer sequencing data. It facilitates large-scale, cross-dataset estimation of mutational signatures, implements existing methods for pattern decomposition, supports extension through user-defined approaches and integrates with existing Bioconductor workflows. AVAILABILITY AND IMPLEMENTATION: The R package SomaticSignatures is available as part of the Bioconductor project. Its documentation provides additional details on the methods and demonstrates applications to biological datasets. CONTACT: julian.gehring@embl.de, whuber@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spectrum of diverse genomic alterations define non-clear cell renal carcinoma subtypes.

To further understand the molecular distinctions between kidney cancer subtypes, we analyzed exome, transcriptome and copy number alteration data from 167 primary human tumors that included renal oncocytomas and non-clear cell renal cell carcinomas (nccRCCs), consisting of papillary (pRCC), chromophobe (chRCC) and translocation (tRCC) subtypes. We identified ten significantly mutated genes in pRCC, including MET, NF2, SLC5A3, PNKD and CPQ. MET mutations occurred in 15% (10/65) of pRCC samples and included previously unreported recurrent activating mutations. In chRCC, we found TP53, PTEN, FAAH2, PDHB, PDXDC1 and ZNF765 to be significantly mutated. Gene expression analysis identified a five-gene set that enabled the molecular classification of chRCC, renal oncocytoma and pRCC. Using RNA sequencing, we identified previously unreported gene fusions, including ACTG1-MITF fusion. Ectopic expression of the ACTG1-MITF fusion led to cellular transformation and induced the expression of downstream target genes. Finally, we observed upregulation of the anti-apoptotic factor BIRC7 in MiTF-high RCC tumors, suggesting a potential therapeutic role for BIRC7 inhibitors.

h5vc: scalable nucleotide tallies with HDF5.

SUMMARY: As applications of genome sequencing, including exomes and whole genomes, are expanding, there is a need for analysis tools that are scalable to large sets of samples and/or ultra-deep coverage. Many current tool chains are based on the widely used file formats BAM and VCF or VCF-derivatives. However, for some desirable analyses, data management with these formats creates substantial implementation overhead, and much time is spent parsing files and collating data. We observe that a tally data structure, i.e. the table of counts of nucleotides × samples × strands × genomic positions, provides a reasonable intermediate level of abstraction for many genomics analyses, including single nucleotide variant (SNV) and InDel calling, copy-number estimation and mutation spectrum analysis. Here we present h5vc, a data structure and associated software for managing tallies. The software contains functionality for creating tallies from BAM files, flexible and scalable data visualization, data quality assessment, computing statistics relevant to variant calling and other applications. Through the simplicity of its API, we envision making low-level analysis of large sets of genome sequencing data accessible to a wider range of researchers. AVAILABILITY AND IMPLEMENTATION: The package H5VC for the statistical environment R is available through the Bioconductor project. The HDF5 system is used as the core of our implementation. CONTACT: pyl@embl.de or whuber@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Highly coordinated proteome dynamics during reprogramming of somatic cells to pluripotency.

Generation of induced pluripotent stem cells (iPSCs) is a process whose mechanistic underpinnings are only beginning to emerge. Here, we applied in-depth quantitative proteomics to monitor proteome changes during the course of reprogramming of fibroblasts to iPSCs. We uncover a two-step resetting of the proteome during the first and last 3 days of reprogramming, with multiple functionally related proteins changing in expression in a highly coordinated fashion. This comprised several biological processes, including changes in the stoichiometry of electron transport-chain complexes, repressed vesicle-mediated transport during the intermediate stage, and an EMT-like process in the late phase. In addition, we demonstrate that the nucleoporin Nup210 is essential for reprogramming by its permitting of rapid cellular proliferation and subsequent progression through MET. Along with the identification of proteins expressed in a stage-specific manner, this study provides a rich resource toward an enhanced mechanistic understanding of cellular reprogramming.