Merkel cell carcinoma (MCC) is an uncommon, but highly malignant, cutaneous tumor. Merkel cell polyoma virus (MCV) has been implicated in a majority of MCC tumors; however, viralnegative tumors have been reported to be more prevalent in some geographic regions subject to high sun exposure. While the impact of MCV and viral T-antigens on MCC development has been extensively investigated, little is known about the etiology of viralnegative tumors. We performed targeted capture and massively parallel DNA sequencing of 619 cancer genes to compare the gene mutations and copy number alterations in MCV-positive (n ¼ 13) and -negative (n ¼ 21) MCC tumors and cell lines. We found that MCV-positive tumors displayed very low mutation rates, but MCV-negative tumors exhibited a high mutation burden associated with a UV-induced DNA damage signature. All viral-negative tumors harbored mutations in RB1, TP53, and a high frequency of mutations in NOTCH1 and FAT1. Additional mutated or amplified cancer genes of potential clinical importance included PI3K (PIK3CA, AKT1, PIK3CG) and MAPK (HRAS, NF1) pathway members and the receptor tyrosine kinase FGFR2. Furthermore, looking ahead to potential therapeutic strategies encompassing immune checkpoint inhibitors such as anti-PD-L1, we also assessed the status of T-cell-infiltrating lymphocytes (TIL) and PD-L1 in MCC tumors. A subset of viral-negative tumors exhibited high TILs and PD-L1 expression, corresponding with the higher mutation load within these cancers. Taken together, this study provides new insights into the underlying biology of viral-negative MCC and paves the road for further investigation into new treatment opportunities. Cancer Res; 75(24); 5228-34. Ó2015 AACR.

Musket is available at http://musket.sourceforge.net.

We isolated and analyzed, at single-nucleotide resolution, cancer-associated neochromosomes from well- and/or dedifferentiated liposarcomas. Neochromosomes, which can exceed 600 Mb in size, initially arise as circular structures following chromothripsis involving chromosome 12. The core of the neochromosome is amplified, rearranged, and corroded through hundreds of breakage-fusion-bridge cycles. Under selective pressure, amplified oncogenes are overexpressed, while coamplified passenger genes may be silenced epigenetically. New material may be captured during punctuated chromothriptic events. Centromeric corrosion leads to crisis, which is resolved through neocentromere formation or native centromere capture. Finally, amplification terminates, and the neochromosome core is stabilized in linear form by telomere capture. This study investigates the dynamic mutational processes underlying the life history of a special form of cancer mutation.

No abstract

The source code of Coral is freely available at http://www.cs.helsinki.fi/u/lmsalmel/coral/.

Reprogramming human somatic cells to primed or naive induced pluripotent stem cells (iPSC) recapitulates the different stages of early human embryonic development [1][2][3][4][5][6] . The molecular mechanism underpinning the reprogramming of human somatic cells to primed or naive induced pluripotency remains largely unexplored, impeding our understanding and limiting rational improvements to reprogramming protocols. To address this, we reconstructed molecular reprogramming trajectories using single-cell transcriptomics. This revealed that reprogramming into primed and naive human pluripotency follows diverging and distinct trajectories. Moreover, genome-wide accessible chromatin analyses showed key changes in regulatory elements of core pluripotency genes, and orchestrated global changes in chromatin accessibility over time. Integrated analysis of these datasets unveiled an unexpected role of trophectoderm (TE) lineage-associated transcription factors and the existence of a subpopulation of cells that enter a TE-like state during reprogramming. Furthermore, this TE-like state could be captured, allowing the derivation of induced Trophoblast Stem Cells (iTSCs). iTSCs are molecularly and functionally similar to TSCs derived from human blastocysts or first-trimester placental trophoblasts 7 . Altogether, these results provide a high-resolution roadmap for transcription factor-mediated human 3 reprogramming, revealing an unanticipated role of the TE-lineage specific regulatory program during this process and facilitating the direct reprogramming of somatic cells into iTSCs.

We present SHREC, a new algorithm for correcting errors in short-read data that uses a generalized suffix trie on the read data as the underlying data structure. Our results show that the method can identify erroneous reads with sensitivity and specificity of over 99% and 96% for simulated data with error rates of up to 3% as well as for real data. Furthermore, it achieves an error correction accuracy of over 80% for simulated data and over 88% for real data. These results are clearly superior to previously published approaches. SHREC is available as an efficient open-source Java implementation that allows processing of 10 million of short reads on a standard workstation.

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis.