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A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.This Review provides a brief historical perspective of our understanding of the role of cancer genes before presenting the Integrative OncoGenomics (IntOGen) platform, a bioinformatics method of mutational driver identification, which is beginning to reveal the compendium of driver genes across many tumour types as well as alluding to their tumorigenic mechanisms.

Somatic mutations are the driving force of cancer genome evolution1. The rate of somatic mutations appears to be greatly variable across the genome due to variations in chromatin organization, DNA accessibility and replication timing2-5. However, other variables that may influence the mutation rate locally are unknown, such as a role for DNA-binding proteins, for example. Here we demonstrate that the rate of somatic mutations in melanomas is highly increased at active transcription factor binding sites and nucleosome embedded DNA, compared to their flanking regions. Using recently available excision-repair sequencing (XR-seq) data6, we show that the higher mutation rate at these sites is caused by a decrease of the levels of nucleotide excision repair (NER) activity. Our work demonstrates that DNA-bound proteins interfere with the NER machinery, which results in an increased rate of DNA mutations at the protein binding sites. This finding has important implications for our understanding of mutational and DNA repair processes and in the identification of cancer driver mutations.

While recent studies have identified higher than anticipated heterogeneity of mutation rate across genomic regions, mutations in exons and introns are assumed to be generated at the same rate. Here we find fewer somatic mutations in exons than expected from their sequence content and demonstrate that this is not due to purifying selection. Instead, we show that it is caused by higher mismatch-repair activity in exonic than in intronic regions. Our findings have important implications for understanding of mutational and DNA repair processes and knowledge of the evolution of eukaryotic genes, and they have practical ramifications for the study of evolution of both tumors and species.

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.

Transposons are mobile genetic elements that are an important source of genetic variation and are useful tools for genome engineering, mutagenesis screens, and vectors for transgenesis including gene therapy. We have used second-generation sequencing to analyze ≈2 × 10⁵ unique de novo transposon insertion sites of the transposon Hermes in the Saccharomyces cerevisiae genome from both in vitro transposition reactions by using purified yeast genomic DNA, to better characterize intrinsic sequence specificity, and sites recovered from in vivo transposition events, to characterize the effect of intracellular factors such as chromatin on target site selection. We find that Hermes transposon targeting in vivo is profoundly affected by chromatin structure: The subset of genome-wide target sites used in vivo is strongly associated (P < 2e-16 by Fisher's exact test) with nucleosome-free chromatin. Our characterization of the insertion site preferences of Hermes not only assists in the future use of this transposon as a molecular biology tool but also establishes methods to more fully determine targeting mechanisms of other transposons. We have also discovered a long-range sequence motif that defines S. cerevisiae nucleosome-free regions.

The main critical step in single-cell transcriptomics is sample preparation. Several methods have been developed to preserve cells after dissociation to uncouple sample handling from library preparation. Yet, the suitability of these methods depends on the cell types to be processed. In this project, we perform a systematic comparison of preservation methods for droplet-based single-cell RNA-seq on neural and glial cells derived from induced pluripotent stem cells. Our results show that while DMSO provides the highest cell quality in terms of RNA molecules and genes detected per cell, it strongly affects the cellular composition and induces the expression of stress and apoptosis genes. In contrast, methanol fixed samples display a cellular composition similar to fresh samples and provide a good cell quality and little expression biases. Taken together, our results show that methanol fixation is the method of choice for performing droplet-based single-cell transcriptomics experiments on neural cell populations. Methanol fixation appears to be the method of choice for droplet-based scRNA-seq on neural cell populations, based on a broad analysis of how various fixation or preservation methods impact the single-cell transcriptomes of hiPSC-derived neural cells.

Single amino acid repeats are prevalent in eukaryote organisms, although the role of many such sequences is still poorly understood. We have performed a comprehensive analysis of the proteins containing homopolymeric histidine tracts in the human genome and identified 86 human proteins that contain stretches of five or more histidines. Most of them are endowed with DNA- and RNA-related functions, and, in addition, there is an overrepresentation of proteins expressed in the brain and/or nervous system development. An analysis of their subcellular localization shows that 15 of the 22 nuclear proteins identified accumulate in the nuclear subcompartment known as nuclear speckles. This localization is lost when the histidine repeat is deleted, and significantly, closely related paralogous proteins without histidine repeats also fail to localize to nuclear speckles. Hence, the histidine tract appears to be directly involved in targeting proteins to this compartment. The removal of DNA-binding domains or treatment with RNA polymerase II inhibitors induces the re-localization of several polyhistidine-containing proteins from the nucleoplasm to nuclear speckles. These findings highlight the dynamic relationship between sites of transcription and nuclear speckles. Therefore, we define the histidine repeats as a novel targeting signal for nuclear speckles, and we suggest that these repeats are a way of generating evolutionary diversification in gene duplicates. These data contribute to our better understanding of the physiological role of single amino acid repeats in proteins.

One goal of regenerative medicine is to rejuvenate tissues and extend lifespan by restoring the function of endogenous aged stem cells. However, evidence that somatic stem cells can be targeted in vivo to extend lifespan is still lacking. Here, we demonstrate that after a short systemic treatment with a specific inhibitor of the small RhoGTPase Cdc42 (CASIN), transplanting aged hematopoietic stem cells (HSCs) from treated mice is sufficient to extend the healthspan and lifespan of aged immunocompromised mice without additional treatment. In detail, we show that systemic CASIN treatment improves strength and endurance of aged mice by increasing the myogenic regenerative potential of aged skeletal muscle stem cells. Further, we show that CASIN modifies niche localization and H4K16ac polarity of HSCs in vivo. Single-cell profiling reveals changes in HSC transcriptome, which underlie enhanced lymphoid and regenerative capacity in serial transplantation assays. Overall, we provide proof-of-concept evidence that a short systemic treatment to decrease Cdc42 activity improves the regenerative capacity of different endogenous aged stem cells in vivo, and that rejuvenated HSCs exert a broad systemic effect sufficient to extend murine health- and lifespan.

Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies.