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Background A source of error in prenatal screening for trisomies is PCR amplification error associated with guanine-cytosine (GC) content of DNA fragments in maternal plasma. We describe a simple method of allowing for this. Methods Data from a Reflex DNA screening programme (67 trisomy 18 and 83 unaffected pregnancies) were used to compare the ratio of chromosome 18 DNA fragment counts to chromosome 8 DNA fragment counts (because chromosome 8 has a similar GC content to chromosome 18) with the percentage of chromosome 18 DNA counts using counts from all autosomes in the denominator, with and without an all autosome correction for the GC content of the DNA fragments. Results A chromosome 18 to 8 ratio of DNA fragment counts was more discriminatory than the percentage of all autosome counts arising from chromosome 18 without, or with an all autosome correction for GC content bias. It achieves a high screening performance, eg. for a 0.25% false-positive rate, a 97% detection rate instead of 49% without a correction for GC content, and 91% with an all autosome correction for GC content. Conclusion Consideration can be given to using the ratio of chromosome 18 DNA fragment counts to chromosome 8 DNA fragment counts in cell-free DNA prenatal screening for trisomy 18, avoiding the need for more complex methods of making a correction for the GC content currently used.

Background: \ud Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene.\ud \ud Objectives: \ud This study sought to test the association between the rs9349379 genotype and SCAD.\ud \ud Methods: \ud Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD.\ud \ud Results: \ud The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence.\ud \ud Conclusions: \ud The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD.

BACKGROUND An estimate of fetal fraction (FF) is needed for DNA-based screening for trisomy 21 and other aneuploidies, but there is no gold standard to validate FF measurement methods. We specify a gold standard and use it to validate a method of measuring FF (SeqFF) in singleton pregnancies. METHODS The gold standard was a formula derived from 2 elements: (a) an estimate of the percentage of DNA fragments in maternal plasma from chromosome 21 (%Ch21) in pregnancies without trisomy 21, 18, or 13 (PU) and (b) calculation of %Ch21 with increasing FF in trisomy 21 pregnancies (P21). The SeqFF method was evaluated by plotting regression lines of %Ch21 and SeqFF estimates of FF in 31 singleton male and 31 female trisomy 21 pregnancies and comparing the regressions with the reference line derived from the gold standard formula. RESULTS The gold standard formula was P21 = (1/2)PUFF + PU, with FF expressed as a proportion, or converting %Ch21 to multiples of the median (MoM), P21(MoM) = (1/2)FF + 1. Based on 3865 pregnancies, the PU was 1.2935%. The regression lines for trisomy 21 pregnancies with male and female fetuses were almost identical to the gold standard reference line (regression slopes in MoMs 0.52 and 0.50, respectively, compared with 0.50 for the gold standard reference line). CONCLUSIONS The proposed gold standard can be used to validate different methods of estimating FF in singleton pregnancies. SeqFF is an accurate method of estimating FF.

RNA editing is a widespread post-transcriptional mechanism that confers specific and reproducible nucleotide changes in selected RNA transcripts and plays a critical role in many human cancers. However, little is known about how RNA editing operates in non-small-cell lung cancers. Here, we measured the sequence and expression level of genes of antizyme inhibitor 1 and adenosine deaminase acting on RNA family in 30 non-small-cell lung cancer patient samples and 13 cell lines and revealed RNA editing S367G in antizyme inhibitor 1 is a high-frequent molecular events. We determined overexpression of antizyme inhibitor 1 with RNA editing, implying the oncogenic function of this alteration. We also detected the association of adenosine deaminase acting on RNA overexpression with RNA editing occurred in antizyme inhibitor 1. Furthermore, the RNA editing could cause a cytoplasmic-to-nuclear translocation of antizyme inhibitor 1 protein and conferred the malignant phenotype of non-small-cell lung cancer cells. The in vivo experiment confirmed that this RNA editing confers higher capacity of tumor migration as well. In conclusion, antizyme inhibitor 1 RNA editing and its involvement in tumorigenesis of non-small-cell lung cancer pave a new way for potential clinical management of non-small-cell lung cancer.

In recent years, genome-wide association studies have identified 58 independent risk loci for coronary artery disease (CAD) on the autosome. However, due to the sex-specific data structure of the X chromosome, it has been excluded from most of these analyses. While females have 2 copies of chromosome X, males have only one. Also, one of the female X chromosomes may be inactivated. Therefore, special test statistics and quality control procedures are required. Thus, little is known about the role of X-chromosomal variants in CAD. To fill this gap, we conducted a comprehensive X-chromosome-wide meta-analysis including more than 43,000 CAD cases and 58,000 controls from 35 international study cohorts. For quality control, sex-specific filters were used to adequately take the special structure of X-chromosomal data into account. For single study analyses, several logistic regression models were calculated allowing for inactivation of one female X-chromosome, adjusting for sex and investigating interactions between sex and genetic variants. Then, meta-analyses including all 35 studies were conducted using random effects models. None of the investigated models revealed genome-wide significant associations for any variant. Although we analyzed the largest-to-date sample, currently available methods were not able to detect any associations of X-chromosomal variants with CAD.

The application of paired-end next generation sequencing approaches has made it possible to systematically characterize rearrangements of the cancer genome to base-pair level. Utilizing this approach, we report the first detailed analysis of ovarian cancer rearrangements, comparing high-grade serous and clear cell cancers, and these histotypes with other solid cancers. Somatic rearrangements were systematically characterized in eight high-grade serous and five clear cell ovarian cancer genomes and we report here the identification of > 600 somatic rearrangements. Recurrent rearrangements of the transcriptional regulator gene, TSHZ3, were found in three of eight serous cases. Comparison to breast, pancreatic and prostate cancer genomes revealed that a subset of ovarian cancers share a marked tandem duplication phenotype with triple-negative breast cancers. The tandem duplication phenotype was not linked to BRCA1/2 mutation, suggesting that other common mechanisms or carcinogenic exposures are operative. High-grade serous cancers arising in women with germline BRCA1 or BRCA2 mutation showed a high frequency of small chromosomal deletions. These findings indicate that BRCA1/2 germline mutation may contribute to widespread structural change and that other undefined mechanism(s), which are potentially shared with triple-negative breast cancer, promote tandem chromosomal duplications that sculpt the ovarian cancer genome. Copyright © 2012 Pathological Society of Great Britain and Ireland.

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Summary Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer's life history is encrypted in the somatic mutations present. We developed algorithms to decipher this narrative and applied them to 21 breast cancers. Mutational processes evolve across a cancer's lifespan, with many emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, and most mutations are found in just a fraction of tumor cells. Every tumor has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent cell lineages capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer's development, triggering diagnosis. PaperClip, Graphical Abstract Highlights ► Genome-wide analyses of mutations emerging through time in 21 breast cancers ► Minimal expansion of subclones occurs until thousands of mutations have accumulated ► Cancer-specific signatures of point mutations and genomic instability emerge late ► ERBB2 amplification begins early but continues to evolve over long molecular time, Newly developed algorithms allow the reconstruction of the genomic history of different breast cancers, tracing the temporal evolution of each tumor and the emergence of the dominant subclones that will eventually trigger diagnosis.

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

Cancer genomes are complex, carrying thousands of somatic mutations including base substitutions, insertions and deletions, rearrangements, and copy number changes that have been acquired over decades. Recently, technologies have been introduced that allow generation of high-resolution, comprehensive catalogs of somatic alterations in cancer genomes. However, analyses of these data sets generally do not indicate the order in which mutations have occurred, or the resulting karyotype. Here, we introduce a mathematical framework that begins to address this problem. By using samples with accurate data sets, we can reconstruct relatively complex temporal sequences of rearrangements and provide an assembly of genomic segments into digital karyotypes. For cancer genes mutated in rearranged regions, this information can provide a chronological examination of the selective events that have taken place.

SUMMARY Multiple somatic rearrangements are often found in cancer genomes. However, the underlying processes of rearrangement and their contribution to cancer development are poorly characterised. Here, we employed a paired-end sequencing strategy to identify somatic rearrangements in breast cancer genomes. There are more rearrangements in some breast cancers than previously appreciated. Rearrangements are more frequent over gene footprints and most are intrachromosomal. Multiple architectures of rearrangement are present, but tandem duplications are common in some cancers, perhaps reflecting a specific defect in DNA maintenance. Short overlapping sequences at most rearrangement junctions suggest that these have been mediated by non-homologous end-joining DNA repair, although varying sequence patterns indicate that multiple processes of this type are operative. Several expressed in-frame fusion genes were identified but none were recurrent. The study provides a new perspective on cancer genomes, highlighting the diversity of somatic rearrangements and their potential contribution to cancer development.

Tandem mass spectrometric data from peptides are routinely used in an unsupervised manner to infer product ion sequence and hence the identity of their parent protein. However, significant variability in relative signal intensity of product ions within peptide tandem mass spectra is commonly observed. Furthermore, instrument-specific patterns of fragmentation are observed, even where a common mechanism of ion heating is responsible for generation of the product ions. This information is currently not fully exploited within database searching strategies; this motivated the present study to examine a large dataset of tandem mass spectra derived from multiple instrumental platforms. Here, we report marked global differences in the product ion spectra of protonated tryptic peptides generated from two of the most common proteomic platforms, namely tandem quadrupole-time-of-flight and quadrupole ion trap instruments. Specifically, quadrupole-time-of-flight tandem mass spectra show a significant under-representation of N-terminal b-type fragments in comparison to quadrupole ion trap product ion spectra. Energy-resolved mass spectrometry experiments conducted upon test tryptic peptides clarify this disparity; b-type ions are significantly less stable than their y-type N-terminal counterparts, which contain strongly basic residues. Secondary fragmentation processes which occur within the tandem quadrupole-time-of-flight device account for the observed differences, whereas this secondary product ion generation does not occur to a significant extent from resonant excitation performed within the quadrupole ion trap. We suggest that incorporation of this stability information in database searching strategies has the potential to significantly improve the veracity of peptide ion identifications as made by conventional database searching strategies.

The use of electron-transfer dissociation as an alternative peptide ion activation method for generation of protein sequence information is examined here in comparison with the conventional method of choice, collisionally activated dissociation, using a linear ion trapping instrument. Direct comparability between collisionally and electron-transfer-activated product ion data were ensured by employing an activation-switching method during acquisition, sequentially activating precisely the same precursor ion species with each fragmentation method in turn. Sequest (Thermo Fisher Scientific, San Jose, CA) searching of product ion data generated an overlapping yet distinct pool of polypeptide identifications from the products of collisional and electron-transfer-mediated activation products. To provide a highly confident set of protein recognitions, identification data were filtered using parameters that achieved a peptide false discovery rate of 1%, with two or more independent peptide assignments required for each protein. The use of electron transfer dissociation (ETD) has allowed us to identify additional peptides where the quality of product ion data generated by collisionally activated dissociation (CAD) was insufficient to infer peptide sequence. Thus, a combined ETD/CAD approach leads to the recognition of more peptides and proteins than are achieved using peptide analysis by CAD- or ETD-based tandem mass spectrometry alone.

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding mass spectrometry scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram, and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.

In this paper, we review two recently developed kernel self-organising maps (SOMs) for classification and further establish their link with an energy function. We demonstrate that the kernel SOM can be derived naturally by minimising the energy function, and the resulting kernel SOM unifies the approaches to kernelise the SOM and can be performed entirely in the feature space. Different forms of kernel functions can be readily adopted. Various kernel SOMs, as well as the original SOM, are compared on classifying several benchmark datasets. The performance of the kernel SOMs depends on the choices of kernel functions and their parameters, as well as the ways to classify the neurons. Although the proposed energy based kernel SOM can produce better classification results than other SOMs in some cases, there is no clear evidence showing that the kernel SOMs are always superior to the common SOM. The computational cost to kernelise the SOM, however, increases significantly.

Proteome science relies on bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. The resulting secondary ion mass spectra are compared with protein sequence databases in order to identify the amino acid sequence. Although these search tools (e.g. SEQUEST, Mascot, X!Tandem, Phenyx) are frequently successful, much is still not understood about the amino acid sequence patterns which promote/protect particular fragmentation pathways, and hence lead to the presence/absence of particular ions from different ion series. In order to advance this area, we have developed a database, PepSeeker (http://nwsr.smith.man.ac.uk/pepseeker), which captures this peptide identification and ion information from proteome experiments. The database currently contains >185 000 peptides and associated database search information. Users may query this resource to retrieve peptide, protein and spectral information based on protein or peptide information, including the amino acid sequence itself represented by regular expressions coupled with ion series information. We believe this database will be useful to proteome researchers wishing to understand gas phase peptide ion chemistry in order to improve peptide identification strategies. Questions can be addressed to ku.ca.retsehcnam@yelles.j.

SummaryAll cancers carry somatic mutations. The patterns of mutation in cancer genomes reflect the DNA damage and repair processes to which cancer cells and their precursors have been exposed. To explore these mechanisms further, we generated catalogs of somatic mutation from 21 breast cancers and applied mathematical methods to extract mutational signatures of the underlying processes. Multiple distinct single- and double-nucleotide substitution signatures were discernible. Cancers with BRCA1 or BRCA2 mutations exhibited a characteristic combination of substitution mutation signatures and a distinctive profile of deletions. Complex relationships between somatic mutation prevalence and transcription were detected. A remarkable phenomenon of localized hypermutation, termed “kataegis,” was observed. Regions of kataegis differed between cancers but usually colocalized with somatic rearrangements. Base substitutions in these regions were almost exclusively of cytosine at TpC dinucleotides. The mechanisms underlying most of these mutational signatures are unknown. However, a role for the APOBEC family of cytidine deaminases is proposed.PaperClip

All cancers carry somatic mutations in their genomes. A subset, known as driver mutations, confer clonal selective advantage on cancer cells and are causally implicated in oncogenesis(1), and the remainder are passenger mutations. The driver mutations and mutational processes operative in breast cancer have not yet been comprehensively explored. Here we examine the genomes of 100 tumours for somatic copy number changes and mutations in the coding exons of protein-coding genes. The number of somatic mutations varied markedly between individual tumours. We found strong correlations between mutation number, age at which cancer was diagnosed and cancer histological grade, and observed multiple mutational signatures, including one present in about ten per cent of tumours characterized by numerous mutations of cytosine at TpC dinucleotides. Driver mutations were identified in several new cancer genes including AKT2, ARID1B, CASP8, CDKN1B, MAP3K1, MAP3K13, NCOR1, SMARCD1 and TBX3. Among the 100 tumours, we found driver mutations in at least 40 cancer genes and 73 different combinations of mutated cancer genes. The results highlight the substantial genetic diversity underlying this common disease.

Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.

El pdf del artículo es el manuscrito de autor (PMCID: PMC3030920).-- et al., The genetics of renal cancer is dominated by inactivation of the VHL tumour suppressor gene in clear cell carcinoma (ccRCC), the commonest histological subtype. A recent large-scale screen of ~3,500 genes by PCR-based exon re-sequencing identified several new cancer genes in ccRCC including UTX (also known as KDM6A)1, JARID1C (also known as KDM5C) and SETD2 (ref. 2). These genes encode enzymes that demethylate (UTX, JARID1C) or methylate (SETD2) key lysine residues of histone H3. Modification of the methylation state of these lysine residues of histone H3 regulates chromatin structure and is implicated in transcriptional control3. However, together these mutations are present in fewer than 15% of ccRCC, suggesting the existence of additional, currently unidentified cancer genes. Here, we have sequenced the protein coding exome in a series of primary ccRCC and report the identification of the SWI/SNF chromatin remodelling complex gene PBRM1 (ref. 4) as a second major ccRCC cancer gene, with truncating mutations in 41% (92/227) of cases. These data further elucidate the somatic genetic architecture of ccRCC and emphasize the marked contribution of aberrant chromatin biology., P.A.F. and M.R.S. would like to acknowledge the Wellcome Trust for support under grant reference 077012/Z/05/Z and A. Coffey, D. Turner and L. Mamanova for assistance with the exon capture. K.F., K.D. and B.T.T. acknowledge the support of the Van Andel Research Institute. B.T.T. would like to acknowledge support from the Lee Foundation. I.V. is supported by a fellowship from The International Human Frontier Science Program Organization. D.J.A. acknowledges the support of Cancer Research UK. D.A.T. and P.A.P.-M. acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampo and thank W. Howatt, A. Hazelhurst and colleagues in the CRI core facilities for their support.

Cancer is driven by somatically acquired point mutations and chromosomal rearrangements, conventionally thought to accumulate gradually over time. Using next-generation sequencing, we characterize a phenomenon, which we term chromothripsis, whereby tens to hundreds of genomic rearrangements occur in a one-off cellular crisis. Rearrangements involving one or a few chromosomes crisscross back and forth across involved regions, generating frequent oscillations between two copy number states. These genomic hallmarks are highly improbable if rearrangements accumulate over time and instead imply that nearly all occur during a single cellular catastrophe. The stamp of chromothripsis can be seen in at least 2%¿3% of all cancers, across many subtypes, and is present in ~25% of bone cancers. We find that one, or indeed more than one, cancer-causing lesion can emerge out of the genomic crisis. This phenomenon has important implications for the origins of genomic remodeling and temporal emergence of cancer., This work was supported by the Wellcome Trust (grant reference 077012/Z/ 05/Z) and the Chordoma Foundation. P.J.C. is personally funded through a Wellcome Trust Senior Clinical Research Fellowship (grant reference WT088340MA). I.V. is supported by a fellowship from The International Human Frontier Science Program Organization.

Databases which capture proteomic data for subsequent interrogation can be extremely useful for our understanding of peptide ion behaviour in the mass spectrometer, leading to novel hypotheses and mechanistic understanding of the underlying mechanisms determining peptide fragmentation behaviour. These, in turn, can be used to improve database searching algorithms for use in automated and unbiased interpretation of peptide product ion spectra. Here, we examine a previously published dataset using our established methods, in order to discover differences in the observation of product ions of different types, following ion activation and unimolecular dissociation either by collisional dissociation or the ion/ion reaction, electron transfer dissociation. Using a target-decoy database searching strategy, a large data set of precursor ions, were confidently predicted as peptide sequence matches (PSMs) at either a 1% or 5% peptide false discovery rate, as reported in our previous study. Using these high quality PSMs, we have conducted a more detailed and novel analysis of the global trends in observed product ions present/absent in these spectra, examining both CID and ETD data. We uncovered underlying trends for an increased propensity for the observation of higher members of the ion series in ETD product ion spectra in comparison to their CID counterparts. Such data-mining efforts will prove useful in the generation of new database searching algorithms which are well suited to the analysis of ETD product ion spectra.

Databases which capture proteomic data for subsequent interrogation can be extremely useful for our understanding of peptide ion behaviour in the mass spectrometer, leading to novel hypotheses and mechanistic understanding of the underlying mechanisms determining peptide fragmentation behaviour. These, in turn, can be used to improve database searching algorithms for use in automated and unbiased interpretation of peptide product ion spectra.Here, we examine a previously published dataset using our established methods, in order to discover differences in the observation of product ions of different types, following ion activation and unimolecular dissociation either by collisional dissociation or the ion/ion reaction, electron transfer dissociation. Using a target-decoy database searching strategy, a large data set of precursor ions, were confidently predicted as peptide sequence matches (PSMs) at either a 1% or 5% peptide false discovery rate, as reported in our previous study. Using these high quality PSMs, we have conducted a more detailed and novel analysis of the global trends in observed product ions present/absent in these spectra, examining both CID and ETD data. We uncovered underlying trends for an increased propensity for the observation of higher members of the ion series in ETD product ion spectra in comparison to their CID counterparts. Such data-mining efforts will prove useful in the generation of new database searching algorithms which are well suited to the analysis of ETD product ion spectra.

Cancer is driven by mutation. Worldwide, tobacco smoking is the principal lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small-cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. A total of 22,910 somatic substitutions were identified, including 134 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general, expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in frame, and another two lines carrying PVT1-CHD7 fusion genes, indicating that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

Clear cell renal cell carcinoma (ccRCC) is the most common form of adult kidney cancer, characterized by the presence of inactivating mutations in the VHL gene in most cases, and by infrequent somatic mutations in known cancer genes. To determine further the genetics of ccRCC, we have sequenced 101 cases through 3,544 protein-coding genes. Here we report the identification of inactivating mutations in two genes encoding enzymes involved in histone modification-SETD2, a histone H3 lysine 36 methyltransferase, and JARID1C (also known as KDM5C), a histone H3 lysine 4 demethylase-as well as mutations in the histone H3 lysine 27 demethylase, UTX (KMD6A), that we recently reported. The results highlight the role of mutations in components of the chromatin modification machinery in human cancer. Furthermore, NF2 mutations were found in non-VHL mutated ccRCC, and several other probable cancer genes were identified. These results indicate that substantial genetic heterogeneity exists in a cancer type dominated by mutations in a single gene, and that systematic screens will be key to fully determining the somatic genetic architecture of cancer.

Tandem mass spectrometric data from peptides are routinely used in an unsupervised manner to infer product ion sequence and hence the identity of their parent protein. However, significant variability in relative signal intensity of product ions within peptide tandem mass spectra is commonly observed. Furthermore, instrument-specific patterns of fragmentation are observed, even where a common mechanism of ion heating is responsible for generation of the product ions. This information is currently not fully exploited within database searching strategies; this motivated the present study to examine a large dataset of tandem mass spectra derived from multiple instrumental platforms. Here, we report marked global differences in the product ion spectra of protonated tryptic peptides generated from two of the most common proteomic platforms, namely tandem quadrupole-time-of-flight and quadrupole ion trap instruments. Specifically, quadrupole-time-of-flight tandem mass spectra show a significant under-representation of N-terminal b-type fragments in comparison to quadrupole ion trap product ion spectra. Energy-resolved mass spectrometry experiments conducted upon test tryptic peptides clarify this disparity; b-type ions are significantly less stable than their y-type N-terminal counterparts, which contain strongly basic residues. Secondary fragmentation processes which occur within the tandem quadrupole-time-of-flight device account for the observed differences, whereas this secondary product ion generation does not occur to a significant extent from resonant excitation performed within the quadrupole ion trap. We suggest that incorporation of this stability information in database searching strategies has the potential to significantly improve the veracity of peptide ion identifications as made by conventional database searching strategies.

The accurate quantification of proteins is important in several areas of cell biology, biotechnology and medicine. Both relative and absolute quantification of proteins is often determined following mass spectrometric analysis of one or more of their constituent peptides. However, in order for quantification to be successful, it is important that the experimenter knows which peptides are readily detectable under the mass spectrometric conditions used for analysis. In this paper, genetic programming is used to develop a function which predicts the detectability of peptides from their calculated physico-chemical properties. Classification is carried out in two stages: the selection of a good classifier using the AUROC objective function and the setting of an appropriate threshold. This allows the user to select the balance point between conflicting priorities in an intuitive way. The success of this method is found to be highly dependent on the initial selection of input parameters. The use of brood recombination and a modified version of the multi-objective FOCUS method are also investigated. While neither has a significant effect on predictive accuracy, the use of the FOCUS method leads to considerably more compact solutions.

The accurate analysis of the proteome using mass spectrometry plays an important role in the understanding of many of the physiological processes that occur in an organism and has become a standard tool used in the identification of proteins. This identification of proteins is a challenging one and relies upon bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. An important problem associated with the accurate identification of peptides from mass spectrometry is whether or not a particular peptide is likely to be detected in a standard proteomics experiment, this can be dependant on a number of factors including the physiochemical properties of the peptide itself as well as the mass spectrometer used in the experiment. A machine learning approach was applied to find peptide fragmentation patterns based on different properties of the peptide sequence and we are able to predict which peptide(s) are likely to be detected in a standard proteomics experiment. The task of protein identification is made even more challenging by the occurrence of partial enzymatic protein cleavage, resulting in peptides with internal missed cleavage sites, as proteases frequently fail to digest proteins to their limit peptides. Typically, up to 1 of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using rules derived from information theory, we were able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines.

The genetics of renal cancer is dominated by inactivation of the VHL tumour suppressor gene in clear cell carcinoma (ccRCC), the commonest histological subtype. A recent large-scale screen of ∼3500 genes by PCR-based exon re-sequencing identified several new cancer genes in ccRCC including UTX (KDM6A), JARID1C (KDM5C) and SETD2. These genes encode enzymes that demethylate (UTX, JARID1C) or methylate (SETD2) key lysine residues of histone H3. Modification of the methylation state of these lysine residues of histone H3 regulates chromatin structure and is implicated in transcriptional control. However, together these mutations are present in fewer than 15% of ccRCC, suggesting the existence of additional, currently unidentified cancer genes. Here, we have sequenced the protein coding exome in a series of primary ccRCC and report the identification of the SWI/SNF chromatin remodeling complex gene PBRM1 as a second major ccRCC cancer gene, with truncating mutations in 41% (92/227) of cases. These data further elucidate the somatic genetic architecture of ccRCC and emphasize the marked contribution of aberrant chromatin biology. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2805. doi:10.1158/1538-7445.AM2011-2805