Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

MOTIVATION

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature.

RESULTS

Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures.

AVAILABILITY AND IMPLEMENTATION

SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers

Analysis of mutational signatures can reveal underlying molecular mechanisms of the processes that have imprinted the somatic mutations found in cancer genomes. Here, we analyze single base substitutions and small insertions and deletions in pediatric cancers encompassing 785 whole-genome sequenced tumors from 27 molecularly defined cancer subtypes. We identified only a small number of mutational signatures active in pediatric cancers, compared with previously analyzed adult cancers. Further, we report a significant difference in the proportion of pediatric tumors showing homologous recombination repair defect signatures compared with previous analyses. In pediatric leukemias, we identified an indel signature, not previously reported, characterized by long insertions in nonrepeat regions, affecting mainly intronic and intergenic regions, but also exons of known cancer genes. We provide a systematic overview of COSMIC v.3 mutational signatures active across pediatric cancers, which is highly relevant for understanding tumor biology and enabling future research in defining biomarkers of treatment response.

TBIO-04. Comprehensive analysis of mutational signatures in pediatric cancers

Analysis of mutational signatures can reveal the underlying molecular processes that have caused the somatic mutations found in genomes. We performed an extensive analysis encompassing mutational signatures of single base substitution and, for the first time, signatures of small insertions/deletions in ~800 whole-genome-sequenced tumor-normal pairs from 25 molecularly defined pediatric cancer types. More than half of this cohort consists of pediatric CNS-tumors (n=416), including high- and low-grade gliomas, ependymomas, and embryonal tumors. These were subsequently compared with COSMIC v.3 signatures to identify overlap with the latest set of known mutational signatures. We identified only a small number of mutational signatures active in pediatric cancers when compared to previously analyzed adult cancers. Amongst these, SBS1 and SBS5 were present in nearly all pediatric tumors analyzed, with a significant correlation of signature activity with age at diagnosis, as expected. Further, we found SBS21 activity in a fraction of high-grade gliomas, which is the result of defective DNA mismatch repair, SBS36 in fractions of ETMR and Group4-medulloblastoma, the result of defective base excision repair, and SBS44 in Group4-medulloblastomas, caused by defective DNA mismatch repair. For these signatures, no consistent genetic alteration was identified. We report a significantly smaller proportion of pediatric tumors which show homologous-recombination repair defect signature SBS3 compared to previously published analyses. Additionally, the previous mutational signature analysis of this cohort based on COSMIC v.2 reference signatures had identified a novel substitution signature (Signature.P1), active in the brain tumors ATRT and ependymoma. Our updated results suggest that Signature.P1 is not a pediatric specific mutational signature, but a treatment-associated signature identified in a small fraction of brain tumors that were annotated as treatment-naïve. This analysis provides a systematic overview of mutational signatures in pediatric cancers, which is relevant for understanding tumor biology and future research in defining biomarkers for treatment response.

Signatures of copy number alterations in human cancer

Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage-fusion-bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3-5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations.

Control of bacterial biofilms in red meat - A systematic review

Biofilm formation is a serious threat in the meat industry, mainly since it aids food-borne pathogen survival. Biofilms are often difficult to eliminate, and it is essential to understand the best possible deployable measures to remove or inactivate biofilms. We systematically reviewed the published in vitro studies that investigated various methods for removing biofilms in red meat. Publicly available databases, including Google Scholar and PubMed, were queried for relevant studies. The search was restricted to articles published in the English language from 2010 to 2021. We mined a total of 394 studies, of which 12 articles were included in this review. In summary, the studies demonstrated the inhibitory effect of various methods, including the use of bacteriophages, dry heat, cold atmospheric pressure, ozone gas, oils, and acids, on red meat extract or red meat culture. This systematic review suggests that in addition to existing sanitation and antibiotic procedures, other methods, such as the use of phage cocktails and different oils as nanoparticles, yield positive outcomes and may be taken from the in vitro setting to industry with prior validation of the techniques.

Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence

Esophageal squamous cell carcinoma (ESCC) shows remarkable variation in incidence that is not fully explained by known lifestyle and environmental risk factors. It has been speculated that an unknown exogenous exposure(s) could be responsible. Here we combine the fields of mutational signature analysis with cancer epidemiology to study 552 ESCC genomes from eight countries with varying incidence rates. Mutational profiles were similar across all countries studied. Associations between specific mutational signatures and ESCC risk factors were identified for tobacco, alcohol, opium and germline variants, with modest impacts on mutation burden. We find no evidence of a mutational signature indicative of an exogenous exposure capable of explaining differences in ESCC incidence. Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC)-associated mutational signatures single-base substitution (SBS)2 and SBS13 were present in 88% and 91% of cases, respectively, and accounted for 25% of the mutation burden on average, indicating that APOBEC activation is a crucial step in ESCC tumor development.

Genomic and evolutionary classification of lung cancer in never smokers

Lung cancer in never smokers (LCINS) is a common cause of cancer mortality but its genomic landscape is poorly characterized. Here high-coverage whole-genome sequencing of 232 LCINS showed 3 subtypes defined by copy number aberrations. The dominant subtype (piano), which is rare in lung cancer in smokers, features somatic UBA1 mutations, germline AR variants and stem cell-like properties, including low mutational burden, high intratumor heterogeneity, long telomeres, frequent KRAS mutations and slow growth, as suggested by the occurrence of cancer drivers' progenitor cells many years before tumor diagnosis. The other subtypes are characterized by specific amplifications and EGFR mutations (mezzo-forte) and whole-genome doubling (forte). No strong tobacco smoking signatures were detected, even in cases with exposure to secondhand tobacco smoke. Genes within the receptor tyrosine kinase-Ras pathway had distinct impacts on survival; five genomic alterations independently doubled mortality. These findings create avenues for personalized treatment in LCINS.

St. Jude Cloud: A Pediatric Cancer Genomic Data-Sharing Ecosystem

Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem-Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community-enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes. SIGNIFICANCE: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community.This article is highlighted in the In This Issue feature, p. 995.

Making plants into cost-effective bioreactors for highly active antimicrobial peptides

As antibiotic-resistant bacterial pathogens become an ever-increasing concern, antimicrobial peptides (AMPs) have grown increasingly attractive as alternatives. Potentially, plants could be used as cost-effective AMP bioreactors; however, reported heterologous AMP expression is much lower in plants than in E. coli expression systems and often results in plant cytotoxicity, even for AMPs fused to carrier proteins. This suggests that there may be a physical characteristic of the previously described heterologous AMPs which impedes efficient expression in plants. Using a meta-analysis of protein databases, this study has determined that native plant AMPs were significantly less cationic than AMPs native to other taxa. To apply this finding to plant expression, the transient expression of 10 different heterologous AMPs, ranging in charge from +7 to -5, was tested in the tobacco, Nicotiana benthamiana. Elastin-like polypeptide (ELP) was used as the carrier protein for AMP expression. ELP fusion allowed for a simple, cost-effective temperature shift purification. Using this system, all five anionic AMPs expressed well, with two at unusually high levels (375 and 563 μg/gfw). Furthermore, antimicrobial activity against Staphylococcus epidermidis was an order of magnitude greater (average minimum inhibitory concentration MIC of 0.26μM) than that typically seen for AMPs expressed in E. coli systems and was associated with the uncleaved fusion peptide. In summary, this study describes a means of expressing AMP fusions in plants in high yield, purified by a simple temperature-shift protocol, resulting in a fusion peptide with high antimicrobial activity and without the need for a peptide cleavage step.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

The repertoire of mutational signatures in human cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature¹. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium² of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses^3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

Protein classification using modified n-grams and skip-grams

Motivation

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG).

Results

A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists.

Availability and implementation

m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg.

Contact

erich_baker@baylor.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Assigning biological function using hidden signatures in cystine-stabilized peptide sequences

Cystine-stabilized peptides have great utility as they naturally block ion channels, inhibit acetylcholine receptors, or inactivate microbes. However, only a tiny fraction of these peptides has been characterized. Exploration for novel peptides most efficiently starts with the identification of candidates from genome sequence data. Unfortunately, though cystine-stabilized peptides have shared structures, they have low DNA sequence similarity, restricting the utility of BLAST and even more powerful sequence alignment-based annotation algorithms, such as PSI-BLAST and HMMER. In contrast, a supervised machine learning approach may improve discovery and function assignment of these peptides. To this end, we employed our previously described m-NGSG algorithm, which utilizes hidden signatures embedded in peptide primary sequences that define and categorize structural or functional classes of peptides. From the generalized m-NGSG framework, we derived five specific models that categorize cystine-stabilized peptide sequences into specific functional classes. When compared with PSI-BLAST, HMMER and existing function-specific models, our novel approach (named CSPred) consistently demonstrates superior performance in discovery and function-assignment. We also report an interactive version of CSPred, available through download ( https://bitbucket.org/sm_islam/cystine-stabilized-proteins/src ) or web interface (watson.ecs.baylor.edu/cspred), for the discovery of cystine-stabilized peptides of specific function from genomic datasets and for genome annotation. We fully describe, in the Availability section following the Discussion, the quick and simple usage of the CsPred website to automatically deliver function assignments for batch submissions of peptide sequences.

Estimation of (41)Ar activity concentration and release rate from the TRIGA Mark-II research reactor

The BAEC TRIGA research reactor (BTRR) is the only nuclear reactor in Bangladesh. Bangladesh Atomic Energy Regulatory Authority (BAERA) regulations require that nuclear reactor licensees undertake all reasonable precautions to protect the environment and the health and safety of persons, including identifying, controlling and monitoring the release of nuclear substances to the environment. The primary activation product of interest in terms of airborne release from the reactor is (41)Ar. (41)Ar is a noble gas readily released from the reactor stacks and most has not decayed by the time it moves offsite with normal wind speed. Initially (41)Ar is produced from irradiation of dissolved air in the primary water which eventually transfers into the air in the reactor bay. In this study, the airborne radioisotope (41)Ar generation concentration, ground level concentration and release rate from the BTRR bay region are evaluated theoretically during the normal reactor operation condition by several governing equations. This theoretical calculation eventually minimizes the doubt about radiological safety to determine the radiation level for (41)Ar activity whether it is below the permissible limit or not. Results show that the estimated activity for (41)Ar is well below the maximum permissible concentration limit set by the regulatory body, which is an assurance for the reactor operating personnel and general public. Thus the analysis performed within this paper is so much effective in the sense of ensuring radiological safety for working personnel and the environment.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides

Background

Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology.

Results

We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86 %, 94.11 %, 84.31 %, 94.30 % and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB.

Conclusion

PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0633-x) contains supplementary material, which is available to authorized users.

Estimation of biologic gasification potential of arsenic from contaminated natural soil by enumeration of arsenic methylating bacteria

Volatile arsenic species are found in gases released from natural environments as a result of natural ambient-temperature biomethylation of arsenic conducted by yeast, fungi, and bacteria. This process is part of arsenic transport in the arsenic geocycle. It is important to determine the flux of gasified arsenic released by microorganisms to determine the quantitative flux of arsenic cycle clearly and also to understand the effect of microorganisms on the transport and distribution of arsenic in the contaminated environment. In this study, biologic gasification potential of natural soil was determined by enumeration of arsenic methylating bacteria (AsMB). Enumeration of AsMB was conducted for 10 contaminated sites in Bangladesh where AsMB concentration varies from 0.2 x 10(4) to 7.8 x 10(4) most probable number (MPN) kg(-1) dry soil. The specific gasification rate of arsenic by microorganisms was estimated as 1.8 x 10(-7) microg As MPN(-1) d(-1) by incubation of soil in a laboratory soil column setup. Natural biologic gasification potential of arsenic was then calculated by multiplying the specific rate by the number of AsMB in different soils. The attempt of this study is a fundamental step in determining the volatilization flux of arsenic from land surface contributed by microorganisms.

Distribution of Radionuclides in Surface Soil and Bottom Sediment in the District of Jessore, Bangladesh and Evaluation of Radiation Hazard

This paper presents the first reports on the natural and anthropogenic radionuclides in soil and sediment of Jessore, a south-western district of Bangladesh. Surface soil and freshwater sediment were collected from in and around some major water-bodies of this district. To assess the radiological hazard of the natural radioactivity, the radium equivalent activity, the absorbed dose rate, and the external and internal hazard indices were calculated. In the soil and sediment in general, the activity concentration of 232Th was found to be higher than that of  226Ra, while that of 40K markedly exceeds the values of both 226Ra and 232Th. The average activities of 226Ra and 232Th in this area were found to be higher than the world average. There was no activity due to fallout (137Cs) in this area. The radium equivalent activity and the absorbed dose rate due to the natural radionuclides were found to be respectively lower and higher than the world average. The external and internal hazard indices were found to be well below the hazard limit of unity. Our results compare fairly well with other published results.  Key words: Soil, Sediment, Natural lake, Radioactivity, Dose rate DOI: 10.3329/jbas.v33i1.2956 Journal of Bangladesh Academy of Sciences, Vol. 33, No. 1, 117-130, 2009