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BACKGROUND: The choice of an appropriate strategy for intracanalicular vestibular schwannoma (ICVS) is still debated. We conducted a systematic review and meta-analysis with the aim to compare treatment outcomes amongst management strategies (conservative surveillance (CS), microsurgical resection (MR), or stereotactic radiosurgery (SRS)) aiming to inform guideline recommendations on behalf of the International Stereotactic Radiosurgery Society (ISRS). METHODS: Using PRISMA guidelines, we reviewed manuscripts published between January 1990 and October 2021 referenced in PubMed or Embase. Inclusion criteria were peer-reviewed clinical studies or case series reporting a cohort of ICVS managed with CS, MR, or SRS. Primary outcome measures included tumor control, the need for additional treatment, hearing outcomes, and posttreatment neurological deficits. These were pooled using meta-analytical techniques and compared using meta-regression with random effect. RESULTS: Forty studies were included (2371 patients). The weighted pooled estimates for tumor control were 96% and 65% in SRS and CS series, respectively (P

BackgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.ResultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.ConclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Abstract Background Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances. The study was designed to represent a real-world scenario. Clams were collected in different seasons across the most important production area in Europe (lagoons along the northern Adriatic coast) to cover the known seasonal variation in microbiome composition for the species. DNA extracted from samples underwent the same depuration process as commercial products (i.e. at least 12 h in open flow systems). Results Machine learning-based analysis of microbiome profiles was carried out using two completely independent sets of data (collected at the same locations but in different years), one for training the algorithm, and the other for testing its accuracy and assessing the temporal stability signal. Briefly, gills (GI) and digestive gland (DG) of clams were collected in summer and winter over two different years (i.e. from 2018 to 2020) in one banned area and four farming sites. 16S DNA metabarcoding was performed on clam tissues and the obtained amplicon sequence variants (ASVs) table was used as input for ML MP. The best-predicting performances were obtained using the combined information of GI and DG (consensus analysis), showing a Cohen K-score > 0.95 when the target was the classification of samples collected from the banned area and those harvested at farming sites. Classification of the four different farming areas showed slightly lower accuracy with a 0.76 score. Conclusions We show here that MP coupled with ML is an effective tool to trace the origin of shellfish products. The tool is extremely robust against seasonal and inter-annual variability, as well as product depuration, and is ready for implementation in routine assessment to prevent the trade of illegally harvested or mislabeled shellfish.

Abstract Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson’s Paradox, and (2) model generalization limitations.

Abstract Background A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (ΔΔG) upon single point mutation may also help the annotation process. The experimental ΔΔG values are affected by uncertainty as measured by standard deviations. Most of the ΔΔG values are nearly zero (about 32% of the ΔΔG data set ranges from −0.5 to 0.5 kcal/mole) and both the value and sign of ΔΔG may be either positive or negative for the same mutation blurring the relationship among mutations and expected ΔΔG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (ΔΔG1.0 kcal/mole) and neutral mutations (−1.0≤ΔΔG≤1.0 kcal/mole). Results In this paper a support vector machine starting from the protein sequence or structure discriminates between stabilizing, destabilizing and neutral mutations. We rank all the possible substitutions according to a three state classification system and show that the overall accuracy of our predictor is as high as 56% when performed starting from sequence information and 61% when the protein structure is available, with a mean value correlation coefficient of 0.27 and 0.35, respectively. These values are about 20 points per cent higher than those of a random predictor. Conclusions Our method improves the quality of the prediction of the free energy change due to single point protein mutations by adopting a hypothesis of thermodynamic reversibility of the existing experimental data. By this we both recast the thermodynamic symmetry of the problem and balance the distribution of the available experimental measurements of free energy changes. This eliminates possible overestimations of the previously described methods trained on an unbalanced data set comprising a number of destabilizing mutations higher than stabilizing ones.

Abstract Background Peptidases are proteolytic enzymes responsible for fundamental cellular activities in all organisms. Apparently about 2–5% of the genes encode for peptidases, irrespectively of the organism source. The basic peptidase function is "protein digestion" and this can be potentially dangerous in living organisms when it is not strictly controlled by specific inhibitors. In genome annotation a basic question is to predict gene function. Here we describe a computational approach that can filter peptidases and their inhibitors out of a given proteome. Furthermore and as an added value to MEROPS, a specific database for peptidases already available in the public domain, our method can predict whether a pair of peptidase/inhibitor can interact, eventually listing all possible predicted ligands (peptidases and/or inhibitors). Results We show that by adopting a decision-tree approach the accuracy of PROSITE and HMMER in detecting separately the four major peptidase types (Serine, Aspartic, Cysteine and Metallo- Peptidase) and their inhibitors among a non redundant set of globular proteins can be improved by some percentage points with respect to that obtained with each method separately. More importantly, our method can then predict pairs of peptidases and interacting inhibitors, scoring a joint global accuracy of 99% with coverage for the positive cases (peptidase/inhibitor) close to 100% and a correlation coefficient of 0.91%. In this task the decision-tree approach outperforms the single methods. Conclusion The decision-tree can reliably classify protein sequences as peptidases or inhibitors, belonging to a certain class, and can provide a comprehensive list of possible interacting pairs of peptidase/inhibitor. This information can help the design of experiments to detect interacting peptidase/inhibitor complexes and can speed up the selection of possible interacting candidates, without searching for them separately and manually combining the obtained results. A web server specifically developed for annotating peptidases and their inhibitors (HIPPIE) is available at http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi

Abstract Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins.

Abstract Background The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone. Methods In this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand. Results We can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes. Conclusions All together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.

Abstract Background Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied. Results We introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) as an extension of Hidden Conditional Random Fields (HCRFs). GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows regular grammar rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins. Conclusion We show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications. Availability GRHCRF software is available under GPLv3 licence at the website http://www.biocomp.unibo.it/~savojard/biocrf-0.9.tar.gz.

BackgroundStereotactic radiosurgery (SRS) for benign intracranial meningiomas is an established treatment.ObjectiveTo summarize the literature and provide evidence-based practice guidelines on behalf of the International Stereotactic Radiosurgery Society (ISRS).MethodsArticles in English specific to SRS for benign intracranial meningioma, published from January 1964 to April 2018, were systematically reviewed. Three electronic databases, PubMed, EMBASE, and the Cochrane Central Register, were searched.ResultsOut of the 2844 studies identified, 305 had a full text evaluation and 27 studies met the criteria to be included in this analysis. All but one were retrospective studies. The 10-yr local control (LC) rate ranged from 71% to 100%. The 10-yr progression-free-survival rate ranged from 55% to 97%. The prescription dose ranged typically between 12 and 15 Gy, delivered in a single fraction. Toxicity rate was generally low.ConclusionThe current literature supporting SRS for benign intracranial meningioma lacks level I and II evidence. However, when summarizing the large number of level III studies, it is clear that SRS can be recommended as an effective evidence-based treatment option (recommendation level II) for grade 1 meningioma.

IntroductionProstate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.MethodsThe TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study’s prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.ResultsThe cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.DiscussionThis study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.

Genetic markers (especially short tandem repeats or STRs) located on the X chromosome are a valuable resource to solve complex kinship cases in forensic genetics in addition or alternatively to autosomal STRs. Groups of tightly linked markers are combined into haplotypes, thus increasing the discriminating power of tests. However, this approach requires precise knowledge of the recombination rates between adjacent markers. The International Society of Forensic Genetics recommends that recombination rate estimation on the X chromosome is performed from pedigree genetic data while taking into account the confounding effect of mutations. However, implementations that satisfy these requirements have several drawbacks: they were never publicly released, they are very slow and/or need cluster-level hardware and strong computational expertise to use. In order to address these key concerns we developed Recombulator-X, a new open-source Python tool. The most challenging issue, namely the running time, was addressed with dynamic programming techniques to greatly reduce the computational complexity of the algorithm. Compared to the previous methods, Recombulator-X reduces the estimation times from weeks or months to less than one hour for typical datasets. Moreover, the estimation process, including preprocessing, has been streamlined and packaged into a simple command-line tool that can be run on a normal PC. Where previous approaches were limited to small panels of STR markers (up to 15), our tool can handle greater numbers (up to 100) of mixed STR and non-STR markers. In conclusion, Recombulator-X makes the estimation process much simpler, faster and accessible to researchers without a computational background, hopefully spurring increased adoption of best practices.

BackgroundNo guidelines have been published regarding stereotactic radiosurgery (SRS) in the management of Spetzler-Martin grade I and II arteriovenous malformations (AVMs).ObjectiveTo establish SRS practice guidelines for grade I-II AVMs on the basis of a systematic literature review.MethodsPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant search of Medline, Embase, and Scopus, 1986-2018, for publications reporting post-SRS outcomes in ≥10 grade I-II AVMs with a follow-up of ≥24 mo. Primary endpoints were obliteration and hemorrhage; secondary outcomes included Spetzler-Martin parameters, dosimetric variables, and "excellent" outcomes (defined as total obliteration without new post-SRS deficit).ResultsOf 447 abstracts screened, 8 were included (n = 1, level 2 evidence; n = 7, level 4 evidence), representing 1102 AVMs, of which 836 (76%) were grade II. Obliteration was achieved in 884 (80%) at a median of 37 mo; 66 hemorrhages (6%) occurred during a median follow-up of 68 mo. Total obliteration without hemorrhage was achieved in 78%. Of 836 grade II AVMs, Spetzler-Martin parameters were reported in 680: 377 were eloquent brain and 178 had deep venous drainage, totaling 555/680 (82%) high-risk SRS-treated grade II AVMs.ConclusionThe literature regarding SRS for grade I-II AVM is low quality, limiting interpretation. Cautiously, we observed that SRS appears to be a safe, effective treatment for grade I-II AVM and may be considered a front-line treatment, particularly for lesions in deep or eloquent locations. Preceding publications may be influenced by selection bias, with favorable AVMs undergoing resection, whereas those at increased risk of complications and nonobliteration are disproportionately referred for SRS.

BackgroundThis systematic review reports on outcomes and toxicities following stereotactic radiosurgery (SRS) for non-functioning pituitary adenomas (NFAs) and presents consensus opinions regarding appropriate patient management.MethodsUsing the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a systematic review was performed from articles of ≥10 patients with NFAs published prior to May 2018 from the Medline database using the key words "radiosurgery" and "pituitary" and/or "adenoma." Weighted random effects models were used to calculate pooled outcome estimates.ResultsOf the 678 abstracts reviewed, 35 full-text articles were included describing the outcomes of 2671 patients treated between 1971 and 2017 with either single fraction SRS or hypofractionated stereotactic radiotherapy (HSRT). All studies were retrospective (level IV evidence). SRS was used in 27 studies (median dose: 15 Gy, range: 5-35 Gy) and HSRT in 8 studies (median total dose: 21 Gy, range: 12-25 Gy, delivered in 3-5 fractions). The 5-year random effects local control estimate after SRS was 94% (95% CI: 93.0-96.0%) and 97.0% (95% CI: 93.0-98.0%) after HSRT. The 10-year local control random effects estimate after SRS was 83.0% (95% CI: 77.0-88.0%). Post-SRS hypopituitarism was the most common treatment-related toxicity observed, with a random effects estimate of 21.0% (95% CI: 15.0-27.0%), whereas visual dysfunction or other cranial nerve injuries were uncommon (range: 0-7%).ConclusionsSRS is an effective and safe treatment for patients with NFAs. Encouraging short-term data support HSRT for select patients, and mature outcomes are needed before definitive recommendations can be made. Clinical practice opinions were developed on behalf of the International Stereotactic Radiosurgery Society (ISRS).

BackgroundThis systematic review reports on outcomes and toxicities following stereotactic radiosurgery (SRS) for non-functioning pituitary adenomas (NFAs) and presents consensus opinions regarding appropriate patient management.MethodsUsing the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a systematic review was performed from articles of ≥10 patients with NFAs published prior to May 2018 from the Medline database using the key words "radiosurgery" and "pituitary" and/or "adenoma." Weighted random effects models were used to calculate pooled outcome estimates.ResultsOf the 678 abstracts reviewed, 35 full-text articles were included describing the outcomes of 2671 patients treated between 1971 and 2017 with either single fraction SRS or hypofractionated stereotactic radiotherapy (HSRT). All studies were retrospective (level IV evidence). SRS was used in 27 studies (median dose: 15 Gy, range: 5-35 Gy) and HSRT in 8 studies (median total dose: 21 Gy, range: 12-25 Gy, delivered in 3-5 fractions). The 5-year random effects local control estimate after SRS was 94% (95% CI: 93.0-96.0%) and 97.0% (95% CI: 93.0-98.0%) after HSRT. The 10-year local control random effects estimate after SRS was 83.0% (95% CI: 77.0-88.0%). Post-SRS hypopituitarism was the most common treatment-related toxicity observed, with a random effects estimate of 21.0% (95% CI: 15.0-27.0%), whereas visual dysfunction or other cranial nerve injuries were uncommon (range: 0-7%).ConclusionsSRS is an effective and safe treatment for patients with NFAs. Encouraging short-term data support HSRT for select patients, and mature outcomes are needed before definitive recommendations can be made. Clinical practice opinions were developed on behalf of the International Stereotactic Radiosurgery Society (ISRS).

Diversity and inclusion are addressed within the economics of innovation in an evolutive perspective, by focusing a specific theme (access to information) along the diagonal line between the Gutenberg printing and the network digital technologies. Technology and the market modify the trade-off between diversity and inclusion and the degree of the system’s variety as well. In the transition from the analogic space (pluralism of standards, intermediaries, markets) to the network digital space (inclusive standardized ecosystems, oligopoly of technological intermediaries) variety of access to information has increased, but it is lower than the potential variety.

Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Background: The exposome drivers are less studied than its consequences but may be crucial in identifying population subgroups with unfavourable exposures. Objectives: We used three approaches to study the socioeconomic position (SEP) as a driver of the early-life exposome in Turin children of the NINFEA cohort (Italy). Methods: Forty-two environmental exposures, collected at 18 months of age (N = 1989), were classified in 5 groups (lifestyle, diet, meteoclimatic, traffic-related, built environment).We performed cluster analysis to identify subjects sharing similar exposures, and intra-exposome-group Principal Component Analysis (PCA) to reduce the dimensionality. SEP at childbirth was measured through the Equivalised Household Income Indicator.SEP-exposome association was evaluated using: 1) an Exposome Wide Association Study (ExWAS), a one-exposure (SEP) one-outcome (exposome) approach; 2) multinomial regression of cluster membership on SEP; 3) regressions of each intra-exposome-group PC on SEP. Results: In the ExWAS, medium/low SEP children were more exposed to greenness, pet ownership, passive smoking, TV screen and sugar; less exposed to NO2, NOX, PM25abs, humidity, built environment, traffic load, unhealthy food facilities, fruit, vegetables, eggs, grain products, and childcare than high SEP children.Medium/low SEP children were more likely to belong to a cluster with poor diet, less air pollution, and to live in the suburbs than high SEP children.Medium/low SEP children were more exposed to lifestyle PC1 (unhealthy lifestyle) and diet PC2 (unhealthy diet), and less exposed to PC1s of the built environment (urbanization factors), diet (mixed diet), and traffic (air pollution) than high SEP children. Conclusions: The three approaches provided consistent and complementary results, suggesting that children with lower SEP are less exposed to urbanization factors and more exposed to unhealthy lifestyles and diet. The simplest method, the ExWAS, conveys most of the information and is more replicable in other populations. Clustering and PCA may facilitate results interpretation and communication.

An open challenge of computational and experimental biology is understanding the impact of non-synonymous DNA variations on protein function and, subsequently, human health. The effects of these variants on protein stability can be measured as the difference in the free energy of unfolding (ΔΔG) between the mutated structure of the protein and its wild-type form. Throughout the years, bioinformaticians have developed a wide variety of tools and approaches to predict the ΔΔG. Although the performance of these tools is highly variable, overall they are less accurate in predicting ΔΔG stabilizing variations rather than the destabilizing ones. Here, we analyze the possible reasons for this difference by focusing on the relationship between experimentally-measured ΔΔG and seven protein properties on three widely-used datasets (S2648, VariBench, Ssym) and a recently introduced one (S669). These properties include protein structural information, different physical properties and statistical potentials. We found that two highly used input features, i.e., hydrophobicity and the Blosum62 substitution matrix, show a performance close to random choice when trying to separate stabilizing variants from either neutral or destabilizing ones. We then speculate that, since destabilizing variations are the most abundant class in the available datasets, the overall performance of the methods is higher when including features that improve the prediction for the destabilizing variants at the expense of the stabilizing ones. These findings highlight the need of designing predictive methods able to exploit also input features highly correlated with the stabilizing variants. New tools should also be tested on a not-artificially balanced dataset, reporting the performance on all the three classes (i.e., stabilizing, neutral and destabilizing variants) and not only the overall results.

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity >0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation.

Abstract Aims Risk stratification in patients with advanced chronic heart failure (HF) is an unmet need. Circulating microRNA (miRNA) levels have been proposed as diagnostic and prognostic biomarkers in several diseases including HF. The aims of the present study were to characterize HF‐specific miRNA expression profiles and to identify miRNAs with prognostic value in HF patients. Methods and results We performed a global miRNome analysis using next‐generation sequencing in the plasma of 30 advanced chronic HF patients and of matched healthy controls. A small subset of miRNAs was validated by real‐time PCR (P

Background Phoenician and Punic expansions have been protagonists of intense trade networks and settlements in the Mediterranean Sea. Aims The maternal genetic variability of ancient Punic samples from the Sardinian necropolis of Tharros was analysed, with the aim to explore genetic interactions and signatures of past population events. Subjects and methods The mtDNA HVS-I and coding region SNPs were analysed in 14 Punic samples and 74 modern individuals from Cabras and Belvì (for which the HVS-II region was also analysed). The results were compared with 5,590 modern Euro-Mediterranean sequences and 127 ancient samples. Results While contemporary groups fall within the genetic variability of other modern Sardinians, our Punic samples reveal proximity to present-day North-African and Iberian populations. Furthermore, Cabras and Belvì cluster mainly with pre-Phoenician groups, while samples from Tharros project with other Punic Sardinian individuals. Conclusion This study provides the first preliminary insights into the population dynamics of the Punic site of Tharros. While the number of currently available samples does not allow definitive investigation of the connection with indigenous Sardinian groups, our results seem to confirm internal migratory phenomena in the central-western Mediterranean and female participation in the Punic mobility.

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Abstract Background Single session radiosurgery represents a widely accepted treatment for intracranial meningiomas. However, this approach could involve a high risk of treatment-related complications when applied to large volume lesions. In these cases and for those not suitable for surgical resection, radiosurgery in multisession setting could represents a viable option. The literature results are reassuring in terms of correlated adverse events as well as in terms of tumor control. However, no prospective long-term results are available. In this scenario, we design a prospective monocentric phase II study, in order to verify the safety of a multisession radiosurgery schedule delivering 25 Gy in 5 daily fractions. Methods Patients diagnosed with large and/or near to critical structures, intracranial meningiomas have been treated by means of multisession radiosurgery in both exclusive and postoperative settings. The primary study aim is safety that has been being prospectively scored based on international scales, including NCI Common Toxicity criteria, version 4.03, Barrow Neurological Institute pain intensity score, Barrow Neurological Institute facial numbness score and House-Brackmann Facial Nerve Grading System for qualitative analysis. Secondary aim is treatment efficacy in terms of local control that has been being assessed on volumetric analysis. Discussion This is the first prospective phase II trial on multisession radiosurgery for large and/or near to critical structures intracranial meningiomas. If positive results will be found, this study could represent the starting point for a phase III trial exploring the role of multisession radiosurgery in the exclusive and postoperative radiation therapy treatment of intracranial meningiomas. Trial registration Trial registration: clinicaltrials.gov platform (Multisession Radiosurgery in Large Meningiomas –MuRaLM- identifier NCT02974127). Registered: November 28, 2016. Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02974127?term=radiosurgery&cond=Intracranial+Meningioma&draw=2&rank=1

During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility.