Benchmarking and improving the performance of variant-calling pipelines with RecallME.

MOTIVATION: The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet. RESULTS: The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves.

Familial Dilated Cardiomyopathy: A Novel MED9 Short Isoform Identification.

Familial dilated cardiomyopathy (DCM) is among the leading indications for heart transplantation. DCM alters the transcriptomic profile. The alteration or activation/silencing of physiologically operating transcripts may explain the onset and progression of this pathological state. The mediator complex (MED) plays a fundamental role in the transcription process. The aim of this study is to investigate the MED subunits, which are altered in DCM, to identify target crossroads genes. RNA sequencing allowed us to identify specific MED subunits that are altered during familial DCM, transforming into human myocardial samples. N = 13 MED subunits were upregulated and n = 7 downregulated. MED9 alone was significantly reduced in patients compared to healthy subjects (HS) (FC = -1.257; p < 0.05). Interestingly, we found a short MED9 isoform (MED9s) (ENSG00000141026.6), which was upregulated when compared to the full-transcript isoform (MED9f). Motif identification analysis yielded several significant matches (p < 0.05), such as GATA4, which is downregulated in CHD. Moreover, although the protein-protein interaction network showed FOG2/ZFPM2, FOS and ID2 proteins to be the key interacting partners of GATA4, only FOG2/ZFPM2 overexpression showed an interaction score of "high confidence" ≥ 0.84. A significant change in the MED was observed during HF. For the first time, the MED9 subunit was significantly reduced between familial DCM and HS (p < 0.05), showing an increased MED9s isoform in DCM patients with respect to its full-length transcript. MED9 and GATA4 shared the same sequence motif and were involved in a network with FOG2/ZFPM2, FOS, and ID2, proteins already implicated in cardiac development.

TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power.

MOTIVATION: Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. RESULTS: We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. AVAILABILITY AND IMPLEMENTATION: R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation. Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019. Supervised machine-learning models were trained (80% of observations) to predict GW using the following information: donor's age, sex, height, weight, and body mass index; graft type (left, right, or left lateral lobe); computed tomography estimated graft volume and total liver volume. Model performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error (MAE) and the mean absolute percentage error and compared with methods currently available for GW estimation. The best-performing machine-learning model showed an MAE value of 50 ± 62 g in predicting GW, with a mean error of 10.3%. These errors were significantly lower than those observed with alternative methods. In addition, 62% of predictions had errors <10%, whereas errors >15% were observed in only 18.4% of the cases compared with the 34.6% of the predictions obtained with the best alternative method ( p < 0.001). The machine-learning model is made available as a web application ( http://graftweight.shinyapps.io/prediction ). Machine learning can improve the precision of GW estimation compared with currently available methods by reducing the frequency of significant errors. The coupling of anthropometric variables to the preoperatively estimated graft volume seems necessary to improve the accuracy of GW estimation.

Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases.

Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.

In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

DeClUt: Decluttering differentially expressed genes through clustering of their expression profiles.

BACKGROUND AND OBJECTIVE: differential expression analysis is one of the most popular activities in transcriptomic studies based on next-generation sequencing technologies. In fact, differentially expressed genes (DEGs) between two conditions represent ideal prognostic and diagnostic candidate biomarkers for many pathologies. As a result, several algorithms, such as DESeq2 and edgeR, have been developed to identify DEGs. Despite their widespread use, there is no consensus on which model performs best for different types of data, and many existing methods suffer from high False Discovery Rates (FDR). METHODS: we present a new algorithm, DeClUt, based on the intuition that the expression profile of differentially expressed genes should form two reasonably compact and well-separated clusters. This, in turn, implies that the bipartition induced by the two conditions being compared should overlap with the clustering. The clustering algorithm underlying DeClUt was designed to be robust to outliers typical of RNA-seq data. In particular, we used the average silhouette function to enforce membership assignment of samples to the most appropriate condition. RESULTS: DeClUt was tested on real RNA-seq datasets and benchmarked against four of the most widely used methods (edgeR, DESeq2, NOISeq, and SAMseq). Experiments showed a higher self-consistency of results than the competitors as well as a significantly lower False Positive Rate (FPR). Moreover, tested on a real prostate cancer RNA-seq dataset, DeClUt has highlighted 8 DE genes, linked to neoplastic process according to DisGeNET database, that none of the other methods had identified. CONCLUSIONS: our work presents a novel algorithm that builds upon basic concepts of data clustering and exhibits greater consistency and significantly lower False Positive Rate than state-of-the-art methods. Additionally, DeClUt is able to highlight relevant differentially expressed genes not otherwise identified by other tools contributing to improve efficacy of differential expression analyses in various biological applications.

Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data.

The potential of precision population health lies in its capacity to utilize robust patient data for customized prevention and care targeted at specific groups. Machine learning has the potential to automatically identify clinically relevant subgroups of individuals, considering heterogeneous data sources. This study aimed to assess whether unsupervised machine learning (UML) techniques could interpret different clinical data to uncover clinically significant subgroups of patients suspected of coronary artery disease and identify different ranges of aorta dimensions in the different identified subgroups. We employed a random forest-based cluster analysis, utilizing 14 variables from 1170 (717 men/453 women) participants. The unsupervised clustering approach successfully identified four distinct subgroups of individuals with specific clinical characteristics, and this allows us to interpret and assess different ranges of aorta dimensions for each cluster. By employing flexible UML algorithms, we can effectively process heterogeneous patient data and gain deeper insights into clinical interpretation and risk assessment.

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93-95% CI 0.917-0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies.

Analysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

Stepwise shortening of agalsidase beta infusion duration in Fabry disease: Clinical experience with infusion rate escalation protocol.

BACKGROUND: Although enzyme replacement therapy with agalsidase beta resulted in a variety of clinical benefits, life-long biweekly intravenous infusion may impact on patients' quality of life. Moreover, regular infusions are time-consuming: although a stepwise shortening of infusion duration is allowed up to a minimum of 1.5 hr, in most centers it remains ≥3 hr, and no data exists about the safety and tolerability of agalsidase beta administration at maximum tolerated infusion rate. METHODS: In this study, we reported our experience with a stepwise infusion rate escalation protocol developed in our center in a cohort of 53 Fabry patients (both already receiving and treatment-naΪve), and explored factors predictive for the infusion rate increase tolerability. RESULTS: Fifty-two patients (98%) reduced infusion duration ≤3 hr; of these, 38 (72%) even reached a duration ≤2 hr. We found a significant difference between the mean duration reached by already treated and naΪve patients (p < .01). More severely affected patients (male patients and those with lower enzyme activity) received longer infusions for higher risk of infusion-associated reactions (IARs). A significant correlation between anti-agalsidase antibodies and IARs was found. CONCLUSION: Our infusion rate escalation protocol is safe and could improve patient compliance, satisfaction and quality of life.

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. METHODS: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. RESULTS: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). CONCLUSIONS: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

miR-34c-3p targets CDK1 a synthetic lethality partner of KRAS in non-small cell lung cancer.

Lung cancer is still the leading cause of death by cancer worldwide despite advances both in its detection and therapy. Multiple oncogenic driver alterations have been discovered, opening the prospective for new potential therapeutic targets. Among them, KRAS mutations represent the most frequent oncogene aberrations in non-small cell lung cancer (NSCLC) patients with a negative prognostic impact, but effective therapies targeting KRAS are not well characterized yet. Here, we demonstrate that the microRNA miR-34c-3p is a positive prognostic factor in KRAS-mutated NSCLC patients. Firstly, looking at the TGCA dataset, we found that high miR-34c-3p expression correlated with longer survival of KRAS-mutated NSCLC patients. In vitro assays on immortalized and patient-derived primary NSCLC cells revealed that miR-34c-3p overexpression increased apoptosis and lowered proliferation rate in KRASmut cells. Computational analysis and in vitro assays identified CDK1, one of the most promising lethal targets for KRAS-mutant cancer, as a target of miR-34c-3p. Moreover, the combination of CDK1 inhibition (mediated by RO3306) and miR-34c-3p overexpression resulted in an additive effect on the viability of KRASmut-expressing cells. Altogether, our findings demonstrate that miR-34c-3p is a novel biomarker that may allow tailored treatment for KRAS-mutated NSCLC patients.

An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein-Protein Interaction Networks in Female-Specific Cancers.

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

Switch from enzyme replacement therapy to oral chaperone migalastat for treating fabry disease: real-life data.

The treatment options for Fabry disease (FD) are enzyme replacement therapy (ERT) with agalsidase alfa or beta, and the oral pharmacological chaperone migalastat. Since few data are available on the effects of switching from ERT to migalastat, we performed a single-center observational study on seven male Fabry patients (18-66 years) to assess the effects of the switch on renal, cardiac, and neurologic function, health status, pain, lyso-Gb3, α-Gal A activity and adverse effects. Data were retrospectively collected at time of diagnosis of FD (baseline, T0), and after 12 months of ERT (T1), and prospectively after 1 year of therapy with migalastat (T2). No patient died or reported renal, cardiac, or cerebrovascular events during the study period. The predefined measures for cardiac, renal and neurologic function, and FD-related symptoms and questionnaires were stable between baseline and the switch, and remained unchanged with migalastat. However, a significant improvement was observed in left ventricular mass index from baseline to T2 (p = 0.016), with a significative difference between the treatments (p = 0.028), and in median proteinuria from T2 vs T1 (p = 0.048). Moreover, scores of the BPI improved from baseline to T1, and remained stable with migalastat. Plasma lyso-Gb3 levels significantly decreased from baseline to T1 (P = 0.007) and T2 (P = 0.003), while did not significantly differ between the two treatments. α-Gal A activity increased from T0 to T2 (p < 0.0001). The frequency of adverse effects under migalastat and ERT was comparable (28% for both drugs). In conclusion, switching from ERT to migalastat is valid, safe and well tolerated.

DNA methylation profiling of CD04+/CD08+ T cells reveals pathogenic mechanisms in increasing hyperglycemia: PIRAMIDE pilot study.

BACKGROUND: DNA methylation can play a pathogenic role in the early stages of hyperglycemia linking homeostasis imbalance and vascular damage. MATERIAL AND METHODS: We investigated DNA methylome by RRBS in CD04+ and CD08+ T cells from healthy subjects (HS) to pre-diabetics (Pre-Diab) and type 2 diabetic (T2D) patients to identify early biomarkers of glucose impairment and vascular damage. Our cross-sectional study enrolled 14 individuals from HS state to increasing hyperglycemia (pilot study, PIRAMIDE trial, NCT03792607). RESULTS: Globally, differentially methylated regions (DMRs) were mostly annotated to promoter regions. Hypermethylated DMRs were greater than hypomethylated in CD04+ T cells whereas CD08+ T showed an opposite trend. Moreover, DMRs overlapping between Pre-Diab and T2D patients were mostly hypermethylated in both T cells. Interestingly, SPARC was the most hypomethylated gene in Pre-Diab and its methylation level gradually decreased in T2D patients. Besides, SPARC showed a significant positive correlation with DBP (+0.76), HDL (+0.54), Creatinine (+0.83), LVDd (+0.98), LVSD (+0.98), LAD (+0.98), LVPWd (+0.84), AODd (+0.81), HR (+0.72), Triglycerides (+0.83), LAD (+0.69) and AODd (+0.52) whereas a negative correlation with Cholesterol (-0.52) and LDL (-0.71) in T2D. CONCLUSION: SPARC hypomethylation in CD08+ T cells may be a useful biomarker of vascular complications in Pre-Diab with a possible role for primary prevention warranting further multicenter clinical trials to validate our findings.

The complete 12 Mb genome and transcriptome of Nonomuraea gerenzanensis with new insights into its duplicated "magic" RNA polymerase.

In contrast to the widely accepted consensus of the existence of a single RNA polymerase in bacteria, several actinomycetes have been recently shown to possess two forms of RNA polymerases due the to co-existence of two rpoB paralogs in their genome. However, the biological significance of the rpoB duplication is obscure. In this study we have determined the genome sequence of the lipoglycopeptide antibiotic A40926 producer Nonomuraea gerenzanensis ATCC 39727, an actinomycete with a large genome and two rpoB genes, i.e. rpoB(S) (the wild-type gene) and rpoB(R) (the mutant-type gene). We next analyzed the transcriptional and metabolite profiles in the wild-type gene and in two derivative strains over-expressing either rpoB(R) or a mutated form of this gene to explore the physiological role and biotechnological potential of the "mutant-type" RNA polymerase. We show that rpoB(R) controls antibiotic production and a wide range of metabolic adaptive behaviors in response to environmental pH. This may give interesting perspectives also with regard to biotechnological applications.

Simvastatin reduces melanoma progression in a murine model.

Statins are a class of drugs that inhibit the rate-limiting step in the cholesterol biosynthetic pathway and show an anticancer effect, probably through the inhibition of cell proliferation. To date, the exact mechanism of cancer cell growth arrest induced by statins is not known. We report that simvastatin is able to induce apoptosis in melanoma cells but not in normal cells and also able to contrast the growth of tumor in an experimental melanoma murine model. We observed a delay in the tumor development in almost the 50% of the simvastatin administered animals and a strong reduction of the tumor volume with a differences of ~150% compared to the controls. Also the survival rate was significantly higher in mice that received the drug with a survival increase of ~130% compared to the controls. The tumor growth reduction in mice was supported by the results of cell migration assay, confirming that simvastatin clearly reduced cell migration. Moreover, simvastatin induced a strong downregulation of NonO gene expression, an important growth factor involved in the splicing regulation. This result could explain the decrease of melanoma cells proliferation, suggesting a possible action mechanism. The results derived from our experiments may sustain the many reports on the anticancer inhibitory property of statins and encourage new studies on this drug for a possible use in therapy, probably in combination with conventional chemotherapy.