An integrated Bayesian analysis of LOH and copy number data

BACKGROUND Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events. RESULTS To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation. CONCLUSIONS We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.This work was supported by Swiss National Science Foundation (grants 205321-112430, 205320-121886/1); Oncosuisse grants OCS-1939-8-2006 and OCS - 02296-08-2008; Cantone Ticino ("Computational life science/Ticino in rete” program); Fondazione per la Ricerca e la Cura sui Linfomi (Lugano, Switzerland)

Surgery for Intraductal Papillary Mucinous Neoplasms of the Pancreas: Preoperative Factors Tipping the Scale of Decision-Making

Background: Decision-making in intraductal papillary mucinous neoplasms (IPMNs) of the pancreas depends on scaling the risk of malignancy with the surgical burden of a pancreatectomy. This study aimed to develop a preoperative, disease-specific tool to predict surgical morbidity for IPMNs. Methods: Based on preoperative variables of resected IPMNs at two high-volume institutions, classification tree analysis was applied to derive a predictive model identifying the risk factors for major morbidity (Clavien-Dindo ≥3) and postoperative pancreatic insufficiency. Results: Among 524 patients, 289 (55.2%) underwent pancreaticoduodenectomy (PD), 144 (27.5%) underwent distal pancreatectomy (DP), and 91 (17.4%) underwent total pancreatectomy (TP) for main-duct (18.7%), branch-duct (12.6%), or mixed-type (68.7%) IPMN. For 98 (18.7%) of the patients, major morbidity developed. The classification tree distinguished different probabilities of major complications based on the type of surgery (area under the surve [AUC] 0.70; 95% confidence interval [CI], 0.63-0.77). Among the DP patients, the presence of preoperative diabetes identified two risk classes with respective probabilities of 5% and 25% for the development of major morbidity, whereas among the PD/TP patients, three different classes with respective probabilities of 15%, 20%, and 36% were identified according to age and body mass index (BMI). Overall, history of diabetes, age, and cyst size segregated three different risk classes for new-onset/worsening diabetes. Conclusions: In presumed IPMNs, the disease-specific risk of major morbidity and pancreatic insufficiency can be determined in the preoperative setting and used to personalize the possible surgical indication. Age and overweight status in case of PD/TP and diabetes in case of DP tip the scale toward less aggressive clinical management in the absence of features suggestive for malignancy

Long-term and real-world safety and efficacy of retroviral gene therapy for adenosine deaminase deficiency

Adenosine deaminase (ADA) deficiency leads to severe combined immunodeficiency (SCID). Previous clinical trials showed that autologous CD34+ cell gene therapy (GT) following busulfan reduced-intensity conditioning is a promising therapeutic approach for ADA-SCID, but long-term data are warranted. Here we report an analysis on long-term safety and efficacy data of 43 patients with ADA-SCID who received retroviral ex vivo bone marrow-derived hematopoietic stem cell GT. Twenty-two individuals (median follow-up 15.4 years) were treated in the context of clinical development or named patient program. Nineteen patients were treated post-marketing authorization (median follow-up 3.2 years), and two additional patients received mobilized peripheral blood CD34+ cell GT. At data cutoff, all 43 patients were alive, with a median follow-up of 5.0 years (interquartile range 2.4-15.4) and 2 years intervention-free survival (no need for long-term enzyme replacement therapy or allogeneic hematopoietic stem cell transplantation) of 88% (95% confidence interval 78.7-98.4%). Most adverse events/reactions were related to disease background, busulfan conditioning or immune reconstitution; the safety profile of the real world experience was in line with premarketing cohort. One patient from the named patient program developed a T cell leukemia related to treatment 4.7 years after GT and is currently in remission. Long-term persistence of multilineage gene-corrected cells, metabolic detoxification, immune reconstitution and decreased infection rates were observed. Estimated mixed-effects models showed that higher dose of CD34+ cells infused and younger age at GT affected positively the plateau of CD3+ transduced cells, lymphocytes and CD4+ CD45RA+ naive T cells, whereas the cell dose positively influenced the final plateau of CD15+ transduced cells. These long-term data suggest that the risk-benefit of GT in ADA remains favorable and warrant for continuing long-term safety monitoring. Clinical trial registration: NCT00598481 , NCT034786

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

Validating the Italian Version of the Disgust and Propensity Scale-Revised

The aim of this work is to evaluate the factor structure and psychometric properties of the Italian version of the Disgust Propensity and Sensitivity Scale-Revised (DPSS-R, 16 items) in two samples taken from the general population. In the first study, 285 participants completed the DPSS-R questionnaire through a web-based survey. Exploratory factor analysis for ordinal Likert-type data supported the existence of four underlying factors, reflecting self-focused disgust, disgust propensity, somatic anxiety and disgust sensitivity. In the second study, an independent sample of 293 participants was enrolled as a test set to validate the factor structure obtained in the exploratory phase. The factor solution was confirmed, but showed quite highly correlated latent factors. We fitted the model and tested whether or not the bifactor structure was better than the previous one (four correlated factors). Actually, we had evidence supporting the presence of a general factor, providing a measure of disgust susceptibility, along with the four specific factors previously defined. This result could be useful also from the clinical perspective since the DPSS-R questionnaire will be used in clinical context, where underlying factors may be related to different and specific psychopathological profiles. Finally, we examined and visualized the interrelationships among the four DPSS-R factors and the external scales (Anxiety Sensitivity, Disgust Scale and Padua) using a graphical model approach

Testing Hypotheses by Regularized Maximum Mean Discrepancy

Abstract — Regularized Maximum Mean Discrepancy (RMMD), our novel measure for kernel-based hypothesis testing, excels at hypothesis tests involving multiple comparisons with power control even when sample sizes are small. We derive asymptotic distributions under the null and alternative hypotheses, and assess power control. Outstanding results are obtained on challenging benchmark datasets. Keywords- kernel-based hypothesis testing, Homogeneity testing, Multiple comparisons, Power I

Treatment effect assessment in observational studies with multi-level treatment and outcome

In observational studies, one of the main difficulties consists in the comparison of treatment effects. In fact, receiving a treatment is not a “purely random” event, and there could be relevant differences between treatment groups. Propensity score is a popular tool to account for this source of bias. However, its use requires a careful modelization of the dependence relationships of the treatment on the covariates. In this work, we consider a general setting with multiple treatments and discrete multi-valued outcome. We propose to estimate the propensity score by using Bayesian Networks and, based on this, we develop an inferential methodology to evaluate the treatments effect. The performance of the proposed approach have been studied through a simulation study with very promising results

A propensity score approach for treatment evaluation based on Bayesian Networks

In observational studies evaluating the treatment effect on a given outcome, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to estimate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance