Maternal asthma symptoms during pregnancy on child behaviour and executive function: A Bayesian phenomics approach.

OBJECTIVE: Maternal history of inflammatory conditions has been linked to offspring developmental and behavioural outcomes. This phenomenon may be explained by the maternal immune activation (MIA) hypothesis, which posits that dysregulation of the gestational immune environment affects foetal neurodevelopment. The timing of inflammation is critical. We aimed to understand maternal asthma symptoms during pregnancy, in contrast with paternal asthma symptoms during the same period, on child behaviour problems and executive function in a population-based cohort. METHODS: Data were obtained from 844 families from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. Parent asthma symptoms during the prenatal period were reported. Asthma symptoms in children were reported longitudinally from two to five years old, while behavioural problems and executive functioning were obtained at seven years old. Parent and child measures were compared between mothers with and without prenatal asthma symptoms. Generalized linear and Bayesian phenomics models were used to determine the relation between parent or child asthma symptoms and child outcomes. RESULTS: Children of mothers with prenatal asthma symptoms had greater behavioural and executive problems than controls (Cohen's d: 0.43-0.75; all p < 0.05). This association remained after adjustments for emerging asthma symptoms during the preschool years and fathers' asthma symptoms during the prenatal period. After adjusting for dependence between child outcomes, the Bayesian phenomics model showed that maternal prenatal asthma symptoms were associated with child internalising symptoms and higher-order executive function, while child asthma symptoms were associated with executive function skills. Paternal asthma symptoms during the prenatal period were not associated with child outcomes. CONCLUSIONS: Associations between child outcomes and maternal but not paternal asthma symptoms during the prenatal period suggests a role for MIA. These findings need to be validated in larger samples, and further research may identify behavioural and cognitive profiles of children with exposure to MIA.

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity.

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

Perinatal Trajectories of Maternal Depressive Symptoms in Prospective, Community-Based Cohorts Across 3 Continents.

Importance: Depressive symptoms during pregnancy influence the development and health of the offspring, underscoring the need for timely intervention. However, the course of depressive symptoms across the perinatal period remains unclear, thus complicating screening and referral guidelines. Objective: To examine the course and stability of depressive symptoms across the perinatal period in multiple, ethnically diverse independent observational cohorts. Design, Setting, and Participants: This cohort study included self-reported depressive symptoms at multiple time points from 7 prospective cohorts spanning 3 continents (United Kingdom: Avon Longitudinal Study of Parents and Children from 1991 to 1995; Canada: Maternal Adversity, Vulnerability and Neurodevelopment from 2003 to 2007; Montreal Antenatal Well-being Study from 2019 to 2022; Alberta Pregnancy Outcomes and Nutrition from 2009 to 2014; and Singapore: Growing Up in Singapore Toward Healthy Outcomes from 2009 to 2013; Singapore Preconception Study of Long-Term Maternal and Child Outcomes from 2015 to 2019; and Mapping Antenatal Maternal Stress from 2019 to 2022). Participants were recruited either during preconception or pregnancy and observed into the postnatal period. All data from each cohort were analyzed from July 2022 to April 2023. Main Outcomes and Measures: Self-reported depressive symptoms from pregnancy to 2 years following childbirth using either the Edinburgh Postnatal Depression Scale or the Center for Epidemiological Studies Depression were analyzed independently within each cohort using item response theory (IRT) techniques. K-means clustering was used to identify groups of participants with similar trajectories. Results: A total of 11 563 pregnant women (mean [SD] age, 29 [5] years; 569 [4.9%] East Asian women; 304 [2.6%] Southeast Asian women; 10 133 [87.6%] White women) self-reported depressive symptoms from pregnancy to 2 years following childbirth. Analytic methods from Item Response Theory identified 3 groups of mothers based on depressive symptoms: low, mild, and high levels in each of the 7 cohorts. Mothers within and across all cohorts had stable trajectories of maternal depressive symptoms from pregnancy onwards. Mothers with clinical levels of depressive symptoms likewise showed stable trajectories from pregnancy into the postnatal period. Conclusions and Relevance: In this study, trajectories of depressive symptoms remained stable from pregnancy across the perinatal period, a finding that conflicts with a continuing emphasis on postpartum or postnatal onset of depression that persists in some health policy guidelines. Interventions and public health initiatives should focus on reducing depressive symptoms during pregnancy in addition to following birth.

Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a heterogeneous haematologic malignancy involving the abnormal proliferation of immature lymphocytes and accounts for most paediatric cancer cases. The management of ALL in children has seen great improvement in the last decades thanks to greater understanding of the disease leading to improved treatment strategies evidenced through clinical trials. Common therapy regimens involve a first course of chemotherapy (induction phase), followed by treatment with a combination of anti-leukemia drugs. A measure of the efficacy early in the course of therapy is the presence of minimal residual disease (MRD). MRD quantifies residual tumor cells and indicates the effiectiveness of the treatment over the course of therapy. MRD positivity is defined for values of MRD greater than 0.01%, yielding left-censored MRD observations. We propose a Bayesian model to study the relationship between patient features (leukemia subtype, baseline characteristics, and drug sensitivity profile) and MRD observed at two time points during the induction phase. Specifically, we model the observed MRD values via an auto-regressive model, accounting for left-censoring of the data and for the fact that some patients are already in remission after the first stage of induction therapy. Patient characteristics are included in the model via linear regression terms. In particular, patient-specific drug sensitivity based on ex vivo assays of patient samples is exploited to identify groups of subjects with similar profiles. We include this information as a covariate in the model for MRD. We adopt horseshoe priors for the regression coefficients to perform variable selection to identify important covariates. We fit the proposed approach to data from three prospective paediatric ALL clinical trials carried out at the St. Jude Children's Research Hospital. Our results highlight that drug sensitivity profiles and leukemic subtypes play an important role in the response to induction therapy as measured by serial MRD measures.

Bayesian clustering of multiple zero-inflated outcomes.

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Ex vivo drug sensitivity screening in multiple myeloma identifies drug combinations that act synergistically.

The management of multiple myeloma (MM) is challenging: An assortment of available drug combinations adds complexity to treatment selection, and treatment resistance frequently develops. Given the heterogeneous nature of MM, personalized testing tools are required to identify drug sensitivities. To identify drug sensitivities in MM cells, we established a drug testing pipeline to examine ex vivo drug responses. MM cells from 44 patients were screened against 30 clinically relevant single agents and 44 double- and triple-drug combinations. We observed variability in responses across samples. The presence of gain(1q21) was associated with low sensitivity to venetoclax, and decreased ex vivo responses to dexamethasone reflected the drug resistance observed in patients. Less heterogeneity and higher efficacy was detected with many combinations compared to the corresponding single agents. We identified new synergistic effects of melflufen plus panobinostat using low concentrations (0.1-10 nm and 8 nm, respectively). In agreement with clinical studies, clinically approved combinations, such as triple combination of selinexor plus bortezomib plus dexamethasone, acted synergistically, and synergies required low drug concentrations (0.1 nm bortezomib, 10 nm selinexor and 4 nm dexamethasone). In summary, our drug screening provided results within a clinically actionable 5-day time frame and identified synergistic drug efficacies in patient-derived MM cells that may aid future therapy choices.

A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.

Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.

The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose-response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

An in vitro assay for biomarker discovery and dose prediction applied to ibrutinib plus venetoclax treatment of CLL.

Recently, several small molecule drugs were approved for treatment of chronic lymphocytic leukemia (CLL), significantly improving patient management. However, knowledge about how to combine these therapies for optimal effects and what patients will best benefit from them is lacking. Here, we show that drug synergies can be identified by single cell signaling analyses. We investigated the effects of idelalisib, ibrutinib, and venetoclax on 35 protein epitopes by phospho flow in CLL cells. The activity of proteins in the B-cell receptor signalosome and the phosphatidylinositol 3-kinase pathway were altered upon drug exposure. Combined treatment with ibrutinib and venetoclax give promising results in clinical studies and we show that this combination exerted synergistic inhibitory effects on cell signaling and cell viability. Cell viability was monitored by flow cytometry and with independent drug sensitivity screens. Our analyses indicate that the standard dosages of ibrutinib and venetoclax can be lowered without loss of efficacy, potentially reducing drug costs, and toxicities. Observed correlation between signaling and viability indicates that signaling molecules could serve as biomarkers to predict response to therapy. We suggest that phospho flow should be considered as a novel approach for dose and synergy prediction in a precision medicine setting for CLL.

Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.

Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate t-distribution, obtained by dividing each component of the data vector by a gamma random variable, is a straightforward generalization to accommodate deviations from normality such as heavy tails. Since different groups of variables may be contaminated to a different extent, Finegold and Drton (2014) introduced the Dirichlet t-distribution, where the divisors are clustered using a Dirichlet process. In this work, we consider a more general class of nonparametric distributions as the prior on the divisor terms, namely the class of normalized completely random measures (NormCRMs). To improve the effectiveness of the clustering, we propose modeling the dependence among the divisors through a nonparametric hierarchical structure, which allows for the sharing of parameters across the samples in the data set. This desirable feature enables us to cluster together different components of multivariate data in a parsimonious way. We demonstrate through simulations that this approach provides accurate graphical model inference, and apply it to a case study examining the dependence structure in radiomics data derived from The Cancer Imaging Atlas.

Single cell profiling of phospho-protein levels in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) has a high incidence and a steeply growing prevalence in the Western world. The heterogeneity of the disease necessitates individual mapping of biology and predicted drug response in each patient as basis for administration of tailored treatments. Cell signaling aberrations may serve as biological indicators for suitable therapy. By applying phospho-specific flow cytometry, we mapped basal and induced phosphorylation levels of 20 phospho-epitopes on proteins relevant to B-cell signaling in B cells from 22 CLL patients and 25 normal controls. The signaling response of the cytostatic drugs fludarabine, doxorubicin and vincristine was also investigated. CLL cells exerted similar or lower basal phosphorylation levels compared to normal B cells, with the exception of STAT3 (pY705) which was increased. Interestingly, STAT3 inhibitors normalized the STAT3 (pY705) level and reduced cell viability. Vincristine treatment significantly modulated phosphorylation levels in CLL cells, while no effect was observed in controls or after fludarabine or doxorubicin treatment. After BCR stimulation, CLL cells showed a tendency towards impaired phosphorylation levels, significant for several of the analyzed proteins. However, the level of Akt (pS473) was more potently induced in IgHV unmutated CLL (UM-CLL) patient samples and was significantly higher than in M-CLL samples. Importantly, the PI3Kδ inhibitor idelalisib potently reversed the effect of anti-IgM on Akt (pS473). Thus, signaling aberrations could be identified by phosphoflow cytometry and aberrant signaling could be normalized by small molecule drugs. This approach can identify relevant drug targets as well as drug effects in the individual patient.

Nut-cracking behaviour in wild-born, rehabilitated bonobos (Pan paniscus): a comprehensive study of hand-preference, hand grips and efficiency.

There has been an enduring interest in primate tool-use and manipulative abilities, most often with the goal of providing insight into the evolution of human manual dexterity, right-hand preference, and what behaviours make humans unique. Chimpanzees (Pan troglodytes) are arguably the most well-studied tool-users amongst non-human primates, and are particularly well-known for their complex nut-cracking behaviour, which has been documented in several West African populations. However, their sister-taxon, the bonobos (Pan paniscus), rarely engage in even simple tool-use and are not known to nut-crack in the wild. Only a few studies have reported tool-use in captive bonobos, including their ability to crack nuts, but details of this complex tool-use behaviour have not been documented before. Here, we fill this gap with the first comprehensive analysis of bonobo nut-cracking in a natural environment at the Lola ya Bonobo sanctuary, Democratic Republic of the Congo. Eighteen bonobos were studied as they cracked oil palm nuts using stone hammers. Individual bonobos showed exclusive laterality for using the hammerstone and there was a significant group-level right-hand bias. The study revealed 15 hand grips for holding differently sized and weighted hammerstones, 10 of which had not been previously described in the literature. Our findings also demonstrated that bonobos select the most effective hammerstones when nut-cracking. Bonobos are efficient nut-crackers and not that different from the renowned nut-cracking chimpanzees of Bossou, Guinea, which also crack oil palm nuts using stones.