MesoGraph: Automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score.

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS.

A Pandemic Lesson for Global Lung Diseases: Exacerbations Are Preventable

A dramatic global reduction in the incidence of common seasonal respiratory viral infections has resulted from measures to limit the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the pandemic. This has been accompanied by falls reaching 50% internationally in the incidence of acute exacerbations of preexisting chronic respiratory diseases that include asthma, chronic obstructive pulmonary disease, and cystic fibrosis. At the same time, the incidence of acute bacterial pneumonia and sepsis has fallen steeply worldwide. Such findings demonstrate the profound impact of common respiratory viruses on the course of these global illnesses. Reduced transmission of common respiratory bacterial pathogens and their interactions with viruses appear also as central factors. This review summarizes pandemic changes in exacerbation rates of asthma, chronic obstructive pulmonary disease, cystic fibrosis, and pneumonia. We draw attention to the substantial body of knowledge about respiratory virus infections in these conditions, and that it has not yet translated into clinical practice. Now that the large scale of benefits that could be gained by managing these pathogens is unmistakable, we suggest that the field merits substantial academic and industrial investment. We consider how pandemic-inspired measures for prevention and treatment of common infections should become a cornerstone for managing respiratory diseases.

Influence of Aerosol Delivered BCG Vaccination on Immunological and Disease Parameters Following SARS-CoV-2 Challenge in Rhesus Macaques

The tuberculosis vaccine, Bacille Calmette-Guerin (BCG), also affords protection against non-tuberculous diseases attributable to heterologous immune mechanisms such as trained innate immunity, activation of non-conventional T-cells, and cross-reactive adaptive immunity. Aerosol vaccine delivery can target immune responses toward the primary site of infection for a respiratory pathogen. Therefore, we hypothesised that aerosol delivery of BCG would enhance cross-protective action against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and be a deployable intervention against coronavirus disease 2019 (COVID-19). Immune parameters were monitored in vaccinated and unvaccinated rhesus macaques for 28 days following aerosol BCG vaccination. High-dose SARS-CoV-2 challenge was applied by intranasal and intrabronchial instillation and animals culled 6–8 days later for assessment of viral, disease, and immunological parameters. Mycobacteria-specific cell-mediated immune responses were detected following aerosol BCG vaccination, but SARS-CoV-2-specific cellular- and antibody-mediated immunity was only measured following challenge. Early secretion of cytokine and chemokine markers associated with the innate cellular and adaptive antiviral immune response was detected following SARS-CoV-2 challenge in vaccinated animals, at concentrations that exceeded titres measured in unvaccinated macaques. Classical CD14+ monocytes and Vδ2 γδ T-cells quantified by whole-blood immunophenotyping increased rapidly in vaccinated animals following SARS-CoV-2 challenge, indicating a priming of innate immune cells and non-conventional T-cell populations. However, viral RNA quantified in nasal and pharyngeal swabs, bronchoalveolar lavage (BAL), and tissue samples collected at necropsy was equivalent in vaccinated and unvaccinated animals, and in-life CT imaging and histopathology scoring applied to pulmonary tissue sections indicated that the disease induced by SARS-CoV-2 challenge was comparable between vaccinated and unvaccinated groups. Hence, aerosol BCG vaccination did not induce, or enhance the induction of, SARS-CoV-2 cross-reactive adaptive cellular or humoral immunity, although an influence of BCG vaccination on the subsequent immune response to SARS-CoV-2 challenge was apparent in immune signatures indicative of trained innate immune mechanisms and primed unconventional T-cell populations. Nevertheless, aerosol BCG vaccination did not enhance the initial clearance of virus, nor reduce the occurrence of early disease pathology after high dose SARS-CoV-2 challenge. However, the heterologous immune mechanisms primed by BCG vaccination could contribute to the moderation of COVID-19 disease severity in more susceptible species following natural infection.

Comparison of the airway microbiota in children with chronic suppurative lung disease

RATIONALE

The airway microbiota is important in chronic suppurative lung diseases, such as primary ciliary dyskinesia (PCD) and cystic fibrosis (CF). This comparison has not previously been described but is important because difference between the two diseases may relate to the differing prognoses and lead to pathological insights and potentially, new treatments.

OBJECTIVES

To compare the longitudinal development of the airway microbiota in children with PCD to that of CF and relate this to age and clinical status.

METHODS

Sixty-two age-matched children (age range 0.5-17 years) with PCD or CF (n=31 in each group) were recruited prospectively and followed for 1.1 years. Throat swabs or sputum as well as clinical information were collected at routine clinical appointments. 16S rRNA gene sequencing was performed.

MEASUREMENTS AND MAIN RESULTS

The microbiota was highly individual and more diverse in PCD and differed in community composition when compared with CF. While Streptococcus was the most abundant genus in both conditions, Pseudomonas was more abundant in CF with Haemophilus more abundant in PCD (P_adj=0.0005). In PCD only, an inverse relationship was seen in the relative abundance of Streptococcus and Haemophilus with age.

CONCLUSIONS

Bacterial community composition differs between children with PCD and those with CF. Pseudomonas is more prevalent in CF and Haemophilus in PCD, at least until infection with Pseudomonas supervenes. Interactions between organisms, particularly members of Haemophilus, Streptococcus and Pseudomonas genera appear important. Study of the interactions between these organisms may lead to new therapies or risk stratification.

Addressing unmet needs in understanding asthma mechanisms: From the European Asthma Research and Innovation Partnership (EARIP) Work Package (WP)2 collaborators

Asthma is a heterogeneous, complex disease with clinical phenotypes that incorporate persistent symptoms and acute exacerbations. It affects many millions of Europeans throughout their education and working lives and puts a heavy cost on European productivity. There is a wide spectrum of disease severity and control. Therapeutic advances have been slow despite greater understanding of basic mechanisms and the lack of satisfactory preventative and disease modifying management for asthma constitutes a significant unmet clinical need. Preventing, treating and ultimately curing asthma requires co-ordinated research and innovation across Europe. The European Asthma Research and Innovation Partnership (EARIP) is an FP7-funded programme which has taken a co-ordinated and integrated approach to analysing the future of asthma research and development. This report aims to identify the mechanistic areas in which investment is required to bring about significant improvements in asthma outcomes.

Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma

The number of genetic factors associated with asthma remains limited. To identify new genes with an undetected individual effect but collectively influencing asthma risk, we conducted a network-assisted analysis that integrates outcomes of genome-wide association studies (GWAS) and protein-protein interaction networks. We used two GWAS datasets, each consisting of the results of a meta-analysis of nine childhood-onset asthma GWASs (5,924 and 6,043 subjects, respectively). We developed a novel method to compute gene-level P-values (fastCGP), and proposed a parallel dense-module search and cross-selection strategy to identify an asthma-associated gene module. We identified a module of 91 genes with a significant joint effect on childhood-onset asthma (P < 10⁻⁵). This module contained a core subnetwork including genes at known asthma loci and five peripheral subnetworks including relevant candidates. Notably, the core genes were connected to APP (encoding amyloid beta precursor protein), a major player in Alzheimer’s disease that is known to have immune and inflammatory components. Functional analysis of the module genes revealed four gene clusters involved in innate and adaptive immunity, chemotaxis, cell-adhesion and transcription regulation, which are biologically meaningful processes that may underlie asthma risk. Our findings provide important clues for future research into asthma aetiology.

The nuclear factor I/A (NFIA) gene is associated with the asthma plus rhinitis phenotype

BACKGROUND

A previous genome-wide linkage scan in 295 families of the French Epidemiological Study on the Genetics and Environment of Asthma (EGEA) showed strong evidence of linkage of the 1p31 region to the combined asthma plus allergic rhinitis (AR) phenotype.

OBJECTIVE

Our purpose was to conduct fine-scale mapping of the 1p31 linkage region to identify the genetic variants associated with asthma plus AR.

METHODS

Association analyses with the asthma plus rhinitis phenotype were first conducted in the EGEA family sample using the family-based association method (FBAT) and logistic regression. The test of homogeneity of association between asthma plus AR versus asthma alone or AR alone was also applied. Replication of EGEA findings was sought in French-Canadian and United Kingdom family samples.

RESULTS

We found a significant association between asthma plus rhinitis and a 1p31 genetic variant (P = 2 × 10(-5) for rs12122228, which reached the multiple testing-corrected threshold) in EGEA using FBAT. There was evidence of heterogeneity of association between asthma plus AR versus asthma alone or AR alone (P = .03). A Meta-analysis of FBAT results from EGEA and French-Canadian families improved evidence for both association and heterogeneity (P = 5 × 10(-6) and P = .008, respectively), whereas a meta-analysis of EGEA, French-Canadian, and United Kingdom samples based on logistic regression slightly increased the evidence for heterogeneity.

CONCLUSION

The single nucleotide polymorphism specifically associated to asthma plus rhinitis is located in the flanking 5' untranslated region of the nuclear factor I/A (NFIA) gene, a strong candidate gene for asthma and AR.

Analgesia and central side-effects: two separate dimensions of morphine response

AIMS

To present a statistical model for defining interindividual variation in response to morphine and to use this model in a preliminary hypothesis-generating multivariate genetic association study.

METHODS

Two hundred and sixty-four cancer patients taking oral morphine were included in a prospective observational study. Pain and morphine side-effect scores were examined using principal components analysis. The resulting principal components were used in an exploratory genetic association study of single nucleotide polymorphisms across the genes coding for the three opioid receptors, OPRM1, OPRK1 and OPRD1. Associations in multivariate models, including potential clinical confounders, were explored.

RESULTS

Two principal components corresponding to residual pain and central side-effects were identified. These components accounted for 42 and 18% of the variability in morphine response, respectively, were independent of each other and only mildly correlated. The genetic and clinical factors associated with these components were markedly different. Multivariate regression modelling, including clinical and genetic factors, accounted for only 12% of variability in residual pain on morphine and 3% of variability in central side-effects.

CONCLUSIONS

Although replication is required, this data-driven analysis suggests that pain and central side-effects on morphine may be two separate dimensions of morphine response. Larger study samples are necessary to investigate potential genetic and clinical associations comprehensively.