Dynamics of Gene Expression Profiling and Identification of High-Risk Patients for Severe COVID-19

The clinical manifestations of SARS-CoV-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of hospitalized adult COVID-19 patients and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 h of admission and on day 7. There were 2572 differently expressed genes in patients with ARDS at baseline and 1149 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to an immune regulation loss. This led, in turn, to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in the latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control

Reproducible data integration and visualization of biological networks in R

Motivation Collaborative workflows in network biology not only require the documentation of the performed analysis steps but also of the network data on which the decisions were based. However, replication of the entire workflow or tracking of the intermediate networks used for a particular visualization remains an intricate task. Also, the amount and heterogeneity of the integrated data requires instruments to explore and thus comprehend the results. Results Here we demonstrate a collection of software tools and libraries for network data integration, exploration, and visualization to document the different stages of the workflow. The integrative steps are performed in R, and the entire process is accompanied by an interchangeable toolset for data exploration and network visualization. Availability The source code of the performed workflow is available as R markdown scripts at https://github.com/frankkramer-lab/reproducible-network-visualization. A compiled HTML version is also hosted on Github pages at https://frankkramer-lab.github.io/reproducible-network-visualization

Diversity of marine microeukaryotes in the global deep ocean

Aquatic Sciences Meeting, Aquatic Sciences: Global And Regional Perspectives - North Meets South, 22-27 February 2015, Granada, SpainThe aim of this work was to study the diversity of bathypelagic microeukaryotes. Seawater samples were taken at around 4000 m depth in 27 stations distributed in the main oceans. Pyrosequencing was used to obtain about half a million tags from the 18S rDNA V4 region, then clustered into 2482 OTUs at 97% similarity. The relative pyrotag abundance of the 20 most abundant OTUs matched well with the results of a parallel metagenomic analysis of 18S rDNA genes, suggesting little PCR bias in the tag-approach. There was a weak trend of genetic similarity among geographically close stations and among samples from the same water-mass, but we did not find a typical global community. Instead, there were four phylogenetic groups (Collodaria, Chrysophytes, Basidiomycota and MALV-II) mixed in different proportions. The amount of phylogenetic novelty was concentrated in three hotspots accounting for 6% of pyrotags globally. Rarefaction curves suggested that there were OTUs still waiting to be discovered. Our study is an essential step for a more detailed investigation of the deep ocean microbiota and suggests idiosyncratic microeukaryotic assemblages in distinct regionsPeer Reviewe

Identifying actionable variants in cancer - The dual web and batch processing tool MTB-report

Next-generation sequencing methods continuously provide clinicians and researchers in precision oncology with growing numbers of genomic variants found in cancer. However, manually interpreting the list of variants to identify reliable targets is an inefficient and cumbersome process that does not scale with the increasing number of cases. Support by computer systems is needed for the analysis of large scale experiments and clinical studies to identify new targets and therapies, and user-friendly applications are needed in molecular tumor boards to support clinicians in their decision-making processes. The MTB-Report tool annotates, filters and sorts genetic variants with information from public databases, providing evidence on actionable variants in both scenarios. A web interface supports medical doctors in the tumor board, and a command line mode allows batch processing of large datasets. The MTB-Report tool is available as an R implementation as well as a Docker image to provide a tool that runs out-of-the-box. Moreover, containerization ensures a stable application that delivers reproducible results over time. A public version of the web interface is available at: http://mtb.bioinf.med.uni-goettingen.de/mtb-report

Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Background: Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions. Methods: Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient. Results: We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. Conclusions: The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board

Genomic predictors of good outcome, recurrence, or progression in high-grade T1 non-muscle-invasive bladder cancer

High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progression (PD) in a cohort of HGT1 patients. Exome sequencing was performed on 62 HGT1 and 15 matched normal tissue samples. Both tumor only (TO) and paired analyses were performed, focusing on 95 genes known to be mutated in bladder cancer. Somatic mutations, copy-number alterations, mutation load, and mutation signatures were studied. Thirty-three GO, 10 R, 18 PD, and 1 unknown outcome patients were analyzed. Tumor mutational burden (TMB) was similar to muscle-invasive disease and was highest in GO, intermediate in PD, and lowest in R patients (P = 0.017). DNA damage response gene mutations were associated with higher TMB (P < 0.0001) and GO (P = 0.003). ERCC2 and BRCA2 mutations were associated with GO. TP53, ATM, ARID1A, AHR, and SMARCB1 mutations were more frequent in PD. Focal copy-number gain in CCNE1 and CDKN2A deletion was enriched in PD or R (P = 0.047; P = 0.06). APOBEC (46%) and COSMIC5 (34%) signatures were most frequent. APOBEC-A and ERCC2 mutant tumors (COSMIC5) were associated with GO (P = 0.047; P = 0.0002). pT1b microstaging was associated with a genomic cluster (P = 0.05) with focal amplifications of E2F3/SOX4, PVRL4, CCNE1, and TP53 mutations. Findings were validated using external public datasets. These findings require confirmation but suggest that management of HGT1 bladder cancer may be improved via molecular characterization to predict outcome. SIGNIFICANCE: Detailed genetic analyses of HGT1 bladder tumors identify features that correlate with outcome, e.g., high mutational burden, ERCC2 mutations, and high APOBEC-A/ERCC2 mutation signatures were associated with good outcome

Non-muscle-invasive micropapillary bladder cancer has a distinct lncRNA profile associated with unfavorable prognosis

Background: molecular subtyping of bladder cancer has revealed luminal tumors generally have a more favourable prognosis. However, some aggressive forms of variant histology, including micropapillary, are often classified luminal. In previous work, we found long non-coding RNA (lncRNA) expression profiles could identify a subgroup of luminal bladder tumors with less aggressive biology and better outcomes. Objective: in the present study, we aimed to investigate whether lncRNA expression profiles could identify high-grade T1 micropapillary bladder cancer with differential outcome. Design, setting, and participants: LncRNAs were quantified from RNA-seq data from a HGT1 bladder cancer cohort that was enriched for primary micropapillary cases (15/84). Unsupervised consensus clustering of variant lncRNAs identified a three-cluster solution, which was further characterised using a panel of micropapillary-associated biomarkers, molecular subtypes, gene signatures, and survival analysis. A single-sample genomic signature was trained using lasso-penalized logistic regression to classify micropapillary-like gene-expression, as characterised by lncRNA clustering. The genomic classifier (GC) was tested on luminal tumors derived from the TCGA cohort (N = 202). Outcome measurements and statistical analysis: patient and tumor characteristics were compared between subgroups by using X2 tests and two-sided Wilcoxon rank-sum tests. Primary endpoints were overall, progression-free and high-grade recurrence-free survival, calculated as the date of high-grade T1 disease at TURBT till date of death from any cause, progression, or recurrence, respectively. Survival rates were estimated using weighted Kaplan-Meier (KM) curves. Results and limitations: primary micropapillary HGT1 showed decreased FGFR3, SHH, and p53 pathway activity relative to tumors with conventional urothelial carcinoma. Many bladder cancer-associated lncRNAs were downregulated in micropapillary tumors, including UCA1, LINC00152, and MALAT1. Unsupervised consensus clustering resulted in a lncRNA cluster 1 (LC1) with worse prognosis that was enriched for primary micropapillary histology and the Luminal Unstable (LumU) molecular subtype. Interestingly, LC1 appeared to better identify aggressive HGT1 disease, compared to stratifying outcomes using primary histologic characteristics. A signature trained to identify LC1 cases showed good performance in the testing cohort, identifying seven cases with significantly worse survival (p < 0.001). Limitations include the retrospective nature of the study and the lack of a validation cohort. Conclusions: using the lncRNA transcriptome we identified a subgroup of aggressive HGT1 bladder cancer that was enriched with micropapillary histology. These data suggest that lncRNAs can facilitate the identification of aggressive micropapillary-like tumors, potentially improving patient management

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance

The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

Abstract Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models