Summary The analysis of patient blood transcriptional profiles offers a means to investigate immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically-relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease datasets. Mapping changes in gene expression at the module-level generated disease-specific transcriptional fingerprints which provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.

Summary Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by breakdown of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA) and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis, and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, which were supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases.

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Individual elements that constitute the immune system have been characterized over the past decades, largely through reductionist approaches. More recently the introduction of large-scale profiling platforms has enabled the assessment of these elements on a global scale. However, the analysis and interpretation of such large-scale data remains a challenge and a barrier for the wider adoption of systems approaches in immunological and clinical studies. Here, we describe an analytic strategy relying on the a priori determination of co-dependent gene sets for a given biological system. Such modular transcriptional repertoires can in turn be used to simplify the analysis and interpretation of large-scale datasets and to design targeted immune fingerprinting assays and web applications that will further facilitate the dissemination of systems approaches in immunology.

Staphylococcus aureus infections are associated with diverse clinical manifestations leading to significant morbidity and mortality. To define the role of the host response in the clinical manifestations of the disease, we characterized whole blood transcriptional profiles of children hospitalized with community-acquired S. aureus infection and phenotyped the bacterial strains isolated. The overall transcriptional response to S. aureus infection was characterized by over-expression of innate immunity and hematopoiesis related genes and under-expression of genes related to adaptive immunity. We assessed individual profiles using modular fingerprints combined with the molecular distance to health (MDTH), a numerical score of transcriptional perturbation as compared to healthy controls. We observed significant heterogeneity in the host signatures and MDTH, as they were influenced by the type of clinical presentation, the extent of bacterial dissemination, and time of blood sampling in the course of the infection, but not by the bacterial isolate. System analysis approaches provide a new understanding of disease pathogenesis and the relation/interaction between host response and clinical disease manifestations.

Objective. To examine the strength of the association between different measures of health-related quality of life (HRQOL), disability, pain, and well-being in children with chronic arthritis. To evaluate whether HRQOL scores vary as a function of disability status beyond chance. To assess the quality of the parent proxy report for HRQOL as compared with disability, pain, and well-being. There was fair to good agreement and moderate consistency of the HRQOL ratings (SG: fair consistency) between patients and parents with marked differences between health domains. Conclusion. HRQOL measured by the PedsQL, JAQQ, and VAS are moderately to highly correlated with each other in children with chronic arthritis. The children's HRQOL significantly decreases with increasing disability. Despite more pronounced differences for some health domains, parents are moderate to good proxy reporters of HRQOL, disability, and well-being of children with chronic arthritis.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.