Immune checkpoint inhibitors increase T cell immunity during SARS-CoV-2 infection.

The COVID-19 pandemic has spread worldwide, yet the role of antiviral T cell immunity during infection and the contribution of immune checkpoints remain unclear. By prospectively following a cohort of 292 patients with melanoma, half of which treated with immune checkpoint inhibitors (ICIs), we identified 15 patients with acute or convalescent COVID-19 and investigated their transcriptomic, proteomic, and cellular profiles. We found that ICI treatment was not associated with severe COVID-19 and did not alter the induction of inflammatory and type I interferon responses. In-depth phenotyping demonstrated expansion of CD8 effector memory T cells, enhanced T cell activation, and impaired plasmablast induction in ICI-treated COVID-19 patients. The evaluation of specific adaptive immunity in convalescent patients showed higher spike (S), nucleoprotein (N), and membrane (M) antigen-specific T cell responses and similar induction of spike-specific antibody responses. Our findings provide evidence that ICI during COVID-19 enhanced T cell immunity without exacerbating inflammation.

Polygenic risk for skin autoimmunity impacts immune checkpoint blockade in bladder cancer.

PD-1 and PD-L1 act to restrict T cell responses in cancer and contribute to self-tolerance. Consistent with this role, PD-1 checkpoint inhibitors have been associated with immune-related adverse events (irAEs), immune toxicities thought to be autoimmune in origin. Analyses of dermatological irAEs have identified an association with improved overall survival (OS) following anti-PD-(L)1 therapy, but the factors that contribute to this relationship are poorly understood. We collected germline whole-genome sequencing data from IMvigor211, a recent phase 3 randomized controlled trial comparing atezolizumab (anti-PD-L1) monotherapy to chemotherapy in bladder cancer. We found that high vitiligo, high psoriasis, and low atopic dermatitis polygenic risk scores (PRSs) were associated with longer OS under anti-PD-L1 monotherapy as compared to chemotherapy, reflecting the Th17 polarization of these diseases. PRSs were not correlated with tumor mutation burden, PD-L1 immunohistochemistry, nor T-effector gene signatures. Shared genetic factors impact risk for dermatological autoimmunity and anti-PD-L1 monotherapy in bladder cancer.

Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.

Human thymopoiesis is influenced by a common genetic variant within the TCRA-TCRD locus.

The thymus is the primary lymphoid organ where naïve T cells are generated; however, with the exception of age, the parameters that govern its function in healthy humans remain unknown. We characterized the variability of thymic function among 1000 age- and sex-stratified healthy adults of the Milieu Intérieur cohort, using quantification of T cell receptor excision circles (TRECs) in peripheral blood T cells as a surrogate marker of thymopoiesis. Age and sex were the only nonheritable factors identified that affect thymic function. TREC amounts decreased with age and were higher in women compared to men. In addition, a genome-wide association study revealed a common variant (rs2204985) within the T cell receptor TCRA-TCRD locus, between the DD2 and DD3 gene segments, which associated with TREC amounts. Strikingly, transplantation of human hematopoietic stem cells with the rs2204985 GG genotype into immunodeficient mice led to thymopoiesis with higher TRECs, increased thymocyte counts, and a higher TCR repertoire diversity. Our population immunology approach revealed a genetic locus that influences thymopoiesis in healthy adults, with potentially broad implications in precision medicine.

Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors.

The quantification and characterization of circulating immune cells provide key indicators of human health and disease. To identify the relative effects of environmental and genetic factors on variation in the parameters of innate and adaptive immune cells in homeostatic conditions, we combined standardized flow cytometry of blood leukocytes and genome-wide DNA genotyping of 1,000 healthy, unrelated people of Western European ancestry. We found that smoking, together with age, sex and latent infection with cytomegalovirus, were the main non-genetic factors that affected variation in parameters of human immune cells. Genome-wide association studies of 166 immunophenotypes identified 15 loci that showed enrichment for disease-associated variants. Finally, we demonstrated that the parameters of innate cells were more strongly controlled by genetic variation than were those of adaptive cells, which were driven by mainly environmental exposure. Our data establish a resource that will generate new hypotheses in immunology and highlight the role of innate immunity in susceptibility to common autoimmune diseases.

Deconvolution of the Response to Bacillus Calmette-Guérin Reveals NF-κB-Induced Cytokines As Autocrine Mediators of Innate Immunity.

Bacillus Calmette-Guérin (BCG) is used as a vaccine and diagnostic test for tuberculosis, as well as immunotherapy in the treatment of bladder cancer. While clinically useful, the response to mycobacterial stimulation is complex and the induced protein signature remains poorly defined. We characterized the cell types directly engaged by BCG, as well as the induced cytokine loops that transmit signal(s) to bystander cells. Standardized whole-blood stimulations and mechanistic studies on single and purified cell populations identified distinct patterns of activation in monocytes as compared to neutrophils and invariant lymphocyte populations. Deconvoluting the role of Toll-like receptor 2/4 and Dectin-1/2 in the inflammatory response to BCG, we revealed Dectin-1/2 as dominant in neutrophils as compared to monocytes, which equally engaged both pathways. Furthermore, we quantified the role of NF-κB and NADPH/reactive oxygen species (ROS)-dependent cytokines, which triggered a JAK1/2-dependent amplification loop and accounted for 40-50% of the induced response to BCG. In sum, this study provides new insight into the molecular and cellular pathways involved in the response to BCG, establishing the basis for a new generation of immunodiagnostic tools.

Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia.

Fusion genes are potent driver mutations in cancer. In this study, we delineate the fusion gene landscape in a consecutive series of 195 paediatric B-cell precursor acute lymphoblastic leukaemia (BCP ALL). Using RNA sequencing, we find in-frame fusion genes in 127 (65%) cases, including 27 novel fusions. We describe a subtype characterized by recurrent IGH-DUX4 or ERG-DUX4 fusions, representing 4% of cases, leading to overexpression of DUX4 and frequently co-occurring with intragenic ERG deletions. Furthermore, we identify a subtype characterized by an ETV6-RUNX1-like gene-expression profile and coexisting ETV6 and IKZF1 alterations. Thus, this study provides a detailed overview of fusion genes in paediatric BCP ALL and adds new pathogenetic insights, which may improve risk stratification and provide therapeutic options for this disease.

A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.

Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.

The projection score--an evaluation criterion for variable subset selection in PCA visualization.

BACKGROUND: In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization. RESULTS: We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA. CONCLUSIONS: We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.

The correlation pattern of acquired copy number changes in 164 ETV6/RUNX1-positive childhood acute lymphoblastic leukemias.

The ETV6/RUNX1 fusion gene, present in 25% of B-lineage childhood acute lymphoblastic leukemia (ALL), is thought to represent an initiating event, which requires additional genetic changes for leukemia development. To identify additional genetic alterations, 24 ETV6/RUNX1-positive ALLs were analyzed using 500K single nucleotide polymorphism arrays. The results were combined with previously published data sets, allowing us to ascertain genomic copy number aberrations (CNAs) in 164 cases. In total, 45 recurrent CNAs were identified with an average number of 3.5 recurrent changes per case (range 0-13). Twenty-six percent of cases displayed a set of recurrent CNAs identical to that of other cases in the data set. The majority (74%), however, displayed a unique pattern of recurrent CNAs, indicating a large heterogeneity within this ALL subtype. As previously demonstrated, alterations targeting genes involved in B-cell development were common (present in 28% of cases). However, the combined analysis also identified alterations affecting nuclear hormone response (24%) to be a characteristic feature of ETV6/RUNX1-positive ALL. Studying the correlation pattern of the CNAs allowed us to highlight significant positive and negative correlations between specific aberrations. Furthermore, oncogenetic tree models identified ETV6, CDKN2A/B, PAX5, del(6q) and +16 as possible early events in the leukemogenic process.

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis.

BACKGROUND: With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. RESULTS: Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. CONCLUSIONS: We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.

Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations.

Global expression profiles of a consecutive series of 121 childhood acute leukemias (87 B lineage acute lymphoblastic leukemias, 11 T cell acute lymphoblastic leukemias, and 23 acute myeloid leukemias), six normal bone marrows, and 10 normal hematopoietic subpopulations of different lineages and maturations were ascertained by using 27K cDNA microarrays. Unsupervised analyses revealed segregation according to lineages and primary genetic changes, i.e., TCF3(E2A)/PBX1, IGH@/MYC, ETV6(TEL)/RUNX1(AML1), 11q23/MLL, and hyperdiploidy (>50 chromosomes). Supervised discriminatory analyses were used to identify differentially expressed genes correlating with lineage and primary genetic change. The gene-expression profiles of normal hematopoietic cells were also studied. By using principal component analyses (PCA), a differentiation axis was exposed, reflecting lineages and maturation stages of normal hematopoietic cells. By applying the three principal components obtained from PCA of the normal cells on the leukemic samples, similarities between malignant and normal cell lineages and maturations were investigated. Apart from showing that leukemias segregate according to lineage and genetic subtype, we provide an extensive study of the genes correlating with primary genetic changes. We also investigated the expression pattern of these genes in normal hematopoietic cells of different lineages and maturations, identifying genes preferentially expressed by the leukemic cells, suggesting an ectopic activation of a large number of genes, likely to reflect regulatory networks of pathogenetic importance that also may provide attractive targets for future directed therapies.

Approximate geodesic distances reveal biologically relevant structures in microarray data.

MOTIVATION: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. RESULTS: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.