A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data

BACKGROUND

Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise.

RESULTS

We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function.

CONCLUSIONS

The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website.

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

IL7 genetic variation and toxicity to immune checkpoint blockade in patients with melanoma

Treatment with immune checkpoint blockade (ICB) frequently triggers immune-related adverse events (irAEs), causing considerable morbidity. In 214 patients receiving ICB for melanoma, we observed increased severe irAE risk in minor allele carriers of rs16906115, intronic to IL7. We found that rs16906115 forms a B cell-specific expression quantitative trait locus (eQTL) to IL7 in patients. Patients carrying the risk allele demonstrate increased pre-treatment B cell IL7 expression, which independently associates with irAE risk, divergent immunoglobulin expression and more B cell receptor mutations. Consistent with the role of IL-7 in T cell development, risk allele carriers have distinct ICB-induced CD8+ T cell subset responses, skewing of T cell clonality and greater proportional repertoire occupancy by large clones. Finally, analysis of TCGA data suggests that risk allele carriers independently have improved melanoma survival. These observations highlight key roles for B cells and IL-7 in both ICB response and toxicity and clinical outcomes in melanoma.

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19.

An immunodominant NP105-113-B*07:02 cytotoxic T cell response controls viral replication and is associated with less severe COVID-19 disease

NP105-113-B*07:02-specific CD8+ T cell responses are considered among the most dominant in SARS-CoV-2-infected individuals. We found strong association of this response with mild disease. Analysis of NP105-113-B*07:02-specific T cell clones and single-cell sequencing were performed concurrently, with functional avidity and antiviral efficacy assessed using an in vitro SARS-CoV-2 infection system, and were correlated with T cell receptor usage, transcriptome signature and disease severity (acute n = 77, convalescent n = 52). We demonstrated a beneficial association of NP105-113-B*07:02-specific T cells in COVID-19 disease progression, linked with expansion of T cell precursors, high functional avidity and antiviral effector function. Broad immune memory pools were narrowed postinfection but NP105-113-B*07:02-specific T cells were maintained 6 months after infection with preserved antiviral efficacy to the SARS-CoV-2 Victoria strain, as well as Alpha, Beta, Gamma and Delta variants. Our data show that NP105-113-B*07:02-specific T cell responses associate with mild disease and high antiviral efficacy, pointing to inclusion for future vaccine design.

Immune checkpoint blockade sensitivity and progression-free survival associates with baseline CD8+ T cell clone size and cytotoxicity

[Figure: see text].

Interferon-Gamma-Producing CD8+ Tissue Resident Memory T Cells Are a Targetable Hallmark of Immune Checkpoint Inhibitor-Colitis

BACKGROUND & AIMS

The pathogenesis of immune checkpoint inhibitor (ICI)-colitis remains incompletely understood. We sought to identify key cellular drivers of ICI-colitis and their similarities to idiopathic ulcerative colitis, and to determine potential novel therapeutic targets.

METHODS

We used a cross-sectional approach to study patients with ICI-colitis, those receiving ICI without the development of colitis, idiopathic ulcerative colitis, and healthy controls. A subset of patients with ICI-colitis were studied longitudinally. We applied a range of methods, including multiparameter and spectral flow cytometry, spectral immunofluorescence microscopy, targeted gene panels, and bulk and single-cell RNA sequencing.

RESULTS

We demonstrate CD8⁺ tissue resident memory T (T_RM) cells are the dominant activated T cell subset in ICI-colitis. The pattern of gastrointestinal immunopathology is distinct from ulcerative colitis at both the immune and epithelial-signaling levels. CD8⁺ T_RM cell activation correlates with clinical and endoscopic ICI-colitis severity. Single-cell RNA sequencing analysis confirms activated CD8⁺ T_RM cells express high levels of transcripts for checkpoint inhibitors and interferon-gamma in ICI-colitis. We demonstrate similar findings in both anti-CTLA-4/PD-1 combination therapy and in anti-PD-1 inhibitor-associated colitis. On the basis of our data, we successfully targeted this pathway in a patient with refractory ICI-colitis, using the JAK inhibitor tofacitinib.

CONCLUSIONS

Interferon gamma-producing CD8⁺ T_RM cells are a pathological hallmark of ICI-colitis and a novel target for therapy.

EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.

Checkpoint-blocker-induced autoimmunity is associated with favourable outcome in metastatic melanoma and distinct T-cell expression profiles

BACKGROUND

Immune checkpoint blockers (ICBs) activate CD8⁺ T cells, eliciting both anti-cancer activity and immune-related adverse events (irAEs). The relationship of irAEs with baseline parameters and clinical outcome is unclear.

METHODS

Retrospective evaluation of irAEs on survival was performed across primary (N = 144) and secondary (N = 211) independent cohorts of patients with metastatic melanoma receiving single agent (pembrolizumab/nivolumab-sICB) or combination (nivolumab and ipilimumab-cICB) checkpoint blockade. RNA from pre-treatment and post-treatment CD8⁺ T cells was sequenced and differential gene expression according to irAE development assessed.

RESULTS

58.3% of patients developed early irAEs and this was associated with longer progression-free (PFS) and overall survival (OS) across both cohorts (log-rank test, OS: P < 0.0001). Median survival for patients without irAEs was 16.6 months (95% CI: 10.9-33.4) versus not-reached (P = 2.8 × 10^-6). Pre-treatment monocyte and neutrophil counts, but not BMI, were additional predictors of clinical outcome. Differential expression of numerous gene pathway members was observed in CD8⁺ T cells according to irAE development, and patients not developing irAEs demonstrating upregulated CXCR1 pre- and post-treatment.

CONCLUSIONS

Early irAE development post-ICB is associated with favourable survival in MM. Development of irAEs is coupled to expression of numerous gene pathways, suggesting irAE development in-part reflects baseline immune activation.

Regulatory Crosstalk of Doxorubicin, Estradiol and TNFα Combined Treatment in Breast Cancer-derived Cell Lines

We present a new model of ESR1 network regulation based on analysis of Doxorubicin, Estradiol, and TNFα combination treatment in MCF-7. We used Doxorubicin as a therapeutic agent, TNFα as marker and mediator of an inflammatory microenvironment and 17β-Estradiol (E2) as an agonist of Estrogen Receptors, known predisposing factor for hormone-driven breast cancer, whose pharmacological inhibition reduces the risk of breast cancer recurrence. Based on the results of transcriptomics analysis, we found 71 differentially expressed genes that are specific for the combination treatment with Doxorubicin + Estradiol + TNFα in comparison with single or double treatments. The responsiveness to the triple treatment was examined for seven genes by qPCR, of which six were validated, and then extended to four additional cell lines differing for p53 and/or ER status. The results of differential regulation enrichment analysis highlight the role of the ESR1 network that included 36 of 71 specific differentially expressed genes. We propose that the combined activation of p53 and NF-kB transcription factors significantly influences ligand-dependent, ER-driven transcriptional responses, also of the ESR1 gene itself. These results provide a model of coordinated interaction of TFs to explain the Doxorubicin, E2 and TNFα induced repression mechanisms.

Context-specific regulation of surface and soluble IL7R expression by an autoimmune risk allele

IL-7 is a key factor in T cell immunity and common variants at IL7R, encoding its receptor, are associated with autoimmune disease susceptibility. IL7R mRNA is induced in stimulated monocytes, yet a function for IL7R in monocyte biology remains unexplored. Here we characterize genetic regulation of IL7R at the protein level in healthy individuals, and find that monocyte surface and soluble IL7R (sIL7R) are markedly induced by lipopolysaccharide. In monocytes, both surface IL7R and sIL7R expression strongly associate with allelic carriage of rs6897932, a disease-associated IL7R polymorphism. Monocytes produce more sIL7R than CD4 + T cells, and the amount is additionally correlated with the expression of DDX39A, encoding a splicing factor. Synovial fluid-derived monocytes from patients with spondyloarthritis are enriched for IL7R+ cells with a unique transcriptional profile that overlaps with IL-7-induced gene sets. Our data thus suggest a previously unappreciated function for monocytes in IL-7 biology and IL7R-associated diseases.

Systematic exploration of cell morphological phenotypes associated with a transcriptomic query

Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds. Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action. Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds. These data provide an opportunity to study the interdependence between transcription and cell morphology. Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology. To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology. Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology. We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line.

Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches

A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.

Nonparametric simulation of signal transduction networks with semi-synchronized update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.

Development of a sensitive deaminated single-strand conformation polymorphism (DSSCP)

The single-strand conformation polymorphism (SSCP), accompanied by sequencing, is a useful methods for identifying mutations in a DNA fragment. In this study, we have developed a modified SSCP with the aid of sodium bisulfite treatment. The corresponding PCR products for exon 3 of Hb gene were sequenced and samples with homozygote and heterozygote single nucleotide substitutions were identified. The PCR products were treated with sodium bisulfite, which deaminates all the cytosine residues. The reaction mixture was then analyzed on non-denaturing polyacrylamide gels. The modified method, which is called deaminated SSCP (DSSCP), was applied successfully in analysis of mutations in the beta-globin gene at positions relevant to codon 6. DSSCP is a very effective and reproducible method providing clear results that are easy to interpret without the involvement of radioactivity.

Methylation status of p16 INK4A tumor suppressor gene in Iranian patients with sporadic breast cancer

INTRODUCTION

p16(INK4A) is a tumor suppressor encoding the Cdk inhibitor protein, which acts to repress Cdk4/6 and pRb phosphorylation. p16(INK4A) gene can be inactivated by a variety of events, including promoter hypermethylation.

MATERIALS AND METHODS

To investigate the methylation status of the p16(INK4A) gene in Iranian patients with breast carcinoma, promoter methylation was studied by methylation-specific PCR (MSP) and restriction enzyme-related PCR (REP). In addition, p16(INK4A) promoter was analyzed by PCR-SSCP in order to detection of mutation and single nucleotide polymorphisms.

RESULTS

Analysis of 70 patients by MPS and REP showed hypermethylation of p16(INK4A) promoter in 35.7% (25/70) and 40% (28/70) of samples, respectively. Comparison of the molecular data and pathological information of the samples suggested that p16(INK4A) gene might be inactivated at the early stages in breast cancer.

CONCLUSION

Therefore, it could be suggested that hypermethylation of p16(INK4A) promoter is one of the epigenetic factors affecting the progress of sporadic breast carcinogenesis in Iranian patients.