Copy number loss of the interferon gene cluster in melanomas is linked to reduced T cell infiltrate and poor patient prognosis

Type I interferon activates MHC class I-dressed CD11b+ conventional dendritic cells to promote protective anti-tumor CD8+ T cell immunity

Tumor-infiltrating dendritic cells (DCs) assume varied functional states that impact anti-tumor immunity. To delineate the DC states associated with productive anti-tumor T cell immunity, we compared spontaneously regressing and progressing tumors. Tumor-reactive CD8+ T cell responses in Batf3-/- mice lacking type 1 DCs (DC1s) were lost in progressor tumors but preserved in regressor tumors. Transcriptional profiling of intra-tumoral DCs within regressor tumors revealed an activation state of CD11b+ conventional DCs (DC2s) characterized by expression of interferon (IFN)-stimulated genes (ISGs) (ISG+ DCs). ISG+ DC-activated CD8+ T cells ex vivo comparably to DC1. Unlike cross-presenting DC1, ISG+ DCs acquired and presented intact tumor-derived peptide-major histocompatibility complex class I (MHC class I) complexes. Constitutive type I IFN production by regressor tumors drove the ISG+ DC state, and activation of MHC class I-dressed ISG+ DCs by exogenous IFN-β rescued anti-tumor immunity against progressor tumors in Batf3-/- mice. The ISG+ DC gene signature is detectable in human tumors. Engaging this functional DC state may present an approach for the treatment of human disease.

Genetic Changes Driving Immunosuppressive Microenvironments in Oral Premalignancy

Oral premalignant lesions (OPLs) are the precursors to oral cavity cancers, and have variable rates of progression to invasive disease. As an intermediate state, OPLs have acquired a subset of the genomic alterations while arising in an oral inflammatory environment. These specific genomic changes may facilitate the transition to an immune microenvironment that permits malignant transformation. Here, we will discuss mechanisms by which OPLs develop an immunosuppressive microenvironment that facilitates progression to invasive cancer. We will describe how genomic alterations and immune microenvironmental changes co-evolve and cooperate to promote OSCC progression. Finally, we will describe how these immune microenvironmental changes provide specific and unique evolutionary vulnerabilities for targeted therapies. Therefore, understanding the genomic changes that drive immunosuppressive microenvironments may eventually translate into novel biomarker and/or therapeutic approaches to limit the progression of OPLs to potential lethal oral cancers.

Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma

ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. T cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of T cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ T cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded T cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action.

New Immunotherapy Combinations Enter the Battlefield of Malignant Mesothelioma

Chimeric antigen receptor (CAR) T cells targeting mesothelin plus pembrolizumab, and atezolizumab plus bevacizumab, have recently shown clinical efficacy in phase I trials in malignant pleural and peritoneal mesothelioma. Despite being tested in a highly selected patient population and requiring a complex engineering that can hardly be upscaled, CAR T cells combined with pembrolizumab bring the first proof of efficacy in cold solid tumors with low genomic heterogeneity, while atezolizumab-bevacizumab offers an easy-to-use combination of antiangiogenics and immunotherapy in an orphan disease.See related article by Raghav et al., p. 2738.See related article by Adusumilli et al., p. 2748.

STING protects breast cancer cells from intrinsic and genotoxic-induced DNA instability via a non-canonical, cell-autonomous pathway

STING (Stimulator of Interferon Genes) is an endoplasmic reticulum-anchored adaptor of the innate immunity best known to trigger pro-inflammatory cytokine expression in response to pathogen infection. In cancer, this canonical pathway can be activated by intrinsic or drug-induced genomic instability, potentiating antitumor immune responses. Here we report that STING downregulation decreases cell survival and increases sensitivity to genotoxic treatment in a panel of breast cancer cell lines in a cell-autonomous manner. STING silencing impaired DNA Damage Response (53BP1) foci formation and increased DNA break accumulation. These newly identified properties were found to be independent of STING partner cGAS and of its canonical pro-inflammatory pathway. STING was shown to partially localize at the inner nuclear membrane in a variety of breast cancer cell models and clinical tumor samples. Interactomics analysis of nuclear STING identified several proteins of the DNA Damage Response, including the three proteins of the DNA-PK complex, further supporting a role of STING in the regulation of genomic stability. In breast and ovarian cancer patients that received adjuvant chemotherapy, high STING expression is associated with increased risk of relapse. In summary, this study highlights an alternative, non-canonical tumor-promoting role of STING that opposes its well-documented function in tumor immunosurveillance.

SLFN11 captures cancer-immunity interactions associated with platinum sensitivity in high-grade serous ovarian cancer

Large independent analyses on cancer cell lines followed by functional studies have identified Schlafen 11 (SLFN11), a putative helicase, as the strongest predictor of sensitivity to DNA-damaging agents (DDAs), including platinum. However, its role as a prognostic biomarker is undefined, partially due to the lack of validated methods to score SLFN11 in human tissues. Here, we implemented a pipeline to quantify SLFN11 in human cancer samples. By analyzing a cohort of high-grade serous ovarian carcinoma (HGSOC) specimens before platinum-based chemotherapy treatment, we show, for the first time to our knowledge, that SLFN11 density in both the neoplastic and microenvironmental components was independently associated with favorable outcome. We observed SLFN11 expression in both infiltrating innate and adaptive immune cells, and analyses in a second, independent, cohort revealed that SLFN11 was associated with immune activation in HGSOC. We found that platinum treatments activated immune-related pathways in ovarian cancer cells in an SLFN11-dependent manner, representative of tumor-immune transactivation. Moreover, SLFN11 expression was induced in activated, isolated immune cell subpopulations, hinting that SLFN11 in the immune compartment may be an indicator of immune transactivation. In summary, we propose SLFN11 is a dual biomarker capturing simultaneously interconnected immunological and cancer cell–intrinsic functional dispositions associated with sensitivity to DDA treatment.

Copy number alteration of the interferon gene cluster in cancer: Individual patient data meta-analysis prospects to personalized immunotherapy

Interferon (IFN) therapy has been the standard of care for a variety of cancers for decades due to the pleiotropic actions of IFNs against malignancies. However, little is known about the role of copy number alteration (CNA) of the IFN gene cluster, located at the 9p21.3, in cancer. This large individual patient data meta-analysis using 9937 patients obtained from cBioportal indicates that CNA of the IFN gene cluster is prevalent among 24 cancer types. Two statistical approaches showed that notably deletion of this cluster is significantly associated with increased mortality in many cancer types particularly uterus (OR = 2.71), kidney (OR = 2.26), and brain (OR = 2.08) cancers. The Cancer Genome Atlas PanCancer analysis also showed that CNA of the IFN gene cluster is significantly associated with decreased overall survival. For instance, the overall survival of patients with brain glioma reduced from 93m (diploidy) to 24m (with the CNA of the IFN gene). In conclusion, the CNA of the IFN gene cluster is associated with increased mortality and decreased overall survival in cancer. Thus, in the prospect of immunotherapy, CNA of IFN gene may be a useful biomarker to predict the prognosis of patients and also as a potential companion diagnostic test to prescribe IFN α/β therapy.

The Yin and Yang of Type I IFNs in Cancer Promotion and Immune Activation

Type I Interferons (IFNs) are key regulators of natural and therapy-induced host defense against viral infection and cancer. Several years of remarkable progress in the field of oncoimmunology have revealed the dual nature of these cytokines. Hence, Type I IFNs may trigger anti-tumoral responses, while leading immune dysfunction and disease progression. This dichotomy relies on the duration and intensity of the transduced signaling, the nature of the unleashed IFN stimulated genes, and the subset of responding cells. Here, we discuss the role of Type I IFNs in the evolving relationship between the host immune system and cancer, as we offer a view of the therapeutic strategies that exploit and require an intact Type I IFN signaling, and the role of these cytokines in inducing adaptive resistance. A deep understanding of the complex, yet highly regulated, network of Type I IFN triggered molecular pathways will help find a timely and immune"logical" way to exploit these cytokines for anticancer therapy.

Interpretable systems biomarkers predict response to immune-checkpoint inhibitors

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis

Sepsis results from the dysregulation of the host immune system. This highly variable disease affects 19 million people globally, and accounts for 5 million deaths annually. In transcriptomic datasets curated from public repositories, we observed a consistent upregulation (3.26-5.29 fold) of ERLIN1-a gene coding for an ER membrane prohibitin and a regulator of inositol 1, 4, 5-trisphosphate receptors and sterol regulatory element-binding proteins-under septic conditions in healthy neutrophils, monocytes, and whole blood. In vitro expression of the ERLIN1 gene and proteins was measured by stimulating the whole blood of healthy volunteers to a combination of lipopolysaccharide and peptidoglycan. Septic stimulation induced a significant increase in ERLIN1 expression; however, ERLIN1 was differentially expressed among the immune blood cell subsets. ERLIN1 was uniquely increased in whole blood neutrophils, and confirmed in the differentiated HL60 cell line. The scarcity of ERLIN1 in sepsis literature indicates a knowledge gap between the functions of ERLIN1, calcium homeostasis, and cholesterol and fatty acid biosynthesis, and sepsis. In combination with experimental data, we bring forth the hypothesis that ERLIN1 is variably modulated among immune cells in response to cellular perturbations, and has implications for ER functions and/or ER membrane protein components during sepsis.

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via:  https://drinchai.shinyapps.io/BloodGen3Module/ .

The tweety Gene Family: From Embryo to Disease

The tweety genes encode gated chloride channels that are found in animals, plants, and even simple eukaryotes, signifying their deep evolutionary origin. In vertebrates, the tweety gene family is highly conserved and consists of three members—ttyh1, ttyh2, and ttyh3—that are important for the regulation of cell volume. While research has elucidated potential physiological functions of ttyh1 in neural stem cell maintenance, proliferation, and filopodia formation during neural development, the roles of ttyh2 and ttyh3 are less characterized, though their expression patterns during embryonic and fetal development suggest potential roles in the development of a wide range of tissues including a role in the immune system in response to pathogen-associated molecules. Additionally, members of the tweety gene family have been implicated in various pathologies including cancers, particularly pediatric brain tumors, and neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Here, we review the current state of research using information from published articles and open-source databases on the tweety gene family with regard to its structure, evolution, expression during development and adulthood, biochemical and cellular functions, and role in human disease. We also identify promising areas for further research to advance our understanding of this important, yet still understudied, family of genes.

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

Comparison of sparse biclustering algorithms for gene expression datasets

MOTIVATION

Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics.

RESULTS

We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis.

AVAILABILITY

Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.

Application of a bioinformatic pipeline to RNA-seq data identifies novel viruslike sequence in human blood

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

BACKGROUND

Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions.

RESULTS

To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions.

CONCLUSIONS

We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.

Loss of p16: A Bouncer of the Immunological Surveillance?

p16INK4A (hereafter called p16) is an important tumor suppressor protein frequently suppressed in human cancer and highly upregulated in many types of senescence. Although its role as a cell cycle regulator is very well delineated, little is known about its other non-cell cycle-related roles. Importantly, recent correlative studies suggest that p16 may be a regulator of tissue immunological surveillance through the transcriptional regulation of different chemokines, interleukins and other factors secreted as part of the senescence-associated secretory phenotype (SASP). Here, we summarize the current evidence supporting the hypothesis that p16 is a regulator of tumor immunity.

Mutations in the IFNγ-JAK-STAT Pathway Causing Resistance to Immune Checkpoint Inhibitors in Melanoma Increase Sensitivity to Oncolytic Virus Treatment

PURPOSE

Next-generation sequencing studies and CRISPR-Cas9 screens have established mutations in the IFNγ-JAK-STAT pathway as an immune checkpoint inhibitor (ICI) resistance mechanism in a subset of patients with melanoma. We hypothesized ICI resistance mutations in the IFNγ pathway would simultaneously render melanomas susceptible to oncolytic virus (OV) therapy.

EXPERIMENTAL DESIGN

Cytotoxicity experiments were performed with a number of OVs on a matched melanoma cell line pair generated from a baseline biopsy and a progressing lesion with complete JAK2 loss from a patient that relapsed on anti-PD-1 therapy, in melanoma lines following JAK1/2 RNA interference (RNAi) and pharmacologic inhibition and in Jak2 knockout (KO) B16-F10 mouse melanomas. Furthermore, we estimated the frequency of genetic alterations in the IFNγ-JAK-STAT pathway in human melanomas.

RESULTS

The melanoma line from an anti-PD-1 progressing lesion was 7- and 22-fold more sensitive to the modified OVs, herpes simplex virus 1 (HSV1-dICP0) and vesicular stomatitis virus (VSV-Δ51), respectively, compared with the line from the baseline biopsy. RNAi, JAK1/2 inhibitor studies, and in vivo studies of Jak2 KOs B16-F10 melanomas revealed a significant increase in VSV-Δ51 sensitivity with JAK/STAT pathway inhibition. Our analysis of The Cancer Genome Atlas data estimated that approximately 11% of ICI-naïve cutaneous melanomas have alterations in IFNγ pathway genes that may confer OV susceptibility.

CONCLUSIONS

We provide mechanistic support for the use of OVs as a precision medicine strategy for both salvage therapy in ICI-resistant and first-line treatment in melanomas with IFNγ-JAK-STAT pathway mutations. Our study also supports JAK inhibitor-OV combination therapy for treatment-naïve melanomas without IFN signaling defects.See related commentary by Kaufman, p. 3278.

Computational deconvolution to estimate cell type-specific gene expression from bulk data

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.

Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy

Only a subgroup of patients with muscle-invasive bladder cancer (MIBC) are responders toward cisplatin-based chemotherapy and PD-L1 blockade immunotherapy. There is a clinical need to identify MIBC molecular subtypes and biomarkers for patient stratification toward the therapies. Here, we performed an integrative clustering analysis of 388 MIBC samples with multi-omics data and identified basal and luminal/differentiated integrative subtypes and derived a 42 gene panel for classification of MIBC. Using nine additional gene expression data (n = 844), we demonstrated the prognostic value of the 42 basal-luminal genes. The basal subtype was associated with worse overall survival in patients receiving no neoadjuvant chemotherapy (NAC), but better overall survival in patients receiving NAC in two clinical trials. Each of the subtypes could be further divided into chr9 p21.3 normal or loss subgroup. The patients with low expression of MTAP/CDKN2A/2B (indicative of chr9 p21.3 loss) had a significantly lower response rate to anti-PD-L1 immunotherapy and worse survival than the patients with high expression of MTAP/CDKN2A/2B. This integrative analysis reveals intrinsic MIBC subtypes and biomarkers with prognostic value for the frontline therapies.

Single-cell RNA-Seq revealed profound immune alteration in the peripheral blood of patients with bacterial infection

OBJECTIVES

Bacterial infection remains one of the greatest threats to human health. However, how human hosts respond to bacterial infection has not been thoroughly understood. Better understanding of this response will improve human health.

METHODS

Here, we conducted an investigation on host response to bacterial infection using unperturbed clinical samples and single-cell RNA-Seq (scRNA-Seq) technology. To evaluate immune alteration upon bacterial infection in scRNA-Seq data of peripheral blood mononuclear cells (PBMCs), we developed a barcode analytical framework named PBMCode.

RESULTS

Using this PBMCode framework, we revealed profound immune alteration in peripheral blood under bacterial infection, including the emergence of natural killer T (NKT) cell cluster, reduction of B cell population, and considerable changes in T cells and monocytes. In addition, we also observed a large quantity of low-density neutrophils.

CONCLUSIONS

Our investigation on single cells provided unprecedented details in the alteration of both cell population and cell state under bacterial infection. These findings may be relevant to clinical decisions. The complexity of host response to bacterial infection revealed by scRNA-Seq deserves further attention in future studies.

Definition of erythroid cell-positive blood transcriptome phenotypes associated with severe respiratory syncytial virus infection

Biomarkers to assess the risk of developing severe respiratory syncytial virus (RSV) infection are needed. We conducted a meta-analysis of 490 unique profiles from six public RSV blood transcriptome datasets. A repertoire of 382 well-characterized transcriptional modules was used to define dominant host responses to RSV infection. The consolidated RSV cohort was stratified according to four traits: "interferon response" (IFN), "neutrophil-driven inflammation" (Infl), "cell cycle" (CC), and "erythrocytes" (Ery). We identified eight prevalent blood transcriptome phenotypes, of which three Ery+ phenotypes comprised higher proportions of patients requiring intensive care. This finding confirms on a larger scale data from one of our earlier reports describing an association between an erythrocyte signature and RSV disease severity. Further contextual interpretation made it possible to associate this signature with immunosuppressive states (late stage cancer, pharmacological immunosuppression), and with a population of fetal glycophorin A+ erythroid precursors. Furthermore, we posit that this erythrocyte cell signature may be linked to a population of immunosuppressive erythroid cells previously described in the literature, and that overabundance of this cell population in RSV patients may underlie progression to severe disease. These findings outline potential priority areas for biomarker development and investigations into the immune biology of RSV infection. The approach that we developed and employed here should also permit to delineate prevalent blood transcriptome phenotypes in other settings.

A Neutrophil-Driven Inflammatory Signature Characterizes the Blood Transcriptome Fingerprint of Psoriasis

Transcriptome profiling approaches have been widely used to investigate the mechanisms underlying psoriasis pathogenesis. Most researchers have measured changes in transcript abundance in skin biopsies; relatively few have examined transcriptome changes in the blood. Although less relevant to the study of psoriasis pathogenesis, blood transcriptome profiles can be readily compared across various diseases. Here, we used a pre-established set of 382 transcriptional modules as a common framework to compare changes in blood transcript abundance in two independent public psoriasis datasets. We then compared the resulting "transcriptional fingerprints" to those obtained for a reference set of 16 pathological or physiological states. The perturbations in blood transcript abundance in psoriasis were relatively subtle compared to the changes we observed in other autoimmune and auto-inflammatory diseases. However, we did observe a consistent pattern of changes for a set of modules associated with neutrophil activation and inflammation; interestingly, this pattern resembled that observed in patients with Kawasaki disease. This similarity between the blood-transcriptome signatures in psoriasis and Kawasaki disease suggests that the immune mechanisms driving their pathogenesis might be partially shared.

Stable gene expression for normalisation and single-sample scoring

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Assessment of long non-coding RNA expression reveals novel mediators of the lung tumour immune response

The tumour immune microenvironment is a crucial mediator of lung tumourigenesis, and characterizing the immune landscape of patient tumours may guide immunotherapy treatment regimens and uncover novel intervention points. We sought to identify the landscape of tumour-infiltrating immune cells in the context of long non-coding RNA (lncRNAs), known regulators of gene expression. We examined the lncRNA profiles of lung adenocarcinoma (LUAD) tumours by interrogating RNA sequencing data from microdissected and non-microdissected samples (BCCRC and TCGA). Subsequently, analysis of single-cell RNA sequencing data from lung tumours and flow-sorted healthy peripheral blood mononuclear cells identified lncRNAs in immune cells, highlighting their biological and prognostic relevance. We discovered lncRNA expression patterns indicative of regulatory relationships with immune-related protein-coding genes, including the relationship between AC008750.1 and NKG7 in NK cells. Activation of NK cells in vitro was sufficient to induce AC008750.1 expression. Finally, siRNA-mediated knockdown of AC008750.1 significantly impaired both the expression of NKG7 and the anti-tumour capacity of NK cells. We present an atlas of cancer-cell extrinsic immune cell-expressed lncRNAs, in vitro evidence for a functional role of lncRNAs in anti-tumour immune activity, which upon further exploration may reveal novel clinical utility as markers of immune infiltration.

A deconvolution method and its application in analyzing the cellular fractions in acute myeloid leukemia samples

BACKGROUND

The identification of cell type-specific genes (markers) is an essential step for the deconvolution of the cellular fractions, primarily, from the gene expression data of a bulk sample. However, the genes with significant changes identified by pair-wise comparisons cannot indeed represent the specificity of gene expression across multiple conditions. In addition, the knowledge about the identification of gene expression markers across multiple conditions is still paucity.

RESULTS

Herein, we developed a hybrid tool, LinDeconSeq, which consists of 1) identifying marker genes using specificity scoring and mutual linearity strategies across any number of cell types, and 2) predicting cellular fractions of bulk samples using weighted robust linear regression with the marker genes identified in the first stage. On multiple publicly available datasets, the marker genes identified by LinDeconSeq demonstrated better accuracy and reproducibility compared to MGFM and RNentropy. Among deconvolution methods, LinDeconSeq showed low average deviations (≤0.0958) and high average Pearson correlations (≥0.8792) between the predicted and actual fractions on the benchmark datasets. Importantly, the cellular fractions predicted by LinDeconSeq appear to be relevant in the diagnosis of acute myeloid leukemia (AML). The distinct cellular fractions in granulocyte-monocyte progenitor (GMP), lymphoid-primed multipotent progenitor (LMPP) and monocytes (MONO) were found to be closely associated with AML compared to the healthy samples. Moreover, the heterogeneity of cellular fractions in AML patients divided these patients into two subgroups, differing in both prognosis and mutation patterns. GMP fraction was the most pronounced between these two subgroups, particularly, in SubgroupA, which was strongly associated with the better AML prognosis and the younger population. Totally, the identification of marker genes by LinDeconSeq represents the improved feature for deconvolution. The data processing strategy with regard to the cellular fractions used in this study also showed potential for the diagnosis and prognosis of diseases.

CONCLUSIONS

Taken together, we developed a freely-available and open-source tool LinDeconSeq ( https://github.com/lihuamei/LinDeconSeq ), which includes marker identification and deconvolution procedures. LinDeconSeq is comparable to other current methods in terms of accuracy when applied to benchmark datasets and has broad application in clinical outcome and disease-specific molecular mechanisms.

Covering all your bases: incorporating intron signal from RNA-seq data

RNA-seq datasets can contain millions of intron reads per library that are typically removed from downstream analysis. Only reads overlapping annotated exons are considered to be informative since mature mRNA is assumed to be the major component sequenced, especially for poly(A) RNA libraries. In this study, we show that intron reads are informative, and through exploratory data analysis of read coverage that intron signal is representative of both pre-mRNAs and intron retention. We demonstrate how intron reads can be utilized in differential expression analysis using our index method where a unique set of differentially expressed genes can be detected using intron counts. In exploring read coverage, we also developed the superintronic software that quickly and robustly calculates user-defined summary statistics for exonic and intronic regions. Across multiple datasets, superintronic enabled us to identify several genes with distinctly retained introns that had similar coverage levels to that of neighbouring exons. The work and ideas presented in this paper is the first of its kind to consider multiple biological sources for intron reads through exploratory data analysis, minimizing bias in discovery and interpretation of results. Our findings open up possibilities for further methods development for intron reads and RNA-seq data in general.

Annexin A3 in sepsis: novel perspectives from an exploration of public transcriptome data

According to publicly available transcriptome datasets, the abundance of Annexin A3 (ANXA3) is robustly increased during the course of sepsis; however, no studies have examined the biological significance or clinical relevance of ANXA3 in this pathology. Here we explored this interpretation gap and identified possible directions for future research. Based on reference transcriptome datasets, we found that ANXA3 expression is restricted to neutrophils, is upregulated in vitro after exposure to plasma obtained from septic patients, and is associated with adverse clinical outcomes. Secondly, an increase in ANXA3 transcript abundance was also observed in vivo, in the blood of septic patients in multiple independent studies. ANXA3 is known to mediate calcium-dependent granules-phagosome fusion in support of microbicidal activity in neutrophils. More recent work has also shown that ANXA3 enhances proliferation and survival of tumour cells via a Caspase-3-dependent mechanism. And this same molecule is also known to play a critical role in regulation of apoptotic events in neutrophils. Thus, we posit that during sepsis ANXA3 might either play a beneficial role, by facilitating microbial clearance and resolution of the infection; or a detrimental role, by prolonging neutrophil survival, which is known to contribute to sepsis-mediated organ damage.

A modular framework for the development of targeted Covid-19 blood transcript profiling panels

BACKGROUND

Covid-19 morbidity and mortality are associated with a dysregulated immune response. Tools are needed to enhance existing immune profiling capabilities in affected patients. Here we aimed to develop an approach to support the design of targeted blood transcriptome panels for profiling the immune response to SARS-CoV-2 infection.

METHODS

We designed a pool of candidates based on a pre-existing and well-characterized repertoire of blood transcriptional modules. Available Covid-19 blood transcriptome data was also used to guide this process. Further selection steps relied on expert curation. Additionally, we developed several custom web applications to support the evaluation of candidates.

RESULTS

As a proof of principle, we designed three targeted blood transcript panels, each with a different translational connotation: immunological relevance, therapeutic development relevance and SARS biology relevance.

CONCLUSION

Altogether the work presented here may contribute to the future expansion of immune profiling capabilities via targeted profiling of blood transcript abundance in Covid-19 patients.

Host and Parasite Transcriptomic Changes upon Successive Plasmodium falciparum Infections in Early Childhood

We show that dual RNA-seq from patient blood samples allows characterization of host/parasite interactions during malaria infections and can provide a solid framework to study the acquisition of antimalarial immunity, as well as the adaptations of P. falciparum to malaria-experienced hosts.

Children are highly susceptible to clinical malaria, and in regions where malaria is endemic, their immune systems must face successive encounters with Plasmodium falciparum parasites before they develop immunity, first against severe disease and later against uncomplicated malaria. Understanding cellular and molecular interactions between host and parasites during an infection could provide insights into the processes underlying this gradual acquisition of immunity, as well as to how parasites adapt to infect hosts that are successively more malaria experienced. Here, we describe methods to analyze the host and parasite gene expression profiles generated simultaneously from blood samples collected from five consecutive symptomatic P. falciparum infections in three Malian children. We show that the data generated enable statistical assessment of the proportions of (i) each white blood cell subset and (ii) the parasite developmental stages, as well as investigations of host-parasite gene coexpression. We also use the sequences generated to analyze allelic variations in transcribed regions and determine the complexity of each infection. While limited by the modest sample size, our analyses suggest that host gene expression profiles primarily clustered by individual, while the parasite gene expression profiles seemed to differentiate early from late infections. Overall, this study provides a solid framework to examine the mechanisms underlying acquisition of immunity to malaria infections using whole-blood transcriptome sequencing (RNA-seq).

IMPORTANCE We show that dual RNA-seq from patient blood samples allows characterization of host/parasite interactions during malaria infections and can provide a solid framework to study the acquisition of antimalarial immunity, as well as the adaptations of P. falciparum to malaria-experienced hosts.

Complete Topological Mapping of a Cellular Protein Interactome Reveals Bow-Tie Motifs as Ubiquitous Connectors of Protein Complexes

The network topology of a protein interactome is shaped by the function of each protein, making it a resource of functional knowledge in tissues and in single cells. Today, this resource is underused, as complete network topology characterization has proved difficult for large protein interactomes. We apply a matrix visualization and decoding approach to a physical protein interactome of a dendritic cell, thereby characterizing its topology with no prior assumptions of structure. We discover 294 proteins, each forming topological motifs called "bow-ties" that tie together the majority of observed protein complexes. The central proteins of these bow-ties have unique network properties, display multifunctional capabilities, are enriched for essential proteins, and are widely expressed in other cells and tissues. Collectively, the bow-tie motifs are a pervasive and previously unnoted topological trend in cellular interactomes. As such, these results provide fundamental knowledge on how intracellular protein connectivity is organized and operates.

Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans

During haematopoiesis, haematopoietic stem cells differentiate into restricted potential progenitors before maturing into the many lineages required for oxygen transport, wound healing and immune response. We have updated Haemopedia, a database of gene-expression profiles from a broad spectrum of haematopoietic cells, to include RNA-seq gene-expression data from both mice and humans. The Haemopedia RNA-seq data set covers a wide range of lineages and progenitors, with 57 mouse blood cell types (flow sorted populations from healthy mice) and 12 human blood cell types. This data set has been made accessible for exploration and analysis, to researchers and clinicians with limited bioinformatics experience, on our online portal Haemosphere: https://www.haemosphere.org. Haemosphere also includes nine other publicly available high-quality data sets relevant to haematopoiesis. We have added the ability to compare gene expression across data sets and species by curating data sets with shared lineage designations or to view expression gene vs gene, with all plots available for download by the user.

Using the circulating proteome to assess type I interferon activity in systemic lupus erythematosus

Type I interferon (IFN) drives pathology in systemic lupus erythematosus (SLE) and can be tracked via IFN-inducible transcripts in blood. Here, we examined whether measurement of circulating proteins, which enter the bloodstream from inflamed tissues, also offers insight into global IFN activity. Using a novel protocol we generated 1,132 aptamer-based protein measurements from anti-dsDNA^pos SLE blood samples and derived an IFN protein signature (IFNPS) that approximates the IFN 21-gene signature (IFNGS). Of 82 patients with SLE, IFNPS was elevated for 89% of IFNGS-high patients (49/55) and 26% of IFNGS-low patients (7/27). IFNGS-high/IFNPS-high patients exhibited activated NK, CD4, and CD8 T cells, while IFNPS-high only patients did not. IFNPS correlated with global disease activity in lymphopenic and non-lymphopenic patients and decreased following type I IFN neutralisation with anifrolumab in the SLE phase IIb study, MUSE. In summary, we developed a protein signature that reflects IFNGS and identifies a new subset of patients with SLE who have IFN activity.

A composite immune signature parallels disease progression across T1D subjects

At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.

Soluble PD-L1 generated by endogenous retroelement exaptation is a receptor antagonist

Immune regulation is a finely balanced process of positive and negative signals. PD-L1 and its receptor PD-1 are critical regulators of autoimmune, antiviral and antitumoural T cell responses. Although the function of its predominant membrane-bound form is well established, the source and biological activity of soluble PD-L1 (sPD-L1) remain incompletely understood. Here, we show that sPD-L1 in human healthy tissues and tumours is produced by exaptation of an intronic LINE-2A (L2A) endogenous retroelement in the CD274 gene, encoding PD-L1, which causes omission of the transmembrane domain and the regulatory sequence in the canonical 3' untranslated region. The alternatively spliced CD274-L2A transcript forms the major source of sPD-L1 and is highly conserved in hominids, but lost in mice and a few related species. Importantly, CD274-L2A-encoded sPD-L1 lacks measurable T cell inhibitory activity. Instead, it functions as a receptor antagonist, blocking the inhibitory activity of PD-L1 bound on cellular or exosomal membranes.

Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer

BACKGROUND

Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions.

RESULTS

In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package.

CONCLUSIONS

Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.

Interferons α and β in cancer: therapeutic opportunities from new insights

Over the past decade, preclinical and clinical research have confirmed the essential role of interferons for effective host immunological responses to malignant cells. Type I interferons (IFNα and IFNβ) directly regulate transcription of >100 downstream genes, which results in a myriad of direct (on cancer cells) and indirect (through immune effector cells and vasculature) effects on the tumour. New insights into endogenous and exogenous activation of type I interferons in the tumour and its microenvironment have given impetus to drug discovery and patient evaluation of interferon-directed strategies. When combined with prior observations or with other effective modalities for cancer treatment, modulation of the interferon system could contribute to further reductions in cancer morbidity and mortality. This Review discusses new interferon-directed therapeutic opportunities, ranging from cyclic dinucleotides to genome methylation inhibitors, angiogenesis inhibitors, chemoradiation, complexes with neoantigen-targeted monoclonal antibodies, combinations with other emerging therapeutic interventions and associations of interferon-stimulated gene expression with patient prognosis - all of which are strategies that have or will soon enter translational clinical evaluation.

Isolated limb perfusion with melphalan activates interferon-stimulated genes to induce tumor regression in patients with melanoma in-transit metastasis

Hyperthermic isolated limb perfusion (ILP) with high-dose melphalan is a treatment option for melanoma patients with metastasis confined to limbs (in-transit metastasis). The therapy entails a complete response (CR) rate of 50–70%. Cellular immunity is proposed to impact on the clinical efficacy of ILP, but the detailed aspects of ILP-induced immune activation remain to be explored. For this study, we explored the potential role of interferon-stimulated gene (ISG) products, including CXCL10, CCL2, PD-L2 and IFN-γ along with expression of their cognate receptors CXCR3, CCR4, CCR5 and PD-1 on lymphocytes, for the clinical efficacy of ILP. Patients with high serum levels of CXCL10, CCL2, PD-L2 and IFN-γ were more likely to achieve CR after ILP. Additionally, the expression of CXCR3, CCR4 and CCR5 on T cells and/or natural killer (NK) cells was enhanced by ILP. Peripheral blood mononuclear cells (PBMCs) secreted high levels of CXCL10, CCL2 and IFN-γ in response to co-culture with melphalan-exposed melanoma cells in vitro. Activated T cells migrated toward supernatants from these co-cultures. Furthermore, melphalan-exposed melanoma cells triggered upregulation of CXCR3, CCR4, CCR5 and PD-1 on co-cultured T cells and/or NK cells. Our results suggest that constituents released from melphalan-exposed melanoma cells stimulate the ISG axis with ensuing formation of chemokines and upregulation of chemokine receptor expression on anti-neoplastic immune cells, which may contribute in ILP-induced tumor regression.

Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor

Background

The goal of this study is to use comprehensive molecular profiling to characterize clinical response to anti-TNF therapy in a real-world setting and identify reproducible markers differentiating good responders and non-responders in rheumatoid arthritis (RA).

Methods

Whole-blood mRNA, plasma proteins, and glycopeptides were measured in two cohorts of biologic-naïve RA patients (n = 40 and n = 36) from the Corrona CERTAIN (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) registry at baseline and after 3 months of anti-TNF treatment. Response to treatment was categorized by EULAR criteria. A cell type-specific data analysis was conducted to evaluate the involvement of the most common immune cell sub-populations. Findings concordant between the two cohorts were further assessed for reproducibility using selected NCBI-GEO datasets and clinical laboratory measurements available in the CERTAIN database.

Results

A treatment-related signature suggesting a reduction in neutrophils, independent of the status of response, was indicated by a high level of correlation (ρ = 0.62; p < 0.01) between the two cohorts. A baseline, response signature of increased innate cell types in responders compared to increased adaptive cell types in non-responders was identified in both cohorts. This result was further assessed by applying the cell type-specific analysis to five other publicly available RA datasets. Evaluation of the neutrophil-to-lymphocyte ratio at baseline in the remaining patients (n = 1962) from the CERTAIN database confirmed the observation (odds ratio of good/moderate response = 1.20 [95% CI = 1.03–1.41, p = 0.02]).

Conclusion

Differences in innate/adaptive immune cell type composition at baseline may be a major contributor to response to anti-TNF treatment within the first 3 months of therapy.

Long-Chain Acyl-CoA Synthetase 1 Role in Sepsis and Immunity: Perspectives From a Parallel Review of Public Transcriptome Datasets and of the Literature

A potential role for the long-chain acyl-CoA synthetase family member 1 (ACSL1) in the immunobiology of sepsis was explored during a hands-on training workshop. Participants first assessed the robustness of the potential gap in biomedical knowledge identified via an initial screen of public transcriptome data and of the literature associated with ACSL1. Increase in ACSL1 transcript abundance during sepsis was confirmed in several independent datasets. Querying the ACSL1 literature also confirmed the absence of reports associating ACSL1 with sepsis. Inferences drawn from both the literature (via indirect associations) and public transcriptome data (via correlation) point to the likely participation of ACSL1 and ACSL4, another family member, in inflammasome activation in neutrophils during sepsis. Furthermore, available clinical data indicate that levels of ACSL1 and ACSL4 induction was significantly higher in fatal cases of sepsis. This denotes potential translational relevance and is consistent with involvement in pathways driving potentially deleterious systemic inflammation. Finally, while ACSL1 expression was induced in blood in vitro by a wide range of pathogen-derived factors as well as TNF, induction of ACSL4 appeared restricted to flagellated bacteria and pathogen-derived TLR5 agonists and IFNG. Taken together, this joint review of public literature and omics data records points to two members of the acyl-CoA synthetase family potentially playing a role in inflammasome activation in neutrophils. Translational relevance of these observations in the context of sepsis and other inflammatory conditions remain to be investigated.

Virotherapy as a Potential Therapeutic Approach for the Treatment of Aggressive Thyroid Cancer

Virotherapy is a novel cancer treatment based on oncolytic viruses (OVs), which selectively infect and lyse cancer cells, without harming normal cells or tissues. Several viruses, either naturally occurring or developed through genetic engineering, are currently under investigation in clinical studies. Emerging reports suggesting the immune-stimulatory property of OVs against tumor cells further support the clinical use of OVs for the treatment of lesions lacking effective therapies. Poorly differentiated thyroid carcinoma (PDTC) and anaplastic thyroid carcinoma (ATC), have a poor prognosis and limited treatment options. Therefore, several groups investigated the therapeutic potential of OVs in PDTC/ATC models producing experimental data sustaining the potential clinical efficacy of OVs in these cancer models. Moreover, the presence of an immunosuppressive microenvironment further supports the potential use of OVs in ATC. In this review, we present the results of the studies evaluating the efficacy of OVs alone or in combination with other treatment options. In particular, their potential therapeutic combination with multiple kinases inhibitors (MKIs) or immune checkpoint inhibitors are discussed.

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns

Immunotherapies have attracted lots of research interests recently. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, e.g., inconsistent cell type-specific gene expression between purified reference samples and tumor samples. We propose a novel statistical method for expression deconvolution called ICeD-T (Immune Cell Deconvolution in Tumor tissues). ICeD-T automatically identifies aberrant genes whose expression are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors.

TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis

In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .

An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets

Background

Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously.

Results

Elastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures.

Conclusions

Developed classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2994-z) contains supplementary material, which is available to authorized users.

A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients

Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15 Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.

Type I interferons and dendritic cells in cancer immunotherapy

Type I interferons (IFNs) facilitate cancer immunosurveillance, antitumor immunity and antitumor efficacy of conventional cell death-inducing therapies (chemotherapy/radiotherapy) as well as immunotherapy. Moreover, it is clear that dendritic cells (DCs) play a significant role in aiding type I IFN-driven immunity. Owing to these antitumor properties several immunotherapies involving, or inducing, type I IFNs have received considerable clinical attention, e.g., recombinant IFNα2 or agonists targeting pattern recognition receptor (PRR) pathways like Toll-like receptors (TLRs), cGAS-STING or RIG-I/MDA5/MAVS. A series of preclinical and clinical evidence concurs that the success of anticancer therapy hinges on responsiveness of both cancer cells and DCs to type I IFNs. In this article, we discuss this link between type I IFNs and DCs in the context of cancer biology, with particular attention to mechanisms behind type I IFN production, their impact on DC driven anticancer immunity, and the implications of this for cancer immunotherapy, including DC-based vaccines.

Detection of condition-specific marker genes from RNA-seq data with MGFR

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR’s predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

Concise Review: Targeting Cancer Stem Cells and Their Supporting Niche Using Oncolytic Viruses

Cancer stem cells (CSCs) have the capacity to self-renew and differentiate to give rise to heterogenous cancer cell lineages in solid tumors. These CSC populations are associated with metastasis, tumor relapse, and resistance to conventional anticancer therapies. Here, we focus on the use of oncolytic viruses (OVs) to target CSCs as well as the OV-driven interferon production in the tumor microenvironment (TME) that can repress CSC properties. We explore the ability of OVs to deliver combinations of immune-modulating therapeutic transgenes, such as immune checkpoint inhibitor antibodies. In particular, we highlight the advantages of virally encoded bi-specific T cell engagers (BiTEs) to not only target cell-surface markers on CSCs, but also tumor-associated antigens on contributing components of the surrounding TME and other cancer cells. We also highlight the crucial role of combination anticancer treatments, evidenced by synergy of OV-delivered BiTEs and chimeric-antigen receptor T cell therapy. Stem Cells 2019;37:716-723.

Elevated T cell levels in peripheral blood predict poor clinical response following rituximab treatment in new-onset type 1 diabetes

Biologic treatment of type 1 diabetes (T1D) with agents including anti-CD3 (otelixizumab and teplizumab), anti-CD20 (rituximab), LFA3Ig (alafacept), and CTLA4Ig (abatacept) results in transient stabilization of insulin C-peptide, a surrogate for endogenous insulin secretion. With the goal of inducing more robust immune tolerance, we used systems biology approaches to elucidate mechanisms associated with C-peptide stabilization in clinical trial blood samples from new-onset T1D subjects treated with the B cell-depleting drug, rituximab. RNA sequencing (RNA-seq) analysis of whole-blood samples from this trial revealed a transient increase in heterogeneous T cell populations, which were associated with decreased pharmacodynamic activity of rituximab, increased proliferative responses to islet antigens, and more rapid C-peptide loss. Our findings illustrate complexity in hematopoietic remodeling that accompanies B cell depletion by rituximab, which impacts and predicts therapeutic efficacy in T1D. Our data also suggest that a combination of rituximab with therapy targeting CD4 + T cells may be beneficial for T1D subjects.