Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution

Identification and validation of DLX4 as a prognostic and diagnostic biomarker for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a lethal cancer, and biomarkers for exact diagnosis and predicting prognosis are urgently needed. The present study aimed to determine the roles of distal-less homeobox (DLX) family genes in ccRCC. The clinicopathological and mRNA expression data of patients with ccRCC were derived from The Cancer Genome Atlas database. Kaplan-Meier curves, univariate and multivariate Cox hazard analyses, in addition to receiver operator characteristic curves were used to evaluate the prognostic and diagnostic values. A single-sample gene set enrichment analysis was used to quantify the infiltration levels of immune cells. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry were conducted to examine the expression levels of DLX4 in tumor and adjacent tissue; the results demonstrated that DLX4 was highly expressed in ccRCC tissues compared with normal renal tissues. Furthermore, DLX4 expression was associated with tumor stage and grade. High proportions of males, advanced pathological stage, higher tumor grade and T, N and M stage were also observed in the high DLX4 expression group. Patients with the high DLX4 expression levels tended to have lower overall survival and disease-free survival rates compared with those with low DLX4 expression. DLX4 expression also showed favorable diagnostic efficiency in ccRCC patients. Based on functional enrichment analysis, cell cycle related pathways, epithelial-mesenchymal transition, glycolysis and inflammatory response were associated with the expression levels of DLX4. Furthermore, DLX4 expression was revealed to be associated with tumor immunosuppressive microenvironment. Overall, the expression level of DLX4 may be considered a novel prognostic indicator in ccRCC and a specific diagnostic biomarker for patients with ccRCC.

Recent advances in the genetics of alopecia areata

Alopecia areata (AA) is a common autoimmune-mediated hair loss disorder in humans with an estimated lifetime risk of approximately 2 %. Episodes of hair loss usually begin with isolated hairless patches that may progress to complete hair loss over the entire body. A familial occurrence of AA is well established, with recurrence risks of about 6-8 % in first-degree relatives. AA is a multifactorial disorder involving both environmental and genetic risk factors. Previous research has identified 14 susceptibility loci, most of which implicate genes involved in the immune response. The following review presents a summary of the latest findings from genome-wide association, sequencing and gene expression studies of AA, as well as their contribution to the recent therapeutic developments.

Primary cicatricial alopecias are characterized by dysregulation of shared gene expression pathways

The primary forms of cicatricial (scarring) alopecia (PCA) are a group of inflammatory, irreversible hair loss disorders characterized by immune cell infiltrates targeting hair follicles (HFs). Lichen planopilaris (LPP), frontal fibrosing alopecia (FFA), and centrifugal cicatricial alopecia (CCCA) are among the main subtypes of PCAs. The pathogenesis of the different types of PCAs are poorly understood, and current treatment regimens yield inconsistent and unsatisfactory results. We performed high-throughput RNA-sequencing on scalp biopsies of a large cohort PCA patients to develop gene expression-based signatures, trained into machine-learning-based predictive models and pathways associated with dysregulated gene expression. We performed morphological and cytokine analysis to define the immune cell populations found in PCA subtypes. We identified a common PCA gene signature that was shared between LPP, FFA, and CCCA, which revealed a significant over-representation of mast cell (MC) genes, as well as downregulation of cholesterogenic pathways and upregulation of fibrosis and immune signaling genes. Immunohistological analyses revealed an increased presence of MCs in PCAs lesions. Our gene expression analyses revealed common pathways associated with PCAs, with a strong association with MCs. The indistinguishable differences in gene expression profiles and immune cell signatures between LPP, FFA, and CCCA suggest that similar treatment regimens may be effective in treating these irreversible forms of hair loss.

Translational impact of omics studies in alopecia areata: recent advances and future perspectives

INTRODUCTION

Alopecia areata (AA) is a non-scarring, hair loss disorder and a common autoimmune-mediated disease with an estimated lifetime risk of about 2%. To date, the treatment of AA is mainly based on suppression or stimulation of the immune response. Genomics and transcriptomics studies generated important insights into the underlying pathophysiology, enabled discovery of molecular disease signatures, which were used in some of the recent clinical trials to monitor drug response and substantiated the consideration of new therapeutic modalities for the treatment of AA such as abatacept, dupilumab, ustekinumab and Janus Kinase (JAK) inhibitors.

AREAS COVERED

In this review, genomics and transcriptomics studies in AA are discussed in detail with particular emphasis on their past and prospective translational impacts. Microbiome studies are also briefly introduced.

EXPERT OPINION

The generation of large datasets using the new high-throughput technologies has revolutionized medical research and AA has also benefited from the wave of omics studies. However, the limitations associated with JAK inhibitors and clinical heterogeneity in AA patients underscore the necessity for continuing omics research in AA for discovery of novel therapeutic modalities and development of clinical tools for precision medicine.

Ikaros Proteins in Tumor: Current Perspectives and New Developments

Ikaros is a zinc finger transcription factor (TF) of the Krüppel family member, which significantly regulates normal lymphopoiesis and tumorigenesis. Ikaros can directly initiate or suppress tumor suppressors or oncogenes, consequently regulating the survival and proliferation of cancer cells. Over recent decades, a series of studies have been devoted to exploring and clarifying the relationship between Ikaros and associated tumors. Therapeutic strategies targeting Ikaros have shown promising therapeutic effects in both pre-clinical and clinical trials. Nevertheless, the increasingly prominent problem of drug resistance targeted to Ikaros and its analog is gradually appearing in our field of vision. This article reviews the role of Ikaros in tumorigenesis, the mechanism of drug resistance, the progress of targeting Ikaros in both pre-clinical and clinical trials, and the potential use of associated therapy in cancer therapy.

Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix

Biomolecular networks are often assumed to be scale-free hierarchical networks. The weighted gene co-expression network analysis (WGCNA) treats gene co-expression networks as undirected scale-free hierarchical weighted networks. The WGCNA R software package uses an Adjacency Matrix to store a network, next calculates the topological overlap matrix (TOM), and then identifies the modules (sub-networks), where each module is assumed to be associated with a certain biological function. The most time-consuming step of WGCNA is to calculate TOM from the Adjacency Matrix in a single thread. In this paper, the single-threaded algorithm of the TOM has been changed into a multi-threaded algorithm (the parameters are the default values of WGCNA). In the multi-threaded algorithm, Rcpp was used to make R call a C++ function, and then C++ used OpenMP to start multiple threads to calculate TOM from the Adjacency Matrix. On shared-memory MultiProcessor systems, the calculation time decreases as the number of CPU cores increases. The algorithm of this paper can promote the application of WGCNA on large data sets, and help other research fields to identify sub-networks in undirected scale-free hierarchical weighted networks. The source codes and usage are available at https://github.com/do-somethings-haha/multi-threaded_calculate_unsigned_TOM_from_unsigned_or_signed_Adjacency_Matrix_of_WGCNA.

Regulatory network analysis defines unique drug mechanisms of action and facilitates patient-drug matching in alopecia areata clinical trials

Not all therapeutics are created equal in regards to individual patients. The problem of identifying which compound will work best with which patient is a significant burden across all disease contexts. In the context of autoimmune diseases such as alopecia areata, several formulations of JAK/STAT inhibitors have demonstrated efficacy in clinical trials. All of these compounds demonstrate different rates of response, and here we observed that this coincided with different molecular effects on patients undergoing treatment. Using these data, we have developed a computational model that is capable of predicting which patient-drug pairs have the highest likelihood of response. We achieved this by integrating inferred mechanism of action data and gene regulatory networks derived from an independent patient cohort with baseline patient data prior to beginning treatment.

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

The study of immune evasion has gained a well-deserved eminence in cancer research by successfully developing a new class of therapeutics, immune checkpoint inhibitors, such as pembrolizumab and nivolumab, anti-PD-1 antibodies. By aiming at the immune checkpoint blockade (ICB), these new therapeutics have advanced cancer treatment with notable increases in overall survival and tumor remission. However, recent reports reveal that 40-60% of patients fail to benefit from ICB therapy due to acquired resistance or tumor relapse. This resistance may stem from increased expression of co-inhibitory immune checkpoints or alterations in the tumor microenvironment that promotes immune suppression. Because these mechanisms are poorly elucidated, the transcription factors that regulate immune checkpoints, known as "master regulators", have garnered interest. These include AP-1, IRF-1, MYC, and STAT3, which are known to regulate PD/PD-L1 and CTLA-4. Identifying these and other potential master regulators as putative therapeutic targets or biomarkers can be facilitated by mining cancer literature, public datasets, and cancer genomics resources. In this review, we describe recent advances in master regulator identification and characterization of the mechanisms underlying immune checkpoints regulation, and discuss how these master regulators of immune checkpoint molecular expression can be targeted as a form of auxiliary therapeutic strategy to complement traditional immunotherapy.

Network Analysis of Large-Scale Data and Its Application to Immunology

Diseases and infections elicit a multilayered immune response which consists of molecular and cellular interaction cascades. Recent advances in high-throughput technologies have facilitated multiparameter investigation of immune cells involved in human immune responses. These multiparameter investigations generate large-scale datasets and advanced computational techniques are required to gain useful information from them. Networks or graphs offer a practical way to represent complex information and develop advanced algorithms to unveil the underlying mechanisms. Here we discuss ways to assemble and analyze networks using genome-wide transcriptional profiles. Additionally, we discuss ways to integrate information available in primary literature and databases with the networks assembled using large-scale datasets. Finally, we describe ways in which network analysis offers insights into human immune responses.

IKZF1 Enhances Immune Infiltrate Recruitment in Solid Tumors and Susceptibility to Immunotherapy

Immunotherapies are some of the most promising emergent treatments for several cancers, yet there remains a majority of patients who do not benefit from them due to immune-resistant tumors. One avenue for enhancing treatment for these patients is by converting these tumors to an immunoreactive state, thereby restoring treatment efficacy. By leveraging regulatory networks we previously characterized in autoimmunity, here we show that overexpression of the master regulator IKZF1 leads to enhanced immune infiltrate recruitment and tumor sensitivity to PD1 and CTLA4 inhibitors in several tumors that normally lack IKZF1 expression. This work provides proof of concept that tumors can be rendered susceptible by hijacking immune cell recruitment signals through molecular master regulators. On a broader scale, this work also demonstrates the feasibility of using computational approaches to drive the discovery of novel molecular mechanisms toward treatment.

Innate Immunity in Systemic Sclerosis Fibrosis: Recent Advances

Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by three interconnected hallmarks (i) vasculopathy, (ii) aberrant immune activation, and (iii) fibroblast dysfunction leading to extracellular matrix deposition and fibrosis. Blocking or reversing the fibrotic process associated with this devastating disease is still an unmet clinical need. Although various components of innate immunity, including macrophages and type I interferon, have long been implicated in SSc, the precise mechanisms that regulate the global innate immune contribution to SSc pathogenesis remain poorly understood. Recent studies have identified new innate immune players, such as pathogen-recognition receptors, platelet-derived danger-associated molecular patterns, innate lymphoid cells, and plasmacytoid dendritic cells in the pathophysiology of SSc, including vasculopathy and fibrosis. In this review, we describe the evidence demonstrating the importance of innate immune processes during SSc development with particular emphasis on their role in the initiation of pathology. We also discuss potential therapeutic options to modulate innate immune cells or signaling in SSc that are emerging from these recent advances.

iNKT cells ameliorate human autoimmunity: Lessons from alopecia areata

Alopecia areata (AA) is understood to be a CD8+/NKG2D+ T cell-dependent autoimmune disease. Here, we demonstrate that human AA pathogenesis of is also affected by iNKT10 cells, an unconventional T cell subtype whose number is significantly increased in AA compared to healthy human skin. AA lesions can be rapidly induced in healthy human scalp skin xenotransplants on Beige-SCID mice by intradermal injections of autologous healthy-donor PBMCs pre-activated with IL-2. We show that in this in vivo model, the development of AA lesions is prevented by recognized the iNKT cell activator, α-galactosylceramide (α-GalCer), which stimulates iNKT cells to expand and produce IL-10. Moreover, in pre-established humanized mouse AA lesions, hair regrowth is promoted by α-GalCer treatment through a process requiring both effector-memory iNKT cells, which can interact directly with CD8+/NKG2D+ T cells, and IL-10. This provides the first in vivo evidence in a humanized model of autoimmune disease that iNKT10 cells are key disease-protective lymphocytes. Since these regulatory NKT cells can both prevent the development of AA lesions and promote hair re-growth in established AA lesions, targeting iNKT10 cells may have preventive and therapeutic potential also in other autoimmune disorders related to AA.

A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis

Background

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology.

Methods

We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test.

Results

We identified a common pathogenic gene expression signature—an immune–fibrotic axis—indicative of pro-fibrotic macrophages (MØs) in multiple tissues (skin, lung, esophagus, and peripheral blood mononuclear cells) affected by SSc. While the co-expression of these genes is common to all tissues, the functional consequences of this upregulation differ by organ. We used this disease-associated signature to query tissue-specific functional genomic networks to identify common and tissue-specific pathologies of SSc and related conditions. In contrast to skin, in the lung-specific functional network we identify a distinct lung-resident MØ signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct MØ alternative activation transcriptional programs in SSc-associated PF lung and in the skin of patients with an “inflammatory” SSc gene expression signature.

Conclusions

Our results suggest that the innate immune system is central to SSc disease processes but that subtle distinctions exist between tissues. Our approach provides a framework for examining molecular signatures of disease in fibrosis and autoimmune diseases and for leveraging publicly available data to understand common and tissue-specific disease processes in complex human diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0417-1) contains supplementary material, which is available to authorized users.

Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata

BACKGROUND

Alopecia areata (AA) is an autoimmune disease characterized by hair loss mediated by CD8⁺ T cells. There are no reliably effective therapies for AA. Based on recent developments in the understanding of the pathomechanism of AA, JAK inhibitors appear to be a therapeutic option; however, their efficacy for the treatment of AA has not been systematically examined.

METHODS

This was a 2-center, open-label, single-arm trial using the pan-JAK inhibitor, tofacitinib citrate, for AA with >50% scalp hair loss, alopecia totalis (AT), and alopecia universalis (AU). Tofacitinib (5 mg) was given twice daily for 3 months. Endpoints included regrowth of scalp hair, as assessed by the severity of alopecia tool (SALT), duration of hair growth after completion of therapy, and disease transcriptome.

RESULTS

Of 66 subjects treated, 32% experienced 50% or greater improvement in SALT score. AA and ophiasis subtypes were more responsive than AT and AU subtypes. Shorter duration of disease and histological peribulbar inflammation on pretreatment scalp biopsies were associated with improvement in SALT score. Drug cessation resulted in disease relapse in 8.5 weeks. Adverse events were limited to grade I and II infections. An AA responsiveness to JAK/STAT inhibitors score was developed to segregate responders and nonresponders, and the previously developed AA disease activity index score tracked response to treatment.

CONCLUSIONS

At the dose and duration studied, tofacitinib is a safe and effective treatment for severe AA, though it does not result in a durable response. Transcriptome changes reveal unexpected molecular complexity within the disease.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02197455 and NCT02312882.

FUNDING

This work was supported by the US Department of Veterans Affairs Office of Research and Development, National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health grant R01 AR47223 and U01 AR67173, the National Psoriasis Foundation, the Swedish Society of Medicine, the Fernström Foundation, the Locks of Love Foundation, the National Alopecia Areata Foundation, and the Ranjini and Ajay Poddar Resource Fund for Dermatologic Diseases Research.

Molecular signatures define alopecia areata subtypes and transcriptional biomarkers

Alopecia areata (AA) is an autoimmune disease typified by nonscarring hair loss with a variable clinical course. In this study, we conducted whole genome gene expression analysis of 96 human scalp skin biopsy specimens from AA or normal control subjects. Based on gene expression profiling, samples formed distinct clusters based on the presence or absence of disease as well as disease phenotype (patchy disease compared with alopecia totalis or universalis). Differential gene expression analysis allowed us to robustly demonstrate graded immune activity in samples of increasing phenotypic severity and generate a quantitative gene expression scoring system that classified samples based on interferon and cytotoxic T lymphocyte immune signatures critical for disease pathogenesis.

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Gene expression analysis of 96 scalp biopsies from patients with alopecia areata (AA) and healthy controls was performed.

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Samples from AA patchy, alopecia universalis/totalis and control patients formed distinct clusters by gene expression.

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A set of gene expression biomarkers, the Alopecia Areata Disease Activity Index (ALADIN), was formulated.

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ALADIN distinguished AA phenotypes and normal controls.

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ALADIN may have utility in clinical trials of AA.

Alopecia areata is a disease characterized by autoimmune attack of the hair follicle. A complete understanding of the signaling pathways involved in the disease is lacking. Based on gene expression profiling of skin samples from 96 patients and controls, a set of biomarkers, termed the Alopecia Areata Disease Activity Index, or ALADIN, was formulated. ALADIN was able to distinguish samples from patients with patchy disease from samples from patients with the more extensive forms of disease. The usefulness of this biomarker tool is ready to be assessed in clinical trials of therapeutics for alopecia areata.