High-throughput genomic profiling of tumor-infiltrating leukocytes

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Circadian rhythm regulates the function of immune cells and participates in the development of tumors

Circadian rhythms are present in almost all cells and play a crucial role in regulating various biological processes. Maintaining a stable circadian rhythm is essential for overall health. Disruption of this rhythm can alter the expression of clock genes and cancer-related genes, and affect many metabolic pathways and factors, thereby affecting the function of the immune system and contributing to the occurrence and progression of tumors. This paper aims to elucidate the regulatory effects of BMAL1, clock and other clock genes on immune cells, and reveal the molecular mechanism of circadian rhythm's involvement in tumor and its microenvironment regulation. A deeper understanding of circadian rhythms has the potential to provide new strategies for the treatment of cancer and other immune-related diseases.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Cyclooxygenase-2 Blockade Is Crucial to Restore Natural Killer Cell Activity before Anti-CTLA-4 Therapy against High-Grade Serous Ovarian Cancer

Chronic inflammation influences the tumor immune microenvironment (TIME) in high-grade serous ovarian cancer (HGSOC). Specifically, cyclooxygenase-2 (COX-2) overexpression promotes cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) expression. Notably, elevated COX-2 levels in the TIME have been associated with reduced response to anti-CTLA-4 immunotherapy. However, the precise impact of COX-2, encoded by PTGS2, on the immune profile remains unknown. To address this, we performed an integrated bioinformatics analysis using data from the HGSOC cohorts (TCGA-OV, n = 368; Australian cohort AOCS, n = 80; GSE26193, n = 62; and GSE30161, n = 45). Employing Gene Set Variation Analysis (GSVA), MIXTURE and Ecotyper cell deconvolution algorithms, we concluded that COX-2 was linked to immune cell ecosystems associated with shorter survival, cell dysfunction and lower NK cell effector cytotoxicity capacity. Next, we validated these results by characterizing circulating NK cells from HGSOC patients through flow cytometry and cytotoxic assays while undergoing COX-2 and CTLA-4 blockade. The blockade of COX-2 improved the cytotoxic capacity of NK cells against HGSOC cell lines. Our findings underscore the relevance of COX-2 in shaping the TIME and suggest its potential as a prognostic indicator and therapeutic target. Increased COX-2 expression may hamper the effectivity of immunotherapies that require NK cell effector function. These results provide a foundation for experimental validation and clinical trials investigating combined therapies targeting COX-2 and CTLA-4 in HGSOC.

Dynamics and specificities of T cells in cancer immunotherapy

Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.

Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference

To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk-profiled samples by using reference single-cell (sc) RNA-sequencing (RNA-seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed-effect models provide similar results as an elegant machine learning algorithm named cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well-known and reliable procedure to reconstruct cell-type abundances and cell-type-specific gene expression profiles. In real data analysis, the mixed-effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA-seq data, it is better to use mixed models with heterogeneous mean and variance-covariance. As a byproduct, the mixed models provide fractions of covariance between subject-specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA-seq data.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

SPTSSA Is a Prognostic Marker for Glioblastoma Associated with Tumor-Infiltrating Immune Cells and Oxidative Stress

Background. SPTSSA encodes the small subunit A of serine palmitoyltransferase. It catalyzes the formation of sphingoid long-chain base backbone of sphingolipids. Its role in glioma prognosis and tumor-infiltrating immune cells remains unclear. Methods. We analyzed SPTSSA expression and association with clinical prognosis using GEPIA and CGGA database. Then, GSEA was performed to identify relevant biological functions of SPTSSA. The correlations between SPTSSA expression and tumor immune infiltrates were investigated using CIBERSORT and TIMER. Finally, IHC and IF were performed to confirm the value of prognosis and the correlation with immune infiltration. Results. SPTSSA expression was significantly upregulated in diffuse glioma compared to normal tissues and associated with poor survival in GEPIA and CGGA database. Then, we identified biological processes and signaling pathways associated with SPTSSA expression. The result showed that SPTSSA enriched in the GO term like oxidative stress. Finally, we showed that SPTSSA expression was significantly associated with tumor-infiltrating immune cells and overall survival via IHC. Conclusion. These findings suggest that SPTSSA expression might be used as a prognostic biomarker for glioma and potential target for novel glioma therapy.

Characterized the diversity of ABCB1 subtypes in immunogenomic landscape for predicting the drug response in breast cancer

ABCB1 is an important gene that closely related to analgesic tolerance to opioids, and plays an important role in their postoperative treatment. Recent studies have demonstrated that ABCB1 genotype is significantly associated with the chemico-resistance and chemical sensitivity in breast cancer patients. So, it is become very important to investigate the important role of ABCB1 for predicting drug response in breast cancer patients. In this study, by conducting the Cox proportional hazards regression analysis in breast cancer patients, significant differences were found in prognosis between the ABCB1 high- and low-expression subtypes. Meanwhile, by using immune infiltration profiles as well as transcriptomics datasets, the ABCB1 high subtype was found to be significantly enriched in many immune-related KEGG pathways and biological processes, and was characterized by the high infiltration levels of immune cell types. Furthermore, bioinformatics inference revealed that the ABCB1 subtypes were associated with the therapeutic effect of immunotherapy, which would be important for patient prognosis. In conclusion, these findings may provide useful help for recognizing the diversity between ABCB1 subtypes in tumor immune microenvironment, and may unravel prognosis outcomes and immunotherapy utility for ABCB1 in breast cancer.

Circadian regulation of cancer cell and tumor microenvironment crosstalk

Circadian rhythms regulate a remarkable variety of physiologic functions in living organisms. Circadian disruption is associated with tumorigenesis and tumor progression through effects on cancer cell biological properties, including proliferation, DNA repair, apoptosis, metabolism, and stemness. Emerging evidence indicates that circadian clocks also play an influential role in the tumor microenvironment (TME). This review outlines recent discoveries on how cancer cell clock components (including circadian clock and clock genes/proteins) regulate TME biology and, reciprocally, how TME clock components affect tumor growth, metastasis, and therapeutic response. An improved understanding of how clock components regulate the symbiosis between cancer cells and the TME will inform the development of novel clock-oriented therapeutic strategies, including immunotherapy.

Combined homologous recombination repair deficiency and immune activation analysis for predicting intensified responses of anthracycline, cyclophosphamide and taxane chemotherapy in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a clinically aggressive disease with abundant variants that cause homologous recombination repair deficiency (HRD). Whether TNBC patients with HRD are sensitive to anthracycline, cyclophosphamide and taxane (ACT), and whether the combination of HRD and tumour immunity can improve the recognition of ACT responders are still unknown.

METHODS

Data from 83 TNBC patients in The Cancer Genome Atlas (TCGA) was used as a discovery cohort to analyse the association between HRD and ACT chemotherapy benefits. The combined effects of HRD and immune activation on ACT chemotherapy were explored at both the genome and the transcriptome levels. Independent cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) were adopted to validate our findings.

RESULTS

HRD was associated with a longer ACT chemotherapy failure-free interval (FFI) with a hazard ratio of 0.16 (P = 0.004) and improved patient prognosis (P = 0.0063). By analysing both HRD status and ACT response, we identified patients with a distinct TNBC subtype (ACT-S&HR-P) that showed higher tumour lymphocyte infiltration, IFN-γ activity and NK cell levels. Patients with ACT-S&HR-P had significantly elevated immune inhibitor levels and presented immune activation associated with the increased activities of both innate immune cells and adaptive immune cells, which suggested treatment with immune checkpoint blockade as an option for this subtype. Our analysis revealed that the combination of HRD and immune activation enhanced the efficiency of identifying responders to ACT chemotherapy (AUC = 0.91, P = 1.06e-04) and synergistically contributed to the clinical benefits of TNBC patients. A transcriptional HRD signature of ACT response-related prognostic factors was identified and independently validated to be significantly associated with improved survival in the GEO cohort (P = 0.0038) and the METABRIC dataset (P < 0.0001).

CONCLUSIONS

These findings highlight that HR deficiency prolongs FFI and predicts intensified responses in TNBC patients by combining HRD and immune activation, which provides a molecular basis for identifying ACT responders.

Identification of two immune subtypes in osteosarcoma based on immune gene sets

Osteosarcoma (OS) is a highly aggressive cancer with poor prognosis, which mainly occurs in teenagers. Recent studies have shown that tumor-infiltrating immune cells play an important role in the progression of OS. In the present study, we identified two immune subtypes of OS (referred to as high and low immune cell infiltration subtypes, respectively) based on immune-related gene sets using TARGET and GEO cohort datasets. Elevated immune scores, increased stromal scores, decreased tumor purities, and higher infiltration of CD8 + T cells and M1 macrophages were observed for the high immune cell infiltration subtype. Moreover, the high immune cell infiltration subtype was characterized by high expression of immune checkpoint molecules. Gene set enrichment analysis showed that "B cell receptor signaling pathway" and "T cell receptor signaling pathway" gene sets were enriched in the high immune cell infiltration subtype. In addition, patients in the high immune cell infiltration subtype had better prognosis than patients in the low immune cell infiltration subtype. Furthermore, differentially expressed genes were screened according to the two OS subtypes and a risk model was generated by multivariate Cox regression analysis to predict the prognosis of OS patients. These results in this study showed that OS patients could be divided into two immune subtypes and offered a novel two-gene risk signature to predict the prognosis of patients with OS.

Impact of storage conditions on peripheral leukocytes transcriptome

Leukocytes reflect the physiological and pathological states of each individual, and transcriptomic data of leukocytes have been used to reflect health conditions. Since the overall impact of ex vivo conditions on the leukocyte transcriptome before RNA stabilization remains unclear, we evaluated the influence of temporary storage conditions on the leukocyte transcriptome through RNA sequencing. We collected peripheral blood with EDTA tubes, which were processed immediately or stored either at 4 °C or room temperature (RT, 18-22 °C) for 2 h, 6 h and 24 h. Total cellular RNA was extracted from 42 leukocyte samples after red blood cells lysis for subsequent RNA sequencing. We applied weighted gene co-expression network analysis to construct co-expression networks of mRNA and lncRNA among the samples, and then performed gene ontology (GO) term enrichment to explore possible biological processes affected by storage conditions. Storage conditions change the gene expression of peripheral leukocytes. Comparing with fresh leukocytes, storage for 24 h at 4 °C and RT affected 1515 (1.51%) and 10,823 (10.82%) genes, respectively. Pathway enrichment analysis identified nucleosome assembly enriched in up-regulated genes at both conditions. When blood was stored at RT for 24 h, genes involved in apoptotic signaling pathway, negative regulation of cell cycle and lymphocyte activation were upregulated, while the relative proportion of neutrophils was significantly decreased. Temporary storage conditions profoundly affect the gene expression profiles of leukocytes and might further change cell viability and state. Storage of blood samples at 4 °C within 6 h largely maintains their original transcriptome.

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.

Immunological landscape of Neuroblastoma and its clinical significance

Immune infiltration in neuroblastoma (NBL) has been associated with clinical outcome. However, the diversity of distinct immune subpopulations that comprise immune infiltrates in NBL has not been examined. To this end, the present study investigated the immunological landscape of NBL tumors and its clinical significance. CIBERSORTx, an established RNA deconvolution algorithm, was used to impute immune cell proportions from 153 primary NBL tumors. Associations between immune proportions and overall/event-free survival were analyzed by Kaplan-Meier curves and evaluated using log-rank test. Of the 22 subpopulations imputed, M2 macrophages were the most abundant subtype in NBL tumors. Furthermore, monocytes, CD4+ naïve T cells, and CD4+ activated memory T cells were significantly associated with survival. Altogether, the findings suggest differences amongst certain immune cell subsets comprising NBL tumor infiltration and these differences may be important determinants of prognosis.

Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma

Background: The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy.

Methods: Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis.

Results: The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD.

Conclusions: The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy.

Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology

MOTIVATION

The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing.

RESULTS

We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures.

AVAILABILITY AND IMPLEMENTATION

A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With this framework, single-cell or bulk-sorted RNA sequencing data can be used to learn molecular signatures of distinct cell types from a small collection of biospecimens. These signatures can then be repeatedly applied to characterize cellular heterogeneity from bulk tissue transcriptomes without physical cell isolation. In this chapter, we provide a detailed primer on CIBERSORTx and demonstrate its capabilities for high-throughput profiling of cell types and cellular states in normal and neoplastic tissues.

Subsets of mononuclear phagocytes are enriched in the inflamed colons of patients with IBD

BACKGROUND

Myeloid cells, especially mononuclear phagocytes, which include monocytes, macrophages and dendritic cells (DC), play vital roles in innate immunity, and in the initiation and maintenance of adaptive immunity. While T cell-associated activation pathways and cytokines have been identified and evaluated in inflammatory bowel disease (IBD) patients (Neurath, Nat Rev Gastroenterol Hepatol 14:269-78, 1989), the role of mononuclear phagocytes are less understood. Recent reports support the crucial role of DC subsets in the development of acute colitis models (Arimura et al., Mucosal Immunol 10:957-70, 2017), and suggest they may contribute to the pathogenesis of ulcerative colitis (UC) by inducing Th1/Th2/Th17 responses (Matsuno et al., Inflamm Bowel Dis 23:1524-34, 2017).

RESULTS

We performed in silico analysis and evaluated the enrichment of immune cells, with a focus on mononuclear phagocytes in IBD patient colonic biopsies. Samples were from different gut locations, with different levels of disease severity, and with treatment response to current therapies. We observe enrichment of monocytes, M1 macrophages, activated DCs (aDCs) and plasmacytoid dendritic cells (pDCs) in inflamed tissues from various gut locations. This enrichment correlates with disease severity. Additionally, the same mononuclear phagocytes subsets are among the top enriched cell types in both infliximab and vedolizumab treatment non-responder samples. We further investigated the enrichment of selected DC and monocyte subsets based on gene signatures derived from a DC- and monocyte-focused single cell RNA-seq (scRNA-seq) study (Villani et al., Science 356:eaah4573, 2017), and verified enrichment in both inflamed tissues and those with treatment resistance. Moreover, we validated an increased mononuclear phagocyte subset abundance in a Dextran Sulphate Sodium (DSS) induced colitis model in C57Bl/6 mice representative of chronic inflammation.

CONCLUSIONS

We conducted an extensive analysis of immune cell populations in IBD patient colonic samples and identified enriched subsets of monocytes, macrophages and dendritic cells in inflamed tissues. Understanding how they interact with other immune cells and other cells in the colonic microenvironment such as epithelial and stromal cells will help us to delineate disease pathogenesis.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Determining cell type abundance and expression from bulk tissues with digital cytometry

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

A Pan-cancer Landscape of Interactions between Solid Tumors and Infiltrating Immune Cell Populations

Purpose: Throughout their development, tumors are challenged by the immune system, and they acquire features to evade its surveillance. A systematic view of these traits, which shed light on how tumors respond to immunotherapies, is still lacking.Experimental Design: Here, we computed the relative abundance of an array of immune cell populations to measure the immune infiltration pattern of 9,174 tumors of 29 solid cancers. We then clustered tumors with similar infiltration pattern to define immunophenotypes. Finally, we identified genomic and transcriptomic traits associated to these immunophenotypes across cancer types.Results: In highly cytotoxic immunophenotypes, we found tumors with low clonal heterogeneity enriched for alterations of genes involved in epigenetic regulation, ubiquitin-mediated proteolysis, antigen presentation, and cell-cell communication, which may drive resistance in combination with the ectopic expression of negative immune checkpoints. Tumors with immunophenotypes of intermediate cytotoxicity are characterized by an upregulation of processes involved in neighboring tissue invasion and remodeling that may foster the recruitment of immunosuppressive cells. Tumors with poorly cytotoxic immunophenotype tend to be of more advanced stages and bear a greater burden of copy number alterations and frequent alterations of cell cycle, hedgehog, β-catenin, and TGFβ pathways, which may cause immune depletion.Conclusions: We provide a comprehensive landscape of the characteristics of solid tumors that may influence (or be influenced by) the characteristics of their immune infiltrate. These results may help interpret the response of solid tumors to immunotherapies and guide the development of novel drug combination strategies. Clin Cancer Res; 24(15); 3717-28. ©2018 AACR.

Immunological network analysis in HPV associated head and neck squamous cancer and implications for disease prognosis

Human papillomavirus-positive (HPV+) head and neck squamous cell cancer (HNSCC) exhibits a better prognosis than HPV-negative (HPV-) HNSCC. This difference may in part be due to enhanced immune activation in the HPV+ HNSCC tumor microenvironment. To characterize differences in immune activation between HPV+ and HPV- HNSCC tumors, we identified and annotated differentially expressed genes based upon mRNA expression data from The Cancer Genome Atlas (TCGA). Immune network between immune cells and cytokines was constructed by using single sample Gene Set Enrichment Analysis and conditional mutual information. Multivariate Cox regression analysis was used to determine the prognostic value of immune microenvironment characterization. A total of 1673 differentially expressed genes were functionally annotated. We found that genes upregulated in HPV+ HNSCC are enriched in immune-associated processes. And the up-regulated gene sets were validated by Gene Set Enrichment Analysis. The microenvironment of HPV+ HNSCC exhibited greater numbers of infiltrating B and T cells and fewer neutrophils than HPV- HNSCC. These findings were validated by two independent datasets in the Gene Expression Omnibus (GEO) database. Further analyses of T cell subtypes revealed that cytotoxic T cell subtypes predominated in HPV+ HNSCC. In addition, the ratio of M1/M2 macrophages was much higher in HPV+ HNSCC. The infiltration of these immune cells was correlated with differentially expressed cytokine-associated genes. Enhanced infiltration of B cells and CD8+ T cells were identified as independent protective factors, while high neutrophil infiltration was a risk enhancing factor for HPV+ HNSCC patients. A schematic model of immunological network was established for HPV+ HNSCC to summarize our findings.

Profiling Tumor Infiltrating Immune Cells with CIBERSORT

Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

As interactions between the immune system and tumour cells are governed by a complex network of cell–cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.

Mathematical approaches can be used to assess immune cell composition from the tumour's bulk expression data. Here the authors optimise the CYBERSORT-based deconvolution algorithm by including cell type-specific reference gene expression profiles generated from tumour-derived single-cell RNA sequencing data.

xCell: digitally portraying the tissue cellular heterogeneity landscape

Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/.

Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping

Background: The tumor microenvironment is an important factor in cancer immunotherapy response. To further understand how a tumor affects the local immune system, we analyzed immune gene expression differences between matching normal and tumor tissue.Methods: We analyzed public and new gene expression data from solid cancers and isolated immune cell populations. We also determined the correlation between CD8, FoxP3 IHC, and our gene signatures.Results: We observed that regulatory T cells (Tregs) were one of the main drivers of immune gene expression differences between normal and tumor tissue. A tumor-specific CD8 signature was slightly lower in tumor tissue compared with normal of most (12 of 16) cancers, whereas a Treg signature was higher in tumor tissue of all cancers except liver. Clustering by Treg signature found two groups in colorectal cancer datasets. The high Treg cluster had more samples that were consensus molecular subtype 1/4, right-sided, and microsatellite-instable, compared with the low Treg cluster. Finally, we found that the correlation between signature and IHC was low in our small dataset, but samples in the high Treg cluster had significantly more CD8⁺ and FoxP3⁺ cells compared with the low Treg cluster.Conclusions: Treg gene expression is highly indicative of the overall tumor immune environment.Impact: In comparison with the consensus molecular subtype and microsatellite status, the Treg signature identifies more colorectal tumors with high immune activation that may benefit from cancer immunotherapy. Cancer Epidemiol Biomarkers Prev; 27(1); 103-12. ©2017 AACR.

Inhibition of p21 activated kinase enhances tumour immune response and sensitizes pancreatic cancer to gemcitabine

Pancreatic ductal adenocarcinoma (PDA) is one of the major types of cancer that exhibit high mortality worldwide because of the late diagnosis and the lack of effective treatment. Immunotherapy appears to be ineffective in PDA treatment due to the existence of a unique immune-suppressive microenvironment in PDA. Gemcitabine-based therapy is still the most commonly used chemotherapy to treat PDA patients with only marginal increased survival rates. This prompted us to continue the search for more effective therapy for PDA treatment. The effects of p21 activated kinases (PAKs) on tumour immune response and gemcitabine response were examined in PDA. An orthotopic murine PDA model, in which pancreatic cancer cells were injected to the tail of pancreas, was used. The mice were treated with PAK inhibitor, PF‑3758309, plus or minus gemcitabine. Tumour growth was measured by volume and weight. Tumour immune response was determined by flow cytometry analysis of splenic cells and immunohistochemical staining of intratumoural lymphocytes. Inhibition of PAKs by PF‑3758309, not only suppressed tumour growth, but also stimulated tumour immune response by increasing the numbers of splenic and intratumoural T lymphocytes. Furthermore, inhibition of PAKs decreased PDA cell growth synergistically with gemcitabine in vitro and in vivo. The dual effects of inhibition of PAKs make PAK-targeted therapy more potent for the treatment of PDA. The combination of PAK inhibitors with gemcitabine may be a more effective therapeutic approach in PDA treatment.

Hydrogel-based suspension array for biomarker detection using horseradish peroxidase-mediated silver precipitation

Advances in medical diagnostics and personalized therapy require robust, sensitive yet cost-effective diagnostic tools for rapid measurement of biomolecules including proteins in body fluids. State-of-the-art technologies are complex and rely on expensive or custom made detection system, and therefore, cannot be readily adapted for point-of-care (POC) analysis. The development of a novel detection platform, which leverages horseradish peroxidase (HRP)-mediated silver precipitation within antibody immobilized porosity tuned poly (ethylene) glycol diacrylate (PEGDA) hydrogel microparticles with the operational advantages of suspension arrays for sensitive quantification of biomarkers, is described. In this study, vascular endothelial growth factor (VEGF) has been used as a model protein. The silver deposition corresponded to the concentration of VEGF in solution. The detection limit of 5.2 ± 1.0 pg/mL and assay time of 2 h highlights that this assay exceeds the conventional technologies in terms of sensitivity and speed. The practical applicability of the hydrogel microparticle based detection system has been established by demonstrating the ability of the system to quantify the production of VEGF by highly aggressive (MDA-MB-231) and non-aggressive (MCF-7) breast cancer cells. The reliance on simple instrument for quantification of clinically relevant markers bolsters the adaptability of the detection platform/method in POC settings.

Data normalization considerations for digital tumor dissection

In a recently published article in Genome Biology, Li and colleagues introduced TIMER, a gene expression deconvolution approach for studying tumor-infiltrating leukocytes (TILs) in 23 cancer types profiled by The Cancer Genome Atlas. Methods to characterize TIL biology are increasingly important, and the authors offer several arguments in favor of their strategy. Several of these claims warrant further discussion and highlight the critical importance of data normalization in gene expression deconvolution applications.Please see related Li et al correspondence: www.dx.doi.org/10.1186/s13059-017-1256-5 and Zheng correspondence: www.dx.doi.org/10.1186/s13059-017-1258-3.

Assessment of tumor-infiltrating TCRV9V2 lymphocyte abundance by deconvolution of human cancers microarrays

Most human blood γδ cells are cytolytic TCRVγ9Vδ2⁺ lymphocytes with antitumor activity. They are currently investigated in several clinical trials of cancer immunotherapy but so far, their tumor infiltration has not been systematically explored across human cancers. Novel algorithms allowing the deconvolution of bulk tumor transcriptomes to find the relative proportions of infiltrating leucocytes, such as CIBERSORT, should be appropriate for this aim but in practice they fail to accurately recognize γδ T lymphocytes. Here, by implementing machine learning from microarray data, we first improved the computational identification of blood-derived TCRVγ9Vδ2⁺ γδ lymphocytes and then applied this strategy to assess their abundance as tumor infiltrating lymphocytes (γδ TIL) in ∼10,000 cancer biopsies from 50 types of hematological and solid malignancies. We observed considerable inter-individual variation of TCRVγ9Vδ2⁺γδ TIL abundance both within each type and across the spectrum of cancers tested. We report their prominence in B cell-acute lymphoblastic leukemia (B-ALL), acute promyelocytic leukemia (M3-AML) and chronic myeloid leukemia (CML) as well as in inflammatory breast, prostate, esophagus, pancreas and lung carcinoma. Across all cancers, the abundance of αβ TILs and TCRVγ9Vδ2⁺ γδ TILs did not correlate. αβ TIL abundance paralleled the mutational load of tumors and positively correlated with inflammation, infiltration of monocytes, macrophages and dendritic cells (DC), antigen processing and presentation, and cytolytic activity, in line with an association with a favorable outcome. In contrast, the abundance of TCRVγ9Vδ2⁺ γδ TILs did not correlate with these hallmarks and was variably associated with outcome, suggesting that distinct contexts underlie TCRVγ9Vδ2⁺ γδ TIL and αβ TIL mobilizations in cancer.

Underexpression of Specific Interferon Genes Is Associated with Poor Prognosis of Melanoma

Because the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10-5). Our model predicted 38 cases as low-risk and 54 cases as high-risk. The probability of surviving at least 5 years was 64% for low-risk and 14% for high-risk cases. In particular, we found that the overexpression of specific genes in the mitotic cell cycle pathway and the underexpression of specific genes in the interferon pathway are both associated with poor prognosis. We show that our predictive model assesses the risk more accurately than the traditional Clark staging method. Therefore, our model can help clinicians design treatment strategies more effectively. Furthermore, our findings shed light on the biology of melanoma and its prognosis. This is the first in vivo study that demonstrates the association between the interferon pathway and the prognosis of melanoma.

Digitally deconvolving the tumor microenvironment

Understanding a tumor’s complex cellular heterogeneity will be crucial for the development of better treatment strategies. A new study suggests a novel method for the in silico dissociation of solid tumors and presents novel insights that have implications for immunotherapy in cancer.

Please see the related Research article: www.dx.doi.org/10.1186/s13059-016-1028-7.