Quantitative Analyses of the Tumor Microenvironment Composition and Orientation in the Era of Precision Medicine

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

The role of mesenchymal stem cells in cancer and prospects for their use in cancer therapeutics

Mesenchymal stem cells (MSCs) are recruited by malignant tumor cells to the tumor microenvironment (TME) and play a crucial role in the initiation and progression of malignant tumors. This role encompasses immune evasion, promotion of angiogenesis, stimulation of cancer cell proliferation, correlation with cancer stem cells, multilineage differentiation within the TME, and development of treatment resistance. Simultaneously, extensive research is exploring the homing effect of MSCs and MSC-derived extracellular vesicles (MSCs-EVs) in tumors, aiming to design them as carriers for antitumor substances. These substances are targeted to deliver antitumor drugs to enhance drug efficacy while reducing drug toxicity. This paper provides a review of the supportive role of MSCs in tumor progression and the associated molecular mechanisms. Additionally, we summarize the latest therapeutic strategies involving engineered MSCs and MSCs-EVs in cancer treatment, including their utilization as carriers for gene therapeutic agents, chemotherapeutics, and oncolytic viruses. We also discuss the distribution and clearance of MSCs and MSCs-EVs upon entry into the body to elucidate the potential of targeted therapies based on MSCs and MSCs-EVs in cancer treatment, along with the challenges they face.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

The Dual Role of Mesenchymal Stem Cells in Cancer Pathophysiology: Pro-Tumorigenic Effects versus Therapeutic Potential

Mesenchymal stem/stromal cells (MSCs) are multipotent cells involved in numerous physiological events, including organogenesis, the maintenance of tissue homeostasis, regeneration, or tissue repair. MSCs are increasingly recognized as playing a major, dual, and complex role in cancer pathophysiology through their ability to limit or promote tumor progression. Indeed, these cells are known to interact with the tumor microenvironment, modulate the behavior of tumor cells, influence their functions, and promote distant metastasis formation through the secretion of mediators, the regulation of cell-cell interactions, and the modulation of the immune response. This dynamic network can lead to the establishment of immunoprivileged tissue niches or the formation of new tumors through the proliferation/differentiation of MSCs into cancer-associated fibroblasts as well as cancer stem cells. However, MSCs exhibit also therapeutic effects including anti-tumor, anti-proliferative, anti-inflammatory, or anti-oxidative effects. The therapeutic interest in MSCs is currently growing, mainly due to their ability to selectively migrate and penetrate tumor sites, which would make them relevant as vectors for advanced therapies. Therefore, this review aims to provide an overview of the double-edged sword implications of MSCs in tumor processes. The therapeutic potential of MSCs will be reviewed in melanoma and lung cancers.

The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left-Right Functional Differences

Breast cancer is a complex and heterogeneous disease that displays diverse molecular subtypes and clinical outcomes. Although it is known that the location of tumors can affect their biological behavior, the underlying mechanisms are not fully understood. In our previous study, we found a differential methylation profile and membrane potential between left (L)- and right (R)-sided breast tumors. In this current study, we aimed to identify the ion channels responsible for this phenomenon and determine any associated phenotypic features. To achieve this, experiments were conducted in mammary tumors in mice, human patient samples, and with data from public datasets. The results revealed that L-sided tumors have a more depolarized state than R-sided. We identified a 6-ion channel-gene signature (CACNA1C, CACNA2D2, CACNB2, KCNJ11, SCN3A, and SCN3B) associated with the side: L-tumors exhibit lower expression levels than R-tumors. Additionally, in silico analyses show that the signature correlates inversely with DNA methylation writers and with key biological processes involved in cancer progression, such as proliferation and stemness. The signature also correlates inversely with patient survival rates. In an in vivo mouse model, we confirmed that KI67 and CD44 markers were increased in L-sided tumors and a similar tendency for KI67 was found in patient L-tumors. Overall, this study provides new insights into the potential impact of anatomical location on breast cancer biology and highlights the need for further investigation into possible differential treatment options.

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.

SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

MOTIVATION

As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists.

RESULTS

We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods.

AVAILABILITY AND IMPLEMENTATION

SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Selection of RNA-based evaluation methods for tumor microenvironment by comparing with histochemical and flow cytometric analyses in gastric cancer

Understanding the tumor microenvironment (TME) and anti-tumor immune responses in gastric cancer are required for precision immune-oncology. Taking advantage of next-generation sequencing technology, the feasibility and reliability of transcriptome-based TME analysis were investigated. TME of 30 surgically resected gastric cancer tissues was analyzed by RNA-Seq, immunohistochemistry (IHC) and flow cytometry (FCM). RNA-Seq of bulk gastric cancer tissues was computationally analyzed to evaluate TME. Computationally analyzed immune cell composition was validated by comparison with cell densities established by IHC and FCM from the same tumor tissue. Immune cell infiltration and cellular function were also validated with IHC and FCM. Cell proliferation and cell death in the tumor as assessed by RNA-Seq and IHC were compared. Computational tools and gene set analysis for quantifying CD8⁺ T cells, regulatory T cells and B cells, T cell infiltration and functional status, and cell proliferation and cell death status yielded an excellent correlation with IHC and FCM data. Using these validated transcriptome-based analyses, the immunological status of gastric cancer could be classified into immune-rich and immune-poor subtypes. Transcriptome-based TME analysis is feasible and is valuable for further understanding the immunological status of gastric cancer.

Tumor Microenvironment Profiling Identifies Prognostic Signatures and Suggests Immunotherapeutic Benefits in Neuroblastoma

The tumor microenvironment (TME) influences disease initiation and progression. Cross-talks of cells within TME can affect the efficacy of immunotherapies. However, a precise, concise, and comprehensive TME landscape in neuroblastoma (NB) has not been established. Here, we profiled the TME landscape of 498 NB-related patients on a self-curated gene list and identified three prognostic TMEsubgroups. The differentially expressed genes in these three TMEsubgroups were used to construct a genetic signature of the TME landscape and characterize three GeneSubgroups. The subgroup with the worst overall survival prognosis, the TMEsubgroup/GeneSubgroup3, lacked immune cell infiltration and received the highest scores of MYCN- and ALK-related signatures and lowest scores of immune pathways. Additionally, we found that the GeneSubgroup3 might be benefited from anti-GD2 instead of anti-PD-1 therapy. We further created a 48-gene signature, the TMEscore, to infer prognosis and validated it in three independent NB cohorts and a pan-cancer cohort of 9,460 patients. We did RNA-seq on 16 samples and verified that TMEscore was higher in patients with stage 3/4 than stage 1/2 diseases. The TMEscore could also predict responses for several immunotherapies. After adding clinical features, we found that the nomogram-based score system, the TMEIndex, surpassed the current risk system at predicting survivals. Our analysis explained TME at the transcriptome level and paved the way for immunotherapies in NB.

DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks

Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNNXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (denoted as DAISM-DNN) pipeline that trains a deep neural network (DNN) with dataset-specific training data populated from a certain amount of calibrated samples using DAISM, a novel data augmentation method with an in silico mixing strategy. The evaluation results demonstrate that the DAISM-DNN pipeline outperforms other existing methods consistently and substantially for all the cell types under evaluation in real-world datasets.

Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing

OBJECTIVES

To reveal the heterogeneity of different cell types of osteoarthritis (OA) synovial tissues at a single-cell resolution, and determine by novel methodology whether bulk-RNA-seq data could be deconvoluted to create in silico scRNA-seq data for synovial tissue analyses.

METHODS

OA scRNA-seq data (102,077 synoviocytes) were provided by 17 patients undergoing total knee arthroplasty; 9 tissues with matched scRNA-seq and bulk RNA-seq data were used to evaluate six in silico gene deconvolution tools. Predicted and observed cell types and proportions were compared to identify the best deconvolution tool for synovium.

RESULTS

We identified seven distinct cell types in OA synovial tissues. Gene deconvolution identified three (of six) platforms as suitable for extrapolating cellular gene expression from bulk RNA-seq data. Using paired scRNA-seq and bulk RNA-seq data, an "arthritis" specific signature matrix was created and validated to have a significantly better predictive performance for synoviocytes than a default signature matrix. Use of the machine learning tool, Cell-type Identification By Estimating Relative Subsets of RNA Transcripts x (CIBERSORTx), to analyze rheumatoid arthritis (RA) and OA bulk RNA-seq data yielded proportions of T cells and fibroblasts that were similar to the gold standard observations from RA and OA scRNA-seq data, respectively.

CONCLUSION

This novel study revealed heterogeneity of synovial cell types in OA and the feasibility of gene deconvolution for synovial tissue.

Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.

Methods

In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.

Results

We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.

Conclusion

Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis

A critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes.

Phenotypic Characterization by Mass Cytometry of the Microenvironment in Ovarian Cancer and Impact of Tumor Dissociation Methods

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the deadliest gynecological malignancy. Despite increasing research on HGSOC, biomarkers for individualized selection of therapy are scarce. In this study, we develop a multiparametric mass cytometry antibody panel to identify differences in the cellular composition of the microenvironment of tumor tissues dissociated to single-cell suspensions. We also investigate how dissociation methods impact results. Application of our antibody panel to HGSOC tissues showed its ability to identify established main cell subsets and subpopulations of these cells. Comparisons between dissociation methods revealed differences in cell fractions for one immune, two stromal, and three tumor cell subpopulations, while functional marker expression was not affected by the dissociation method. The interpatient disparities identified in the tumor microenvironment were more significant than those identified between differently dissociated tissues from one patient, indicating that the panel facilitates the mapping of individual tumor microenvironments in HGSOC patients.

Abstract

Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.

A novel co-culture assay to assess anti-tumor CD8+ T cell cytotoxicity via luminescence and multicolor flow cytometry

T cell immunotherapies have shown great promise in patients with advanced cancer disease, revolutionizing treatment. T cell cytotoxicity is crucial in its efficacy, therefore developing ex vivo methods testing tumor and T cell interactions is pivotal. Increasing efforts have been made in developing co-culture assays with sophisticated materials and platforms aiming to mimic the tumor microenvironment (TME), but its complexity makes it difficult to develop the ideal model. In this study, we developed a simple co-culture assay, reproducible in any lab, but respecting the multicellular nature of the TME. Our goal is to combine in a single assay well-established techniques such as a luciferase assay for target cell viability analysis, a CD107a degranulation assay, and multicolor flow cytometry for the detection of cytokines and cytotoxicity markers. Cell suspensions of whole spleens and tumors containing splenic or tumor-infiltrating effector T cells of mice bearing Lewis lung carcinoma (LLC) or CT26 colon carcinoma tumors treated with radiation alone or in combination with immunotherapies were used for co-culture. LLC and CT26 cell lines transduced with the firefly luciferase gene were used as target cells. We demonstrated that splenocytes and tumor-infiltrating T cells derived from mice treated with combination therapy were able to kill approximately 50% of target cells after 48 h of co-culture. This effect was tumor cell-specific and dependent on CD8+ T cells evidenced by in vitro CD8+ T cell depletion. Flow cytometry demonstrated increased expression of CD107a and production of granzyme B, IFNγ, and TNFα by CD8+ T cells. Our co-culture assay is therefore suitable as proof of principle for in vivo therapeutic studies testing immunotherapies, and specifically to assess the involvement of cytotoxic CD8+ T cells in treatment response in LLC and CT26 tumor models. We also propose this assay as an ex vivo platform for high-throughput screening of immunomodulating agents to be tested in these two murine tumor models. This assay can be adapted to other tumor models after optimizations.

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.

Identification of tumor-infiltrating immune cells and microenvironment-relevant genes in nasopharyngeal carcinoma based on gene expression profiling

AIMS

The purpose of this study was to investigate the prognostic significance of tumor-infiltrating immune cells and microenvironment-relevant genes in nasopharyngeal carcinoma (NPC) and their correlations.

MATERIALS AND METHODS

The "xCell" algorithm was used to calculate the enrichment scores for 33 immune cells in the samples of GSE12452, GSE40290, GSE53819, GSE68799, and GSE102349. The difference of immune cells between NPC group and non-cancerous group and the prognostic value of the immune cells were analyzed. Besides, based on the Microenvironment scores, the differentially expressed genes (DEGs) between the high- and low-score groups were screened to identify the microenvironment-relevant hub genes. Furthermore, the DEGs were used to establish a risk score model for predicting progression-free survival (PFS) via LASSO penalized Cox regression.

KEY FINDINGS

The scores of B-cells and Memory B-cells of NPC were significantly lower than those of non-cancerous tissues, and they were positively associated with PFS. Moreover, 10 hub genes (PTPRC, CD19, CD79B, BTK, CD79A, SELL, MS4A1, CD38, CD52, and CD22) were identified and positively correlated with B-cells, Memory B-cells, and Microenvironment scores in GSE12452, GSE68799, and GSE102349. High expression levels of CD22, CD38, CD79B, MS4A1, SELL, and PTPRC were associated with longer PFS. Besides, a risk score model composed of DARC, IL33, IGHG1, and SLC6A8 was established with a good performance for PFS prediction.

SIGNIFICANCE

These results enhance our understanding of the composition and prognostic significance of tumor-infiltrating immune cells in NPC lesions, and provide potential targets for prognostication and immunotherapy for NPC patients.

The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression

Quantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases. While numerous methods can quantify immune or stromal cells in human tissue samples from transcriptomic data, few are available for mouse studies. We introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that accurately quantify 16 immune and stromal murine cell populations. We validated mMCP-counter with flow cytometry data and showed that mMCP-counter outperforms existing methods. We showed that mMCP-counter scores are predictive of response to immune checkpoint blockade in cancer mouse models and identify early immune impacts of Alzheimer's disease.

Dendritic cells in the tumor microenvironment: prognostic and theranostic impact

Among all immune cells, dendritic cells (DC) are the most potent APCs in the immune system and are central players of the adaptive immune response. There are phenotypically and functionally distinct DC populations derived from blood and lymphoid organ including plasmacytoid DC (pDC), conventional DC (cDC1 and cDC2) and monocyte-derived DC (moDC). The interaction between these different DCs and tumors is a dynamic process where DC-mediated cross-priming of tumor specific T cells is critical in initiating and sustaining anti-tumor immunity. Their presence within the tumor tends to induce T cell responses and to reduce cancer progression and is associated with improved patient survival. This review will focus on the distinct tumor-associated DCs (TADC) subsets in the tumor microenvironment (TME), their roles in tumor immunology and their prognostic and/or predictive impact in human cancers. The development of therapeutic immunity strategies targeting TADC is promising to enhance their immune-stimulatory capacity in cancers and improve the efficacy of current immunotherapies including immune checkpoint inhibitor (ICI) blockade and DC-based therapies.

Identification of Key Genes of Prognostic Value in Clear Cell Renal Cell Carcinoma Microenvironment and a Risk Score Prognostic Model

Objective

We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model.

Materials and Methods

Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores.

Results

The median survival time of the low immune score group was longer than that of the high immune score group (p = 0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p < 0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237-1.0641, p < 0.0001).

Conclusions

Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

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.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Novel Nuclear Medicine Imaging Applications in Immuno-Oncology

The global immuno-oncology pipeline has grown progressively in recent years, leading cancer immunotherapy to become one of the main issues of the healthcare industry. Despite their success in the treatment of several malignancies, immune checkpoint inhibitors (ICIs) perform poorly in others. Again, ICIs action depends on such a multitude of clinico-pathological features, that the attempt to predict responders/long-responders with ad-hoc built immunograms revealed to be quite complex. In this landscape, the role of nuclear medicine might be crucial, with first interesting evidences coming from small case series and pre-clinical studies. Positron-emission tomography (PET) techniques provide functional information having a predictive and/or prognostic value in patients treated with ICIs or adoptive T-cell therapy. Recently, a characterization of the tumor immune microenvironment (TiME) pattern itself has been shown to be feasible through the use of different radioactive tracers or image algorithms, thus adding knowledge about tumor heterogeneity. Finally, nuclear medicine exams permit an early detection of immune-related adverse events (irAEs), with on-going clinical trials investigating their correlation with patients’ outcome. This review depicts the recent advances in molecular imaging both in terms of non-invasive diagnosis of TiME properties and benefit prediction from immunotherapeutic agents.

The Tumor Microenvironment in the Response to Immune Checkpoint Blockade Therapies

Tumor cells constantly interact with their microenvironment, which comprises a variety of immune cells together with endothelial cells and fibroblasts. The composition of the tumor microenvironment (TME) has been shown to influence response to immune checkpoint blockade (ICB). ICB takes advantage of immune cell infiltration in the tumor to reinvigorate an efficacious antitumoral immune response. In addition to tumor cell intrinsic biomarkers, increasing data pinpoint the importance of the TME in guiding patient selection and combination therapies. Here, we review recent efforts in determining how various components of the TME can influence response and resistance to ICB. Although a large body of evidence points to the extent and functional orientation of the T cell infiltrate as important in therapy response, recent studies also confirm a role for other components of the TME, such as B cells, myeloid lineage cells, cancer-associated fibroblasts, and vasculature. If the ultimate goal of curative cancer therapies is to induce a long-term memory T cell response, the other components of the TME may positively or negatively modulate the induction of efficient antitumor immunity. The emergence of novel high-throughput methods for analyzing the TME, including transcriptomics, has allowed tremendous developments in the field, with the expansion of patient cohorts, and the identification of TME-based markers of therapy response. Together, these studies open the possibility of including TME-based markers for selecting patients that are likely to respond to specific therapies, and pave the way to personalized medicine in oncology.

Tumor immune microenvironment modulation-based drug delivery strategies for cancer immunotherapy

The past years have witnessed promising clinical feedback for anti-cancer immunotherapies, which have become one of the hot research topics; however, they are limited by poor delivery kinetics, narrow patient response profiles, and systemic side effects. To the best of our knowledge, the development of cancer is highly associated with the immune system, especially the tumor immune microenvironment (TIME). Based on the comprehensive understanding of the complexity and diversity of TIME, drug delivery strategies focused on the modulation of TIME can be of great significance for directing and improving cancer immunotherapy. This review highlights the TIME modulation in cancer immunotherapy and summarizes the versatile TIME modulation-based cancer immunotherapeutic strategies, medicative principles and accessory biotechniques for further clinical transformation. Remarkably, the recent advances of cancer immunotherapeutic drug delivery systems and future prospects of TIME modulation-based drug delivery systems for much more controlled and precise cancer immunotherapy will be emphatically discussed.

EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data

Gene expression profiling is nowadays routinely performed on clinically relevant samples (e.g., from tumor specimens). Such measurements are often obtained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor in downstream analyses.Many tools have been developed to estimate the proportion of the different cell types from bulk gene expression data. Here, we provide guidelines and examples on how to use these tools, with a special focus on our recent computational method EPIC (Estimating the Proportions of Immune and Cancer cells). EPIC includes RNA-seq-based gene expression reference profiles from immune cells and other nonmalignant cell types found in tumors. EPIC can additionally manage user-defined gene expression reference profiles. Some unique features of EPIC include the ability to account for an uncharacterized cell type, the introduction of a renormalization step to account for different mRNA content in each cell type, and the use of single-cell RNA-seq data to derive biologically relevant reference gene expression profiles. EPIC is available as a web application ( http://epic.gfellerlab.org ) and as an R-package ( https://github.com/GfellerLab/EPIC ).

Bioinformatics for Cancer Immunotherapy

Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.

Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data

Since the performance of in silico approaches for estimating immune-cell fractions from bulk RNA-seq data can vary, it is often advisable to compare results of several methods. Given numerous dependencies and differences in input and output format of the various computational methods, comparative analyses can become quite complex. This motivated us to develop immunedeconv, an R package providing uniform and user-friendly access to seven state-of-the-art computational methods for deconvolution of cell-type fractions from bulk RNA-seq data. Here, we show how immunedeconv can be installed and applied to a typical dataset. First, we give an example for obtaining cell-type fractions using quanTIseq. Second, we show how dimensionless scores produced by MCP-counter can be used for cross-sample comparisons. For each of these examples, we provide R code illustrating how immunedeconv results can be summarized graphically.

The Role of the Lymphocyte Functional Crosstalk and Regulation in the Context of Checkpoint Inhibitor Treatment-Review

During the last decade, the dynamics of the cellular crosstalk have highlighted the significance of the host vs. tumor interaction. This resulted in the development of novel immunotherapeutic strategies in order to modulate/inhibit the mechanisms leading to escape of tumor cells from immune surveillance. Different monoclonal antibodies directed against immune checkpoints, e.g., the T lymphocyte antigen 4 and the programmed cell death protein 1/ programmed cell death ligand 1 have been successfully implemented for the treatment of cancer. Despite their broad activity in many solid and hematologic tumor types, only 20–40% of patients demonstrated a durable treatment response. This might be due to an impaired T cell tumor interaction mediated by immune escape mechanisms of tumor and immune cells as well as alterations in the composition of the tumor microenvironment, peripheral blood, and microbiome. These different factors dynamically regulate different steps of the cancer immune process thereby negatively interfering with the T cell –mediated anti-tumoral immune responses. Therefore, this review will summarize the current knowledge of the different players involved in inhibiting tumor immunogenicity and mounting resistance to checkpoint inhibitors with focus on the role of tumor T cell interaction. A better insight of this process might lead to the development of strategies to revert these inhibitory processes and represent the rational for the design of novel immunotherapies and combinations in order to improve their efficacy.

The tumour microenvironment as an integrated framework to understand cancer biology

Cancer cells all share the feature of being immersed in a complex environment with altered cell-cell/cell-extracellular element communication, physicochemical information, and tissue functions. The so-called tumour microenvironment (TME) is becoming recognised as a key factor in the genesis, progression and treatment of cancer lesions. Beyond genetic mutations, the existence of a malignant microenvironment forms the basis for a new perspective in cancer biology where connections at the system level are fundamental. From this standpoint, different aspects of tumour lesions such as morphology, aggressiveness, prognosis and treatment response can be considered under an integrated vision, giving rise to a new field of study and clinical management. Nowadays, somatic mutation theory is complemented with study of TME components such as the extracellular matrix, immune compartment, stromal cells, metabolism and biophysical forces. In this review we examine recent studies in this area and complement them with our own research data to propose a classification of stromal changes. Exploring these avenues and gaining insight into malignant phenotype remodelling, could reveal better ways to characterize this disease and its potential treatment.

Prognostic impact of immune gene expression signature and tumor infiltrating immune cells in localized clear cell renal cell carcinoma

Background

The tumor immune microenvironment has become the focus of research in clear cell renal cell carcinoma (ccRCC) due to its important role in immune surveillance post nephrectomy. This study investigates the correlation of tumor infiltrating immune cell characteristics with rates of recurrence following surgery in localized ccRCC.

Methods

We morphologically identified and scored tumor infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) stained slides of patients with localized ccRCC (stage ≥T1b excluding stage IV). The University of Alabama at Birmingham (UAB) dataset (n = 159) was used to discover and the Fox Chase Cancer Center (FCCC) dataset (n = 198) was used to validate the results of morphologic immune cell analysis. We then performed gene expression analysis using the Immune Profile panel by NanoString in the UAB cohort and identified immune cells and pathways associated with recurrence, followed by validation in the Cancer Genome Atlas (TCGA) ccRCC dataset. Infiltrating immune cell types were identified by gene expression deconvolution.

Results

The presence of TILs identified by morphology correlated with higher T cell, Th1, CD8+ T and Treg gene signatures. Recurrence was associated with lower T cells and higher neutrophils. Higher Teffector (Teff)/Treg ratio correlated with lower rate of recurrence and was validated in the TCGA dataset. Genes associated with adaptive immune response were downregulated in tumors that recurred. Unsupervised hierarchical clustering identified a subset of patients with over-expression of adaptive response genes including CD8, CD3, GZMA/B, PRF1, IDO1, CTLA4, PDL1, ICOS and TIGIT. These patients had higher morphologic lymphocyte infiltration and T cell gene expression. Higher levels of TILs identified by morphology correlated with higher rates of recurrence in our discovery dataset but not in our validation set.

Conclusions

Recurrence of ccRCC following surgery was associated with lower T cell infiltrate, lower adaptive immune response and higher neutrophil gene expression. Presence of higher Teff/Treg ratio correlated with lower recurrence.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0621-1) contains supplementary material, which is available to authorized users.

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data.quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients' responses to checkpoint blockers.Availability: quanTIseq is available at http://icbi.at/quantiseq .