Systems immune monitoring in cancer therapy

Immune-monitoring of myelodysplastic neoplasms: Recommendations from the i4MDS consortium

Advancements in comprehending myelodysplastic neoplasms (MDS) have unfolded significantly in recent years, elucidating a myriad of cellular and molecular underpinnings integral to disease progression. While molecular inclusions into prognostic models have substantively advanced risk stratification, recent revelations have emphasized the pivotal role of immune dysregulation within the bone marrow milieu during MDS evolution. Nonetheless, immunotherapy for MDS has not experienced breakthroughs seen in other malignancies, partly attributable to the absence of an immune classification that could stratify patients toward optimally targeted immunotherapeutic approaches. A pivotal obstacle to establishing "immune classes" among MDS patients is the absence of validated accepted immune panels suitable for routine application in clinical laboratories. In response, we formed International Integrative Innovative Immunology for MDS (i4MDS), a consortium of multidisciplinary experts, and created the following recommendations for standardized methodologies to monitor immune responses in MDS. A central goal of i4MDS is the development of an immune score that could be incorporated into current clinical risk stratification models. This position paper first consolidates current knowledge on MDS immunology. Subsequently, in collaboration with clinical and laboratory specialists, we introduce flow cytometry panels and cytokine assays, meticulously devised for clinical laboratories, aiming to monitor the immune status of MDS patients, evaluating both immune fitness and identifying potential immune "risk factors." By amalgamating this immunological characterization data and molecular data, we aim to enhance patient stratification, identify predictive markers for treatment responsiveness, and accelerate the development of systems immunology tools and innovative immunotherapies.

A multicenter phase Ib trial of the histone deacetylase inhibitor entinostat in combination with pembrolizumab in patients with myelodysplastic syndromes/neoplasms or acute myeloid leukemia refractory to hypomethylating agents

Patients with myelodysplastic syndromes/neoplasms (MDS) or acute myeloid leukemia (AML) with hypomethylating agent failure have a poor prognosis. Myeloid-derived suppressor cells (MDSCs) can contribute to MDS progression and mediate resistance to anti-PD1 therapy. As histone deacetylase inhibitors (HDACi) decrease MDSCs in preclinical models, we conducted an investigator-initiated, NCI-Cancer Therapy Evaluation Program-sponsored, multicenter, dose escalation, and expansion phase Ib trial (NCT02936752) of the HDACi entinostat and the anti-PD1 antibody pembrolizumab. Twenty-eight patients (25 MDS and 3 AML) were enrolled. During dose escalation (n=13 patients), there was one dose-limiting toxicity (DLT) on dose level (DL) 1 (G5 pneumonia/bronchoalveolar hemorrhage) and two DLTs at DL 2 (G3 pharyngeal mucositis and G3 anorexia). Per the 3 + 3 dose escalation design, DL 1 (entinostat 8 mg PO days 1 and 15 + pembrolizumab 200 mg IV day 1 every 21 days) was expanded and another 15 patients were enrolled. Hematologic adverse events (AEs) were common. The most common non-hematologic ≥G3 AEs were infection (32%), hypoxia/respiratory failure (11%), and dyspnea (11%). There were no protocol-defined responses among the 28 patients enrolled. Two patients achieved a marrow complete remission (mCR). Using a systems immunology approach with mass cytometry and machine learning analysis, mCR patients had increased classical monocytes and macrophages but there was no significant change of MDSCs. In conclusion, combining entinostat with pembrolizumab in patients with advanced MDS and AML was associated with limited clinical efficacy and substantial toxicity. Absence of an effect on MDSCs could be a potential explanation for the limited efficacy of this combination. ClinicalTrial.gov Identifier: NCT02936752.

Intravesical oncolytic virotherapy and immunotherapy for non-muscle-invasive bladder cancer mouse model

OBJECTIVES

To test if intravesical instillation of both an anti-programmed cell death protein 1 (PD-1) inhibitor and an oncolytic reovirus would demonstrate a greater effect than either treatment alone, as non-muscle-invasive bladder cancer that is refractory to intravesical bacillus Calmette-Guérin can be treated by systemic anti-PD-1 immunotherapy and we previously demonstrated improved overall survival (OS) with six once-weekly instillations of intravesical anti-PD-1 in a murine model.

MATERIALS AND METHODS

Using an orthotopic syngeneic C3H murine model of MBT2 urothelial bladder cancer, groups of 10 mice were compared between no treatment, intravesical anti-PD-1, intravesical oncolytic reovirus, or intravesical reovirus + anti-PD-1. A single intravesical treatment session was given. The primary outcome was OS, and the secondary outcomes included long-term immunity and tumour-immune profile.

RESULTS

With a median follow-up of 9 months, all mice that received no treatment died with a median survival of 41 days, while the comparison median OS was not reached for reovirus (hazard ratio [HR] 14.4, 95% confidence interval [CI] 3.9-32.6; P < 0.001), anti-PD-1 (HR 28.4, 95% CI 7.0-115.9; P < 0.001), and reovirus + anti-PD-1 (HR 28.4, 95% CI 7.0-115.9; P < 0.001). Monotherapy with anti-PD-1 or reovirus demonstrated no significant differences in survival (P = 0.067). Mass cytometry showed that reovirus + anti-PD-1 treatment enriched monocytes and decreased myeloid-derived suppressor cells, generating an immuno-responsive tumour microenvironment. Depletion of CD8+ T cells eliminated the survival advantage provided by the intravesical treatment.

CONCLUSIONS

Treatment of murine orthotopic bladder tumours with a single instillation of intravesical reovirus, anti-PD-1 antibody, or the combination confers superior survival compared to controls. Tumour-immune microenvironment differences indicated myeloid-derived suppressor cells and CD8+ T cells mediate the treatment response.

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

Boosting the Immune Response—Combining Local and Immune Therapy for Prostate Cancer Treatment

Due to its slow progression and susceptibility to radical forms of treatment, low-grade PC is associated with high overall survival (OS). With the clinical progression of PC, the therapy is becoming more complex. The immunosuppressive tumor microenvironment (TME) makes PC a difficult target for most immunotherapeutics. Its general immune resistance is established by e.g., immune evasion through Treg cells, synthesis of immunosuppressive mediators, and the defective expression of surface neoantigens. The success of sipuleucel-T in clinical trials initiated several other clinical studies that specifically target the immune escape of tumors and eliminate the immunosuppressive properties of the TME. In the settings of PC treatment, this can be commonly achieved with radiation therapy (RT). In addition, focal therapies usually applied for localized PC, such as high-intensity focused ultrasound (HIFU) therapy, cryotherapy, photodynamic therapy (PDT), and irreversible electroporation (IRE) were shown to boost the anti-cancer response. Nevertheless, the present guidelines restrict their application to the context of a clinical trial or a prospective cohort study. This review explains how RT and focal therapies enhance the immune response. We also provide data supporting the combination of RT and focal treatments with immune therapies.

Routine flow cytometry approach for the evaluation of solid tumor neoplasms and immune cells in minimally invasive samples

BACKGROUND

Multidimensional flow cytometry (MFC) is routinely used for the diagnosis and follow-up of hematolymphoid neoplasms but its contribution to the identification of non-hematolymphoid malignant tumors is limited.

METHODS

The presence of non-hematolymphoid cells in clinical samples obtained via minimally invasive methods was ascertained by using a panel of monoclonal antibodies previously developed in our laboratory comprising a mixture of antibodies: CD9-PacB/CD45-OC515/CD57-FITC/CD56-PE/CD3-PerCP-Cy5.5/CD117-PE-Cy7/CD326-APC/CD81-APC-C750. Histopathological studies were performed using standard techniques.

RESULTS

164 specimens of different origins were included. Malignancy was finally confirmed in 142 (86.5%), while 22 non neoplastic samples were identified. The most frequent diagnosis was small cell lung carcinoma (SCLC) (50%). High sensitivity (S = 98.6%) was reached combining MFC and conventional pathology. Individual markers differed according to the cellular origin of the neoplasm, with neuroendocrine tumors showing a unique immunophenotypic profile (CD56+ CD326+ CD117-/+ and variable tetraspanins expression). Principal component analysis efficiently distinguished SCLC from other tumor samples. In immune cell populations, differences between reactive and malignant biopsies were found in different cell compartments, especially in B cells and Plasma cells. Differences also emerged in the percentage of CD4+ CD8- T cells, CD4-CD8+ T cells and NK cells and these were dependent on the origin of the tumor cells.

CONCLUSIONS

These results support the use of MFC as a rapid and valuable technique to detect non-hematolymphoid tumoral cells in clinical specimens, providing an initial orientation to complement hystopathological studies and allow a more precise diagnosis, especially in neuroendocrine neoplasms. The impact of different immune cell patterns warrants further research.

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Two New Neutrophil Subsets Define a Discriminating Sepsis Signature

RATIONALE

Sepsis is the leading cause of death in adult intensive care units. At present, sepsis diagnosis relies on non-specific clinical features. It could transform clinical care to have immune cell biomarkers that could predict sepsis diagnosis and guide treatment. For decades, neutrophil phenotypes have been studied in sepsis, but a diagnostic cell subset has yet to be identified.

OBJECTIVES

To identify an early specific immune signature of sepsis severity that does not overlap with other inflammatory biomarkers, and that distinguishes patients with sepsis from those with non- infectious inflammatory syndrome.

METHODS

Mass cytometry combined with computational high-dimensional data analysis were used to measure 42 markers on whole blood immune cells from sepsis patients and controls, and automatically and comprehensively characterize circulating immune cells, which enables identification of novel, disease-specific cellular signatures.

MEASUREMENTS AND MAIN RESULTS

Unsupervised analysis of high-dimensional mass cytometry data characterized previously unappreciated heterogeneity within the CD64+ immature neutrophils and revealed two new subsets distinguished by CD123 and PD-L1 expression. These immature neutrophils exhibited diminished activation and phagocytosis functions. The proportion of CD123-expressing neutrophils correlated with clinical severity.

CONCLUSIONS

This study showed that these two new neutrophil subsets were specific to sepsis and detectable by routine flow cytometry using seven markers. The demonstration here that a simple blood test distinguishes sepsis from other inflammatory conditions represents a key biological milestone that can be immediately translated into improvements in patient care.

DebarcodeR increases fluorescent cell barcoding capacity and accuracy

Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between different cell types and Gaussian mixture modeling provided a robust method to probabilistically assign cells to samples. Assignment tolerance parameters are available to allow users to balance high cell recovery with accurate assignments. Improvements to experimental methods include: (1) inclusion of an "external standard" control where a pool of all cells was stained a single level of each barcoding dyes and (2) an "internal standard" where each cell is stained with a single level of a separate dye. DebarcodeR significantly improved speed, accuracy, and reproducibility of FCB while avoiding selective loss of unusual cell subsets when debarcoding microtiter plates of cell lines and heterogenous mixtures of primary cells. DebarcodeR is available on Github as an R package that works with flowCore and Cytoverse packages at github.com/cytolab/DebarcodeR.

High-dimensional immune-profiling in cancer: implications for immunotherapy

Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data

Summary

Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework’s analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research.

Availability and implementation

CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2.

Supplementary information

Supplementary data are available at Bioinformatics online.

In vitro assessment of antitumor immune responses using tumor antigen proteins produced by transgenic silkworms

The evaluation of antitumor immune responses is essential for immune monitoring to predict clinical outcomes as well as treatment efficacies in cancer patients. In this study, we produced two tumor antigen (TA) proteins, melanoma antigen family A4 and wild type p53, using TG silkworm systems and evaluated anti-TA-specific immune responses by enzyme-linked immunosorbent spot assays in patients with head and neck cancer. Eleven (61.1%) of 18 patients showed significant IFN-γ production in response to at least one TA; however, the presence of TA-specific immune responses did not significantly contribute to better prognosis (overall survival, p = 0.1768; progression-free survival, p = 0.4507). Further studies will need to be performed on a larger scale to better assess the clinical significance of these systems. The production of multiple TA proteins may provide new avenues for the development of immunotherapeutic strategies to stimulate a potent and specific immune response against tumor cells as well as precise assessment of antitumor immune responses in cancer patients.

Modulation of intratumoural myeloid cells, the hallmark of the anti-tumour efficacy induced by a triple combination: tumour-associated peptide, TLR-3 ligand and α-PD-1

BACKGROUND

Anti-programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) monoclonal antibodies (mAbs) show remarkable clinical anti-tumour efficacy. However, rational combinations are needed to extend the clinical benefit to primary resistant tumours. The design of such combinations requires the identification of the kinetics of critical immune cell populations in the tumour microenvironment.

METHODS

In this study, we compared the kinetics of immune cells in the tumour microenvironment upon treatment with immunotherapy combinations with different anti-tumour efficacies in the non-inflamed tumour model TC-1/A9. Tumour-bearing C57BL/6J mice were treated with all possible combinations of a human papillomavirus (HPV) E7 long peptide, polyinosinic-polycytidylic acid (PIC) and anti-PD-1 mAb. Tumour growth and kinetics of the relevant immune cell populations were assessed over time. The involvement of key immune cells was confirmed by depletion with mAbs and immunophenotyping with multiparametric flow cytometry.

RESULTS

The maximum anti-tumour efficacy was achieved after intratumoural administration of HPV E7 long peptide and PIC combined with the systemic administration of anti-PD-1 mAb. The intratumoural immune cell kinetics of this combination was characterised by a biphasic immune response. An initial upsurge of proinflammatory myeloid cells led to a further rise in effector CD8⁺ T lymphocytes at day 8. Depletion of either myeloid cells or CD8⁺ T lymphocytes diminished the anti-tumour efficacy of the combination.

CONCLUSIONS

The anti-tumour efficacy of a successful immunotherapy combination in a non-inflamed tumour model relies on an early inflammatory process that remodels the myeloid cell compartment.

A Palette of Cytokines to Measure Anti-Tumor Efficacy of T Cell-Based Therapeutics

Cytokines are key molecules within the tumor microenvironment (TME) that can be used as biomarkers to predict the magnitude of anti-tumor immune responses. During immune monitoring, it has been customary to predict outcomes based on the abundance of a single cytokine, in particular IFN-γ or TGF-β, as a readout of ongoing anti-cancer immunity. However, individual cytokines within the TME can exhibit dual opposing roles. For example, both IFN-γ and TGF-β have been associated with pro- and anti-tumor functions. Moreover, cytokines originating from different cellular sources influence the crosstalk between CD4+ and CD8+ T cells, while the array of cytokines expressed by T cells is also instrumental in defining the mechanisms of action and efficacy of treatments. Thus, it becomes increasingly clear that a reliable readout of ongoing immunity within the TME will have to include more than the measurement of a single cytokine. This review focuses on defining a panel of cytokines that could help to reliably predict and analyze the outcomes of T cell-based anti-tumor therapies.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review

The landscape for medical treatment of lung cancer has irreversibly changed since the development of immuno-oncology (IO). Yet, while immune checkpoint blockade (ICB) revealed that T lymphocytes play a major role in lung cancer, the precise dynamic of innate and adaptive immune cells induced by anticancer treatments including chemotherapy, targeted therapy, and/or ICB is poorly understood. In lung cancer, studies evaluating specific immune cell populations as predictors of response to medical treatment are scarce, and knowledge is fragmented. Here, we review the different techniques allowing the detection of immune cells in the tumor and blood (multiplex immunohistochemistry and immunofluorescence, RNA-seq, DNA methylation pattern, mass cytometry, functional tests). In addition, we present data that consider different baseline immune cell populations as predictors of response to medical treatments of lung cancer. We also review the potential for assessing dynamic changes in cell populations during treatment as a biomarker. As powerful tools for immune cell detection and data analysis are available, clinicians and researchers could increase understanding of mechanisms of efficacy and resistance in addition to identifying new targets for IO by developing translational studies that decipher the role of different immune cell populations during lung cancer treatments.

The Paradox of Cancer Immune Exclusion: Immune Oncology Next Frontier

Checkpoint inhibitor therapy (CIT) has revolutionized cancer treatment but it has also reached a standstill when an absent dialog between cancer and immune cells makes it irrelevant. This occurs with high prevalence in the context of "immune silent" and, even perhaps, "immune-excluded" tumors. The latter are characterized by T cells restricted to the periphery of cancer nests. Since in either case T cells do not come in direct contact with most cancer cells, CIT rests immaterial. Adoptive cell therapy (ACT), may also be affected by limited access to antigen-bearing cancer cells. While lack of immunogenicity intuitively explains the immune silent phenotype, immune exclusion is perplexing. The presence of T cells at the periphery suggests that chemo-attraction recruits them and an immunogenic stimulus promotes their persistence. However, what stops the T cells from infiltrating the tumors' nests and reaching the germinal center (GC)? Possibly, a concentric gradient of increased chemo-repulsion or decreased chemo-attraction demarcates an abrupt "do not trespass" warning. Various hypotheses suggest physical or functional barriers but no definitive consensus exists over the weight that each plays in human cancers. On one hand, it could be hypothesized that the intrinsic biology of cancer cells may degenerate from a "cancer stem cell" (CSC)-like phenotype in the GC toward a progressively more immunogenic phenotype prone to immunogenic cell death (ICD) at the periphery. On the other hand, the intrinsic biology of the cancer cells may not change but it is the disorderly architecture of the tumor microenvironment (TME) that alters in a centripetal direction cancer cell metabolism, both directly and indirectly, the function of surrounding stromal cells. In this chapter, we examine whether the paradoxical exclusion of T cells from tumors may serve as a model to understand the requirements for tumor immune infiltration and, correspondingly, we put forth strategies to restore the dialog between immune cells and cancer to enhance the effectiveness of immune oncology (IO) approaches.

Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types

Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information, incorporate changes over time, and address diverse immune monitoring challenges. The four complementary properties characterized were (i) systemic plasticity, (ii) change in population abundance, (iii) change in signature population features, and (iv) novelty of cellular phenotype. Three systems immune monitoring studies were selected to challenge this ensemble approach. In serial biopsies of melanoma tumors undergoing targeted therapy, the ensemble approach revealed enrichment of double-negative (DN) T cells. Melanoma tumor-resident DN T cells were abnormal and phenotypically distinct from those found in nonmalignant lymphoid tissues, but similar to those found in glioblastoma and renal cell carcinoma. Overall, ensemble systems immune monitoring provided a robust, quantitative view of changes in both the system and cell subsets, allowed for transparent review by human experts, and revealed abnormal immune cells present across multiple human tumor types.

Topography of cancer-associated immune cells in human solid tumors

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.

The role of automated cytometry in the new era of cancer immunotherapy

The introduction in the clinical practice of several new approaches to cancer immunotherapy has greatly increased the interest in analytical methodologies that can define the immunological profile of patients in the clinical setting. This requires huge effort to obtain reliable monitoring tools that could be used to improve the patient's clinical outcome. The clinical applications of flow cytometry (FCM) in oncology started with the measurement of DNA content for the evaluation of both ploidy and cell cycle profile as potential prognostic parameters in the majority of human solid cancer types. The availability of monoclonal antibodies widely broadened the spectrum of clinical applications of this technique, which rapidly became a fundamental tool for the diagnosis and prognosis of malignant hematological diseases. Among the emerging clinical applications of FCM, the study of minimal residual disease in hematological malignancies, the quantification of blood dendritic cells in various types of tumors, the study of metastatic spread in solid tumors throughout both the analysis of circulating endothelial progenitor cells and the identification and characterization of circulating tumor cells, all appear very promising. More recently, an advanced single cell analysis technique has been developed that combines the precision of mass spectrometry with the unique advantages of FCM. This approach, termed mass cytometry, utilizes antibodies conjugated to heavy metal ions for the analysis of cellular proteins by a mass spectrometer. It provides measurement of over 100 simultaneous cellular parameters in a single sample and has evolved from a promising technology to a high recognized platform for multi-dimensional single-cell analysis. Should a careful standardization of the analytical procedures be reached, both FCM and mass cytometry could effectively become ideal tools for the optimization of new immunotherapeutic approaches in cancer patients.

Current status and future directions of cancer immunotherapy

In the past decades, our knowledge about the relationship between cancer and the immune system has increased considerably. Recent years' success of cancer immunotherapy including monoclonal antibodies (mAbs), cancer vaccines, adoptive cancer therapy and the immune checkpoint therapy has revolutionized traditional cancer treatment. However, challenges still exist in this field. Personalized combination therapies via new techniques will be the next promising strategies for the future cancer treatment direction.

Genomics and emerging biomarkers for immunotherapy of colorectal cancer

Colorectal cancer (CRC) is a common and lethal disease with a high therapeutic need. For most patients with metastatic CRC, chemotherapy is the only viable option. Currently, immunotherapy is restricted to the particular genetic subgroup of mismatch-repair deficient (MMRd)/microsatellite instable (MSI) CRC. Anti-PD1 therapy was recently FDA-approved as a second-line treatment in this subgroup. However, in a metastatic setting, these MMRd/MSI tumors are vastly outnumbered by mismatch-repair proficient (MMRp)/microsatellite stable (MSS) tumors. These MMRp/MSS tumors do not meaningfully respond to any traditional immunotherapy approach including checkpoint blockade, adoptive cell transfer and vaccination. This resistance to immunotherapy is due to a complex tumor microenvironment that counteracts antitumor immunity through a combination of poorly antigenic tumor cells and an immunosuppressive tumor microenvironment. To find ways of overcoming immunotherapy resistance in the majority of CRC patients, it is necessary to analyze the immunological makeup in an in-depth and personalized way and in the context of their tumor genetic makeup. Flexible, biomarker-guided early-phase immunotherapy trials are needed to optimize this workflow. In this review, we detail key mechanisms for immune evasion and emerging immune biomarkers for personalized immunotherapy in CRC. Also, we present a template for biomarker-guided clinical trials that are needed to move new immunotherapy approaches closer to clinical application.

Discovery of human cell selective effector molecules using single cell multiplexed activity metabolomics

Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations.

Mass Cytometry of Follicular Lymphoma Tumors Reveals Intrinsic Heterogeneity in Proteins Including HLA-DR and a Deficit in Nonmalignant Plasmablast and Germinal Center B-Cell Populations

BACKGROUND

Follicular lymphoma (FL) is an indolent non-Hodgkin lymphoma that has a risk of transformation to more aggressive lymphoma. Relatively little is known about the nonmalignant B-cell and T-cell subset composition within the tumor microenvironment and whether altered phenotypes are associated with patterns of lymphoma B-cell heterogeneity.

METHODS

Two mass cytometry (CyTOF) panels were designed to immunophenotype B and T cells in FL tumors. Populations of malignant B cells, nonmalignant B cells, and T cells from each FL tumor were identified and their phenotypes compared to B and T cells from healthy human tonsillar tissue.

RESULTS

Diversity in cellular phenotype between tumors was greater for the malignant B cells than for nonmalignant B or T cells. The malignant B-cell population bore little phenotypic similarity to any healthy B-cell subset, and unexpectedly clustered closer to naïve B-cell populations than GC B-cell populations. Among the nonmalignant B cells within FL tumors, a significant lack of GC and plasmablast B cells was observed relative to tonsil controls. In contrast, nonmalignant T cells in FL tumors were present at levels similar to their cognate tonsillar T-cell subsets.

CONCLUSION

Mass cytometry revealed that diverse HLA-DR expression on FL cells within individual tumors contributed greatly to tumor heterogeneity. Both malignant and nonmalignant B cells in the tumor bore little phenotypic resemblance to healthy GC B cells despite the presence of T follicular helper cells in the tumor. These findings suggest that ongoing signaling interactions between malignant B cells and intra-tumor T cells shape the tumor microenvironment. © 2016 International Clinical Cytometry Society.

Resistance to immunotherapy: clouds in a bright sky

Two major challenges persist for an optimal management of immunotherapy: i) identifying those patients who will benefit from this type of therapy, and ii) determining the biological, cellular and molecular mechanisms that trigger disease progression while on therapy. There is a consensual view in favor of standardizing practices currently used to measure programmed death ligand 1 (PD-L1) expression that relates to innate resistance. The tumor mutation landscape has been widely explored as a potential predictor of treatment efficacy. In contrast, our knowledge is rather limited as concerns the mechanisms sustaining acquired resistance to checkpoint blockade immunotherapy in patients under treatment. Upregulation of T cell immunoglobulin mucin domain 3 (TIM-3) in CD8+ T-cells has been reported in patients developing acquired resistance to anti-PD-1 treatment. Resistance mechanisms are even more complex for combinatorial strategies linking immunotherapeutic agents and conventional therapies, an area that is expanding rapidly. However, with the arrival of advanced analytical methods such as mass cytometry, there is reason for optimism. These methods can identify cellular mechanisms governing response to therapy and resistance. The clinical use of inhibitors of tumor-microenvironment-modulated pathways, such as those targeting indoleamine 2, 3-dioxygenase (IDO), hold promise for resistance management. Graphical abstract Clouds in a bright sky - Joseph Mallord William Turner.

Preparing Viable Single Cells from Human Tissue and Tumors for Cytomic Analysis

Mass cytometry is a single-cell biology technique that samples >500 cells per second, measures >35 features per cell, and is sensitive across a dynamic range of >104 relative intensity units per feature. This combination of technical assets has powered a series of recent cytomic studies where investigators used mass cytometry to measure protein and phospho-protein expression in millions of cells, characterize rare cell types in healthy and diseased tissues, and reveal novel, unexpected cells. However, these advances largely occurred in studies of blood, lymphoid tissues, and bone marrow, since the cells in these tissues are readily obtained in single-cell suspensions. This unit establishes a primer for single-cell analysis of solid tumors and tissues, and has been tested with mass cytometry. The cells obtained from these protocols can be fixed for study, cryopreserved for long-term storage, or perturbed ex vivo to dissect responses to stimuli and inhibitors. © 2017 by John Wiley & Sons, Inc.

Characterizing cell subsets using marker enrichment modeling

Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.