Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy

Utilizing a Proximity Dependent Labeling Strategy to Study Cancer-Immune Intercellular Interactions In Vitro and In Vivo

Immune cells play a critical role in surveilling and defending against cancer, emphasizing the importance of understanding how they interact and communicate with cancer cells to determine cancer status, treatment response, and the formation of the tumor microenvironment (TME). To this end, we conducted a study demonstrating the effectiveness of an enzyme-mediated intercellular proximity labeling (EXCELL) method, which utilizes a modified version of the sortase A enzyme known as mgSrtA, in detecting and characterizing immune-tumor cell interactions. The mgSrtA enzyme is expressed on the membrane of tumor cells, which is able to label immune cells that interact with tumor cells in a proximity-dependent manner. Our research indicates that the EXCELL technique can detect and characterize immune-tumor cell interactions in a time- and concentration-dependent manner, both in vitro and in vivo, without requiring pre-engineering of the immune cells. We also highlight its ability to detect various types of immune cell subpopulations in vivo that have migrated out of the tumor into the spleen, providing insights into the role of peripheral T-cell recruitment in tumor progression. Overall, our findings suggest that the EXCELL method has great potential for improving our understanding of immune cell dynamics within the TME, ultimately leading to more potent pharmacological effects and cancer immunotherapy strategies. SIGNIFICANCE STATEMENT: The enzyme-mediated intercellular proximity labeling method holds promise for detecting immune cell interactions with cancer cells, both in vitro and in vivo. It has important implications for studying immune tumor cell dynamics and potentially uncovering novel subtypes of immune cells within the tumor microenvironment, both prior to and during immunotherapeutic interventions.

Photoactivatable Aptamer-CRISPR Nanodevice Enables Precise Profiling of Interferon-Gamma Release in Humanized Mice

Real-time dynamic imaging of immunoactivation-related cytokines is crucial for evaluating the efficacy of immune checkpoint blockade therapy and optimizing the treatment regimen. We introduce herein a spatiotemporally controlled nanodevice that allows in situ photoactivated imaging of interferon-gamma (IFN-γ) secretion from T cells in vitro and in vivo. The nanodevice is constructed by rational engineering of an aptamer-embedded, UV-cleavable PC-DNA probe and further integration with upconversion nanoparticles- and CRISPR-Cas12a-enhanced fluorescence systems. Using human peripheral blood mononuclear cells (PBMC)-engrafted mouse models, this nanodevice allows for the quantitative imaging of endogenous IFN-γ and its intratumoral dynamics responding to antiprogrammed cell death receptor 1 (anti-PD-1) therapy. This study thus provides a toolbox for boosting the sensitivity and precision of cytokine imaging during immune checkpoint blockade therapy, enlightening research toward imaging-guided tumor therapy.

Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance

Effector T cells need to form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. Using classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as our model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our models highlight the bone marrow as a potential sanctuary site permitting tumor evolution and antigen escape. The models also suggest that optimal dosing regimens are a function of tumor growth, CD19 expression, and patient T cell abundance, which confer adequate tumor control with reduced disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

The role of interferons in ovarian cancer progression: Hinderer or promoter?

Ovarian cancer (OC) is a common gynecologic malignancy with poor prognosis and high mortality. Changes in the OC microenvironment are closely related to the genesis, invasion, metastasis, recurrence, and drug-resistance. The OC microenvironment is regulated by Interferons (IFNs) known as a type of important cytokines. IFNs have a bidirectional regulation for OC cells growth and survival. Meanwhile, IFNs positively regulate the recruitment, differentiation and activation of immune cells. This review summarizes the secretion and the role of IFNs. In particular, we mainly elucidate the actions played by IFNs in various types of therapy. IFNs assist radiotherapy, targeted therapy, immunotherapy and biotherapy for OC, except for some IFN pathways that may cause chemo-resistance. In addition, we present some advances in OC treatment with the help of IFN pathways. IFNs have the ability to powerfully modulate the tumor microenvironment and can potentially provide new combination strategies for OC treatment.

Immunotherapy in non-small cell lung cancer: Past, present, and future directions

Many decades in the making, immunotherapy has demonstrated its ability to produce durable responses in several cancer types. In the last decade, immunotherapy has shown itself to be a viable therapeutic approach for non-small cell lung cancer (NSCLC). Several clinical trials have established the efficacy of immune checkpoint blockade (ICB), particularly in the form of anti-programmed death 1 (PD-1) antibodies, anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies and anti-programmed death 1 ligand (PD-L1) antibodies. Many trials have shown progression free survival (PFS) and overall survival (OS) benefit with either ICB alone or in combination with chemotherapy when compared to chemotherapy alone. The identification of biomarkers to predict response to immunotherapy continues to be evaluated. The future of immunotherapy in lung cancer continues to hold promise with the development of combination therapies, cytokine modulating therapies and cellular therapies. Lastly, we expect that innovative advances in technology, such as artificial intelligence (AI) and machine learning, will begin to play a role in the future care of patients with lung cancer.

Immunotherapeutic Approaches for Glioblastoma Treatment

Glioblastoma remains a challenging disease to treat, despite well-established standard-of-care treatments, with a median survival consistently of less than 2 years. In this review, we delineate the unique disease-specific challenges for immunotherapies, both brain-related and non-brain-related, which will need to be adequately overcome for the development of effective treatments. We also review current immunotherapy treatments, with a focus on clinical applications, and propose future directions for the field of GBM immunotherapy.

A pan-cancer immunogenomic atlas for immune checkpoint blockade immunotherapy

The ability to identify robust genomic signatures that predict response to immune checkpoint blockade is restricted by limited sample sizes and ungeneralizable performance across cohorts. To address these challenges, we established Cancer-Immu (http://bioinfo.vanderbilt.edu/database/Cancer-Immu/) a comprehensive platform that integrates large-scale multidimensional omics data, including genetic, bulk, and single-cell transcriptomic, proteomic, and dynamic genomic profiles, with clinical phenotypes to explore consistent and rare immunogenomic connections. Currently Cancer-Immu has incorporated data for 3,652 samples for 16 cancer types. It provides easy access to immunogenomic data and empowers researchers to translate omics datasets into biological insights and clinical applications.

Immu-Mela: An open resource for exploring immunotherapy-related multidimensional genomic profiles in melanoma

There are increasing studies aimed to reveal genomic hallmarks predictive of immune checkpoint blockade (ICB) treatment response, which generated a large number of data and provided an unprecedented opportunity to identify response-related features and evaluate their robustness across cohorts. However, those valuable data sets are not easily accessible to the research community. To take full advantage of existing large-scale immuno-genomic profiles, we developed Immu-Mela (http://bioinfo.vanderbilt.edu/database/Immu-Mela/), a multidimensional immuno-genomic portal that provides interactive exploration of associations between ICB responsiveness and multi-omics features in melanoma, including genetic, transcriptomics, immune cells, and single-cell populations. Immu-Mela also enables integrative analysis of any two genomic features. We demonstrated the value of Immu-Mela by identifying known and novel genomic features associated with ICB response. In addition, Immu-Mela allows users to upload their data sets (unrestricted to any cancer types) and co-analyze with existing data to identify and validate signatures of interest. Immu-Mela reduces barriers between researchers and complex genomic data, facilitating discoveries in cancer immunotherapy.

Which factors matter the most? Revisiting and dissecting antibody therapeutic doses

Factors such as antibody clearance and target affinity can influence antibodies' effective doses for specific indications. However, these factors vary considerably across antibody classes, precluding direct and quantitative comparisons. Here, we apply a dimensionless metric, the therapeutic exposure affinity ratio (TEAR), which normalizes the therapeutic doses by antibody bioavailability, systemic clearance and target-binding property to enable direct and quantitative comparisons of therapeutic doses. Using TEAR, we revisited and dissected the doses of up to 60 approved antibodies. We failed to detect a significant influence of target baselines, turnovers or anatomical locations on antibody therapeutic doses, challenging the traditional perceptions. We highlight the importance of antibodies' modes of action for therapeutic doses and dose selections; antibodies that work through neutralizing soluble targets show higher TEARs than those working through other mechanisms. Overall, our analysis provides insights into the factors that influence antibody doses, and the factors that are crucial for antibodies' pharmacological effects.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

Immune Profile Analysis in Peripheral Blood and Tumor in Patients with Malignant Melanoma

Melanoma is a severe and life-threatening malignancy derived from melanocytes. The traditional treatment for melanoma could not sustain satisfactory outcomes long term; however, the recent immune checkpoint treatment has made a breakthrough in these problems. Nivolumab is a representative immune checkpoint treatment, and this PD-1-targeted therapy has evolutionally developed and improved the clinical outcome in a recent decade. On the other hand, the clinical application of immune checkpoint treatment presents clinicians with novel questions, especially how to obtain additional efficacy and overcome the disadvantage by using this treatment. To answer these problems, we first investigated the distribution of PD-L1 in various organs to clarify the organs most affected by anti-PD-1 antibody treatment. Among various organs, lung, placenta, spleen, heart, and thyroid highly expressed PD-L1, while skin, thalamus, hippocampus, ovary, stomach, testis, and prostate showed lower expressions of PD-L1. Furthermore, the immune profiles were also examined in tumors and peripheral blood in patients with melanoma. PD-1 was highly expressed in CD8 and CD4 cells, and B cells also highly expressed PD-1 compared with NK cells. However, there was no significant difference in Th1/Th2/Th17 cytokines and inhibitory cytokine IL-10. Although nevus showed a low expression of PD-L1 compared with healthy skin, PD-L1 expression was increased in growth-phase melanoma. Finally, we analyzed the peripheral blood profiles in patients treated with nivolumab. PD-1-bearing dendritic cells (DCs) were increased during nivolumab treatment and Lin-CD11c+HLA-DR+ cells were highly increased during nivolumab treatment. These findings indicate a clue to answering the problems during nivolumab treatment and suggest to us the importance of multiple aspect observation during immune checkpoint treatment.

Tissue MicroRNA Expression as a Predictor of Response to Immunotherapy in NSCLC Patients

Introduction

Expression of PD-L1 protein on tumor cells, which is so far the only validated predictive factor for immunotherapy, is regulated by epigenetic and genetic factors. Among the most important ones that regulate gene expression are microRNAs.

Materials and Methods

The study included 60 patients with NSCLC who underwent first or second line immunotherapy with pembrolizumab or nivolumab. FFPE materials were collected before the start of immunotherapy. We examined relative expression of microRNAs (miR-141, miR-200a, miR-200b, miR-200c, miR-429, miR-508-3p, miR-1184, miR-1255a) and PD-L1 mRNA expression. Copy number variation (CNV) of PD-L1 gene by qPCR and FISH methods were assessed. Two single nucleotide polymorphisms (SNPs) in promoter region of PD-L1 gene (rs822335 and rs822336) were examined. Expression of PD-L1 protein on tumor cells was assessed by immunohistochemistry (IHC). The response rate to immunotherapy and progression free survival (PFS) measured in weeks and overall survival (OS) measured in months from the start of immunotherapy were evaluated.

Results

Response to immunotherapy was observed in nine patients (15%, including one complete response), disease stabilization in 22 patients (36.7%), and progression in 29 patients (48.3%). Significantly higher (p=0.015) expression of miR-200b and significantly lower (p=0.043) expression of miR-429 were observed in responders compared to patients who did not respond to immunotherapy. The median PFS in the whole group of patients was 16 weeks, and the median OS was 10.5 month. In univariate analysis, the median PFS was significantly higher in patients with high miR-200b expression (HR=0.4253, 95%CI: 0.1737–1.0417, p=0.05) and high miR-508 expression (HR=0.4401, 95%CI: 0.1903–1.0178, p=0.05) and with low expression of miR-429 (HR=0.1288, 95%CI: 0.01727–0.9606, p=0.0456) compared to patients with low and high expression of these molecules, respectively. The median OS was higher in patients with low expression of miR-429 (HR=0,6288, 95%CI: 0,3053–1,2949, p=0.06) compared with patients with high expression of this microRNA. In multivariate analysis, we found that patients with PD-L1 expression on ≥1% of tumor cells compared to patients without PD-L1 expression on cancer cells had a significantly lower risk of progression (HR=0.3857, 95%CI: 0.1612–0.9226, p=0.0323) and death (HR=0.377, 95%CI: 0.1636–0.8688, p=0.022).

Conclusion

The miR-200b and miR-429 molecules in tumor cells seem to have greatest impact on the effectiveness of immunotherapy in NSCLC patients.

Profiling the Non-genetic Origins of Cancer Drug Resistance with a Single-Cell Functional Genomics Approach Using Predictive Cell Dynamics

Non-genetic heterogeneity observed in clonal cell populations is an immediate cause of drug resistance that remains challenging to profile because of its transient nature. Here, we coupled three single-cell technologies to link the predicted drug response of a cell to its own genome-wide transcriptomic profile. As a proof of principle, we analyzed the response to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) in HeLa cells to demonstrate that cell dynamics can discriminate the transient transcriptional states at the origin of cell decisions such as sensitivity and resistance. Our same-cell approach, named fate-seq, can reveal the molecular factors regulating the efficacy of a drug in clonal cells, providing therapeutic targets of non-genetic drug resistance otherwise confounded in gene expression noise. A record of this paper's transparent peer review process is included in the Supplemental Information.

Biomarkers for immunotherapy for treatment of glioblastoma

Immunotherapy is a promising new therapeutic field that has demonstrated significant benefits in many solid-tumor malignancies, such as metastatic melanoma and non-small cell lung cancer. However, only a subset of these patients responds to treatment. Glioblastoma (GBM) is the most common malignant primary brain tumor with a poor prognosis of 14.6 months and few treatment advancements over the last 10 years. There are many clinical trials testing immune therapies in GBM, but patient responses in these studies have been highly variable and a definitive benefit has yet to be identified. Biomarkers are used to quantify normal physiology and physiological response to therapies. When extensively characterized and vigorously validated, they have the potential to delineate responders from non-responders for patients treated with immunotherapy in malignancies outside of the central nervous system (CNS) as well as GBM. Due to the challenges of current modalities of radiographic diagnosis and disease monitoring, identification of new predictive and prognostic biomarkers to gauge response to immune therapy for patients with GBM will be critical in the precise treatment of this highly heterogenous disease. This review will explore the current and future strategies for the identification of potential biomarkers in the field of immunotherapy for GBM, as well as highlight major challenges of adapting immune therapy for CNS malignancies.

Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients' overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, IRF1, JAK2, CD8A, IRF8, STAT5B, and SELL may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.