Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

Immune Checkpoint Inhibitors and Immunosuppressive Tumor Microenvironment: Current Challenges and Strategies to Overcome Resistance

Immune checkpoint inhibitors (ICIs) are shown to improve cancer treatment effectiveness by boosting the immune system of the patient. Nevertheless, the unique and highly suppressive TME poses a significant challenge, causing heterogeneity of response or resistance in a considerable number of patients. This review focuses on the evasive attributes of the TME. Immune evasion mechanism in TME include immunosuppressive cells, cytokine and chemokine signaling, metabolic alterations and overexpression of immune checkpoint molecules such as PD-1, CTLA-4, LAG-3, TIM-3, TIGIT, BTLA and their interactions within the TME. In addition, this review focuses on the overcoming resistance by targeting immunosuppressive cells, normalizing tumor blood vessels, blocking two or three checkpoints simultaneously, combining vaccines, oncolytic viruses and metabolic inhibitors with ICIs or other therapies. This review also focuses on the necessity of finding predictive markers for the stratification of patients and to check response of ICIs treatment. It remains to be made certain by new research and intelligent innovations how these discoveries of the TME and its interplay facilitate ICI treatment and change the face of cancer treatment.

Denfivontinib Activates Effector T Cells Through the NLRP3 Inflammasome, Yielding Potent Anticancer Effects by Combination with Pembrolizumab

Various combination therapies have been investigated to overcome the limitations of using immune checkpoint inhibitors. However, determining the optimal combination therapy remains challenging. To overcome the therapeutic limitation, we conducted a translational research to elucidate the mechanisms by which AXL inhibition enhances antitumor effects when combined with anti-PD-1 antibody therapy. Herein, we demonstrated improved antitumor effects through combination treatment with denfivontinib and pembrolizumab which resulted in enhanced differentiation into effector CD4+ and CD8+ memory T cells, accompanied by an increase in IFN-γ expression in the YHIM-2004 xenograft model derived from patients with non-small cell lung cancer. Concurrently, a reduction in the number of immunosuppressive M2 macrophages and myeloid-derived suppressor cells was observed. Mechanistically, denfivontinib potentiated the NOD-like receptor pathway, thereby facilitating NLRP3 inflammasome formation. This leads to macrophage activation via NF-κB signaling pathway activation. We have confirmed that the positive interaction between macrophages and T cells arises from the enhanced antigen-presenting machinery of activated macrophages. Furthermore, the observed tumor effects in AXL knockout mice confirmed that AXL inhibition by denfivontinib enhances the antitumor effects, thus opening new avenues for therapeutic interventions aimed at overcoming limitations in immunotherapy. To demonstrate the extent to which our findings reflect clinical results, we analyzed bulk RNA sequencing data from 21 patients with non-small cell lung cancer undergoing anti-PD-1 immunotherapy. The NLRP3 inflammasome score influenced enhanced immune responses in patient data undergoing anti-PD-1 immunotherapy, suggesting a role for the NLRP3 inflammasome in activating immune responses during treatment.

Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study

BACKGROUND/OBJECTIVES

T cell receptor fusion constructs (TRuCs), a next generation engineered T cell therapy, hold great promise. To accelerate the clinical development of these therapies, improving patient selection is a crucial pathway forward.

METHODS

We retrospectively analyzed 23 mesothelioma patients (85 target tumors) treated in a phase 1/2 single arm clinical trial (NCT03907852). Five imaging sites were involved, the settings for the evaluations were Blinded Independent Central Reviews (BICRs) with double reads. The reproducibility of 3416 radiomics and delta-radiomics (Δradiomics) was assessed. The univariate analysis evaluated correlations at the target tumor level with (1) tumor diameter response; (2) tumor volume response, according to the Quantitative Imaging Biomarker Alliance; and (3) the mean standard uptake value (SUV) response, as defined by the positron emission tomography response criteria in solid tumors (PERCISTs). A random forest model predicted the response of the target pleural tumors.

RESULTS

Tumor anatomical distribution was 55.3%, 17.6%, 14.1%, and 10.6% in the pleura, lymph nodes, peritoneum, and soft tissues, respectively. Radiomics/Δradiomics reproducibility differed across tumor localizations. Radiomics were more reproducible than Δradiomics. In the univariate analysis, none of the radiomics/Δradiomics correlated with any response criteria. With an accuracy ranging from 0.75 to 0.9, three radiomics/Δradiomics were able to predict the response of target pleural tumors. Pivotal studies will require a sample size of 250 to 400 tumors.

CONCLUSIONS

The prediction of responding target pleural tumors can be achieved using a machine learning-based radiomics/Δradiomics analysis. Tumor-specific reproducibility and the average values indicated that using tumor models to create an effective patient model would require combining several target tumor models.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

TGF-β trap of AdAPT-001 turns up the heat on tumors and turns down checkpoint blocker resistance

At the ASCO 2024 meeting, Anthony P Conley, coauthor on this editorial, presented promising data from the phase 1/2 clinical trial called BETA PRIME (ClinicalTrials.gov NCT04673942) with AdAPT-001 plus a checkpoint inhibitor (CI). All participants gave informed consent to participate in BETA PRIME before taking part. AdAPT-001 is an oncolytic adenovirus that expresses a transforming growth factor beta (TGF-β) trap to neutralize active TGF-β. This editorial proposes that the TGF-β trap of AdAPT-001 reverses the immunosuppressive environment of tumor cells, and thus makes these tumors susceptible to CIs like the anti-PD-1 agent, nivolumab, and potentially other therapies as well.

Disulfidptosis-related lncRNAs signature predicting prognosis and immunotherapy effect in lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients.

METHODS

The study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines.

RESULTS

A total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line.

CONCLUSIONS

The prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.

CAR-NKT Cells in Asthma: Use of NKT as a Promising Cell for CAR Therapy

NKT cells, unique lymphocytes bridging innate and adaptive immunity, offer significant potential for managing inflammatory disorders like asthma. Activating iNKT induces increasing IFN-γ, TGF-β, IL-2, and IL-10 potentially suppressing allergic asthma. However, their immunomodulatory effects, including granzyme-perforin-mediated cytotoxicity, and expression of TIM-3 and TRAIL warrant careful consideration and targeted approaches. Although CAR-T cell therapy has achieved remarkable success in treating certain cancers, its limitations necessitate exploring alternative approaches. In this context, CAR-NKT cells emerge as a promising approach for overcoming these challenges, potentially achieving safer and more effective immunotherapies. Strategies involve targeting distinct IgE-receptors and their interactions with CAR-NKT cells, potentially disrupting allergen-mast cell/basophil interactions and preventing inflammatory cytokine release. Additionally, targeting immune checkpoints like PDL-2, inducible ICOS, FASL, CTLA-4, and CD137 or dectin-1 for fungal asthma could further modulate immune responses. Furthermore, artificial intelligence and machine learning hold immense promise for revolutionizing NKT cell-based asthma therapy. AI can optimize CAR-NKT cell functionalities, design personalized treatment strategies, and unlock a future of precise and effective care. This review discusses various approaches to enhancing CAR-NKT cell efficacy and longevity, along with the challenges and opportunities they present in the treatment of allergic asthma.

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.

Design of a novel chimeric peptide via dual blockade of CD47/SIRPα and PD-1/PD-L1 for cancer immunotherapy

Although immune checkpoint inhibition has been shown to effectively activate antitumor immunity in various tumor types, only a small subset of patients can benefit from PD-1/PD-L1 blockade. CD47 expressed on tumor cells protects them from phagocytosis through interaction with SIRPα on macrophages, while PD-L1 dampens T cell-mediated tumor killing. Therefore, dual targeting PD-L1 and CD47 may improve the efficacy of cancer immunotherapy. A chimeric peptide Pal-DMPOP was designed by conjugating the double mutation of CD47/SIRPα blocking peptide (DMP) with the truncation of PD-1/PD-L1 blocking peptide OPBP-1(8-12) and was modified by a palmitic acid tail. Pal-DMPOP can significantly enhance macrophage-mediated phagocytosis of tumor cells and activate primary T cells to secret IFN-γ in vitro. Due to its superior hydrolysis-resistant activity as well as tumor tissue and lymph node targeting properties, Pal-DMPOP elicited stronger anti-tumor potency than Pal-DMP or OPBP-1(8-12) in immune-competent MC38 tumor-bearing mice. The in vivo anti-tumor activity was further validated in the colorectal CT26 tumor model. Furthermore, Pal-DMPOP mobilized macrophage and T-cell anti-tumor responses with minimal toxicity. Overall, the first bispecific CD47/SIRPα and PD-1/PD-L1 dual-blockade chimeric peptide was designed and exhibited synergistic anti-tumor efficacy via CD8+ T cell activation and macrophage-mediated immune response. The strategy could pave the way for designing effective therapeutic agents for cancer immunotherapy.

Hypofractionated Stereotactic Re-irradiation and Anti-PDL1 Durvalumab Combination in Recurrent Glioblastoma: STERIMGLI Phase I Results

BACKGROUND

Hypofractionated stereotactic radiotherapy (hFSRT) is a salvage option for recurrent glioblastoma (GB) which may synergize anti-PDL1 treatment. This phase I study evaluated the safety and the recommended phase II dose of anti-PDL1 durvalumab combined with hFSRT in patients with recurrent GB.

METHODS

Patients were treated with 24 Gy, 8 Gy per fraction on days 1, 3, and 5 combined with the first 1500 mg Durvalumab dose on day 5, followed by infusions q4weeks until progression or for a maximum of 12 months. A standard 3 + 3 Durvalumab dose de-escalation design was used. Longitudinal lymphocytes count, cytokines analyses on plasma samples, and magnetic resonance imaging (MRI) were collected.

RESULTS

Six patients were included. One dose limiting toxicity, an immune-related grade 3 vestibular neuritis related to Durvalumab, was reported. Median progression-free interval (PFI) and overall survival (OS) were 2.3 and 16.7 months, respectively. Multi-modal deep 
learning-based analysis including MRI, cytokines, and lymphocytes/neutrophil ratio isolated the patients presenting pseudoprogression, the longest PFI and those with the longest OS, but statistical significance cannot be established considering phase I data only.

CONCLUSION

Combination of hFSRT and Durvalumab in recurrent GB was well tolerated in this phase I study. These encouraging results led to an ongoing randomized phase II. (ClinicalTrials.gov Identifier: NCT02866747).

STING Protein-Based In Situ Vaccine Synergizes CD4+ T, CD8+ T, and NK Cells for Tumor Eradication

Stimulator of interferon genes (STING) signaling is a promising target in cancer immunotherapy, with many ongoing clinical studies in combination with immune checkpoint blockade (ICB). Existing STING-based therapies largely focus on activating CD8+ T cell or NK cell-mediated cytotoxicity, while the role of CD4+ T cells in STING signaling has yet to be extensively studied in vivo. Here, a distinct CD4-mediated, protein-based combination therapy of STING and ICB as an in situ vaccine, is reported. The treatment eliminates subcutaneous MC38 and YUMM1.7 tumors in 70-100% of mice and protected all cured mice against rechallenge. Mechanistic studies reveal a robust TH 1 polarization and suppression of Treg of CD4+ T cells, followed by an effective collaboration of CD4+ T, CD8+ T, and NK cells to eliminate tumors. Finally, the potential to overcome host STING deficiency by significantly decreasing MC38 tumor burden in STING KO mice is demonstrated, addressing the translational challenge for the 19% of human population with loss-of-function STING variants.

The progress of novel strategies on immune-based therapy in relapsed or refractory diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) can be cured with standard front-line immunochemotherapy, whereas nearly 30-40% of patients experience refractory or relapse. For several decades, the standard treatment strategy for fit relapsed/refractory (R/R) DLBCL patients has been high-dose chemotherapy followed by autologous hematopoietic stem cell transplant (auto-SCT). However, the patients who failed in salvage treatment or those ineligible for subsequent auto-SCT have dismal outcomes. Several immune-based therapies have been developed, including monoclonal antibodies, antibody-drug conjugates, bispecific T-cell engaging antibodies, chimeric antigen receptor T-cells, immune checkpoint inhibitors, and novel small molecules. Meanwhile, allogeneic SCT and radiotherapy are still necessary for disease control for fit patients with certain conditions. In this review, to expand clinical treatment options, we summarize the recent progress of immune-related therapies and prospect the future indirections in patients with R/R DLBCL.

The tumor immune microenvironment in pancreatic cancer and its potential in the identification of immunotherapy biomarkers

INTRODUCTION

Pancreatic cancer (PC) has an extremely poor prognosis, even with surgical resection and triplet chemotherapy treatment. Cancer immunotherapy has been recently approved for tumor-agnostic treatment with genome analysis, including in PC. However, it has limited efficacy.

AREAS COVERED

In addition to the low tumor mutation burden, one of the difficulties of immunotherapy in PC is the presence of abundant stromal cells in its microenvironment. Among stromal cells, cancer-associated fibroblasts (CAFs) play a major role in immunotherapy resistance, and CAF-targeted therapies are currently under development, including those in combination with immunotherapies. Meanwhile, microbiomes and tumor-derived exosomes (TDEs) have been shown to alter the behavior of distant receptor cells in PC. This review discusses the role of CAFs, microbiomes, and TDEs in PC tumor immunity.

EXPERT OPINION

Elucidating the mechanisms by which CAFs, microbiomes, and TDEs are involved in the tumorigenesis of PC will be helpful for developing novel immunotherapeutic strategies and identifying companion biomarkers for immunotherapy. Spatial single-cell analysis of the tumor microenvironment will be useful for identifying biomarkers of PC immunity. Furthermore, given the complexity of immune mechanisms, artificial intelligence models will be beneficial for predicting the efficacy of immunotherapy.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Recent advances in sonodynamic immunotherapy

Tumor immunotherapy has become an important means of tumor therapy by enhancing the immune response and triggering the activation of immune cells. However, currently, only a small number of patients respond to immunotherapy alone, and patients may experience immune-related adverse events (irAEs) during the course of treatment. Sonodynamic therapy (SDT) can produce cytotoxic substances to tumor tissue, induce apoptosis and enhance immunity. SDT combined with immunotherapy is considered a promising strategy for cancer treatment. In this mini review, we summarize the role of SDT in immunotherapy in recent years, including the application of SDT-triggered immunotherapy and the combination of SDT and immunotherapy.

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.

Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma

Background and purpose

Programmed cell death protein-1 (PD-1) and programmed cell death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of specimens, can discriminate patients with hepatocellular carcinoma (HCC) who can derive the most benefits from immune checkpoint inhibitor (ICI) therapy. A non-invasive method of measuring PD-1/PD-L1 expression is urgently needed for clinical decision support.

Materials and methods

We included a cohort of 87 patients with HCC from the West China Hospital and analyzed 3094 CT images to develop and validate our prediction model. We propose a novel deep learning-based predictor, Contrastive Learning Network (CLNet), which is trained with self-supervised contrastive learning to better extract deep representations of computed tomography (CT) images for the prediction of PD-1 and PD-L1 expression.

Results

Our results show that CLNet exhibited an AUC of 86.56% for PD-1 expression and an AUC of 83.93% for PD-L1 expression, outperforming other deep learning and machine learning models.

Conclusions

We demonstrated that a non-invasive deep learning-based model trained with self-supervised contrastive learning could accurately predict the PD-1 and PD-L1 expression status, and might assist the precision treatment of patients withHCC, in particular the use of immune checkpoint inhibitors.

Identification and verification of a prognostic ferroptosis-related lncRNAs signature for patients with lung adenocarcinoma

Lung cancer has been identified as one of the deadliest malignant tumors worldwide. Mounting evidence suggests that ferroptosis is a well-known non-apoptotic cell death process that participates in pathological mechanisms and is a new cancer treatment strategy. Aberrantly expressed long non-coding RNAs (lncRNAs) that drive lung cancer progression have attracted increasing attention. Herein, we explored the prognostic significance of ferroptosis-related lncRNAs in lung cancer patients. LUAD gene expression patterns and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on LASSO-Cox regression, A 14 ferroptosis-related differentially expressed lncRNAs (FRDELs) signature was constructed. Subsequently, a nomogram model for predicting the prognosis of LUAD patients was constructed based on clinicopathological data and the 14 - FRDELs signature. The signature was shown to be correlated with tumor mutational burden (TMB) and immune cell infiltration within the tumor microenvironment. Furthermore, Gene Set Enrichment Analysis (GSEA) confirmed that the signature was correlated with LUAD-related biological functions such as the P53 signaling pathway, DNA replication, and cell cycle. The roles and mechanisms of PACERR in the signature were explored by si-lncRNA-mediated knockdown and transfection-mediated overexpression via in vitro experiments in A549 and H1299 cells. PACERR was significantly upregulated in A549 and H1299 cells, and higher expression promoted LUAD cell proliferation, migration, and invasion via in vitro experiments, while knockdown of PACERR presented the opposite effects. In conclusion, our study provided information regarding ferroptosis-related lncRNA expression and established a prognostic nomogram based on 14 FRDELs to predict overall survival in LUAD accurately. Additionally, our results in vitro revealed that PACERR played an oncogenic role in LUAD proliferation and metastasis, which provides mechanistic insights into the roles of ferroptosis-related lncRNA in LUAD progression and that it may be a potential biomarker for LUAD treatment.

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

Simple Summary

The introduction of immune checkpoint inhibitors has represented a milestone in cancer treatment. Despite PD-L1 expression being the standard biomarker used before the start of therapy, there is still a strict need to identify complementary non-invasive biomarkers in order to better select patients. In this context, radiomics is an emerging approach for examining medical images and clinical data by capturing multiple features hidden from human eye and is potentially able to predict response assessment and survival in the course of immunotherapy. We reviewed the available studies investigating the role of radiomics in cancer patients, focusing on non-small cell lung cancer treated with immune checkpoint inhibitors. Although preliminary research shows encouraging results, different issues need to be solved before radiomics can enter into clinical practice.

Abstract

Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an “ideal” biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.

The Past, Present, and Future of Clinically Applied Chimeric Antigen Receptor-T-Cell Therapy

Immunotherapy represents the fourth pillar of cancer therapy after surgery, chemotherapy, and radiation. Chimeric antigen receptor (CAR)-T-cell therapy is an artificial immune cell therapy applied in clinical practice and is currently indicated for hematological malignancies, with cluster of differentiation 19 (CD19) as its target molecule. In this review, we discuss the past, present, and future of CAR-T-cell therapy. First, we summarize the various clinical trials that were conducted before the clinical application of CD19-targeted CAR-T-cell therapies began. Second, we discuss the accumulated real-world evidence and the barriers associated with applying clinical trials to clinical practices from the perspective of the quality and technical aspects. After providing an overview of all the moving parts involved in the production of CAR-T-cell products, we discuss the characteristics of immune cells (given that T cells are the raw materials for CAR-T-cell therapy) and elucidate the relationship between lifestyle, including diet and exercise, and immune cells. Finally, we briefly highlight future trends in the development of immune cell therapy. These advancements may help position CAR-T-cell therapy as a standard of care.

Applying artificial intelligence for cancer immunotherapy

Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

Expression, prognosis value, and immune infiltration of lncRNA ASB16-AS1 identified by pan-cancer analysis

Long non-coding RNA known as ASB16 antisense RNA1 (ASB16-AS1) has been proven to be an oncogene, and the relationship between ASB16-AS1 and immunity is still under studied. This study aims to explore the expression and prognostic potential of ASB16-AS1, and to visualize the relationship between ASB16-AS1 expression and immune infiltration in pan-cancer analysis. We clarified ASB16-AS1 expression patterns and its relationship with prognosis through multi-platform and multi-database sources. We also verified the function of ASB16-AS1 in liver hepatocellular carcinoma (LIHC). A  variety of immune cell content evaluation methods were used to mutually verify the correlation between ASB16-AS1 and immune infiltration. Finally, the relationships between ASB16-AS1 and molecular characteristics were further explored. In terms of comprehensive analysis, compared with non-tumor tissues, ASB16-AS1 was highly expressed in tumor tissues, and indicated the value of poor prognosis in multiple cancer types. Functional assays, such as counting kit-8 assay, transwell assay and scratch-wound assay verified that high ASB16-AS1 expression promoted tumor progression in LIHC. ASB16-AS1 was positively correlated with B cells, T cells CD4+ and T cells CD8+ in most cancer types, and negatively correlated with macrophages, dendritic cells and neutrophils in some cancer types. In addition, there were different interaction modes between ASB16-AS1 and molecular features, such as the relationship with oncogenic signaling pathways, showing that the high ASB16-AS1 expression was related to alterations in oncogenic signaling pathways. Our study emphasizes that ASB16-AS1 is a potential pan-cancer prognostic marker, whichs is associated with the immune infiltration in multiple cancer types.

Chemotherapeutic drug-induced immunogenic cell death for nanomedicine-based cancer chemo-immunotherapy

Chemotherapy has been a conventional paradigm for cancer treatment, and multifarious chemotherapeutic drugs have been widely employed for decades with significant performances in suppressing tumors. Moreover, some of the antitumor chemotherapeutic agents, such as doxorubicin (DOX), oxaliplatin (OXA), cyclophosphamide (CPA) and paclitaxel (PTX), can also tackle tumors through the induction of immunogenic cell death (ICD) in tumor cells to trigger specific antitumor immune responses of the body and improve chemotherapy efficacy. In recent years, chemo-immunotherapy has attracted increasing attention as one of the most promising combination therapies to struggle with malignant tumors. Many effective antitumor therapies have benefited from the successful induction of ICD in tumors, which could incur the release of endogenous danger signals and tumor-associated antigens (TAAs), further stimulating antigen-presenting cells (APCs) and ultimately initiating efficient antitumor immunity. In this review, several well-characterized damage-associated molecular patterns (DAMPs) were introduced and the progress of ICD induced by representative chemotherapeutic drugs for nanomedicine-based chemo-immunotherapy was highlighted. In addition, the combination strategies involving ICD cooperated with other therapies were discussed. Finally, we shared some perspectives in chemotherapeutic drug-induced ICD for future chemo-immunotherapy. It was hoped that this review would provide worthwhile presentations and enlightenments for cancer chemo-immunotherapy.

"Built-in" PD-1 blocker to rescue NK-92 activity from PD-L1-mediated tumor escape mechanisms

Success of adoptive cell therapy mainly depends on the ability of immune cells to persist and function optimally in the immunosuppressive tumor microenvironment. Although present at the cancer site, immune cells become exhausted and/or inhibited, due to the presence of inhibitory receptors such as PD-L1 on malignant cells. Novel genetic strategies to manipulate the PD1/PD-L1 axis comprise (i) PD-1 reversion where the receptor intracellular domain is replaced with an activating unit, (ii) the use of anti-PD-L1 CAR or (iii) the disruption of the PD-1 gene. We here present an alternative strategy to equip therapeutic cells with a truncated PD-1 (tPD-1) to abrogate PD-1/PD-L1 inhibition. We show that engagement of tPD-1 with PD-L1-positive tumor unleashes NK-92 activity in vitro. Furthermore, this binding was sufficiently strong to induce killing of targets otherwise not recognized by NK-92, thus increasing the range of targets. In vivo treatment with NK-92 tPD-1 cells led to reduced tumor growth and improved survival. Importantly, tPD-1 did not interfere with tumor recognition in PD-L1 negative conditions. Thus, tPD-1 represents a straightforward method for improving antitumor immunity and revealing new targets through PD-L1 positivity.

Interplay between Neutrophils, NETs and T-Cells in SARS-CoV-2 Infection-A Missing Piece of the Puzzle in the COVID-19 Pathogenesis?

Since the end of 2019, a new, dangerous virus has caused the deaths of more than 3 million people. Efforts to fight the disease remain multifaceted and include prophylactic strategies (vaccines), the development of antiviral drugs targeting replication, and the mitigation of the damage associated with exacerbated immune responses (e.g., interleukin-6-receptor inhibitors). However, numerous uncertainties remain, making it difficult to lower the mortality rate, especially among critically ill patients. While looking for a new means of understanding the pathomechanisms of the disease, we asked a question-is our immunity key to resolving these uncertainties? In this review, we attempt to answer this question, and summarize, interpret, and discuss the available knowledge concerning the interplay between neutrophils, neutrophil extracellular traps (NETs), and T-cells in COVID-19. These are considered to be the first line of defense against pathogens and, thus, we chose to emphasize their role in SARS-CoV-2 infection. Although immunologic alterations are the subject of constant research, they are poorly understood and often underestimated. This review provides background information for the expansion of research on the novel, immunity-oriented approach to diagnostic and treatment possibilities.

Immunotherapy for cholangiocarcinoma: a 2021 update

Cholangiocarcinoma (CCA) is a rare malignancy with generally dismal prognosis. Immunotherapy has revolutionized the management of cancer patients during the last decade, offering durable responses with an acceptable safety profile, but there are still no significant advances regarding CCA. Novel immunotherapeutic methods, such as cancer vaccines, oncolytic viruses, adoptive cell therapy and combinations of immune checkpoint inhibitors with other agents are currently under investigation and may improve prognosis. Efforts to find robust biomarkers for response are also ongoing. In this review, we discuss the rationale for the use of immunotherapy in CCA and available clinical data. Ongoing trials will also be presented, as well as key findings from each study.

Evidence for Non-Cancer-Specific T Cell Exhaustion in the Tcl1 Mouse Model for Chronic Lymphocytic Leukemia

The reinvigoration of anti-cancer immunity by immune checkpoint therapies has greatly improved cancer treatment. In chronic lymphocytic leukemia (CLL), patients as well as in the Tcl1 mouse model for CLL, PD1-expressing, exhausted T cells significantly expand alongside CLL development; nevertheless, PD1 inhibition has no clinical benefit. Hence, exhausted T cells are either not activatable by simple PD1 blocking in CLL and/or only an insufficient number of exhausted T cells are CLL-specific. In this study, we examined the latter hypothesis by exploiting the Tcl1 transgenic CLL mouse model in combination with TCR transgene expression specific for a non-cancer antigen. Following CLL tumor development, increased PD1 levels were detected on non-CLL specific T cells that seem dependent on the presence of (tumor-) antigen-specific T cells. Transcriptome analysis confirmed a similar exhaustion phenotype of non-CLL specific and endogenous PD1pos T cells. Our results indicate that in the CLL mouse model, a substantial fraction of non-CLL specific T cells becomes exhausted during disease progression in a bystander effect. These findings have important implications for the general efficacy assessment of immune checkpoint therapies in CLL.

Safety and Efficacy of Personalized Cancer Vaccines in Combination With Immune Checkpoint Inhibitors in Cancer Treatment

Cancer immunotherapy can induce sustained responses in patients with cancers in a broad range of tissues, however, these treatments require the optimized combined therapeutic strategies. Despite immune checkpoint inhibitors (ICIs) have lasting clinical benefit, researchers are trying to combine them with other treatment modalities, and among them the combination with personalized cancer vaccines is attractive. Neoantigens, arising from mutations in cancer cells, can elicit strong immune response without central tolerance and out-target effects, which is a truly personalized method. Growing studies show that the combination can elevate the antitumor efficacy with acceptable safety and minimal additional toxicity compared with single agent vaccine or ICI. Herein, we have searched these preclinical and clinical trials and summarized safety and efficacy of personalized cancer vaccines combined with ICIs in several malignancies. Meanwhile, we discuss the rationale of the combination and future challenges.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BACKGROUND

This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.

METHOD

LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.

RESULTS

Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.

CONCLUSIONS

The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.

miRNA-Based Therapeutics in the Era of Immune-Checkpoint Inhibitors

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to complementary target regions on gene transcripts. Thus, miRNAs fine-tune gene expression profiles in a cell-type-specific manner and thereby regulate important cellular functions, such as cell growth, proliferation and cell death. MiRNAs are frequently dysregulated in cancer cells by several mechanisms, which significantly affect the course of the disease. In this review, we summarize the current knowledge on how dysregulated miRNAs contribute to cancer and how miRNAs can be exploited as predictive factors and therapeutic targets, particularly in regard to immune-checkpoint inhibitor therapies.

Metabolic regulation of the HBV-specific T cell function

Chronically HBV infected subjects are more than 260 million worldwide; cirrhosis and liver cancer represent possible outcomes which affect around 700,000 patients per year. Both innate and adaptive immune responses are necessary for viral control and both have been shown to be defective in chronic patients. Metabolic remodeling is an essential process in T cell biology, particularly for T cell activation, differentiation and survival. Cellular metabolism relies on the conversion of nutrients into energy to support intracellular processes, and to generate fundamental intermediate components for cell proliferation and growth. Adaptive immune responses are the central mechanisms for the resolution of primary human infections leading to the activation of pathogen-specific B and T cell functions. In chronic HBV infection the anti-viral immune response fails to contain the virus and leads to persistent hepatic tissue damage which may finally result in liver cirrhosis and cancer. This T cell failure is associated with metabolic alterations suggesting that control of nutrient uptake and intracellular utilization as well as correct regulation of intracellular metabolic pathways are strategic for T cell differentiation during persistent chronic infections. This review will discuss some of the main features of the T cell metabolic processes which are relevant to the generation of an efficient antiviral response, with specific focus on their clinical relevance in chronic HBV infection in the perspective of possible strategies to correct deregulated metabolic pathways underlying T cell dysfunction of chronic HBV patients.

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Adenocarcinoma is the most common type of lung cancer, and patients have varying prognoses. RNA-binding proteins (RBP) are deemed to be closely associated with tumorigenesis and development, but the exact mechanism is currently unknown. This study was aimed at constructing a new robust prognostic model based on RNA-binding protein-related gene pair scores for better clinical guidance. The model for this study was constructed based on data of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Prognosis-related RBP gene pair models were created based on differentially expressed genes, and the accuracy of the models was verified in a different age, staging, and other subdatasets. A total of 379 RNA-binding protein-related genes were differentially expressed in tumor tissue. From these genes, we constructed a prognostic model consisting of 33 gene pairs, which were found to be significantly associated with survival in TCGA dataset (P < 0.0001, hazard ratio (HR) = 4.380 (3.139 to 6.111)) and different subdatasets. As expected, the results were verified in the GEO validation cohort (P = 7.8 × 10⁻³, HR = 1.597 (1.095 to 2.325)). We found that the signature exhibited an independent prognostic factor in both the univariate and multivariate Cox regression analyses (P < 0.001). CIBERSORT was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. CD8 T cells, activated dendritic cells, regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group (P < 0.01). Patients in the high-risk group had significantly higher tumor mutational burden (TMB) (P = 4.953e − 04) and lower levels of immune cells (P = 3.473e − 05) and stromal cells (P = 0.005) in the tumor microenvironment than those in the low-risk group. Furthermore, the Protein-protein interaction (PPI) network and various enrichment analyses have genuinely uncovered the interrelationships and potential functions of the RBP genes within the model. The results of the present study validated the importance of RNA-binding proteins in tumorigenesis and progression and support the RBP gene-related signature as a promising marker for prognosis prediction in lung adenocarcinoma.

How can artificial intelligence and humans work together to fight against cancer?

This editorial will focus on and discuss growing artificial intelligence (AI) and the utilization of AI in human cancer therapy. The databases and big data related to genomes, genes, proteins and molecular networks are rapidly increasing all worldwide where information on human diseases, including cancer and infection resides. To overcome diseases, prevention and therapeutics are being developed with the abundant data analyzed by AI. AI has so much potential for handling considerable data, which requires some orientation and ambition. Appropriate interpretation of AI is essential for understanding disease mechanisms and finding targets for prevention and therapeutics. Collaboration with AI to extract the essence of cancer data and model intelligent networks will be explored. The utilization of AI can provide humans with a predictive future in disease mechanisms and treatment as well as prevention.

Cancer neoantigen: Boosting immunotherapy

Tumor neoantigen has a high degree of immunogenicity. As one of the emerging methods of tumor immunotherapy, the vaccine developed against it has served to clinical trials of various solid tumors, especially in the treatment of melanoma. Currently, a variety of immunotherapy methods have been applied to the treatment of the tumor. However, other therapeutic methods have the disadvantages of low specificity and prominent side effects. Treatments require tumor antigen with higher immunogenicity as the target of immune attack. This review will recommend the identification of neoantigen, the influencing factors of neoantigen, and the application of personalized vaccines for neoantigen in metastatic tumors such as malignant melanoma.

PD-1/PD-L1 axis importance and tumor microenvironment immune cells

Triple-negative breast cancer (TNBC) is heterogeneous cancer with poor prognosis among the other breast tumors. Rapid recurrence and increased progression rate could be reasons for the poor prognosis of this type of breast cancer. Recently, because of the lack of specific targets in multiple cancer treatment, immune checkpoint blockade therapies with targeting PD-1/PD-L1 axis have displayed significant advances and improved survival. Among different types of breast cancers, TNBC is considered more immunogenic with high T-cell and other immune cells infiltration compared to other breast cancer subtypes. This immunogenic characteristic of TNBC is a beneficial marker in the immunotherapy of these tumors. Clinical studies with a focus on immune checkpoint therapy have demonstrated promising results in TNBC treatment. In this review, we summarize clinical trials with the immunotherapy-based treatment of different cancers and also discuss the interaction between infiltrating immune cells and breast tumor microenvironment. In addition, we focus on the signaling pathway that controls PD-L1 expression and continues with CAR T-cell therapy and siRNA as novel strategies and potential tools in targeted therapy.