Immunotherapy Response Research Articles

IMMUNE AND MOLECULAR CORRELATES OF RESPONSE TO IMMUNOTHERAPY REVEALED BY BRAIN-METASTATIC MELANOMA MODELS.

Despite the promising results of immune checkpoint blockade (ICB) therapy, outcomes for patients with brain metastasis (BrM) remain poor. Identifying resistance mechanisms has been hindered by limited access to patient samples and relevant preclinical models. Here, we developed two mouse melanoma BrM models that recapitulate the disparate responses to ICB seen in patients. We demonstrate that these models capture the cellular and molecular complexity of human disease and reveal key factors shaping the tumor microenvironment and influencing ICB response. BR1-responsive tumor cells express inflammatory programs that polarize microglia into reactive states, eliciting robust T cell recruitment. In contrast, BR3-resistant melanoma cells are enriched in neurological programs and exploit tolerance mechanisms to maintain microglia homeostasis and limit T cell infiltration. In humans, BR1 and BR3 expression signatures correlate positively or negatively with T cell infiltration and BrM patient outcomes, respectively. Our study provides clinically relevant models and uncovers mechanistic insights into BrM ICB responses, offering potential biomarkers and therapeutic targets to improve therapy efficacy.

Assessing the Tumor Immune Landscape Across Multiple Spatial Scales to Differentiate Immunotherapy Response in Metastatic Non-Small Cell Lung Cancer

Although immune checkpoint inhibitor-based therapy has shown promising results in non-small cell lung cancer patients with high programmed death-ligand 1 expression, not all patients respond to therapy. The tumor microenvironment (TME) is complex and heterogeneous, making it challenging to understand the key agents and features that influence response to therapies. In this study, we leverage multiplex fluorescent immunohistochemistry to quantitatively assess interactions between tumor and immune cells in an effort to identify patterns occurring at multiple spatial levels of the TME. To do so, we introduce several computational methods novel to a data set of 1,269 multiplex fluorescent immunohistochemistry images from a cohort of 52 patients with metastatic non-small cell lung cancer. With the spatial G-cross function, we quantify the degree of cell interaction at an entire image level, where we see significantly increased activity of cytotoxic T cells and helper T cells with epithelial tumor cells in responders to immune checkpoint inhibitor-based (P = .022 and P < .001, respectively) and decreased activity of T-regulatory cells with epithelial tumor cells compared with nonresponders (P = .010). By leveraging spatial overlap methods, we define tumor subregions (which we call the tumor “periphery,” “edge.” and “center”) and discover more localized immune-immune interactions influencing positive response, including those between cytotoxic T cells and helper T cells with antigen presenting cells in these subregions specifically. Finally, we trained an interpretable deep learning model that identified key cellular regions of interest that most influenced response classification (area under the curve = 0.71 ± 0.02). Assessing spatial interactions within these subregions further revealed new insights that were not significant at the whole image level, particularly the elevated association of antigen presenting cells and T-regulatory cells with one another in responder groups (P = .024). Altogether, we demonstrate that elucidating patterns of cell composition and interplay across multiple levels of spatial analyses can improve our understanding of the TME and better differentiate patient responses to immunotherapy.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Salivary biomarkers: a promising approach for predicting immunotherapy response in head and neck cancers.

Head and neck cancers, including cancers of the mouth, throat, voice box, salivary glands, and nose, are a significant global health issue. Radiotherapy and surgery are commonly used treatments. However, due to treatment resistance and disease recurrence, new approaches such as immunotherapy are being explored. Immune checkpoint inhibitors (ICIs) have shown promise, but patient responses vary, necessitating predictive markers to guide appropriate treatment selection. This study investigates the potential of non-invasive biomarkers found in saliva, oral rinses, and tumor-derived exosomes to predict ICI response in head and neck cancer patients. The tumor microenvironment significantly impacts immunotherapy efficacy. Oral biomarkers can provide valuable information on composition, such as immune cell presence and checkpoint expression. Elevated tumor mutation load is also associated with heightened immunogenicity and ICI responsiveness. Furthermore, the oral microbiota may influence treatment outcomes. Current research aims to identify predictive salivary biomarkers. Initial studies indicate that tumor-derived exosomes and miRNAs present in saliva could identify immunosuppressive pathways and predict ICI response. While tissue-based markers like PD-L1 have limitations, combining multiple oral fluid biomarkers could create a robust panel to guide treatment decisions and advance personalized immunotherapy for head and neck cancer patients.

Development of a Mitochondrial Membrane Permeability Transition Prognosis System in Colon Adenocarcinoma: Risk Stratification and Therapeutic Target Identification.

Explore the role of mitochondrial membrane permeability transition (MPT) in colon adenocarcinoma (COAD). Further exploration of risk stratification for COAD prognostic assessment has important clinical value. MPT-related pathways play a key role in the pathogenesis of many human diseases, including tumorigenesis. Its impact on COAD is still unknown. Bioinformatics analysis was conducted by analyzing the GEO database and TCGA database, and the bioinformatics results were verified by in vitro experiments. Through the analysis of the transcriptome data of 1008 COAD samples in the GEO database and TCGA database, the differential expressions of MPT-related genes in COAD were explored, followed by molecular subtype analysis based on MPT characteristics by univariate Cox algorithm analysis and the consensus clustering algorithm. The gene signature associated with MPT molecular subtypes was further identified and the MPT scoring system was established by the LASSO-univariate Cox analysis algorithm. After evaluating the prognostic value of the MPT scoring system in COAD patients via nomogram establishment, the clinical value of the MPT scoring system was comprehensively analyzed through somatic mutation characteristics analysis, immunotherapy response analysis, immunoinfiltration analysis, and drug sensitivity analysis. CCK-8, WB, PCR, colony formation method, and Transwell method were used to verify the effect of the screened target on the proliferation and invasion of COAD cells. We successfully established a scoring system related to MPT and validated the prognostic value of COAD patients. The potential clinical value of the MPT scoring system was also analyzed. VSIG4 was selected for further in vitro experiments to verify the effect of the screened targets on the proliferation and invasion ability of COAD cells. We established an MPT scoring system for effective risk stratification of COAD patients, demonstrating the impact of MPT on the development of COAD and its potential value as an intervention factor.

Correspondence on Letter regarding "Transcriptomic signature in advanced hepatocellular carcinoma tissue to predict combination immunotherapy response."

Correspondence on Letter regarding "Transcriptomic signature in advanced hepatocellular carcinoma tissue to predict combination immunotherapy response."

Developing a prognostic model for hepatocellular carcinoma based on MED19 and clinical stage and determining MED19 as a therapeutic target

BackgroudMediator complex subunit 19 (MED19), a member of the mediator complex, has been demonstrated to involve in tumorigenesis of hepatocellular carcinoma (HCC). However, the regulation mechanisms of MED19, the immune landscape linking MED19 to HCC and its predictive value of immunotherapy treatment in HCC are so far unknown.MethodsHere, we analyzed data from The Cancer Genome Atlas and other databases to assess the expression of MED19 and its prognosis and therapeutical-targets impact in HCC.ResultsMED19 expression was upregulated in HCC tissues compared to non-tumorous liver tissues and that its upregulation was positively associated with advanced clinicopathology features. The multivariate analysis showed that MED19 was an independent predictor of outcome in HCC. In vitro experiments revealed that MED19 knockdown suppressed hepG2 cells proliferation, colony forming and invasion and induced apoptosis. Furthermore, MED19 inhibition resulted in G0/G1 phase arrest in hepG2 cells. We screened differentially expressed genes between low and high MED19 expression groups. Enrichment analyses showed that these genes were mainly linked to nuclear division and cell cycle. The pattern of tumor-infiltrating immune was demonstrated to be related with MED19 expression in HCC. TIDE analyses showed that patients in the low-expression group presented significantly better immunotherapy. Moreover, we developed a predicted model for HCC patient’s prognosis. Receiver operating characteristic analyses revealed that this model processed a favorable performance in predicting the prognosis of HCC patients. Finally, a nomogram was built for predicting survival probability of individual HCC patient.ConclusionThese findings suggest that MED19 as a novel biomarker that has significant association with immune landscape and immunotherapy response in HCC. The proposed prediction model composed of MED19 and pathological stage has a better role in determining prognosis and stratifying of HCC.

Construction and validation of an immune-related gene signature predictive of survival and response to immunotherapy for colorectal cancer.

Colorectal cancer is a common malignant tumor with the second incidence rate and the third mortality rate worldwide. In this study, we identified and validated an immune-related gene signature, explored the clinical and molecular characteristics of the signature-defined risk groups, and assessed its ability in predicting prognosis, immune cell infiltration and immunotherapy responses. The Cancer Genome Atlas database was used as the training set while GSE39582 database as the validation set. Immune-related hub genes were selected by the Least Absolute Shrinkage and Selection Operator-penalized Cox regression model, and the signature was then constructed by the selected genes and their relevant coefficients. Prognostic performance of the signature and the signature-base nomogram models were assessed by time-dependent receiver operating characteristic curves and calibration plots in both training and validation cohorts. Clinical and mutation-related data were downloaded and analyzed to explore their associations with signature-defined risk groups. Proportions of infiltrated immune cells was estimated via CIBERSORT algorithm and immunotherapy response was evaluated by immunophenoscore and tumor immune dysfunction and exclusion scores. Seven among 790 immune-related differentially-expressed genes were selected and use to construct the signature. The signature and signature-base nomograms showed promising prognostic performance in both training and validation cohorts. Signature-defined high-risk group was associated with advanced disease, poor pathological prognostic factors and less active immune infiltration microenvironment. Besides, the response to immunotherapy of high-risk group was predicted to be poorer by immunophenoscore and tumor immune dysfunction and exclusion scores. Our signature proved its efficacy in predicting prognosis, tumor immune microenvironment and responses to immunotherapy in colorectal cancer.

A modified model of PANoptosisto identify prognosis and immunotherapy response in bladder cancer

BackgroundCell death, including apoptosis and necrosis, collectively known as widespread apoptosis. The present study aims to investigate the mechanism of action in widespread apoptosis-related modification patterns in bladder cancer. MethodsUsing a clinical genomics database, we obtained transcriptomic data and related clinical information of bladder cancer patients. By employing the least absolute shrinkage analysis, we were able to construct a risk model and single-cell sequencing analysis of differential genes in bladder cancer. ResultsFive differentially expressed genes (TMPRSS4, TPST1, FOXD1, ELOVL4, EMP1) associated with widespread apoptosis were identified as features for predicting the prognosis of bladder cancer patients. Survival curve analysis revealed significant differences in prognosis (P<0.05). Additionally, multivariate Cox regression analysis determined the independent risk factor for bladder cancer prognosis as the risk score (P<0.001), with high confidence in the scoring model validated internally (P<0.001). Single-cell sequencing reveals high expression of CDKN2A, ERBB2, and TMPRSS4 in B cells, while HRAS is significantly expressed in fibroblasts. ConclusionPANscore, as a potential prognostic and immunotherapeutic biomarker, will provide more precise and rational basis for personalized treatment strategies

Multi-omics analysis unveils the predictive value of IGF2BP3/SPHK1 signaling in cancer stem cells for prognosis and immunotherapeutic response in muscle-invasive bladder cancer

BackgroundMuscle invasive bladder cancer (MIBC) is a life-threatening malignant tumor characterized by high metastasis rates, poor prognosis, and limited treatment options. Immune checkpoint inhibitors (ICIs) targeting PD-1 and PD-L1 represent an emerging treatment for MIBC immunotherapy. However, the characteristics of patients likely to benefit from immunotherapy remain unclear.MethodsWe performed single-cell mass cytometry (CyTOF) analysis of 179,483 single cells to characterize potential immunotherapy-related cancer stem cells (CSCs)-like populations in the tumor microenvironment of 38 MIBC tissues. The upregulated expression of IGF2BP3 in CD274 + ALDH + CSC-like cells, which was associated with poor clinical prognosis, was analyzed by bulk RNA-sequencing data from an in-house cohort. The functional role of IGF2BP3 was determined through cell proliferation, colony formation, cell apoptosis and sphere formation assays. The regulation of SPHK1 expression by IGF2BP3 was investigated using methylated RNA immunoprecipitation sequencing (MeRIP-seq) and bulk RNA-sequencing (bulk RNA-seq). We further utilized single-nucleus RNA sequencing (snRNA-seq) data from 67,988 cells of 25 MIBC tissues and single-cell RNA sequencing (scRNA-seq) data from MIBC patient-derived organoids to characterize the molecular features of bladder cancer cells co-expressing IGF2BP3 and SPHK1. Spatial transcriptomics (ST) and co-detection by indexing (CODEX) analysis were used to describe the spatial distribution and interactions of IGF2BP3 + SPHK1 + bladder cancer cells and immune cells.ResultsA subset of CD274 + ALDH + CSC-like cells was identified, associating with immunosuppression and low survival rates in MIBC patients. IGF2BP3, an m6A reader gene, was found to be upregulated in the CD274 + ALDH + CSC-like cell population and linked to poor clinical prognosis in MIBC. Knockout of IGF2BP3 dramatically promoted cell apoptosis and reduced cell proliferation in T24 cells. By integrating MeRIP-seq and bulk RNA-seq analyses, we identified SPHK1 served as a substrate for IGF2BP3 in an m6A-dependent manner. Further snRNA-seq, scRNA-seq, ST, and CODEX analysis revealed a closer topographical distance between IGF2BP3 + SPHK1 + bladder cancer cells and exhausted CD8 + T cells, providing one explanation for the superior response to immunotherapy in IGF2BP3 + SPHK1 + bladder cancer cells-enriched patients. Finally, an ICI-associated signature was developed based on the enriched genes of IGF2BP3 + SPHK1 + bladder cancer cells, and its potential ability to predict the response to immunotherapy was validated in two independent immunotherapy cohort.ConclusionsOur study highlighted the critical involvement of the IGF2BP3/SPHK1 signaling in maintaining the stemness of CSCs and promoting MIBC progression. Additionally, these findings suggested that the IGF2BP3/SPHK1 signaling might serve as a biomarker for prognosis and immunotherapy response in MIBC.

Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer

Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.

Comprehensive analysis reveals that P4HA3 is a prognostic and diagnostic gastric cancer biomarker that can predict immunotherapy efficacy

Gastric cancer (GC) is one of the most challenging malignant tumors worldwide, primarily because of its high incidence and mortality rates. Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) has been established as a pivotal factor for facilitating cell proliferation, invasion, and metastasis across multiple human tumors. Nevertheless, the precise role of P4HA3in GC has not been fully elucidated. In this study, we used data from The Cancer Genome Atlas (TCGA) to examine the role of P4HA3 as a potential biomarker for predicting immunotherapy response in patients with GC. Our comprehensive analysis of data from the TCGA, TIMER, and other databases revealed a significant association between elevated P4HA3 expression in GC and adverse prognostic outcomes. Furthermore, we confirmed that P4HA3 expression was strongly correlated with immune infiltrating cells, immune infiltration markers, the tumor mutational burden (TMB), microsatellite instability (MSI), the immune score, the stromal score, and immune checkpoints, thus highlighting P4HA3 as a crucial and dependable therapeutic target within the context of immune-based antitumor strategies. Our findings suggest that P4HA3 may function as an immune-related biomarker in the pathogenesis and treatment of GC, indicating that P4HA3 is a promising prognostic and therapeutic target for this malignancy.

The baseline hemoglobin level is a positive biomarker for immunotherapy response and can improve the predictability of tumor mutation burden for immunotherapy response in cancer.

Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance. We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method. HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity. The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB.

Profiling hypoxia signaling reveals a lncRNA signature contributing to immunosuppression in high-grade glioma.

Hypoxic conditions in glioma are linked to tumor aggressiveness, poor prognosis, and treatment resistance. Long non-coding RNAs (lncRNAs) play key roles in the hypoxic and immune microenvironment of cancers, but their link to hypoxia-induced immunosuppression in high-grade glioma (HGG) is not well-studied. Gene expression profiles from TCGA and CGGA, along with clinical and genomic data, were analyzed. Bioinformatics methods including Consensus Clustering, Pearson correlation, and Cox regression analyses were used. Cell proliferation was assessed using cell counting kit-8 and colony formation assays. Glioma-macrophage interactions were evaluated using a co-culture model. Hypoxia subtype clustering showed hypoxic stress correlates with worse HGG prognosis. Eight hypoxia-related lncRNAs (AP000695.4, OSMR-AS1, AC078883.3, RP11-545E17.3, LINC01057, LINC01503, TP73-AS1, and LINC00672) with prognostic value were identified, forming a risk signature that separated patients into distinct prognostic groups. Multivariate Cox regression confirmed the signature as an independent prognostic factor. High-risk patients had greater hypoxia, leading to an immunosuppressive environment and immunotherapy resistance via tumor-associated macrophages (TAMs). TP73-AS1 significantly influenced hypoxia-induced TAM infiltration and M2 polarization. We profiled hypoxic stress in HGG and developed an 8-lncRNA hypoxia-related signature predicting patient survival and immunotherapy response, emphasizing its role in hypoxia-induced immunosuppression.

Discovery of Imidazo[1,2-a]pyrazine Derivatives as Potent ENPP1 Inhibitors.

ENPP1 acts as a negative regulator of the cGAS-STING pathway through the hydrolysis of 2'3'-cGAMP. Inhibitors of ENPP1 are regarded as promising agents for stimulating the immune response in cancer immunotherapy. This study describes the identification and optimization of imidazo[1,2-a]pyrazine derivative 7 as a highly potent and selective ENPP1 inhibitor. Compound 7 demonstrated substantial inhibitory activity against ENPP1 with an IC50 value of 5.70 or 9.68 nM while showing weak inhibition against ENPP2 and ENPP3. Furthermore, compound 7 was shown to enhance the mRNA expression of cGAMP-induced STING pathway downstream target genes, such as IFNB1, CXCL10, and IL6. In vivo pharmacokinetic and antitumor studies showed promising results, with 7 not only exhibiting efficient pharmacokinetic properties but also enhancing the antitumor efficacy of the anti-PD-1 antibody. Treatment with 7 (80 mg/kg) combined with anti-PD-1 antibody achieved a tumor growth inhibition rate of 77.7% and improved survival in a murine model.

Rapid Assessment of Biomarkers on Single Extracellular Vesicles Using "Catch and Display" on Ultrathin Nanoporous Silicon Nitride Membranes.

Extracellular vesicles (EVs) are particles released from cells that facilitate intercellular communication and have tremendous diagnostic and therapeutic potential. Bulk assays lack the sensitivity to detect rare EV subsets relevant to disease, and while single EV analysis techniques remedy this, they are often undermined by complicated detection schemes and prohibitive instrumentation. To address these issues, a microfluidic technique for EV characterization called "catch and display for liquid biopsy (CAD-LB)" is proposed. In this method, minimally processed samples are pipette-injected and fluorescently labeled EVs are captured in the nanopores of an ultrathin membrane. This enables the rapid assessment of EV number and biomarker colocalization by light microscopy. Here, nanoparticles are used to define the accuracy and dynamic range for counting and colocalization. The same assessments are then made for purified EVs and for unpurified EVs in plasma. Biomarker detection is validated using CD9 and Western blot analysis to confirm that CAD-LB accurately reports relative protein expression levels. Using unprocessed conditioned media,CAD-LB captures the known increase in EV-associated ICAM-1 following endothelial cell cytokine stimulation. Finally, to demonstrate CAD-LB's clinical potential, EV biomarkers indicative of immunotherapy responsiveness are successfully detected in the plasma of bladder cancer patients treated with immune checkpoint blockade.

The branched N-glycan of PD-L1 predicts immunotherapy responses in patients with recurrent/metastatic HNSCC

Immunotherapy has revolutionized cancer treatment, but the lack of a reliable predictive biomarker for treatment response remains a challenge. Alpha-1,6-Mannosylglycoprotein 6-β-N-Acetylglucosaminyltransferase 5 (MGAT5) is a key regulator of complex N-glycan synthesis, and its dysregulation is associated with cancer progression. The lectin Phaseolus vulgaris leukoagglutinin (PHA-L) specifically binds to mature MGAT5 products. Previous studies have indicated elevated PHA-L staining in head and neck squamous cell carcinoma (HNSCC), which implies increased activity of MGAT5. However, the specific role of MGAT5 in HNSCC remains unclear. In this study, we found significantly higher PHA-L staining and MGAT5 expression in HNSCC tumors compared to adjacent non-tumor tissues. Using a mass spectrometry (MS)-based glycoproteomic approach, we identified 163 potential protein substrates of MGAT5. Functional analysis revealed that protein substrates of MGAT5 regulated pathways related to T cell proliferation and activation. We further discovered that PD-L1 was among the protein substrates of MGAT5, and the expression of MGAT5 protected tumor cells from cytotoxic T lymphocyte (CTL) killing. Treatment of nivolumab alleviated the protective effects of MGAT5 on CTL activity. Consistently, patients with MGAT5-positive tumors showed improved responses to immunotherapy compared to those with MGAT5-negative tumors. Using purified PD-L1 from HNSCC cells and a glycoproteomic approach, we further deciphered that the N35 and N200 sites carry the majority of complex N-glycans on PD-L1. Our findings highlight the critical role of MGAT5-mediated branched N-glycans on PD-L1 in modulating the interaction with the immune checkpoint receptor PD-1. Consequently, we propose that MGAT5 could serve as a biomarker to predict patients’ responses to anti-PD-1 therapy. Furthermore, targeting the branched N-glycans at N35 and N200 of PD-L1 may lead to the development of novel diagnostic and therapeutic approaches.

Spatial resolution of the head and neck cancer tumor microenvironment to identify tumor and stromal features associated with therapy response.

Head and neck cancer (HNC) is the seventh most common cancer globally, resulting in 440 000 deaths per year. While there have been advancements in chemoradiotherapy and surgery, relapse occurs in more than half of HNCs, and these patients have a median survival of 10 months and a 2-year survival of < 20%. Only a subset of patients displays durable benefits from immunotherapies in metastatic and recurrent HNC, making it critical to understand the tumor microenvironment (TME) underpinning therapy responses in HNC. To recognize biological differences within the TME that may be predictive of immunotherapy response, we applied cutting-edge geospatial whole-transcriptome profiling (NanoString GeoMx Digital Spatial Profiler) and spatial proteomics profiling (Akoya PhenoCycler-Fusion) on a tumor microarray consisting of 25 cores from 12 patients that included 4 immunotherapy-unresponsive (8 cores) and 2 immunotherapy-responsive patients (5 cores), as well as 6 immunotherapy naïve patients (12 cores). Through high-plex, regional-based transcriptomic mapping of the tumor and TME, pathways involved with the complement system and hypoxia were identified to be differentially expressed in patients who went on to experience a poor immunotherapy response. Single-cell, targeted proteomic analysis found that immune cell infiltration of the cancer cell mass and interactions of CD8 Tcells with tumor and other immune cells were associated with positive immunotherapy response. The relative abundance of specific tumor phenotypes and their interactions with various immune cells was identified to be different between response groups. This study demonstrates how spatial transcriptomics and proteomics can resolve novel alterations in the TME of HNC that may contribute to therapy sensitivity and resistance.

Characterization of prognostic signature related with twelve types of programmed cell death in lung squamous cell carcinoma

ObjectiveThis study aimed to develop a prognostic cell death index (CDI) based on the expression of genes related with various types of programmed cell death (PCD), and to assess its clinical relevance in lung squamous cell carcinoma (LUSC).MethodsPCD-related genes were gathered and analyzed in silico using the transcriptomic data from the LUSC cohorts of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Differentially expressed PCD genes were analyzed, and a prognostic model was subsequently constructed. CDI scores were calculated for each patient, and their correlations with clinical features, survival outcomes, tumor mutation burden, gene clusters, and tumor microenvironment were investigated. Unsupervised consensus clustering was performed based on CDI model genes. Furthermore, the correlation of CDI for sensitivity of targeted drugs, chemotherapy efficacy, and immunotherapy responses was assessed.ResultsBased on 351 differentially expressed PCD genes in LUSC, a CDI signature comprising FGA, GAB2, JUN, and CDKN2A was identified. High CDI scores were significantly associated with poor survival outcomes (p < 0.05). Unsupervised clustering revealed three distinct patient subsets with varying survival rates. CDKN2A exhibited significantly different mutation patterns between patients with high and low CDI scores (p < 0.01). High CDI scores were also linked to increased immune cell infiltration of specific subsets and altered expression of immune-related genes. Patients with high-CDI showed reduced sensitivity to several chemotherapeutic drugs and a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating potential resistance to immunotherapy.ConclusionThe CDI signature based on PCD genes offers valuable prognostic insights into LUSC, reflecting molecular heterogeneity, immune microenvironment associations, and potential therapeutic challenges. The CDI holds potential clinical utility in predicting treatment responses and guiding the selection of appropriate therapies for patients with LUSC. Future studies are warranted to further validate the prognostic value of CDI in combination with clinical factors and to explore its application across diverse patient cohorts.

Stratifying hepatocellular carcinoma based on immunophenotypes for immunotherapy response and prognosis

Immunotherapy has transformed the management of hepatocellular carcinoma (HCC), but effectiveness varies among patients. This study aimed to identify biomarkers and HCC subtypes responsive to immunotherapy. Patients were classified into Immunity-High (Immunity-H) and Immunity-Low (Immunity-L) subtypes using ssGSEA scores. Prognostic genes were identified through Cox regression, and immune cell infiltration was quantified with TIMER 2.0. BOC expression, analyzed via immunohistochemistry, correlated with immunotherapy responses. Flow cytometry assessed immune cell infiltration relative to BOC levels, while CCK-8 and Transwell assays evaluated BOC overexpression's effects on cell proliferation and invasiveness. Clinically, Immunity-H patients had better survival outcomes. Three hub genes—BOC, VSTM1, and PRDM12—were identified as significantly associated with prognosis. Among these, BOC and VSTM1 demonstrated positive correlations with immune cell infiltration. Elevated expression of BOC was found to be predictive of favorable responses to immunotherapy and was associated with enhanced infiltration of T cells, dendritic cells, and B cells in the tumor microenvironment. Conversely, BOC overexpression in liver cancer cell lines led to decreased cell proliferation and invasiveness. This study underscores the prognostic significance of HCC subtypes defined by immunogenomic profiles and identifies BOC as a potential biomarker for immunotherapy selection and outcome prediction.