Systematic Profiling of Immune Risk Model to Predict Survival and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Lymph node metastasis regulation by peritumoral tonsillar tissue mitochondria-related pathway activation in oropharyngeal cancer

Immune-related gene expression profiles of peritumoral tonsillar tissues are modified by oropharyngeal cancer (OPC) nodal status. This study explored immunometabolism and immune cell count alterations in peritumoral tonsillar tissue according to OPC nodal status. Microarray data analysis of 27 peritumoral tonsillar tissue samples, using a newly generated mitochondrial metabolism-related gene set comprised of 948 genes, detected 228 differentially expressed genes (DEGs) (206 up- and 22 downregulated) in metastasis-negative cases compared to metastasis-positive ones. REACTOME pathway analysis of the 206 upregulated genes revealed the Toll-like receptor 4 cascade were most enriched. Immune cell proportion analysis using the CIBERSORTx algorithm revealed a significantly higher rate of naïve B cells, but lower rates of regulatory T cells and resting natural killer cells in metastasis-negative cases. Digital spatial profiling of the 6 OPC tissues detected 9 DEGs in the lymphoid regions, in contrast, no DEGs were identified in tumor regions according to nodal status. Cancer cell nests and pair matched normal epithelia mitochondrial DNA (mtDNA) from 5 OPC tissues were analyzed by next generation sequencing for variant detection. However, no significant mtDNA variation was found. This study identified mitochondria-related immune cell transcriptional programs and immune cell profiles associated with OPC lymphatic spread in peritumoral tonsil tissue, further evaluation of which will elucidate targetable immune mechanisms associated with OPC lymphatic dissemination.

Association of a gene-expression subtype to outcome and treatment response in patients with recurrent/metastatic head and neck squamous cell carcinoma treated with nivolumab

BACKGROUND

Immune checkpoint inhibitors have been approved and currently used in the clinical management of recurrent and metastatic head and neck squamous cell carcinoma (R/M HNSCC) patients. The reported benefit in clinical trials is variable and heterogeneous. Our study aims at exploring and comparing the predictive role of gene-expression signatures with classical biomarkers for immunotherapy-treated R/M HNSCC patients in a multicentric phase IIIb trial.

METHODS

Clinical data were prospectively collected in Nivactor tiral (single-arm, open-label, multicenter, phase IIIb clinical trial in platinum-refractory HNSCC treated with nivolumab). Findings were validated in an external independent cohort of immune-treated HNSCC patients, divided in long-term and short-term survivors (overall survival >18 and <6 months since the start of immunotherapy, respectively). Pretreatment tumor tissue specimen from immunotherapy-treated R/M HNSCC patients was used for PD-L1 (Tumor Proportion Score; Combined Positive Score (CPS)) and Tumor Mutational Burden (Oncopanel TSO500) evaluation and gene expression profiling; classical biomarkers and immune signatures (retrieved from literature) were challenged in the NIVACTOR dataset.

RESULTS

Cluster-6 (Cl6) stratification of NIVACTOR cases in high score (n=16, 20%) and low score (n=64, 80%) demonstrated a statistically significant and clinically meaningful improvement in overall survival in the high-score cases (p=0.00028; HR=4.34, 95% CI 1.84 to 10.22) and discriminative ability reached area under the curve (AUC)=0.785 (95% CI 0.603 to 0.967). The association of high-score Cl6 with better outcome was also confirmed in: (1) NIVACTOR progression-free survival (p=4.93E-05; HR=3.71, 95% CI 1.92 to 7.18) and objective-response-rate (AUC=0.785; 95% CI 0.603 to 0.967); (2) long survivors versus short survivors (p=0.00544). In multivariate Cox regression analysis, Cl6 was independent from Eastern Cooperative Oncology Group performance status, PDL1-CPS, and primary tumor site.

CONCLUSIONS

These data highlight the presence of underlying biological differences able to predict survival and response following treatment with immunotherapy in platinum-refractory R/M HNSCC that could have translational implications improving treatment selection.

TRIAL REGISTRATION NUMBER

EudraCT Number: 2017-000562-30.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.

Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).

Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.

Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.

A damage-associated molecular patterns-related gene signature for the prediction of prognosis and immune microenvironment in children stage III acute lymphoblastic leukemia

Background

Immunogenic cell death (ICD)-mediated immune response provides a strong rationale to overcome immune evasion in acute lymphoblastic leukemia (ALL). ICD will produce damage-associated molecular patterns (DAMPs) in tumor microenvironment. However, there are few studies on the application of DAMPs-related molecular subtypes in clinically predicting stage III of ALL prognosis. The current study is to identify the DAMPs-associated genes and their molecular subtypes in the stage III of ALL and construct a reliable risk model for prognosis as well as exploring the potential immune-related mechanism.

Materials and methods

We used Target and EBI database for differentially expressed genes (DEGs) analysis of the stage III pediatric ALL samples. Three clusters were identified based on a consistent clustering analysis. By using Cox regression and LASSO analysis, we determined DEGs that attribute to survival benefit. In addition, the Gene Set Enrichment Analysis (GSEA) was performed to identify potential molecular pathways regulated by the DAMPs-related gene signatures. ESTIMATE was employed for evaluating the composition of immune cell populations.

Results

A sum of 146 DAMPs-associated DEGs in ALL were determined and seven transcripts among them were selected to establish a risk model. The DAMPs-associated gene signature significantly contributed to worse prognosis in the high-risk group. We also found that the high-risk group exhibited low immune cell infiltration and high expression of immune checkpoints.

Conclusion

In summary, our study showed that the DAMPs-related DEGs in the stage III of children ALL could be used to predict their prognosis. The risk model of DAMPs we established may be more sensitive to immunotherapy prediction.

Prognosis-related genes participate in immunotherapy of renal clear cell carcinoma possibly by targeting dendritic cells

Tumor immunotherapy has become one of the most promising approaches to tumor treatment. This study aimed to screen genes involved in the response of clear cell renal cell carcinoma (ccRCC) to immunotherapy and analyze their function. Based on the Gene Expression Omnibus and The Cancer Genome Atlas datasets, we screened out nine differentially expressed genes (TYROBP, APOC1, CSTA, LY96, LAPTM5, CD300A, ALOX5, C1QA, and C1QB) associated with clinical traits and prognosis. A risk signature constructed by these nine genes could predict the survival probability for patients at 1 year, 3 years, and 5 years. The immune checkpoint blockade response rate in the high-risk group was significantly higher than in the low-risk group (49.25% vs. 24.72%, p ≤ 0.001). The nine prognosis-related genes were negatively correlated with activated dendritic cells in the low-risk group but not in the high-risk group. qRT-PCR, immunohistochemistry, and immunofluorescence showed that the nine prognosis-related genes were associated with dendritic cell activity and the PD-1 positive staining rate. In conclusion, the nine prognosis-related genes have a high prognostic value. The patients in the high-risk group were more likely to benefit from immunotherapy, and the mechanism might be related to the release of dendritic cell-mediated immunosuppression.

An Immune-Related Gene Signature Can Predict Clinical Outcomes and Immunotherapeutic Response in Oral Squamous Cell Carcinoma

Objective: Immune landscape is a key feature that affects cancer progression, survival, and treatment response. Herein, this study sought to comprehensively characterize the immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC) and conduct an immune-related risk score (IRS) model for prognosis and therapeutic response prediction.

Methods: Transcriptome profiles and follow-up data of OSCC cohorts were curated from TCGA, GSE41613, and IMvigor210 datasets. An IRS model was established through univariate Cox, Random Survival Forest, and multivariate Cox analyses. Prognostic significance was evaluated with Kaplan–Meier curves, ROC, uni- and multivariate Cox, and subgroup analyses. A nomogram was conducted and assessed with C-index, ROC, calibration curves, and decision curve analyses. Immune cell infiltration and immune response were estimated with ESTIMATE and ssGSEA methods.

Results: An IRS model was constructed for predicting the overall survival and disease-free survival of OSCC, containing MASP1, HBEGF, CCL22, CTSG, LBP, and PLAU. High-risk patients displayed undesirable prognosis, and the predictive efficacy of this model was more accurate than conventional clinicopathological indicators. Multivariate Cox analyses demonstrated that this model was an independent risk factor. The nomogram combining IRS, stage, and age possessed high clinical application values. The IRS was positively associated with a nonflamed tumor microenvironment. Moreover, this signature enabled to predict immunotherapeutic response and sensitivity to chemotherapeutic agents (methotrexate and paclitaxel).

Conclusion: Collectively, our study developed a robust IRS model with machine learning method to stratify OSCC patients into subgroups with distinct prognosis and benefits from immunotherapy, which might assist identify biomarkers and targets for immunotherapeutic schemes.

Identification of Novel Biomarkers for Predicting Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Based on ceRNA Network and Immune Infiltration Analysis

Objectives

Patients with head and neck squamous cell carcinoma (HNSCC) have poor prognosis and show poor responses to immune checkpoint (IC) inhibitor (ICI) therapy. Competing endogenous RNA (ceRNA) networks, tumor-infiltrating immune cells (TIICs), and ICIs may influence tumor prognosis and response rates to ICI therapy. This study is aimed at identifying prognostic and IC-related biomarkers and key TIIC signatures to improve prognosis and ICI therapy response in HNSCC patients.

Methods and Results

Ninety-five long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and 1746 mRNAs were identified using three independent methods. We constructed a ceRNA network and estimated the proportions of 22 immune cell subtypes. Ten ceRNAs were related to prognosis according to Kaplan–Meier analysis. Two risk signatures based, respectively, on nine ceRNAs (ANLN, CFL2, ITGA5, KDELC1, KIF23, NFIA, PTX3, RELT, and TMC7) and three immune cell types (naïve B cells, neutrophils, and regulatory T cells) via univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses could accurately and independently predict the prognosis of HNSCC patients. Key mRNAs in the ceRNA network were significantly correlated with naïve B cells and regulatory T cells and with stage, grade, and immune and molecular subtype. Eight IC genes exhibited higher expression in tumor tissues and were correlated with eight key mRNAs in the ceRNA network in HNSCC patients with different HPV statuses according to coexpression and TIMER 2.0 analyses. Most drugs were effective in association with expression of these key signatures (ANLN, CFL2, ITGA5, KIF23, NFIA, PTX3, RELT, and TMC7) based on GSCALite analysis. The prognostic value of key biomarkers and associations between key ceRNAs and IC genes were validated using online databases. Eight key ceRNAs were confirmed to predict response to ICI in other cancers based on TIDE analysis.

Conclusions

We constructed two risk signatures to accurately predict prognosis in HNSCC. Key IC-related signatures may be associated with response to ICI therapy. Combinations of ICIs with inhibitors of eight key mRNAs may improve survival outcomes of HNSCC patients.

Identification of factors related to immunotherapy efficacy and prognosis in patients with advanced head and neck squamous cell carcinoma

BACKGROUND

Immunotherapy is an important treatment in oncology, but only a fraction of patients with head and neck squamous cell carcinoma (HNSCC) benefit from it. Therefore, the aim of this study was to identify predictive biomarkers of immunotherapy response for HNSCC in order to improve treatment outcomes.

METHODS

Survival analyses and comparative efficacy evaluation were performed to investigate prognostic and therapeutic impact factors in patients with advanced HNSCC following immunotherapy, and to examine the effects of factors including gene mutations, tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH), and immune cell infiltration on the survival and efficacy.

RESULTS

Anti-PD-1 treatment led to a prolonged overall survival (OS) in HNSCC patients with gene mutations compared with those without the mutations, while no significant difference in the OS was found between the two groups of patients. And no marked association between the MATH value and OS was detected in HNSCC patients, whereas patients with either high TMB scores in tissues and blood or high immune cell infiltration displayed a significantly longer OS. Further analysis with efficacy as the primary endpoint revealed no significant differences in the tissue TMB, blood TMB, and MATH value between the patients who responded to immunotherapy and those who did not. Moreover, no significant differences in the expression percentages of positive immune cells in tumor, stroma, and total regions were identified between the above two groups of patients.

CONCLUSION

HNSCC is characterized by high mutation rate, high mutation burden, and high level of immune cell infiltration, and a subset of HNSCC patients respond to immunotherapy. Here, we showed that high mutation burden and immune cell infiltration may improve the prognosis of HNSCC patients with immunotherapy, while there was no remarkable effect on the efficacy.

Effector T cell responses unleashed by regulatory T cell ablation exacerbate oral squamous cell carcinoma

Immune suppression by CD4+FOXP3+ regulatory T (Treg) cells and tumor infiltration by CD8+ effector T cells represent two major factors impacting response to cancer immunotherapy. Using deconvolution-based transcriptional profiling of human papilloma virus (HPV)-negative oral squamous cell carcinomas (OSCCs) and other solid cancers, we demonstrate that the density of Treg cells does not correlate with that of CD8+ T cells in many tumors, revealing polarized clusters enriched for either CD8+ T cells or CD4+ Treg and conventional T cells. In a mouse model of carcinogen-induced OSCC characterized by CD4+ T cell enrichment, late-stage Treg cell ablation triggers increased densities of both CD4+ and CD8+ effector T cells within oral lesions. Notably, this intervention does not induce tumor regression but instead induces rapid emergence of invasive OSCCs via an effector T cell-dependent process. Thus, induction of a T cell-inflamed phenotype via therapeutic manipulation of Treg cells may trigger unexpected tumor-promoting effects in OSCC.