Peripheral immune signature resembles tumor microenvironment and predicts clinical outcomes in head and neck squamous cell carcinoma

Hu, Zixuan; Zhou, Jiaju; Li, Yupeng; Luan, Yizhao; Li, Huan; Jia, Bo; Xie, Zhi; Cheng, Bin; Wu, Tong

doi:10.3389/fimmu.2022.915207

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…Studies have shown that immune cells such as neutrophils and lymphocytes can regulate tumor cell activity by secreting large amounts of cytokines [28]. In addition, high levels of neutrophil-to-lymphocyte ratio (NLR) are thought to be strongly associated with poor tumor outcomes [29,30]. Therefore, neutrophils, lymphocytes and NLR in peripheral blood were included in this study and analyzed for their association with progression after rst-line chemotherapy for TC as well as with PFS.…”

Section: Relationship Of Rs and Combined Radiomics Model To Pfs After...mentioning

confidence: 99%

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Wu,

Wang,

Fan

et al. 2024

Preprint

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Objectives Platinum-based chemotherapy as first-line treatment have shown promising results against tumors in thymic carcinoma (TC). However, clinical benefit has been limited to a small proportion of patients. We developed and validated a combined radiomics model to predict progressive disease (PD) in patients suffering from TC following first-line chemotherapy.Methods Patients receiving platinum-based chemotherapy as first-line treatment from four centers in Shandong, China, were retrospectively included (n = 134); 93 and 41 were entered into the training and validation sets. Radiomics features were extracted from pretreatment enhanced CT. After feature selection, radiomics score (RS) was developed by Linear Discriminant Analysis (LDA) and TNM, clinicopathological and clinicopathological-radiomics models (combined radiomics model) were developed by using logistic regression algorithm. Models were assessed for performance, incremental predictive value of radiomics features versus clinicopathological features, and the relationship of RS and clinical factors to survival.Results The clinicopathological model was a modest predictor of PD, with area under curve (AUC) of 0.879 (95% CI: 0.709–0.890) and 0.799 (95% CI: 0.778–0.980) in the training and validation sets. The AUC of the combined radiomics model was 0.937 (95% CI: 0.891–0.984) and 0.899 (95% CI: 0.800-0.997), which is of good calibration and clinical application. The incremental predictive value of radiomics features for clinicopathological features was 27% (p < 0.001) and 0.4% (p = 0.108) in the training and validation sets. In addition, RS, CD5, and distant metastasis were associated with progression-free survival in both the training and validation sets.Conclusion Radiomics features extracted from pretreatment enhanced CT allow prediction of individualized objective responses to platinum-based chemotherapy as first-line treatment in TC, providing incremental predictive value for clinicopathological features, and are associated with progression-free survival after initiation of this combination regimen.

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Section: Relationship Of Rs and Combined Radiomics Model To Pfs After...mentioning

confidence: 99%

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Wu,

Wang,

Fan

et al. 2024

Preprint

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“…Prediction by the IRG-Based Signature. For the verification of the IRG-based signature's prognostic performance, the signature's impact on OS in both risk groups was subjected to comparison by Kaplan-Meier (K-M) survival analysis utilizing the "survival" package in R software, followed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves to assess IRG-based signature's accuracy through the "survival ROC" package in R software [24]. Subsequently, the GEO database (GSE27020) was used for external validation.…”

Section: Prognosismentioning

confidence: 99%

Seven Immune-Related Genes’ Prognostic Value and Correlation with Treatment Outcome in Head and Neck Squamous Cell Carcinoma

Shen

Guo

et al. 2023

Mediators of Inflammation

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Background. Head and neck squamous cell carcinoma (HNSCC) is a growing concern worldwide, due to its poor prognosis, low responsiveness to treatment, and drug resistance. Since immunotherapy effectively improves HNSCC patients’ survival status, it is important to continuously explore new immune-related predictive factors to accurately predict the immune landscape and clinical outcomes of individuals suffering from HNSCC. Methods. The HNSCC transcriptome profiling of RNA-sequencing data was retrieved from TCGA database, and the microarray of GSE27020 was obtained from the GEO database for validation. The differentially expressed genes (DEGs) between HNSCC and normal samples were identified by multiple test corrections in TCGA database. The univariate and multivariate Cox analyses were performed to identify proper immune-related genes (IRGs) to construct a risk model. The Cox regression coefficient was employed for calculation of the risk score (RS) of IRG signature. The median value of RS was utilized as a basis to classify individuals with HNSCC into high- and low-risk groups. The Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves were employed for the identification of the prognostic significance and precision of the IRG signature. The signature was also evaluated based on clinical variables, predictive nomogram, mutation analysis, infiltrating immune cells, immune-related pathways, and chemotherapeutic efficacy. The protein-protein interaction (PPI) network and functional enrichment pathway investigations were utilized to explore possible potential molecular mechanisms. Finally, the hub gene’s differential mRNA expression levels were evaluated by means of the Gene Expression Profiling Interactive Analysis (GEPIA), and the Human Protein Atlas (HPA) was utilized for the validation of their translational levels. Results. Collectively, 1593 DEGs between HNSCC and normal samples were identified, of which 136 IRGs were differentially expressed. Then, the 136 immune-related DEGs were mostly enriched in the cytokine-related signaling pathways by GO and KEGG analyses. After that, a valuable signature based on seven genes (DKK1, GAST, IGHM, IL12RB2, SLURP1, STC2, and TNFRSF4) was designed. The HNSCC patients into the low-risk group and the high-risk group were divided by using the median RS; the HNSCC patients in the high-risk group had a worse survival than those in the low-risk group. The risk signature was verified to be an independent predictive marker for HNSCC patients. Meanwhile, the RS had the largest contribution to survival of these patients based on the predictive nomogram. In addition, the low-risk HNSCC patients exhibited significantly enriched immune cells, along with an association with high chemosensitivity. Conclusion. The constructed gene signature can independently function as a predictive indicator for the clinical features of HNSCC patients. The low-risk HNSCC subjects might benefit from immunotherapy and chemotherapy.

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Heterogeneous characterization of neutrophilic cells in head and neck cancers

Fay,

Clavijo,

Allen

2024

Head & Neck

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BackgroundNeutrophilic cells are among the most abundant immune populations within the head and neck tumor microenvironment (TME) and harbor multiple mechanisms of immunosuppression. Despite these important features, neutrophilic cells may be underrepresented in contemporary studies that aim to comprehensively characterize the immune landscape of the TME due to discrepancies in tissue processing and analysis techniques. Here, we review the role of pathologically activated neutrophilic cells within the TME and pitfalls of various approaches used to study their frequency and function in clinical samples.MethodsThe literature was identified by searching PubMed for “immune landscape” and “tumor immune microenvironment” in combination with keywords describing solid tumor malignancies. Key publications that assessed the immune composition of solid tumors derived from human specimens were included. The tumor and blood processing methodologies in each study were reviewed in depth and correlated with the reported abundance of neutrophilic cells.ResultsNeutrophilic cells do not survive cryopreservation, and many studies fail to identify and study neutrophilic cell populations due to cryopreservation of clinical samples for practical reasons. Additional single‐cell transcriptomic studies filter out neutrophilic cells due to low transcriptional counts.ConclusionsThis report can help readers critically interpret studies aiming to comprehensively study the immune TME that fail to identify and characterize neutrophilic cells.

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Peripheral immune signature resembles tumor microenvironment and predicts clinical outcomes in head and neck squamous cell carcinoma

Cited by 6 publications

References 53 publications

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Tumor Radiomics Features on Pretreatment CT to Predict Response to First-Line Chemotherapy in Thymic Carcinoma: A Multicenter Study

Seven Immune-Related Genes’ Prognostic Value and Correlation with Treatment Outcome in Head and Neck Squamous Cell Carcinoma

Heterogeneous characterization of neutrophilic cells in head and neck cancers

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