scholarly journals Age and Mutations as Predictors of the Response to Immunotherapy in Head and Neck Squamous Cell Cancer

2020 ◽  
Vol 8 ◽  
Author(s):  
Yueming Zhang ◽  
Anqi Lin ◽  
Yonghe Li ◽  
Weimin Ding ◽  
Hui Meng ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Cell Cancer ◽  
Cancer Center ◽  
Cox Regression Analysis ◽  
Immune Checkpoint Molecules ◽  
Immunosuppressive Tumor Microenvironment

The immunosuppressive tumor microenvironment plays an essential role in the treatment of head and neck squamous cell carcinoma (HNSC). Compared to traditional chemoradiotherapy, immune checkpoint inhibitors (ICIs) have become increasingly important in HNSC therapy. Prior studies linked the efficacy of ICIs to PD-L1, microsatellite instability (MSI), HPV infection, tumor mutation burden (TMB), and tumor lymphocyte infiltration in patients with HNSC, but further verification is needed. Additional predictors are needed to recognize HNSC patients with a good response to ICIs. We collected the clinical information and mutation data of HNSC patients from Memorial Sloan Kettering Cancer Center (MSKCC) and The Cancer Genome Atlas (TCGA) databases to generate two clinical cohorts. The MSKCC cohort was used to recognize predictors related to the efficacy of ICIs, and the TCGA cohort was used to further examine the immune microenvironment features and signaling pathways that are significantly enriched in the subgroups of predictors. Multivariate Cox regression analysis indicated that age (HR = 0.50, p = 0.014) and ARID1A (HR = 0.13, p = 0.048), PIK3CA (HR = 0.45, p = 0.021), and TP53 (HR = 1.82, p = 0.035) mutations were potential predictors for ICI efficacy in HNSC patients. Age > 65 years and ARID1A or PIK3CA mutations correlated with good overall survival (OS). TP53 mutant-type (MT) patients experienced a worse prognosis than TP53 wild-type (WT) patients. The subgroups associated with a good prognosis (age > 65 years, ARID1A-MT, and PIK3CA-MT) universally had a high TMB and increased expression of immune checkpoint molecules. Although TP53-MT was associated with a high TMB, the expression of most immune checkpoint molecules and immune-related genes was lower in TP53-MT patients than TP53-WT patients, which may reflect low immunogenicity. Pathways related to the immunosuppressive tumor microenvironment were mostly enriched in the subgroups associated with a poor prognosis (age ≤ 65 years, low TMB, ARID1A-WT, PIK3CA-WT, and TP53-MT). In conclusion, the factors age > 65 years, PIK3CA-MT, and ARID1A-MT predicted favorable efficacy for ICI treatment in HNSC patients, and TP53 mutation was a negative predictor.

scholarly journals Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoqing Luo ◽  
Shunli Peng ◽  
Sijie Ding ◽  
Qin Zeng ◽  
Rong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Tissue Array ◽  
Lung Cancers ◽  
Cox Regression Analysis ◽  
Immune Checkpoint Molecules ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Infiltration Models

Abstract Background Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the functions and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers. Methods SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated. Results SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns. Conclusions This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and it illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

scholarly journals Neutrophil to Lymphocyte Ratio as Prognostic Indicator for Patients with Esophageal Squamous Cell Cancer

2017 ◽  
Vol 32 (4) ◽  
pp. 409-414 ◽  
Author(s):  
Guo-Dong Gao ◽  
Bo Sun ◽  
Xian-Bin Wang ◽  
Shi-Meng Wang
Keyword(s):  
Overall Survival ◽  
Squamous Cell ◽  
Cox Regression ◽  
Cell Cancer ◽  
Prognostic Indicator ◽  
Neutrophil To Lymphocyte Ratio ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Leukocyte Counts ◽  
Clinical Records

Background This study aimed to evaluate the correlation between neutrophil to lymphocyte ratio (NLR) with overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. Method Records of patients with diagnosed ESCC were reviewed. Leukocyte counts and patients' characteristics were extracted from their clinical records to calculate NLR. Correlation between NLR and baseline characteristics with overall survival (OS) was then analyzed using Cox regression. The patients were then separated into higher and lower NLR groups according to median NLR. OS was further compared between the 2 groups. Results A total of 1281 patients were included in the study. Cox regression analysis showed a significant correlation of NLR with OS of ESCC patients. The median pretreatment NLR was identified as 2.86. Higher NLR was associated with worse prognosis in terms of OS. Conclusions Pretreatment NLR is independently associated with OS of ESCC patients. Therefore, NLR may be used as a predictive indicator for pretreatment evaluation and adjustment of treatment regimen.

scholarly journals Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
He-San Luo ◽  
Ying-Ying Chen ◽  
Wei-Zhen Huang ◽  
Sheng-Xi Wu ◽  
Shao-Fu Huang ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Local Recurrence ◽  
Squamous Cell ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cell Cancer ◽  
Clinical Factors ◽  
Free Survival ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Purpose To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT). Methods We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy. Results A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort. Conclusion We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.

scholarly journals Immune-related miRNA signature identifies prognosis and immune landscape in head and neck squamous cell carcinomas

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Bo Ma ◽  
Hui Li ◽  
Jia Qiao ◽  
Tao Meng ◽  
Riyue Yu
Keyword(s):  
Head And Neck ◽  
Squamous Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Cox Regression Analysis ◽  
Immunosuppressive Microenvironment ◽  
Selection Operator

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is recognised as an immune active cancer, but little is known about the role of microRNAs (miRNAs) in it. In the present study, we aim to determine a prognostic and immune-related miRNAs signature (IRMS) in HNSCC. Methods: Spearman correlation analysis was used to screen out prognostic immune-related miRNAs based on single-sample gene set enrichment analysis (ssGSEA). Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish IRMS in HNSCC. Then, the influence of the IRMS on HNSCC was comprehensively analysed. Results: We obtained 11 prognostic immune-related miRNAs based on ssGSEA. Then an IRMS integrated with six miRNAs was established through LASSO Cox regression analysis. The stratification survival analysis indicated that IRMS was independent from other characteristics and performed favourably in the overall survival (OS) prediction. The function annotation suggested that IRMS was highly associated with the immune-related response biological processes and pathways which are so important for tumorigenesis of HNSCC. Moreover, the nomogram demonstrated that our model was identified as an independent prognostic factor. In addition, we found that IRMS was significantly correlated with the immune infiltration and expression of critical immune checkpoints, indicating that the poor prognosis might be caused partly by immunosuppressive microenvironment. Conclusion: We established a novel IRMS, which exhibited a potent prognostic value and could be representative of immune status in HNSCC.

scholarly journals Prognostic factors affecting survival and recurrence in patients with early cervical squamous cell cancer following radical hysterectomy

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051988974
Author(s):  
Dan Li ◽  
Xiaoxian Xu ◽  
Dingding Yan ◽  
Shuhui Yuan ◽  
Juan Ni ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Squamous Cell ◽  
Radical Hysterectomy ◽  
Tumor Size ◽  
Cox Regression ◽  
Squamous Cell Cancer ◽  
Postoperative Radiotherapy ◽  
Cell Cancer ◽  
Cox Regression Analysis

Objective This study aimed to investigate the clinical and histological features affecting the survival of patients with early cervical squamous cell cancer treated with radical hysterectomy. Methods We retrospectively analyzed clinical and histological data for patients with stage IB-IIA cervical cancer treated by radical hysterectomy at Zhejiang Cancer Hospital from August 2008 to January 2013. Results A total of 1435 patients were included in the study. Cox regression analysis identified tumor size >4 cm, lymphovascular space involvement (LVSI), lymph node ratio (LNR), and squamous cell carcinoma antigen (SCC-Ag) >2.65 ng/mL as independent prognostic risk factors. Among 1096 patients without high pathological risk factors, the 5-year local recurrence rates for SCC-Ag ≤2.65 and >2.65 ng/mL were 6.6% and 25.7%, respectively. Among 332 patients with lymph node positivity, the overall survival rates for LNR ≤0.19 and >0.19 were 87.8% and 55.6%, respectively. Conclusions LVSI, tumor size >4 cm, LNR >0.19, and SCC-Ag >2.65 ng/mL may predict a poor prognosis in patients with early cervical squamous cell cancer treated with radical hysterectomy. SCC-Ag >2.65 ng/mL may be a useful prognostic factor guiding the use of postoperative radiotherapy in patients without pathologic risk factors.

scholarly journals Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

2021 ◽  
Author(s):  
Xiaoqing Luo ◽  
Shunli Peng ◽  
Sijie Ding ◽  
Qin Zeng ◽  
Rong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Tissue Array ◽  
Lung Cancers ◽  
Cox Regression Analysis ◽  
Immune Checkpoint Molecules ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Infiltration Models

Abstract Background: Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the functions and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers.Methods: SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated.Results: SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns.Conclusions: This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and it illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

scholarly journals Immune‐checkpoint molecules on regulatory T‐cells as a potential therapeutic target in head and neck squamous cell cancers

2020 ◽  
Vol 111 (6) ◽  
pp. 1943-1957 ◽  
Author(s):  
Susumu Suzuki ◽  
Tetsuya Ogawa ◽  
Rui Sano ◽  
Taishi Takahara ◽  
Daisuke Inukai ◽  
...  
Keyword(s):  
T Cells ◽  
Regulatory T Cells ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Checkpoint ◽  
Therapeutic Target ◽  
Potential Therapeutic Target ◽  
Immune Checkpoint Molecules

scholarly journals Development and Validation of a Radiomics-based Model to Predict Local Progression-free Survival After Chemo-radiotherapy in Patients With Esophageal Squamous Cell Cancer

2021 ◽  
Author(s):  
He-San Luo ◽  
Ying-Ying Chen ◽  
Sheng-Xi Wu ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Local Recurrence ◽  
Squamous Cell ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cell Cancer ◽  
Clinical Factors ◽  
Free Survival ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Purpose: To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with chemoradiotherapy. Methods: We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomics features calculating Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analysis were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy.Results: A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. After LASSO COX regression analysis, seventeen radiomics features were selected to calculate Rad-score for predicting LPFS. The patients with a Rad-score≥0.1411 had high risk of local recurrence, and those with a Rad-score<0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95%CI: 0.7700 -0.790) in training cohort and 0.723(95%CI：0.654-0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort.Conclusion: We developed and validated a prediction model based on radiomics features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to making individualized chemoradiotherapy strategy and providing scientific basis for subsequent intensive adjuvant therapy for ESCC patients.

Sign in / Sign up

Export Citation Format

Share Document