scholarly journals Immunogenomic Gene Signature of Cell-Death Associated Genes with Prognostic Implications in Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 155
Author(s):  
Pankaj Ahluwalia ◽  
Meenakshi Ahluwalia ◽  
Ashis K. Mondal ◽  
Nikhil Sahajpal ◽  
Vamsi Kota ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Cell Death ◽  
T Cell ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Group ◽  
High Risk Group ◽  
Free Survival ◽  
Cell Death Pathways

Lung cancer is one of the leading causes of death worldwide. Cell death pathways such as autophagy, apoptosis, and necrosis can provide useful clinical and immunological insights that can assist in the design of personalized therapeutics. In this study, variations in the expression of genes involved in cell death pathways and resulting infiltration of immune cells were explored in lung adenocarcinoma (The Cancer Genome Atlas: TCGA, lung adenocarcinoma (LUAD), 510 patients). Firstly, genes involved in autophagy (n = 34 genes), apoptosis (n = 66 genes), and necrosis (n = 32 genes) were analyzed to assess the prognostic significance in lung cancer. The significant genes were used to develop the cell death index (CDI) of 21 genes which clustered patients based on high risk (high CDI) and low risk (low CDI). The survival analysis using the Kaplan–Meier curve differentiated patients based on overall survival (40.4 months vs. 76.2 months), progression-free survival (26.2 months vs. 48.6 months), and disease-free survival (62.2 months vs. 158.2 months) (Log-rank test, p < 0.01). Cox proportional hazard model significantly associated patients in high CDI group with a higher risk of mortality (Hazard Ratio: H.R 1.75, 95% CI: 1.28–2.45, p < 0.001). Differential gene expression analysis using principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters. To analyze the immune parameters in two risk groups, cytokines expression (n = 265 genes) analysis revealed the highest association of IL-15RA and IL 15 (> 1.5-fold, p < 0.01) with the high-risk group. The microenvironment cell-population (MCP)-counter algorithm identified the higher infiltration of CD8+ T cells, macrophages, and lower infiltration of neutrophils with the high-risk group. Interestingly, this group also showed a higher expression of immune checkpoint molecules CD-274 (PD-L1), CTLA-4, and T cell exhaustion genes (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, and LYN) (p < 0.01). Furthermore, functional enrichment analysis identified significant perturbations in immune pathways in the higher risk group. This study highlights the presence of an immunocompromised microenvironment indicated by the higher infiltration of cytotoxic T cells along with the presence of checkpoint molecules and T cell exhaustion genes. These patients at higher risk might be more suitable to benefit from PD-L1 blockade or other checkpoint blockade immunotherapies.

scholarly journals GDPLichi: a DNA Damage Repair-Related Gene Classifier for Predicting Lung Adenocarcinoma Immune Checkpoint Inhibitors Response

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Leng ◽  
Shiying Dang ◽  
Fei Yin ◽  
Tianshun Gao ◽  
Xing Xiao ◽  
...  
Keyword(s):  
Dna Damage ◽  
T Cells ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Risk Group ◽  
Checkpoint Inhibitors ◽  
High Risk Group ◽  
Damage Repair

Lung cancer is one of the most common and mortal malignancies, usually with a poor prognosis in its advanced or recurrent stages. Recently, immune checkpoint inhibitors (ICIs) immunotherapy has revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD), and significantly improved patients’ prognoses. However, the prognostic and predictive outcomes differ because of tumor heterogeneity. Here, we present an effective method, GDPLichi (Genes of DNA damage repair to predict LUAD immune checkpoint inhibitors response), as the signature to predict the LUAD patient’s response to the ICIs. GDPLichi utilized only 7 maker genes from 8 DDR pathways to construct the predictive model and classified LUAD patients into two subgroups: low- and high-risk groups. The high-risk group was featured by worse prognosis and decreased B cells, CD8+ T cells, CD8+ central memory T cells, hematopoietic stem cells (HSC), myeloid dendritic cells (MDC), and immune scores as compared to the low-risk group. However, our research also suggests that the high-risk group was more sensitive to ICIs, which might be explained by increased TMB, neoantigen, immune checkpoint molecules, and immune suppression genes’ expression, but lower TIDE score as compared to the low-risk group. This conclusion was verified in three other LUAD cohort datasets (GSE30219, GSE31210, GSE50081).

scholarly journals Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12547
Author(s):  
Zihao Yan ◽  
Siwen Chu ◽  
Chen Zhu ◽  
Yunhe Han ◽  
Qingyu Liang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
T Cell Activation ◽  
Cell Activation ◽  
Risk Group ◽  
Immune Checkpoint Blockade ◽  
High Risk Group ◽  
Related Risk

Background Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM. Materials and Methods This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting. Results The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy. Conclusions As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

A new model using artificial intelligence to predict recurrence after surgical resection of stage I-II non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8537-8537
Author(s):  
Natalie Lui ◽  
Nien Wei ◽  
Winston Trope ◽  
Shannon Nesbit ◽  
Prasha Bhandari ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
High Risk ◽  
Surgical Resection ◽  
Risk Group ◽  
Disease Free Survival ◽  
High Risk Group ◽  
Low Risk ◽  
Stage I ◽  
Free Survival

8537 Background: Five-year survival for stage I-II lung cancer is quite low even after complete surgical resection. Current guidelines recommend adjuvant treatment only for selected patients with stage II or higher disease. A prediction model that identifies patients at high risk of recurrence who may benefit from adjuvant treatment is greatly needed. Many existing prediction models include a small number of genes that were found to be significant in previous studies. We propose using artificial intelligence to analyze a microarray of > 20,000 well-annotated genes to create a model that predicts recurrence after surgical resection of stage I-II lung cancer. Methods: We identified 275 patients who underwent surgical resection for pathologic stage I-II lung adenocarcinoma or squamous cell carcinoma from 2009 to 2019 in our institution’s prospective surgical database. We excluded patients who had follow up time less than 3 years or received adjuvant therapy and had not had a recurrence, as well as patients with missing specimen blocks. Patient characteristics and recurrence information were obtained from chart review. The patients were divided into training (192 patients) and validation (83 patients) cohorts, and the recurrence status for the validation cohort was initially blinded. Gene expression levels were generated using Clariom S human array (ThermoFisher) from 10um sections cut from the formalin-fixed, paraffin-embedded surgical specimen blocks. The artificial intelligence algorithm Support Vector Machine (SVM) was used to create a prediction model for recurrence using the gene expression and recurrence status of the patients in the training cohort. The model was then tested on the validation cohort using Kaplan-Meier analysis and the area under the receiver operator curve (AUROC). Results: The recurrence prediction model separated the validation cohort into 15 (18.1%) patients in the high-risk group and 68 (81.9%) patients in the low-risk group. Kaplan-Meier analysis showed the five-year disease-free survival was significantly higher in the low-risk group compared to the high-risk group (86 vs. 50%, HR = 4.41, p = 0.0025). The AUROC for predicting recurrence was 0.744. Conclusions: Our model uses artificial intelligence to successfully predict recurrence after surgical resection for stage I-II non-small cell lung cancer. With an AUROC of 0.744, our model outperforms previously described models with AUROC up to 0.6. Our model separates patients into high-risk and low-risk groups, which will make management decisions clearer compared to other models that also include an intermediate-risk group. Patients in the low-risk group had 86% five-year disease-free survival; patients in the high-risk group had 50% five-year disease-free survival and may benefit from increased postoperative surveillance or adjuvant therapy.

scholarly journals Identification and Validation of 17-lncRNA Related to Regulatory T Cell Heterogeneity as a Prognostic Signature for Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Qi Sun ◽  
Yumei Li ◽  
Xin Yang ◽  
Xinxin Wu ◽  
Zhen Liu ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
High Risk ◽  
Clinical Decision Making ◽  
Cell Function ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Signature

Successful eradication of tumors by the immune system depends on generation of antigen-specific T cells that migrate to tumor sites and kill cancerous cells. However, presence of suppressive Treg populations inside tumor microenvironment hinders effector T cell function and decreases antitumor immunity. In this study we independently evaluated and confirmed prognostic signature of 17-Treg-related-lncRNA. Immune cell infiltration analysis using 17-lncRNA signature as a probe, accurately described Treg populations in tumor immune microenvironment. 17-lncRNA signature model predicted prognosis with excellent accuracy in all three cohorts: training cohort (AUC=0.82), testing cohort (AUC=0.61) and total cohort (AUC=0.72). The Kaplan-Meier analysis confirmed that the overall survival of patients in the low-risk group was significantly better than those in the high-risk group(P&lt;0.001). CIBERSORT analysis confirmed that low risk group had higher infiltration of tumor killer CD8 T cells, memory activated CD4 T cells, follicular helper T cells and T cells regulatory (Tregs), and lower expression of M0 macrophages and Mast cells activated. These results indicate that the 17-lncRNA signature is a novel prognostic and support the use of lncRNA as a stratification tool to help guide the course of treatment and clinical decision making in patients at high risk of HNSCC.

Exploration of Tumor Mutation Burden Combined with Immune Infiltrates in the Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Zhenyu Zhao ◽  
Boxue He ◽  
Qidong Cai ◽  
Pengfei Zhang ◽  
Xiong Peng ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Clinical Application ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Tumor Mutation Burden ◽  
Immune Signature ◽  
Immune Infiltration ◽  
Mutation Burden

Abstract Background: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. A recent study shows that immunotherapy is an effective method of LUAD treatment, and tumor mutation burden (TMB) was associated with the immune microenvironment and affected the immunotherapy. Exploration of the gene signature associated with tumor mutation burden and immune infiltrates in predicting prognosis in lung adenocarcinoma in this study, we explored the correlation of TMB with immune infiltration and prognosis in LUAD.Materials and Methods: In this study, we firstly got mutation data and LUAD RNA-Seq data of the LUAD from The Cancer Genome Atlas (TCGA), and according to the TMB we divided the patients into high/low-TMB levels groups. The gene ontology (GO) pathway enrichment analysis and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were utilized to explore the molecular function of the differentially expressed genes (DEGs) between the two groups. The function enrichment analyses of DEGs were related to the immune pathways. Then, the ESTIMATE algorithm, CIBERSORT, and ssGSEA analysis were utilized to identify the relationship between TMB subgroups and immune infiltration. According to the results, Venn analysis was utilized to select the immune-related genes in DEGs. Univariate and Lasso Cox proportional hazards regression analyses were performed to construct the signature which positively associated with the immune infiltration and affected the survival. Finally, we verified the correlation between the signature and immune infiltration. Result: The exploration of the immune infiltration suggested that high-TMB subgroups positively associated with the high level of immune infiltration in LUAD patients. According to the TMB-related immune signature, the patients were divided into High/Low-risk groups, and the high-risk group was positively associated with poor prognostic. The results of the PCA analysis confirmed the validity of the signature. We also verified the effectiveness of the signature in GSE30219 and GSE72094 datasets. The ROC curves and C-index suggested the good clinical application of the TMB-related immune signature in LUAD prognosis. Another result suggested that the patients of the high-risk group were positively associated with higher TMB levels, PD-L1expression, and immune infiltration levels.Conclusion: In conclusion, our signature provides potential biomarkers for studying aspects of the TMB in LUAD such as TMB affected immune microenvironment and prognosis. This signature may provide some biomarkers which could improve the biomarkers of PD-L1 immunotherapy response and were inverted for the clinical application of the TMB in LUAD. LUAD male patients with higher TMB-levels and risk scores may benefit from immunotherapy. The high-risk patients along with higher PD-L1 expression of the signature may suitable for immunotherapy and improve their survival by detecting the TMB of LUAD.

scholarly journals Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

2020 ◽  
Vol 29 ◽  
pp. 096368972097713
Author(s):  
Xueping Jiang ◽  
Yanping Gao ◽  
Nannan Zhang ◽  
Cheng Yuan ◽  
Yuan Luo ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Gene Pair ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
M2 Macrophage ◽  
Related Gene ◽  
Immune Related Gene ◽  
Potential Biomarker

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

scholarly journals Infusion of Donor Lymphocytes Genetically Engineered to Express the Herpes Simplex Virus Thymidine Kinase (HSV-TK) Suicide Gene after Haploidentical Hematopoietic Stem Cell Transplantation (HSCT): Preliminary Efficacy Data from the Randomized TK008 Study

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2535-2535 ◽  
Author(s):  
Chiara Bonini ◽  
Fabio Ciceri ◽  
Arnon Nagler ◽  
Evangelia Yannaki ◽  
Maria Teresa Lupo Stanghellini ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
High Risk ◽  
Suicide Gene ◽  
Suppressive Therapy ◽  
Phase 3 ◽  
Free Survival ◽  
Cell Dose ◽  
Immune Suppressive Therapy ◽  
Ongoing Phase

Abstract Background: Haploidentical family donors represent the ideal solution to offer a potential cure for every high-risk leukemia patient undergoing HSCT. Widespread use of haploidentical HSCT has been historically limited by high rates of late non-relapse mortality (NRM) and relapse, which are associated with the delayed immune reconstitution (IR) due to either ex vivo T-cell depletion or in vivo post-HSCT cyclophosphamide given as graft-vs-host disease (GvHD) prevention. We have previously shown in a phase 2 trial (TK007, Lancet Oncol 2009;10:489) that TK cells (donor T cells genetically modified to express the HSV-TK suicide gene) can safely induce an early IR when given after T-cell depleted haploidentical HSCT Methods: In a confirmatory, ongoing phase 3 trial (TK008, NCT00914628), up to 4 monthly infusions of TK cells are given at 1x107for kg of patient body weight, starting 21 to 49 days after T-cell depleted haploidentical HSCT in the experimental arm. Control arm consists of either T-cell depleted or post-HSCT cyclophosphamide haploidentical HSCT, at physician discretion. High-risk acute leukemia patients lacking an HLA-matched donor are included. So far, 34 patients have been enrolled from eight EU and US sites, with 19 being in complete remission, 18 presenting with AML and 22 having PS of 0 at HSCT. Hypothesis testing for primary efficacy endpoint: 1-year disease-free survival (DFS) of 30% (control arm) vs 52% (TK arm). Secondary endpoints include overall survival (OS), NRM and relapse incidence, proportion and timing to IR, incidence and control of GvHD by suicide-gene induction with ganciclovir Results: Data refer only to 24 patients randomly assigned to experimental arm. Median follow-up was 1.2 years. TK cells were timely given at a median of 28 days after HSCT (95% CI, 25 to 35). IR (defined as a CD3+ cell count > 100/µL) was achieved by 15 of 19 patients (79%) who received TK cells, after a median number of 2 doses (range, 1 to 4) and a median cumulative TK-cell dose of 2.4x107 (range, 1.0 to 3.9). The median time to reach IR computed from HSCT and last TK-cell infusion was 105 days (95% CI, 68 to 125) and 27 days (95% CI, 23 to 33), respectively. No patient received post-HSCT immune-suppressive therapy as GvHD prophylaxis. Acute GvHD developed in 7 patients (6 grade II and 1 grade III) for a 1-year cumulative incidence of 33% (±10) and was rapidly abrogated by suicide-gene induction with ganciclovir administered for a median of 14 days. None of the patients who experienced GvHD subsequently received prophylaxis with long-term immune-suppressive therapy (IST). No progression from acute to chronic GvHD and no GvHD-related death occurred. TK-cell-induced IR was characterized by a wide T-cell repertoire and high frequency of T cells specific for opportunistic pathogens and was associated with high survival rates. By ITT analysis at 1 year, OS was 85% (±8), DFS and IST-free survival were both 74% (±10) and NRM was 10% (±7). For patients achieving IR, the corresponding values of OS, DFS and NRM were 100%, 86% and 0%, respectively. Relapse incidence at 1 year was 16% (±8) for all patients and resulted related to the cumulative TK-cell dose, being 0% for patients who had received higher doses (≥3x107) compared to 20% for those treated with lower doses. By pooling data from TK007 and TK008 trial, these dose-related outcomes were confirmed in a landmark analysis set at 100 days after HSCT to mitigate the guarantee-time bias, with 5-years relapse incidence being inversely related to the TK-cell dose received (0%, 19% and 64% for doses ≥ 3x107, 1.1-2.9x107 and ≤ 1.0x107, respectively; p=0.008) Conclusions: Preliminary results of this ongoing phase 3 trial confirm the potential clinical benefit of T-cell gene transfer technology integrated with T-cell depleted haploidentical HSCT, and highlight the role of early IR as surrogate endpoint for survival outcomes and the dose-related antileukemic effects of TK cells Disclosures Niederwieser: Novartis, Gentium, Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Colombi:MolMed: Employment. Antonio:MolMed: Employment. Bordignon:MolMed: Employment.

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