A non-linear association between blood tumor mutation burden and prognosis in NSCLC patients receiving atezolizumab.
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ABSTRACT:
SUBMITTER: Nie W
PROVIDER: S-EPMC7051187 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAt present, there is a lack of studies focusing on the survival prediction of patients with non-small cell lung cancer (NSCLC) receiving atezolizumab in light of gene mutation characteristic.MethodsPatients with NSCLC receiving atezolizumab from the OAK study were defined as the training group. LASSO Cox regressions were applied to establish the gene mutation signature model to predict the overall survival (OS) rate of the training group. NSCLC patients receiving atezolizumab from the POPLAR study were defined as the testing group to validate the gene mutation signature model. In addition, we compared the OS rate between patients receiving atezolizumab and docetaxel classified according to their risk score based on our gene mutation signature model.ResultsWe successfully established a 5-genomic mutation signature that included CREBBP, KEAP1, RAF1, STK11 and TP53 mutations. We found it was superior to the blood tumor mutation burden (bTMB) score and programmed death ligand 1 (PDL1) expression in the prediction of the OS rate for patients receiving atezolizumab. High-risk patients receiving atezolizumab had a worse OS rate compared with low-risk patients in the training (P = 0.0004) and testing (P = 0.0001) groups. In addition, low-risk patients using atezolizumab had a better OS rate compared with those in use of docetaxel for the training (P <0.0001) and testing groups (P = 0.0095). High-risk patients of the training group (P = 0.0265) using atezolizumab had a better OS rate compared with those using docetaxel. However, the OS difference between atezolizumab and docetaxel was not found in high-risk patients from the testing group (P = 0.6403). Multivariate Cox regression analysis showed that the risk model in light of 5-genomic mutation signature was an independent prognostic factor on OS for patients receiving atezolizumab (P <0.0001). In addition, significant OS benefit could only be found in low-risk patients receiving atezolizumab compared with docetaxel (P <0.0001).ConclusionsThe 5-genomic mutation signature could predict OS benefit for patients with NSCLC receiving atezolizumab. Therefore, the establishment of the 5-genomic mutation panel will guide clinicians to identify optimal patients who could benefit from atezolizumab treatment.
| S-EPMC8261129 | biostudies-literature
Project description:Background: Esophageal adenocarcinoma (EAC) remains a leading cause of cancer-related deaths worldwide and demonstrates a predominant rising incidence in Western countries. Recently, immunotherapy has dramatically changed the landscape of treatment for many advanced cancers, with the benefit in EAC thus far been limited to a small fraction of patients. Methods: Using somatic mutation data of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium, we delineated the somatic mutation landscape of EAC patients from US and England. Based on the expression data of TCGA cohort, multiple bioinformatics algorithms were utilized to perform function annotation, immune cell infiltration analysis, and immunotherapy response assessment. Results: We found that RYR2 was a common frequently mutated gene in both cohorts, and patients with RYR2 mutation suggested higher tumor mutation burden (TMB), better prognosis, and superior expression of immune checkpoints. Moreover, RYR2 mutation upregulated the signaling pathways implicated in immune response and enhanced antitumor immunity in EAC. Multiple bioinformatics algorithms for assessing immunotherapy response demonstrated that patients with RYR2 mutation might benefit more from immunotherapy. In order to provide additional reference for antitumor therapy of different RYR2 status, we identified nine latent antitumor drugs associated with RYR2 status in EAC. Conclusion: This study reveals a novel gene whose mutation could be served as a potential biomarker for prognosis, TMB, and immunotherapy of EAC patients.
| S-EPMC8166246 | biostudies-literature
Project description:ObjectiveTo explore the mutated genes in esophageal cancer (ESCA), and evaluate its relationship with tumor mutation burden (TMB) and prognosis of ESCA, and analyze the advantages of FAT3 as a potential prognostic marker in ESCA.MethodsThe somatic mutation landscape was analyzed according to ESCA samples from the TCGA and ICGC database. The differences of TMB between mutant type and wild type of frequently mutated genes were compared by Mann-Whitney U test. The association of gene mutations with prognosis was analyzed by Kaplan-Meier method. The relative abundance of 22 tumor-infiltrating lymphocyte subsets in ESCA was calculated by CIBERSORT algorithm.ResultsFAT3 was a high frequency mutation in both TCGA and ICGC samples from the somatic mutation landscape. Then, the mutation type of FAT3 had significantly higher TMB in patients with ESCA compared the wild type (P<0.05). Meanwhile, the prognosis of FAT3 mutation type was significantly worse in patients with ESCA(P<0.05), and the FAT3 mutation status might be an independent factor for prognosis of patients with ESCA (HR: 1.262-5.922, P=0.011). The GSEA analysis revealed the potential mechanism of FAT3 mutation on the occurrence and development of ESCA. Finally, naive B cells were significantly enriched in FAT3 mutation samples of the ESCA microenvironment (P<0.05).ConclusionsFAT3 mutation is related to TMB and poor prognosis in ESCA. FAT3 mutation may be a prognostic marker of ESCA, and reveal the potential mechanism of FAT3 mutation on ESCA.
| S-EPMC8018597 | biostudies-literature
Project description:The causes underlying racial disparities in cancer are multifactorial. In addition to socioeconomic issues, biological factors may contribute to these inequities, especially in disease incidence and patient survival. To date, there have been few studies that relate the disparities in these aspects to genetic aberrations. In this work, we studied the impacts of race on the patient survival and tumor mutation burden using the data released by the Cancer Genome Atlas (TCGA). The potential relationship between mutation burden and disease incidence is further inferred by an integrative analysis of TCGA data and the data from the Surveillance, Epidemiology, and End Results (SEER) Program. The results show that disparities are present (p < 0.05) in patient survival of five cancers, such as head and neck squamous cell carcinoma. The numbers of tumor driver mutations are differentiated (p < 0.05) over the racial groups in five cancers, such as lung adenocarcinoma. By treating a specific cancer type and a racial group as an "experimental unit", driver mutation numbers demonstrate a significant (r = 0.46, p < 0.002) positive correlation with cancer incidence rates, especially when the five cancers with mutational disparities are exclusively focused (r = 0.88, p < 0.00002). These results enrich our understanding of racial disparities in cancer and carcinogenic process.
| S-EPMC5651797 | biostudies-literature
Project description:BackgroundIntrahepatic cholangiocarcinoma (iCCA) is a highly lethal malignancy of the biliary tract. Analysis of somatic mutational profiling can reveal new prognostic markers and actionable treatment targets. In this study, we explored the utility of genomic mutation signature and tumor mutation burden (TMB) in predicting prognosis in iCCA patients.MethodsWhole-exome sequencing and corresponding clinical data were collected from the ICGC portal and cBioPortal database to detect the prognostic mutated genes and determine TMB values. To identify the hub prognostic mutant signature, we used Cox regression and Lasso feature selection. Mutation-related signature (MRS) was constructed using multivariate Cox regression. The predictive performances of MRS and TMB were assessed using Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC). We performed a functional enrichment pathway analysis using gene set enrichment analysis (GSEA) for mutated genes. Based on the MRS, TMB, and the TNM stage, a nomogram was constructed to visualize prognosis in iCCA patients.ResultsThe mutation landscape illustrated distributions of mutation frequencies and types in iCCA, and generated a list of most frequently mutated genes (such as Tp53, KRAS, ARID1A, and IDH1). Thirty-two mutated genes associated with overall survival (OS) were identified in iCCA patients. We obtained a six-gene signature using the Lasso and Cox method. AUCs for the MRS in the prediction of 1-, 3-, and 5-year OS were 0.759, 0.732, and 0.728, respectively. Kaplan-Meier analysis showed a significant difference in prognosis for patients with iCCA having a high and low MRS score (P < 0.001). GSEA was used to show that several signaling pathways, including MAPK, PI3K-AKT, and proteoglycan, were involved in cancer. Conversely, survival analysis indicated that TMB was significantly associated with prognosis. GSEA indicated that samples with high MRS or TMB also showed an upregulated expression of pathways involved in tumor signaling and the immune response. Finally, the predictive nomogram (that included MRS, TMB, and the TNM stage) demonstrated satisfactory performance in predicting survival in patients with iCCA.ConclusionsMutation-related signature and TMB were associated with prognosis in patients with iCCA. Our study provides a valuable prognostic predictor for determining outcomes in patients with iCCA.
| S-EPMC7860034 | biostudies-literature
Project description:Breast cancer is the second most common malignancy in women and considered as a severe health burden. PEG3 mutations have been observed in several cancers. However, the associations of PEG3 mutation with tumor mutation burden (TMB) and prognosis in breast cancer have not been investigated. In our study, the somatic mutation data of 986 breast cancer patients from The Cancer Genome Atlas (TCGA) were analyzed. It showed that PEG3 had a relatively high mutation rate (2%). After calculated the TMB in PEG3 mutant and PEG3 wild-type groups, we found the TMB value was significantly higher in PEG3 mutant samples than that in PEG3 wild-type samples (P = 5.6e-07), which was independent of the confounding factors including age, stage, mutations of BRCA1, BRCA2 and POLE (odd ratio, 0.45; 95% CI, 0.20-0.98; P=0.044). Survival analysis revealed that PEG3 mutant samples had inferior survival outcome compared with the PEG3 wild-type samples after adjusted for the confounding factors above (hazard ratio, 0.27; 95% CI: 0.12-0.57; P<0.001). These results illustrated that PEG3 mutation was associated with high TMB and inferior prognosis, suggesting PEG3 mutation might play a guiding role in prognosis prediction and immunotherapy selection in breast cancer.
| S-EPMC7419805 | biostudies-literature
Project description:BackgroundLiver hepatocellular carcinoma (LIHC) is the most common primary liver cancer and the main cause of death in patients with cirrhosis. LRP1B is found to involve in a variety of cancers, but the association of LRP1B mutation with tumor mutation burden (TMB) and prognosis of LIHC is rarely studied.Methods and resultsHerein, we analyzed the somatic mutation data of 364 LIHC patients from The Cancer Genome Atlas (TCGA) and found that LRP1B showed elevated mutation rate. Calculation of the TMB in LRP1B mutant and LRP1B wild-type groups showed that LRP1B mutant group had higher TMB compared with that in LRP1B wild-type group. Then survival analysis was performed and the survival curve showed that LRP1B mutation was associated with poor survival outcome, and this association remained to be significant after adjusting for multiple confounding factors including age, gender, tumor stage, mutations of BRCA1, BRCA2, and POLE.ConclusionCollectively, our results revealed that LRP1B mutation was related to high TMB value and poor prognosis in LIHC, indicating that LRP1B mutation is probably helpful for the selection of immunotherapy and prognosis prediction in LIHC.
| S-EPMC9239668 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is a heterogeneous disease related to a broad spectrum of molecular alterations. The successes of immunotherapies treating solid tumors and a deeper understanding of the immune systems of patients with hematologic malignancies have promoted the development of immunotherapies for the treatment of AML. And high tumor mutational burden (TMB) is an emerging predictive biomarker for response to immunotherapy. However, the association of gene mutation in AML with TMB and immunological features still has not been clearly elucidated. In our study, based on The Cancer Genome Atlas (TCGA) and BeatAML cohorts, 20 frequently mutated genes were found to be covered by both datasets in AML. And TP53 mutation was associated with a poor prognosis, and its mutation displayed exclusiveness with other common mutated genes in both datasets. Moreover, TP53 mutation correlated with TMB and the immune microenvironment. Gene Set Enrichment Analysis (GSEA) showed that TP53 mutation upregulated signaling pathways involved in the immune system. In summary, TP53 mutation is frequently mutated in AML, and its mutation is associated with dismal outcome, TMB, and immunological features, which may serve as a biomarker to predict immune response in AML.
| S-EPMC8566372 | biostudies-literature
Project description:ObjectiveA nomogram model based on clinical variables was conducted to predict the survival in patients with non-small cell lung cancer (NSCLC) receiving second-line atezolizumab.MethodsFour hundred and twenty-four patients with NSCLC receiving atezolizumab from OAK study were regarded as the training cohort. Next, a nomogram model based on clinical variables in the training cohort was established to predict the survival of patients receiving atezolizumab. The concordance index, area under curve (AUC), and calibration plots were used to assess the performance of the nomogram model. In addition, 144 patients with NSCLC receiving atezolizumab from POPLAR study were regarded as the test cohort to validate the nomogram model. Using Kaplan-Meier and log-rank test, we compared the survival difference between the high- and low-risk groups, atezolizumab and docetaxel treatment groups, respectively.ResultsWe successfully constructed a nomogram model based on different variable scores for predicting the survival in NSCLC patients receiving atezolizumab using the training cohort. According to risk score, patients receiving atezolizumab were divided into the high- and low-risk groups. Importantly, in the training cohort, patients had worse overall survival (OS) in high-risk group compared with the low-risk group (median survival: 252.3 vs. 556.9 days; p < 0.0001). As expected, in the test cohort, the high-risk patients also showed a worse OS (median survival: 288.8 vs. 529.3 days, p = 0.0003). In addition, all the patients from the training and test cohorts could be found the OS benefit from atezolizumab compared with docetaxel (all, p < 0.05).ConclusionsThe clinical variable-based nomogram model could predict the survival benefit for NSCLC patients receiving second-line atezolizumab.
| S-EPMC8446569 | biostudies-literature
Project description:Background: Melanoma is highly immunogenic and therefore suitable for immunotherapy, but the efficacy is limited by response rate. In several types of tumor, tumor mutation burden (TMB) and immune infiltration have been reported to predict the response to immunotherapy, although each has its limitations. In the current study, we aimed to explore the association of TMB with immune infiltration and prognosis in cutaneous melanoma. Methods: The data of cutaneous melanoma used for analyses was downloaded from The Cancer Genome Atlas (TCGA) database. The mutation data was sorted using "maftools" R package. TMB was estimated and then patients were divided into two groups based on TMB. The association of TMB with prognosis and clinical characteristics was explored. Differential analysis between two TMB groups was performed using "DESeq2" R package to identify differentially expressed genes (DEGs). The function enrichment analyses of DEGs were conducted to screen critical pathways. Besides, DEGs were further filtered to identify two hub genes, based on which a risk score model and nomogram for predicting prognosis were conducted, and the validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Finally, CIBERSORT algorithm and TIMER database were used to assess the effect of TMB and hub genes on immune infiltration. Results: The most common mutation was C > T, and the top three frequently mutated genes were TTN, MUC16, and BRAF. Higher TMB indicated better survival outcomes and lower pathological stages. 735 DEGs were identified and mainly involved in immune-related and adhesion-related pathways. The risk score model and nomogram were validated using receiver operating characteristic (ROC) curves and calibration curves, and exhibited relatively high predictive capability. Decision curve analysis (DCA) was used to assess clinical benefit. As for immune infiltration, the proportion was higher for macrophages M1 and M2 in the high-TMB group, while lower for memory B cells and regulatory T cells. Conclusions: In cutaneous melanoma, TMB was positively correlated with prognosis. The risk score model and nomogram can be conveniently used to predict prognosis. The association of TMB with immune infiltration can help improve the predicting methods for the response to immunotherapy.
| S-EPMC7531222 | biostudies-literature