Prognostic value of tumor mutation burden in patients with advanced gastric cancer receiving first-line chemotherapy

Duan, Xidong; Liu, Ke; Jiao, Xiao‐Dong; Qin, Bao‐Dong; Li, Bing; He, X. T.; Ling, Yan; Wu, Ying; Chen, Shiqi; Zang, Yuan‐Sheng

doi:10.3389/fonc.2022.1007146

Cited by 3 publications

(1 citation statement)

References 55 publications

(58 reference statements)

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“…McGrail et al reported that onesize-fits-all high-TMB cut point could not applied for predicting ICIs efficacy for all types of tumors (McGrail et al, 2021). Several cutoff values have been used to define high TBM in GC by different studies (Wang et al, 2019;Duan et al, 2022). In 2020, pembrolizumab was approved for the treatment of metastatic GC with high-TMB (≥10 mut/Mb) by the US Food and Drug Administration.…”

Section: Discussionmentioning

confidence: 99%

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Ma,

Zhang,

Zhao

et al. 2023

Front. Genet.

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Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.

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Section: Discussionmentioning

confidence: 99%

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Ma,

Zhang,

Zhao

et al. 2023

Front. Genet.

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Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach

Niu

Jia

Wang

et al. 2023

Funct Integr Genomics

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Multi-omics profiling and experimental verification of tertiary lymphoid structure-related genes: molecular subgroups, immune infiltration, and prognostic implications in lung adenocarcinoma

Wu,

Pan,

Pan

et al. 2024

Front. Immunol.

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Lung adenocarcinoma (LUAD), characterized by a low 5-year survival rate, is the most common and aggressive type of lung cancer. Recent studies have shown that tertiary lymphoid structures (TLS), which resemble lymphoid structures, are closely linked to the immune response and tumor prognosis. The functions of the tertiary lymphoid structure-related genes (TLS-RGs) in the tumor microenvironment (TME) are poorly understood. Based on publicly available data, we conducted a comprehensive study of the function of TLS-RGs in LUAD. Initially, we categorized LUAD patients into two TLS and two gene subtypes. Subsequently, risk scores were calculated, and prognostic models were constructed using seven genes (CIITA, FCRL2, GBP1, BIRC3, SCGB1A1, CLDN18, and S100P). To enhance the clinical application of TLS scores, we have developed a precise nomogram. Furthermore, drug sensitivity, tumor mutational burden (TMB), and the cancer stem cell (CSC) index were found to be substantially correlated with the TLS scores. Single-cell sequencing results reflected the distribution of TLS-RGs in cells. Finally, we took the intersection of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) prognosis-related genes and then further validated the expression of these genes by qRT-PCR. Our in-depth investigation of TLS-RGs in LUAD revealed their possible contributions to the clinicopathological features, prognosis, and characteristics of TME. These findings underscore the potential of TLS-RGs as prognostic biomarkers and therapeutic targets for LUAD, thereby paving the way for personalized treatment strategies.

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Prognostic value of tumor mutation burden in patients with advanced gastric cancer receiving first-line chemotherapy

Cited by 3 publications

References 55 publications

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach

Multi-omics profiling and experimental verification of tertiary lymphoid structure-related genes: molecular subgroups, immune infiltration, and prognostic implications in lung adenocarcinoma

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