Dataset Information

Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study.

ABSTRACT:

Background

At present, there is no accurate biomarker for immune checkpoint inhibitors (ICIs). Since the efficacy of ICIs is associated with a variety of indicators, establishing a model to predict its efficacy is more clinically significant and in line with clinical needs.

Methods

We collected and retrospectively analyzed the relationship between immunotherapy efficacy and clinicopathologic features in lung adenocarcinoma patients treated with ICIs. Progression-free survival (PFS) and overall survival (OS) were analyzed. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify prognostic factors associated with PFS. Besides, a clinical prediction model was established based on the results of the multivariate Cox regression analyses to predict PFS.

Results

A total of 201 lung adenocarcinoma patients treated with ICIs were assessed. Univariate analysis showed that male gender [hazard ratio (HR) =0.521, 95% confidence interval (CI): 0.356-0.761, P=0.001], smoking (HR =0.595, 95% CI: 0.420-0.843, P=0.003), epidermal growth factor receptor (EGFR) wild type (HR =2.766, 95% CI: 1.719-4.452, P<0.001), Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation (HR =0.449, 95% CI: 0.271-0.743, P=0.001), positive programmed death ligand 1 (PD-L1) expression (HR =0.527, 95% CI: 0.336-0.825, P=0.004), early tumor node metastasis (TNM) stage (HR =0.581, 95% CI: 0.344-0.983, P=0.039), no liver metastasis (HR =1.801, 95% CI: 1.046-3.102, P=0.031), ICIs combined with chemotherapy (HR =0.560, 95% CI: 0.384-0.815, P=0.002), having immune-related adverse effects (HR =0.354, 95% CI: 0.228-0.511, P<0.001) and first-line immunotherapy (HR =0.596, 95% CI: 0.420-0.845, P=0.003) were significantly associated with better PFS in patients with lung adenocarcinoma receiving immunotherapy. Multivariate analysis showed that smoking status, KRAS mutation, PD-L1 expression, line of immunotherapy and immune-related adverse effects were independent prognostic factors affecting PFS. A clinical prediction model was established to predict the PFS of lung adenocarcinoma patients treated with ICIs. The model showed good predictive ability via C-index, calibration curve and receiver operating characteristic (ROC) curve validation.

Conclusions

The clinical prediction model developed in this study can be used to some extent to predict PFS after immunotherapy in lung adenocarcinoma patients. However, the model still needs to be validated in studies with large sample size.

SUBMITTER: Hu F

PROVIDER: S-EPMC9641362 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study.

Hu Fang F Peng Jin J Niu Yanjie Y Mao Xiaowei X Zhao Yizhuo Y Jiang Liyan L

Journal of thoracic disease 20221001 10

<h4>Background</h4>At present, there is no accurate biomarker for immune checkpoint inhibitors (ICIs). Since the efficacy of ICIs is associated with a variety of indicators, establishing a model to predict its efficacy is more clinically significant and in line with clinical needs.<h4>Methods</h4>We collected and retrospectively analyzed the relationship between immunotherapy efficacy and clinicopathologic features in lung adenocarcinoma patients treated with ICIs. Progression-free survival (PFS ...[more]

PMID: 36389292

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Project description:Objective: Metformin as a common antidiabetic drug, has recently found to exert its anti-cancer and immunomodulatory effect in numerous preclinical studies. This study aims to clarify the prognostic impact of metformin use in solid cancer patients receiving immune checkpoint inhibitors (ICIs). Methods: A retrospective cohort enrolling 516 solid cancer patients who received ICI-based therapy between 2018 and 2023 at three hospitals was analyzed. The primary endpoints included overall survival (OS) and progression-free survival (PFS). In addition, a bioinformatics analysis based on TCGA and GSE cohort was performed to investigate the prognostic significance of metformin target genes (MTGs) and their correlation with immune infiltration in non-small cell lung cancer (NSCLC) patients. Results: In the entire cohort, a total of 76 patients received metformin before and/or during ICI therapy. The global analysis demonstrated that metformin use was unrelated with the OS (p = 0.064) and PFS (p = 0.059) of ICI-treated cancer patients, which was confirmed in the subgroups of esophagus, hepatobiliary or pancreatic cancer (all p > 0.05). However, metformin use was significantly correlated with better OS (p = 0.012) and PFS (p = 0.005) in ICI-treated lung cancer patients. Metformin use was also identified as an independent favorable prognostic factor for these patients. The bioinformatics analysis identified five favorable prognostic MTGs (RPS6KA5, RORA, SH3BP5, NUPR1, and CD40LG) for NSCLC patients, all of which was downregulated in lung cancer tissues as compared with normal tissues. The expressions of five MTGs not only could effectively stratify the OS of NSCLC patients, but also was correlated with infiltration of immune cells such as CD4+ and CD8+ T cells. Conclusion: Metformin use was significantly correlated with better OS and PFS in ICI-treated lung cancer patients. MTGs has the potential to serve as novel clinical biomarkers or druggable targets for cancer immunotherapy. Considering study limitations, the actual impact of metformin use on ICI therapy needs to be clarified by more clinical trials.

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Dataset Information

Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study.

Background

Methods

Results

Conclusions

Publications

Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study.

Similar Datasets

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