A nomogram for predicting brain metastases of EGFR-mutated lung adenocarcinoma patients and estimating the efficacy of therapeutic strategies

Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor-mutant non-small-cell lung cancer

PURPOSE

To evaluate the value of computed tomography (CT)-based radiomics combined with clinical-genetic features in predicting brain metastasis in patients with stage III/IV epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC).

METHODS

The study included 147 eligible patients treated at our institution between January 2018 and May 2021. Patients were randomly divided into two cohorts for model training (n = 102) and validation (n = 45). Radiomics features were extracted from the chest CT images before treatment, and a radiomics signature was constructed using the Least Absolute Shrinkage and Selection Operator regression. Kaplan-Meier survival analysis was used to describe the differences in brain metastasis-free survival (BM-FS) risk. A clinical-genetic model was developed using Cox regression analysis. Radiomics, genetic, and combined prediction models were constructed, and their predictive performances were evaluated by the concordance index (C-index).

RESULTS

Patients with a low radiomics score had significantly longer BM-FS than those with a high radiomics score in both the training (p < 0.0001) and the validation (p = 0.0016) cohorts. The C-indices of the nomogram, which combined the radiomics signature and N stage, overall stage, third-generation tyrosine kinase inhibitor treatment, and EGFR mutation status, were 0.886 (95% confidence interval [CI] 0.823-0.949) and 0.811 (95% CI 0.719-0.903) in the training and validation cohorts, respectively. The combined model achieved a higher discrimination and clinical utility than the single prediction models.

CONCLUSIONS

The combined radiomics-genetic model could be used to predict BM-FS in stage III/IV NSCLC patients with EGFR mutations.

Construction and validation of a novel prognostic nomogram for predicting overall survival in lung adenocarcinoma patients with different patterns of metastasis

OBJECTIVE

Metastasis of lung cancer is an important factor affecting survival. The present study proposed to establish and verify a nomogram for predicting overall survival (OS) in lung adenocarcinoma (LUAD) patients with different patterns of metastasis.

METHODS

A total of 9727 patients diagnosed with metastatic LUAD patients from 2010 to 2015 were enrolled based on surveillance, epidemiology and end results (SEER) Database and then randomly divided into training and validation cohorts, and 136 patients in our Cancer Center were enrolled as the external validation cohort. Univariate and multivariate analyses were performed to evaluate the prognostic impact on OS. A prognostic nomogram was constructed and evaluated by C-index, calibration curve, decision curve analysis (DCA), and risk stratification system.

RESULTS

Ultimately, 6809 and 2918 patients diagnosed with metastatic LUAD in the training and validation cohorts were enrolled in the study, respectively. A male sex, a later T and N stage, a larger tumor size, treatment including no surgery, no chemotherapy and no radiotherapy, metastasis sites were found to be independent risk factors in LUAD patients for worse OS, and then incorporated into the nomogram. The frequency of bone metastasis was the highest, and in single site metastasis, the prognosis of liver metastasis was the worst. Two-site metastasis is more common than three-site and four-site metastasis, and co-metastasis eventually leads to a worse survival outcome. The C-index value of nomogram for predicting OS were 0.798, 0.703 and 0.698 in the internal training, validation and external validation cohorts, separately. The calibration curves for the 6-months, 1-year and 2-year showed significant agreement between nomogram models and actual observations. The DCA curves indicated nomogram was more beneficial than the AJCC TNM stage. Patients were further divided into low-risk and high-risk groups according to nomogram predicted scores and developed a survival risk classification system.

CONCLUSIONS

Our prognostic nomogram is expected to be an accurate and individualized clinical predictive tool for predicting OS in LUAD patients with different patterns of metastasis.

Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study

Background

Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM.

Methods

A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging.

Results

The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS.

Conclusion

The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.

Effectiveness and Safety of PD-1 Inhibitor Monotherapy for Elderly Patients with Advanced Non-Small Cell Lung Cancer: A Real-World Exploratory Study

Background

Immunotherapy represented by PD-1 blockades had become the standard of care for advanced non-small cell lung cancer (NSCLC) gradually. Unfortunately, several PD-1 inhibitor-related studies excluded elderly patients with NSCLC over 75 years of age, resulting in relatively limited evidence regarding the efficacy and safety of PD-1 in elderly patients with NSCLC clinically.

Objective

This study aimed to identify the effectiveness and safety of PD-1 blockade monotherapy among elderly patients with advanced NSCLC.

Methods

Elderly patients with advanced NSCLC (≥65 years) who received PD-1 blockade monotherapy from September 2018 to December 2021 were screened retrospectively, and a total of 68 elderly patients with NSCLC were eligible for inclusion ultimately. The PD-1 blockades in the study were the available PD-1 monoclonal antibodies that had been approved for marketing in China, including camrelizumab, sintilimab, pembrolizumab, and nivolumab. The effectiveness and safety of the patients was collected retrospectively. Additionally, the correlation between prognosis and baseline characteristic subgroups was analyzed to identify the potential risk factors for progression-free survival (PFS).

Results

The median age of the 68 elderly patients with advanced NSCLC was 73 years (range: 65–82 years). Best overall response during PD-1 blockade administration suggested that no patients were found with complete response, partial response was found in 14 patients, stable disease was noted in 29 patients, and 25 patients had progressive disease, yielding an objective response rate (ORR) of 20.6% (95%CI: 11.7%–32.1%) and a disease control rate (DCR) of 63.2% (95%CI: 50.7%–74.6%). Furthermore, prognostic analysis exhibited that the median progression-free survival (PFS) of the 68 patients with advanced NSCLC was 3.5 months (95%CI: 2.4–4.6) and the median overall survival (OS) was 10.5 months (95%CI: 6.3–14.7). Additionally, a total of 48 patients were observed with the treatment-related adverse reaction (70.6%) of the 68 elderly patients with NSCLC, and the incidence of grade 3 or above adverse reactions was 16.2%. Specifically, the most common adverse reactions were fatigue, diarrhea, rash, and abnormal liver function with the incidence of 25.0%, 22.1%, 16.2%, and 14.7%, respectively. Exploratory analysis between PFS and baseline characteristic subgroups suggested that ECOG performance status and number of metastatic lesions might be independent factors for PFS.

Conclusion

PD-1 blockade monotherapy exhibited potential effectiveness and acceptable toxicity for elderly patients with NSCLC. ECOG performance status and number of metastatic lesions might be potential risk factors to predict the PFS of elderly patients with advanced NSCLC.

Risk Stratification Using a Novel Nomogram for 2190 EGFR-Mutant NSCLC Patients Receiving the First or Second Generation EGFR-TKI

Simple Summary

No comprehensive and simple prognostic model based on pretreatment factors exists for patients with epidermal growth factor receptor mutation-positive (EGFRm+) non-small cell lung cancer (NSCLC) undergoing EGFR-tyrosine kinase inhibitors (EGFR-TKIs). A total of 11 independent prognostic factors were identified by multivariate analysis, including performance status, morphology, mutation, stage, EGFR-TKIs, and metastasis to liver, brain, bone, pleura, adrenal gland, and distant lymph nodes. We established a nomogram based on independent pretreatment factors and used it to stratify EGFRm+ NSCLC patients undergoing EGFR-TKI treatment into five different risk groups for survival using recursive partitioning analysis. The performance of this nomogram was good and feasible, providing clinicians and patients with additional information for evaluating therapeutic options.

Abstract

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the standard treatment for EGFR mutation-positive (EGFRm+) non-small cell lung cancer (NSCLC). This study aimed to create a novel nomogram to help physicians suggest the optimal treatment for patients with EGFRm+ NSCLC. Records of 2190 patients with EGFRm+ NSCLC cancer who were treated with EGFR-TKIs (including gefitinib, erlotinib, and afatinib) at the branches of a hospital group between 2011 and 2018 were retrospectively reviewed. Their clinicopathological characteristics, clinical tumor response, progression-free survival (PFS), and overall survival (OS) data were collected. Univariate and multivariate analyses were performed to identify potential prognostic factors to create a nomogram for risk stratification. Univariate analysis identified 14 prognostic factors, and multivariate analysis confirmed the pretreatment independent factors, including Eastern Cooperative Oncology Group performance status, morphology, mutation, stage, EGFR-TKIs (gefitinib, erlotinib, or afatinib), and metastasis to liver, brain, bone, pleura, adrenal gland, and distant lymph nodes. Based on these factors, a novel nomogram was created and used to stratify the patients into five different risk groups for PFS and OS using recursive partitioning analysis. This risk stratification can provide additional information to clinicians and patients when determining the optimal therapeutic options for EGFRm+ NSCLC.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.