Identification of risk factors of EGFR-TKIs primary resistance in lung adenocarcinoma patients and construction of a risk predictive model: a case-control study.

BACKGROUND

Lung cancer is one of the malignancies with the highest incidence and mortality rates. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are recommended as the first-line treatment for patients with EGFR-mutated lung adenocarcinoma (LUAD). However, some patients with EGFR-sensitive mutations develop primary resistance to EGFR-TKIs. This study aims to analyze the clinical characteristics of LUAD patients with primary resistance to EGFR-TKIs, identify independent risk factors for primary resistance, and establish a risk predictive model to provide reference for clinical decision-making.

METHODS

We collected data from LUAD patients with EGFR-sensitive mutations (19del/21L858R) who were hospitalized in our institution between 2020 and 2022 and received first-generation EGFR-TKIs with follow-up exceeding 6 months. These patients were categorized into primary resistance and sensitive groups based on treatment outcomes. We compared general clinical data, laboratory tests, and tumor-related characteristics between the two groups, analyzed risk factors for primary resistance to EGFR-TKIs, and constructed a risk predictive model. The model's predictive value was comprehensively assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves.

RESULTS

Serum neuron-specific enolase (NSE) concentration (P=0.03), serum pro-gastrin-releasing peptide (ProGRP) concentration (P=0.01), and Ki67 expression (P<0.001) were identified as independent risk factors for primary resistance to EGFR-TKIs in LUAD. The combined presence of these three risk factors had the highest predictive value [area under the curve (AUC) =0.975, P<0.001]. We constructed a predictive model for the risk of primary resistance to EGFR-TKIs in LUAD patients, incorporating these three parameters, and represented it through a visually interpretable nomogram. The calibration curve of the nomogram demonstrated its strong predictive ability. Further decision curve analysis indicated the model's clinical utility.

CONCLUSIONS

Based on a single-center retrospective case-control study, we identified serum NSE concentration, ProGRP concentration, and Ki67 expression as independent risk factors for primary resistance to EGFR-TKIs in LUAD patients. We constructed and validated a risk predictive model based on these findings. This predictive model holds promise for clinical application, aiding in the development of personalized treatment strategies and providing a scientific basis for early identification of primary resistance patients.