Development and validation of a nomogram model based on blood-based genomic mutation signature for predicting the risk of brain metastases in non-small cell lung cancer.

Abstract

Purpose: Available research indicates that the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway is significantly correlated with lung cancer brain metastasis (BM). This study established a clinical predictive model for assessing the risk of BM based on the mTORC1-related single nucleotide polymorphisms (SNPs).

Methods: In this single-center retrospective study, 395 patients with non-small cell lung cancer were included. Clinical, pathological, imaging, and mTORC1-related single nucleotide polymorphism data were collected. Lasso regression was used to identify variables related to the risk of BM in lung cancer, and a nomogram was constructed. Internal validation was performed using 1,000 bootstrap samples. We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). The calibration of the model was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) was plotted to evaluate the net clinical benefit.

Results: The nomogram's predictive factors included lung cancer histology, clinical N stage, CEA, neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), RPTOR: rs1062935, and RPTOR: rs3751934. The AUC of the model in the training set and internal validation were 0.849 and 0.801, respectively. The calibration curves and Hosmer-Lemeshow test both indicated a good fit.

Conclusion: The nomogram has practicality and efficacy in predicting the high risk of BM in lung cancer patients, confirming that single nucleotide polymorphisms in the mTORC1 pathway genes may be good predictors in clinical prediction models.