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Objective: To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA). Methods: A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models. Results: The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models. Conclusions: The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials.
RESUMEN
OBJECTIVES: Leptomeningeal metastases (LM) had increased in advanced non-small-cell lung cancer (NSCLC) over the last 10 years. The survival outcome remained overall poor, heterogeneous and was reported in association with genotypes in lung cancer patients with LM. Graded prognostic assessment model integrated with molecular alterations (molGPA) might be accurate for outcome prediction of LM patients, but needs to be established. MATERIALS AND METHODS: We retrospectively screened 8921 consecutive lung cancer patients from January 2011 to March 2018. A total of 301 patients diagnosed as LM were enrolled, and randomly divided into training and validation sets after stratified by gender and age. A molGPA score for each patient was calculated based on the weighted significant parameters including gene mutations. RESULT: The median OS for the 301 patients was 9.2 months (95%CI: 7.9-10.5). In the training set, EGFR/ALK positivity, Karnofsky performance score (KPS) score≥60 and absence of extracranial metastasis (ECM) independently predicted better OS. We developed a molGPA model based on above significant prognostic factors. This molGPA model classified LM patients into three prognosis groups of high, intermediate and low risk (molGPA score of 0, 0.5-1.0 and 1.5-2.0, respectively. The median OS of high, intermediate and low risk LM patients in the training set was 0.3, 3.5 and 15.9 months, respectively (p < 0.001). In the validation set, the median OS was 0.9, 5.8 and 17.7 months in the three molGPA subgroups, accordingly (p < 0.001). The C-index of this model in training and validation sets was 0.70 (95%CI: 0.66-0.73) and 0.64 (95%CI: 0.58-0.70) respectively. CONCLUSION: The LM molGPA model with integration of gene status, KPS and ECM can accurately classify lung cancer patients with LM into diverse prognosis.
