A molecular graded prognostic assessment (molGPA) model specific for estimating survival in lung cancer patients with leptomeningeal metastases. (May 2019) - Yin, Kai; Li, Yang-Si; Zheng, Mei-Mei; Jiang, Ben-Yuan; Li, Wen-Feng; Yang, Jin-Ji; Tu, Hai-Yan; Zhou, Qing; Zhong, Wen-Zhao; Yang, Xue-Ning; Chen, Hua-Jun; Yan, Hong-Hong; et al.
Highlights: This was the first and largest study focusing on prognosis model in LM of lung cancer patients. EGFR/ALK positivity, KPS score≥60 and absence of ECM independently predict better OS in LM of lung cancer patients. A novel LM molGPA model was established and internally validated to classify the heterogeneous patients. Abstract: 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.