Development of a lipoprotein(a)-based model for predicting progression-free survival and grade3/4 adverse events in driver gene negative metastatic lung adenocarcinoma patients with PD-L1 TPS <50.

Objective: This study evaluated the value of lipoprotein(a) (LPA) in lung adenocarcinoma (LUAD) patients receiving first-line chemoimmunotherapy and developed a model to predict progression-free survival (PFS) and grade 3/4 adverse events (G3/4 AEs). Methods: A prospective cohort study was conducted on driver gene negative metastatic LUAD patients with PD-L1 TPS <50%, who received first-line chemoimmunotherapy. The data were randomly sampled into training and internal validation sets following a 7:3 proportion. We constructed a prognostic model for progression-free survival (PFS) via LASSO and multivariate Cox regression analyses. We explored five methods-random forest, AdaBoost, elastic-net, LASSO, and support vector machine (SVM)-to develop a prediction model for G3/4 AEs. Results: A total of 227 patients completed the follow-up. The AUC was 0.78(0.62-0.94) for 365-day PFS in the training cohort and 0.95(0.84-1.00) in the internal validation cohort. The serum LPA level independently predicted disease progression in patients receiving first-line chemoimmunotherapy. AdaBoost outperformed other machine learning methods in terms of accuracy, precision, recall, and F1 scores on both the training and validation sets, leading to its selection for the final G3/4 AE prediction model. Conclusion: High LPA expression in the serum was a risk factor for metastatic driver gene-negative lung adenocarcinoma patients receiving first-line chemoimmunotherapy. Our models had favorable value in predicting PFS and G3/4 AEs, which might assist in identifying patients less likely to benefit from initial chemoimmunotherapy.