Intrathecal pemetrexed for leptomeningeal metastasis from lung adenocarcinoma: a clinical efficacy and multiplex liquid proteomics exploration study
The Oncologist, 2025
Wang Z., Ding P., Zhou H., Kuang B., Zhang R., Chen L., Zhang X., Ge M., Liu Y., Tong F., Dong X.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Oncology | Patient Stratification | Plasma CSF |  Olink Target 96 |
Abstract
Background
Leptomeningeal metastasis (LM) is an advanced complication of lung cancer with a poor prognosis. It remains unknown whether intrathecal pemetrexed (IP) can improve the outcomes of patients with LM from lung adenocarcinoma (LUAD). This study explored the efficacy of intrathecal pemetrexed (IP) and proteomic biomarkers in LM from LUAD.
Patients and Methods
We conducted a retrospective survival analysis of 157 confirmed LM patients (54 IP vs 103 non-IP). To balance the baseline, propensity score matching (PSM) was implemented. We investigated clinical baseline characteristics and treatment factors to identify those influencing the outcomes of LM patients. Plasma and cerebrospinal fluid (CSF) samples underwent proteomic profiling using Olink Immuno-Oncology panels to identify IP response biomarkers and build a prediction model.
Results
After PSM, 82 patients were included in two matched cohort (41 IP vs 41 non-IP). IP treatment (HR = 0.427, P = .022) and ECOG performance status (HR = 2.737, P = .005) were independent predictors of overall survival (OS). Stratified analysis showed IP significantly improved OS in patients with poor performance status (ECOG 3-4: IP vs non-IP, 11.2 vs 2.5 months, P = .0029). Proteomics revealed low plasma MCP-2 (AUC = 0.873) and high CSF ARG1 (AUC = 0.875) levels distinguished IP responders from nonresponders.
Conclusions
IP therapy improves OS in LUAD patients with LM, particularly offering significant benefit as salvage treatment for those with ECOG 3-4. Proteomic analysis suggests plasma MCP-2 and CSF ARG1 are promising predictive biomarkers for IP response. The small sample size in biomarker analysis may limit these findings, necessitating validation through multicenter studies.