BIOM-08. LEVERAGING CSF PROTEOMICS TO OPTIMIZE CNS LYMPHOMA MANAGEMENT

Abstract INTRODUCTION Despite exquisite responses to initial therapy for CNS lymphoma (CNSL), relapse is common and overall responses are disappointing compared to other non-CNS diffuse large B-cell lymphomas. Incorporation of multi-agent intraventricular chemotherapy (MAIVC) alongside systemic therapy is novel. Identification of minimally-invasive biomarkers of response to MAIVC are necessary but lacking. The objectives of this study were to 1) identify baseline cerebrospinal fluid (CSF)-based proteomic predictive biomarkers of MAIVC treatment response and 2) identify tumor burden markers for longitudinal monitoring of MAIVC treatment response in CNSL. METHODS A cohort of patients with primary or secondary CNSL that were treated with MAIVC at Penn State Health (2015-2021) was included. Each MAIVC cycle was administered via Ommaya reservoir and included a 2-drug combination comprised of methotrexate, rituximab, cytarabine, etoposide, topotecan, gemcitabine, or thiotepa. Patient-matched CSF was sampled at pre- and post-therapeutic endpoints and profiled using shotgun proteomics. Patients were grouped by treatment response status. Early responders were defined as patients that achieved malignant cell-free CSF within 6 MAIVC cycles, whereas never responders were those patients that i) did not achieve malignant cell-free CSF by endpoint or ii) required salvage surgery or radiation during MAIVC. RESULTS 59 patients were included (21 primary; 38 secondary CNSL) with a median age of 66 years (range 25-90) and median number of MAIVC cycles of 8 (range 2-23). A proteomic-based classifier, comprised of SGCE, LCP1 and AGRN, discriminated between early and never responders with an AUROC of 0.95. Comparison of CSF at baseline (<3 cycles MAIVC) vs. post-treatment (>12 cycles MAIVC) identified H3F3A, YWHAE, LCP1, CA1 and GAPDH as tumor burden biomarkers. CONCLUSION Here we developed a proteomic signature capable of predicting CNSL patient response to MAIVC, with potential to improve clinical decision making. Furthermore, the biomarkers associated with tumor burden represent important indicators of treatment response. Ongoing efforts are underway to evaluate these candidates as actionable therapeutic targets.