Proteomic based profiling of csf for cnsl management

Abstract Central nervous system lymphoma (CNSL) is a rare subtype of non-Hodgkin lymphoma, primarily affecting the brain and spinal cord. CNSL exhibits a dismal prognosis, as reflected by a low 5-year survival rate of 30%, with the current standard of care. The advent of multi-agent intrathecal chemotherapy (MAITC) in conjunction with systemic therapy has demonstrated encouraging outcomes, enhancing both progression-free survival and overall survival in patients with CNSL. Given the potential morbidity associated with MAITC, identification of minimally-invasive biomarkers for guiding patient management are necessary. Leveraging the longitudinal, large volume of Cerebrospinal fluid (CSF) afforded through our MAITC program, the objective of this study is to identify CSF-based proteomic biomarkers that can serve as reliable indicators of MAITC treatment response and CNSL management. Patient-matched CSF samples from the Penn State Health Neuroscience biorepository were banked at pre- and post-therapeutic endpoint in 22 primary and 36 secondary CNSL patients. The samples were profiled using high-throughput fluid sample protocol coupled with mass-spectrometry that only requires 30 μl of CSF. More than 1000 unique proteins were detected using shotgun proteomics, and 797 proteins were present in 70% of the analyzed samples, which were used for downstream analysis. We were able to effectively discriminate between primary and secondary CNSL based on protein abundance. Furthermore, the findings suggest significant changes in the cellular processes related to glycoprotein metabolism and redox signalling in response to the treatment, indicating a potential impact on protein modification and the cellular response to oxidative stress. Our comprehensive, pathway-level analysis uncovered significant molecular alterations, shedding light on their potential prognostic significance in the context of CNS lymphoma. Future work is underway to identify biomarker signatures that would predict optimal response to MAITC therapy, exhibiting potential to improve clinical decision making.