P08.03.A CSF-BASED PROTEOMIC BIOMARKERS OF RESPONSE TO MULTI-AGENT INTRAVENTRICULAR CHEMOTHERAPY IN CNS LYMPHOMA
Bibliographic record
Abstract
Abstract BACKGROUND Despite promising responses to initial therapy for central nervous system lymphoma (CNSL), relapse is common and overall responses are disappointing compared to other non-CNS diffuse large B-cell lymphomas. Literature reports 1- and 3-year survival rates of 50% and 30-40% respectively, with standard treatment. Incorporation of multi-agent intrathecal chemotherapy (MAITC) alongside systemic therapy is novel but not widely adopted. Biomarkers predictive of response could help select patients most likely to benefit. Our retrospective analysis of CNSL patients treated with MAITC between 2015-2021 indicates a significant overall survival advantage. Cerebrospinal fluid (CSF) is a less invasive alternative for diagnosis and management of CNSL compared to traditional biopsies, while also reducing sampling bias. The objective of this study is to identify CSF-based proteomic biomarkers that can serve as reliable indicators of MAITC treatment response and guide CNSL management. MATERIAL AND METHODS Patient-matched CSF samples 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 microliters of CSF. Clinically, patients were categorized as “early responders” (objective imaging response and cytology clearance by cycle 6, both maintained until cycle 12) vs. “non responders”. Tumor burden (based on MRI and cytology) was correlated with proteomic markers to identify potential correlative markers. RESULTS SGCE, LCP1 and AGRN were found to reliably (AUROC 0.95, 95% CI 0.81-0.98, Sensitivity: 0.62, Specificity: 0.76) stratify patients based on MAITC response (responder vs. non-responder). Through analysis of matched pre- and post-treatment samples (>3 cycles) from the same patient cohort and correlation with brain MRI and CSF cytology, we identified H3F3A, YWHAE, LCP1, CA1 and GAPDH as candidate tumor burden biomarkers (AUROC 0.9, 95% CI 0.79-1.0). Conclusion: Our low input proteomic analyses of CSF has identified candidates potentially predictive of response to MAITC along with potential tumor burden markers. Validation studies with a larger cohort and additional timepoints are currently underway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".