Abstract 1022: Integration of cerebrospinal fluid methylome and proteome can obviate the need for biopsy in central nervous system lymphoma
Bibliographic record
Abstract
Abstract Background: The diagnosis of intra-axial brain tumors requires histopathological examination of tissue obtained by neurosurgery in current clinical practice, which comes with inherent risks. Some patients, particularly those with primary CNS lymphoma (PCNSL), only undergo surgery to obtain a diagnosis and do not derive any therapeutic benefit from surgical resection or debulking. Less-invasive techniques for diagnosis, such as liquid biopsy, therefore provides an opportunity to mitigate surgical risk in these patients. Methods: Patients with histopathology confirmed glioblastoma (GBM, IDH wild type), brain metastases (BM), and PCNSL with accompanying cerebrospinal fluid (CSF) samples were included in our study. Cell-free DNA methylation profiling and shotgun proteomics were obtained for all patients and used to train classifiers to distinguish each tumour entity from others. Specifically, binomial elastic net regression models were built by combining both data modalities using established early and late integration paradigms and performance was compared to classifiers built from individual data types alone. Each model was repeated 100 fold and performance assessed on an untouched testing subset. Results: Our cohort includes 20 patients with GBM, 17 with BM, and 14 with PCNSL, each with matching CSF cell-free DNA methylation and shotgun proteomic profiling. We show that these data can be integrated to fully discriminate PCNSL from its major diagnostic counterparts with a perfect median AUC of 1.00 (95% CI 1-1) and 100% specificity. Integrated "lymphoma vs other" models significantly outperform models trained on methylation or protein data alone, though the same dramatic improvement was not demonstrated in GBM or BM, suggesting synergistic biological information is specific to lymphoma. Conclusions: There is a critical need to diagnose patients with intra-axial tumours, particularly PCNSL, without relying on invasive and costly surgery. We present the most specific and accurate CNS lymphoma classifier to date by integrating the methylome and proteome of CSF. This has the potential for immediate clinical utility, eliminating the need for biopsy in an important subset of these patients. Citation Format: Alex P. Landry, Jeffrey A. Zuccato, Vikas Patil, Mathew Voisin, Justin Z. Wang, Yosef Ellenbogen, Chloe Gui, Andrew Ajisebutu, Farshad Nassiri, Gelareh Zadeh. Integration of cerebrospinal fluid methylome and proteome can obviate the need for biopsy in central nervous system lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1022.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".