GR.5 Establishing the utility of multi-platform liquid biopsy by integrating the CSF methylome and proteome in CNS malignancy
Bibliographic record
Abstract
Background: Liquid biopsy represents a major development in cancer research, with significant translational potential. Similarly, the integration of multiple molecular platforms has yielded novel insights into disease biology and heterogeneity. We hypothesise that applying contemporary multi-omic approaches to liquid biopsies will improve the power of current models. Methods: We have compiled a cohort of 51 patients with glioblastoma, brain metastasis, and primary CNS lymphoma who underwent CSF sampling as part of clinical care. Cell free methylated DNA and shotgun proteomic profiling was obtained from the CSF of each patient and used to build tumour-specific classifiers. Integrated classifiers were compared with single platform classifiers using multiple approaches. Results: In this study, we show that the DNA methylation and protein profiles of cerebrospinal fluid can be combined to fully discriminate lymphomas from their major diagnostic counterparts with perfect AUCs of 1 (95% confidence interval 1-1) and 100% specificity. Each integrated lymphoma classifier significantly outperforms single-platform classifiers, suggesting synergistic biology is obtained using multiple molecular platforms. Conclusions: We present the most specific and accurate CNS lymphoma classifier to date by integrating the methylome and proteome of CSF. This has important implications for the future of cancer diagnostics and generates immediate utility for patients with CNS lymphoma.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".