BIOM-72. INTEGRATION OF ULTRASENSITIVE ELECTROLUMINESCENT IMMUNOASSAY AND CELL-FREE DNAMETHYLATION ANALYSIS FOR NON-INVASIVE MONITORING OF ADULT DIFFUSE GLIOMAS
Bibliographic record
Abstract
Abstract Gliomas are highly aggressive brain tumors with nearly universal recurrence rate. Despite this, the ability to accurately predict and identify tumor recurrence relies solely on serial MRI imaging, which is marred by treatment effects such as radiation necrosis, and necessarily identifies progression retrospectively. It is believe that tumor undergo molecular changes upon tumor progression, however the resampling of tumors upon recurrence imposes significant risks. This highlights the need for novel non-invasive biomarkers capable of identifying tumor recurrence. Due to the low accuracies of individual biomarkers, we have proposed the use of an integrated, multi-platform approach to biomarker discovery. A cohort of 107 glioma plasma samples, including 30 pairs, underwent plasma proteomic analysis, consisting of a panel of serum proteins (FABP4, GFAP, NFL, Tau and MMP3,4 &7) quantified through ultrasensitive electrochemiluminescence multiplexed immunoassays, and plasma DNA methylation analysis, captured through cell-free methylated DNA immunoprecipitation and high-throughput sequencing. Unsupervised hierarchal clustering revealed robust separation of primary and recurrent tumors through plasma proteomics, associated with a distinct plasma methylation signature. NFL, Tau and MMP3 levels differed between primary and recurrent samples; pair-wise analysis revealed increased in NFL and Tau concentrations upon recurrence. Tau levels predicted outcome independent of WHO Grade and IDH status. A predictive generalized linear regression model created through the integration of the proteomic and methylation signatures allowed for the discrimination of primary and recurrent samples in 83% of cases. This work suggests that the combination of DNA methylation and plasma proteomics may improve the ability of these techniques for the serial monitoring of gliomas patients.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".