Identification and validation of potential diagnostic plasma biomarkers for diffuse gliomas by multiplex immunoassays.
Bibliographic record
Abstract
2044 Background: Diffuse gliomas are aggressive malignant tumors with poor prognosis. The current standard of care includes measurement of molecular biomarkers in biopsy samples. One unmet clinical need is to identify non-invasive biomarkers that may be used for differential diagnosis of gliomas from other brain tumors. Pre-clinical and clinical validation of such biomarkers could eliminate the need for biopsy, and support the implementation of more personalized and/or emerging treatments and the earlier enrolment of patients into clinical trials. Our objective is to use multidimensional proteomics to identify and validate potential plasma biomarkers for glioma management. Methods: We used the proximity extension assay from Olink Proteomics to analyze 3,000 proteins in plasma of patients with diffuse gliomas and meningiomas (as controls). By data visualization, we identified several plasma proteins that were increased or decreased in gliomas in comparison to meningiomas. Several candidate markers were selected for validation with an independent set of retrospectively collected samples by using quantitative research-use-only electrochemiluminescence assays available from Meso Scale Discovery. In the validation set, which included longitudinal data from patients, patient information included biopsy-requiring molecular tumor abnormalities such as IDH1 status, ATRX expression, MGMT promoter methylation, CDKN2A/B/p16 status, V1p 19q co-deletion and NF1 status. In the validation stage, we focused on diffuse gliomas. Results: In the discovery phase, associations between proteins were plotted to determine potential predictive ability for discriminating diffuse gliomas vs. meningiomas. A partitioning algorithm was fit to determine the optimal combination of GFAP (the strongest biochemical marker), age and sex, as well as with other candidate proteins. Differential expression was seen for a few other proteins such as NEFL, PROK1, FABP4, MMP3 and LMOD1. In the cross-sectional validation phase, we verified strong associations between GFAP and FABP4 plasma concentration and GBM, astrocytomas, oligodendrogliomas and meningiomas, where these markers could differentiate between the groups. Within diffuse gliomas, NEFL, GFAP, FABP4 and IL13 were significantly different. Conclusions: This study highlights the potential of plasma biomarkers to revolutionize glioma patient management through liquid biopsy applications. The strong associations observed between plasma protein concentrations and glioma subtypes support a diagnostic power that addresses a critical unmet need in neuro-oncology. More specifically, these biomarkers can help with patient differential diagnosis at initial presentation, with future aims to investigate the prognostic value and the possibility of acting as surrogates of molecular changes that are currently used for optimizing therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".