Integrated proteomic analysis of low-grade gliomas reveals contributions of 1p-19q co-deletion to oligodendroglioma
Bibliographic record
Abstract
Diffusely infiltrative low-grade gliomas (LGG) are primary brain tumours that arise predominantly in the cerebral hemispheres of younger adults. LGG can display either astrocytic or oligodendroglial histology and do not express malignant histological features. Vast majority of LGG are unified by IDH mutations. Other genomic features including ATRX as well as copy number status of chromosomes 1p and 19q serve to molecularly segregate this tumor group. Despite the exponential gains in molecular profiling and understanding of LGG, survival rates and treatment options have stagnated over the past few decades with few advancements. In this study, we utilize low grade glioma RNA-seq data from the Cancer Genome Atlas (TCGA-LGG) and tandem mass-spectrometry on an in-house cohort of 54 formalin-fixed paraffin-embedded (FFPE) LGG specimens to investigate the transcriptomic and proteomic profiles across the three molecular subtypes of LGG (Type I: IDH mutant - 1p19q co-deleted, Type II: IDH mutant - 1p19q retained, Type III: IDH wildtype). Within the 3 LGG subtypes, gene expression was driven heavily by IDH mutation and 1p19q co-deletion. In concordance with RNA expression, we were able to identify decreased expressions of proteins coded in 1p19q in Type I LGG. Further proteomic analysis identified 54 subtype specific proteins that were used to classify the three subtypes using a multinomial regression model (AUC = 0.911). Type I LGG were found to have increased protein expression of several metabolic proteins while Type III LGG were found to have increased immune infiltration and inflammation related proteins. Here we present the largest proteomic cohort of LGG and show that proteomic profiles can be successfully analyzed from FFPE tissues. We uncover previously known and novel subtype specific markers that are useful for the proteomic classification of LGG subtypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".