MPTH-38. TRANSFORMING GROWTH FACTOR-BETA GENE EXPRESSION AS A NEW PROGNOSTIC BIOMARKER FOR GLIOBLASTOMA
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumour in adults. Response to standard therapy is transitory, recurrence is inevitable and patients median overall survival is inferior to 15 months. The GBM phenotype is characterized by anarchic proliferation and invasiveness together with radio-/chemoresistance abilities. These features are strongly upregulated by transforming growth factor-beta (TGF-b). Therefore, we hypothesized that TGF-b gene expression could correlate with GBM patients overall survival (OS) and be utilized as a prognostic biomarker. Using targeted qPCR, we investigated the expression of TGF-b1 and -b2 in 159 GBM specimens harvested during surgery and 18 non-tumoral brain tissue. We found that both isoforms were significantly upregulated in GBM tumours (33- and 11-fold respectively). While TGF-b1 was the dominant isoform in newly diagnosed tumours (2-fold), no significant difference was observed in recurrent GBMs. Furthermore, TGF-b1 expression levels significantly correlated with OS and progression-free survival (PFS) in newly diagnosed patients. Indeed, multivariate analysis (Cox model) using expression levels as well as numerous clinical surrogates, revealed that high and moderate TGF-β1 expressing patients had a much poorer prognosis than the low TGF-β1 expressing tumours (hazard ratio [95% CI] was 1.998 [1.103 - 3.620]; p=0.022). Interestingly, at recurrence, neither isoforms had meaningful influence on clinical surrogates. In this study we show that TGF-b1 is the prevailing isoform in newly diagnosed GBM rather than the previously recognized TGF-b2. To our knowledge, this study is the first to reveal a significant correlation between TGF-b1 gene expression and OS or PFS in newly diagnosed GBM. Therefore, we believe that TGF-b1 could be used as a prognostic biomarker and/or target which could influence treatment planning and clinical followup of GBM 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".