SCDT-18. EXPRESSION OF ABC TRANSPORTERS AS PROGNOSTIC BIOMARKERS FOR GLIOBLASTOMA
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumour in adults. Standard therapy, consisting in surgery followed by concomitant radio- and chemotherapy, only offers palliative benefits. Indeed, recurrence is inevitable and this disease remains incurable with an overall survival of 14.6 months. We believe that the chemoresistant phenotype of GBM is supported by their expression of several efflux pumps (ABC transporters). We thus hypothesized that gene expression of these transporters could correlate with patients clinical surrogates and be used as prognostic biomarkers. We investigated the expression of ABCB1, ABCC1, ABCC3 and ABCG2 by qPCR in 159 GBM specimens collected during surgery and 22 non-tumoral brain tissue. With the exception of ABCB1, all three efflux pumps were significantly overexpressed in GBM. While ABCB1 had no effect on clinical surrogates, ABCC1 and ABCC3 expression correlated with drastically shorter overall survival (OS) and progression-free survival (PFS) in newly diagnosed patients. Moreover, our multivariate analysis showed that patients with high and moderate levels of ABCC3 had a considerably poorer prognosis than patients with low expressing tumours (hazard ratio [95% CI] was 2.424 [1.340 - 4.531]; p<0.001). However, in recurrent tumours, these transporters showed no correlation with clinical outcome. Interestingly, high ABCG2 expression correlated with better prognosis in both newly diagnosed and recurrent tumours (hazard ratio [95% CI] was 0.564 [0.323 - 0.986]; p=0.044). This study shows that ABCC1, ABCC3 and ABCG2 transporters are upregulated in GBM tumours. Moreover, our data suggest that expression of these efflux pumps could be quantified upon diagnosis and serve as prognostic biomarkers for GBM patients. Therefore, we truly feel that ABCC1, ABCC3 and ABCG2 expression levels could be used to influence treatment and clinical management of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".