Expression of ABCB1, ABCC1 and 3 and ABCG2 in glioblastoma and their relevance in relation to clinical survival surrogates.
Bibliographic record
Abstract
Abstract Purpose Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumors in adults. Patients invariably relapse during or after first-line therapy and the median overall survival is 14.6 months. Such poor clinical response is partly ascribed to the activity of ATP-binding cassette (ABC) transporters. The activity of these proteins, severely reduces the amount of therapeutics that penetrates the tumor cells. We hypothesized that ABC transporter expression could correlate with survival surrogates. In this study, we assessed the expression of four commonly expressed ABC transporters in GBM samples and investigated if mRNA levels could serve as a prognostic biomarker. Methods Human specimens were analyzed by qPCR to assess ABCB1, ABCC1/3 and ABCG2 expression. Kaplan-Meier and multivariate analyses were then used to evaluate the correlation with overall survival (OS) and progression-free survival (PFS). Results Our cohort included 22 non-tumoral samples as well as 159 GBM tumor specimens. ABC transporters were significantly more expressed in GBM samples compared to non-tumoral tissue. Moreover ABCC1 and 3 mRNA expression were significantly increased at recurrence. Statistical analyses revealed that increased expression of either ABCC1 or ABCC3 did not confer a poorer prognosis. However, increased ABCC1 mRNA levels did correlate with a significantly shorter PFS. Conclusion In this manuscript, the analyses we conducted suggest that the expression of the four ABC transporters evaluated would not be suitable prognostic biomarkers. We believe that, when estimating prognosis, the plethora of mechanisms implicated in chemoresistance should be analyzed as a multi-facetted entity rather than isolated units.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".