ABCB1 (MDR1) polymorphisms and ovarian cancer progression and survival: A comprehensive analysis from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas
Bibliographic record
Abstract
OBJECTIVE: ABCB1 encodes the multi-drug efflux pump P-glycoprotein (P-gp) and has been implicated in multi-drug resistance. We comprehensively evaluated this gene and flanking regions for an association with clinical outcome in epithelial ovarian cancer (EOC). METHODS: The best candidates from fine-mapping analysis of 21 ABCB1 SNPs tagging C1236T (rs1128503), G2677T/A (rs2032582), and C3435T (rs1045642) were analysed in 4616 European invasive EOC patients from thirteen Ovarian Cancer Association Consortium (OCAC) studies and The Cancer Genome Atlas (TCGA). Additionally we analysed 1,562 imputed SNPs around ABCB1 in patients receiving cytoreductive surgery and either 'standard' first-line paclitaxel-carboplatin chemotherapy (n=1158) or any first-line chemotherapy regimen (n=2867). We also evaluated ABCB1 expression in primary tumours from 143 EOC patients. RESULT: Fine-mapping revealed that rs1128503, rs2032582, and rs1045642 were the best candidates in optimally debulked patients. However, we observed no significant association between any SNP and either progression-free survival or overall survival in analysis of data from 14 studies. There was a marginal association between rs1128503 and overall survival in patients with nil residual disease (HR 0.88, 95% CI 0.77-1.01; p=0.07). In contrast, ABCB1 expression in the primary tumour may confer worse prognosis in patients with sub-optimally debulked tumours. CONCLUSION: Our study represents the largest analysis of ABCB1 SNPs and EOC progression and survival to date, but has not identified additional signals, or validated reported associations with progression-free survival for rs1128503, rs2032582, and rs1045642. However, we cannot rule out the possibility of a subtle effect of rs1128503, or other SNPs linked to it, on overall survival.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".