Germline Polymorphisms in the Nuclear Receptors PXR and VDR as Novel Prognostic Markers in Metastatic Colorectal Cancer Patients Treated With FOLFIRI
Bibliographic record
Abstract
Nuclear receptors act as mediators of cancer-related inflammation and gene expression. They have a regulatory effect on genes encoding proteins related to drug adsorption, distribution, metabolism, and excretion. The aim of the present study was to highlight novel prognostic markers among polymorphisms in genes encoding for nuclear receptor proteins and inflammation-related cytokines in patients treated with a FOLFIRI regimen. This study included two independent cohorts comprising a total of 337 mCRC patients homogeneously treated with first-line FOLFIRI. Genotyping of 246 haplotype-tagging polymorphisms in 22 genes was performed using bead array technology. The NR1I2 (PXR)-rs1054190 and VDR-rs7299460 polymorphisms were significantly associated with patient overall survival (OS). A detrimental effect of the NR1I2 rs1054190-TT genotype on OS was observed in both the discovery and replication cohorts (HR=6.84, P=0.0021, q-value=0.1278 and HR=3.56, P=0.0414, respectively). Patients harboring the NR1I2 rs1054190-TT genotype had a median OS of 9 months vs. 21 months in patients with C-allele (P<0.0001 log-rank test). VDR rs7299460-T was consistently associated with a longer OS in both cohorts (discovery: HR=0.61, P=0.0075, q-value=0.1535; replication: HR=0.57, P=0.0477). Patients with the VDR rs7299460-T allele had a median OS of 23 months compared to 18 months in those with the CC genotype (P=0.0489, log-rank test). The NR1I2-rs1054190 polymorphism also had an effect on the duration of progression-free survival, consistent with the effect observed on OS. Two novel prognostic markers for mCRC treated with FOLFIRI were described and, if validated by prospective trials, have a potential application in the management of these 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".