Quantitative analysis of nucleoside transporter and metabolism gene expression in chronic lymphocytic leukemia (CLL): identification of fludarabine-sensitive and -insensitive populations
Bibliographic record
Abstract
Resistance to fludarabine is observed in the clinic, and molecular predictive assays for benefit from chemotherapy are required. Our objective was to determine if expression of nucleoside transport and metabolism genes was associated with response to fludarabine therapy in patients with chronic lymphocytic leukemia (CLL). CLL cells from 56 patients were collected prior to treatment with fludarabine. Quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) was performed on sample RNA to determine the relative levels of mRNA of 3 nucleoside transporters that mediate fludarabine uptake (human equilibrative nucleoside transporter 1 [hENT1], human equilibrative nucleoside transporter 2 [hENT2], and human concentrative nucleoside transporter 3 [hCNT3]), deoxycytidine kinase (dCK), and 3 5'-nucleotidases (ecto-5'nucleotidase [CD73], deoxynucleotidase-1 [dNT-1], and cytoplasmic high-Km 5-nucleotidase [CN-II]). Two-dimensional hierarchical cluster analysis of gene expression identified 2 distinct populations of CLL. Cluster 2 patients experienced a 3.4-fold higher risk of disease progression than cluster 1 patients (P = .0058, log-rank analysis). Furthermore, independent analysis of the individual genes of interest revealed statistically significant differences for risk of disease progression (adjusted hazard ratios [HRs]) with underexpression of dNT-1 (HR = 0.45; P = .042), CD73 (HR = 0.40; P = .022), and dCK (HR = 0.0.48; P = .035), and overexpression of hCNT3 (HR = 4.7; P = .0007) genes. Subjects with elevated hCNT3 expression experienced a lower complete response rate to fludarabine therapy (11% vs 69%; P = .002). No hCNT3-mediated plasma membrane nucleoside transport was detected in CLL samples expressing hCNT3 message, and hCNT3 protein was localized to the cytoplasm with immunohistochemical and confocal microscopy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".