The combination of HLA-B*15:01 and DRB1*15:01 is associated with gemcitabine plus erlotinib-induced interstitial lung disease in patients with advanced pancreatic cancer
Bibliographic record
Abstract
PURPOSE: In a phase III study of gemcitabine plus erlotinib for advanced pancreatic cancer conducted in Canada, the incidence of interstitial lung disease (ILD) was 3.5 %. However, the incidence of ILD was reported as high as 8.5 % in a Japanese phase II study. These results suggest the influence of ethnic factors in the association of the use of gemcitabine plus erlotinib with the incidence of ILD. Here, we conducted a prospective study to analyze the relationship between human leukocyte antigen (HLA) alleles and ILD in Japanese patients with advanced pancreatic cancer receiving gemcitabine plus erlotinib. METHODS: Patients were treated with gemcitabine (1000 mg/m(2); administered by intravenous infusion on days 1, 8, and 15 every 4 weeks) and erlotinib (given orally at 100 mg/day). We compared the frequencies of HLA alleles in patients who did and did not develop ILD. RESULTS: A total of 57 patients were treated, and 4 patients (7.0 %) developed ILD. The combination of HLA-B*15:01 and DRB1*15:01 was observed in 2 of 4 patients (50 %) with ILD and in only 1 of 53 patients without ILD (2 %) resulting in odds ratio of 52.0 (95 % CI 3.2-842.5; p = 0.011). CONCLUSION: These results suggest that the combination of HLA-B*15:01 and DRB1*15:01 is associated with ILD in Japanese patients with advanced pancreatic cancer receiving gemcitabine plus erlotinib.
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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 | 0.001 |
| 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".