Upfront DPYD genotyping and toxicity associated with fluoropyrimidine-based concurrent chemoradiotherapy for oropharyngeal carcinomas.
Bibliographic record
Abstract
6580 Background: The combination of carboplatin and 5-fluorouracil (5-FU) is effective when used concurrently with radiotherapy for locoregionally advanced oropharyngeal carcinomas (Calais et al. 1999). DPYD polymorphisms can be associated with an increased risk of severe toxicity to fluoropyrimidines (Deenen et al. 2016). Upfront screening for the DPYD*2A allele is available in the province of Québec, Canada since March 2017. This study aimed to determine the effect of upfront genotyping on grade ≥3 toxicities. Methods: The studied population included all consecutive cases of oropharyngeal carcinomas treated with 5-FU based chemoradiotherapy one year before and after the implementation of upfront DPYD*2A genotyping. All patients were treated at the Centre Hospitalier de l’Université de Montréal (CHUM) between March 2016 and April 2018. Clinical data were extracted from chart review. Extended screening for 3 supplemental at-risk DPYD variants was also retrospectively performed in August 2019. Results: 181 patients were included in the analysis (87 patients before and 94 patients after DPYD*2A screening implementation). 91% of patients (n = 86) were prospectively genotyped for the DPYD*2A allele. Of those screened, 2% (n = 2/87) demonstrated a heterozygous DPYD*2A mutation. Those two patients received cisplatin-based treatment and thus avoided 5-FU toxicities. Extended genotyping of DPYD*2A-negative patients later allowed for the retrospective identification of 6 additional patients with alternative DPYD variants (two c.2846A > T and four c.1236G > A allele mutations). Conclusions: The DPYD*2A, c.2846A > T and c.1236G > A polymorphisms are associated with an increased risk of G3-4 toxicity to 5-FU, as well as higher hospitalization rates. Upfront DPYD genotyping can identify patients in whom fluoropyrimidine-related toxicity should be avoided. This represents an interesting addition in terms of pharmacovigilance. [Table: see text]
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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) | 0.000 | 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".