Pharmacogenetic (PGx) guided cancer pain management in an oncology palliative medicine (PM) clinic.
Bibliographic record
Abstract
117 Background: About 30% of cancer patients presenting with pain have symptomatic improvement using conventional strategies within one month. PGx may help personalize opioid selection and improve cancer pain management. Methods: This is a pragmatic pilot trial investigating the feasibility and application of PGx testing to improve pain management in adults with uncontrolled cancer pain referred to an oncology PM clinic. PM providers assessed patients using Edmonton Symptom Assessment Scale at baseline and opioid therapy was initiated or modified. A buccal swab was obtained for genotyping single nucleotide polymorphisms in: COMT, CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, and OPRM1. The first assessment occurred within one week of baseline and a second within another week if intervention was required. PGx results were available before the first assessment and utilized, if applicable, throughout the one-month study period. Pain improvement rate (≥ 2-point reduction on a 0-10 scale) from baseline to final visit, was compared to historical control data by a one-sided exact binomial test of proportions. Results: Of 75 undergoing PGx testing, 52 were evaluable for the primary endpoint (54% female, 81% white, 17% black, median age 63, 75% stage 3 or 4 disease, median personalized pain goal 3 [0-6]). 56% had pain improvement compared to 30% in historical controls (p < 0.001). At final assessment, 35% met their personalized pain goal. Of 26 (50%) requiring opioid adjustments, 18 (69%) had an actionable genotype with a 61% pain improvement rate. The two most common genes for opioid adjustment were CYP2D6 (16/18; 89%) and COMT (8/18; 44%). The most common PGx-guided modification involved switching from a CYP2D6-metabolized drug (hydrocodone, oxycodone, tramadol) to a non-CYP2D6-metabolized drug (fentanyl, hydromorphone, methadone, morphine). Conclusions: PGx implementation in an oncology PM clinic was feasible and improved pain management. Half of those requiring opioid adjustments had an actionable genotype, with the largest impact from CYP2D6 polymorphisms. Future studies should focus on preemptive PGx testing to guide initial drug selection and confirm clinical utility in a randomized trial. Clinical trial information: NCT02542397.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".