Identification of pharmacogenetic variants influencing the likelihood of developing cancer treatment-induced mucositis using pathway analyses
Bibliographic record
Abstract
Methotrexate (MTX), a cornerstone cancer treatment, has contributed to improved 5-year event-free survival rates but is associated with 20-40% occurrence of mucositis, which is characterized by the development of painful inflammatory lesions largely focused along the alimentary tract that often leads to premature termination of cancer treatment and impacts survival. Previous studies identified individual genetic variants implicated in mucositis development. Considering the complex biological pathways involved in mucositis development highlighted in recent studies, we hypothesize that multiple genetic variants within these shared biological pathways contribute to the onset of MTX-induced mucositis. To identify gene pathways that are likely to impact mucositis risk, we captured genes associated with: (i) methotrexate pharmacokinetics/pharmacodynamics from PharmGKB; (ii) published pathobiological pathways (e.g., WNT/β-catenin signaling) predicted to underlie mucositis development using MSigDB/Enrichr; and novel pathways based on genes previously associated with mucositis from literature using StringDB. To pinpoint pathways highly enriched for genetic variations associated with mucositis development in methotrexate-treated children, we examined the joint association of genetic variants using the raw genome-wide genotyping and patient clinical data for a set of pediatric mucositis cases (n=131) and controls (n=366) treated with intravenous methotrexate across 6 Canadian academic hospitals. A final set of 18 non-redundant pathways with a priori evidence for association with treatment-induced mucositis were selected for analysis. Our study used a phenotypic permutation test to identify significant enrichment in the IL-6 and WNT/β-catenin signaling pathways in patients developing mucositis due to high-dose MTX (> 1000 mg/m2). Using these genetic findings and clinical data, we developed a machine learning algorithm for mucositis risk stratification in patients receiving high-dose IV-MTX and identified key features impacting risk. Our future direction will focus on replicating the findings. If validated, the results can guide clinical care for high-risk patients and inform diagnosis and prescribing decisions. Moreover, the highlighted pathobiological pathways may serve as future therapeutic targets for mitigating mucositis during cancer treatment. Our goal is to minimize MTX-induced adverse reactions and improve patient quality of life during cancer treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".