Leveraging large-scale datasets and single cell omics data to develop a polygenic score for cisplatin-induced ototoxicity
Bibliographic record
Abstract
Abstract Background Cisplatin-induced ototoxicity (CIO), characterized by irreversible and progressive bilateral hearing loss, is a prevalent adverse effect of cisplatin chemotherapy. Alongside clinical risk factors, genetic variants contribute to CIO and genome-wide association studies (GWAS) have highlighted the polygenicity of this adverse drug reaction. Polygenic scores (PGS), which integrate information from multiple genetic variants across the genome, offer a promising tool for the identification of individuals who are at higher risk for CIO. Integrating large-scale hearing loss GWAS data with single cell omics data holds potential to overcome limitations related to small sample sizes associated with CIO studies, enabling the creation of PGSs to predict CIO risk. Results We utilized a large-scale hearing loss GWAS and murine inner ear single nuclei RNA-sequencing (snRNA-seq) data to develop two polygenic scores: a hearing loss PGS (PGS HL ) and a biologically informed PGS for CIO (PGS CIO ). The PGS CIO included only variants which mapped to genes that were differentially expressed within cochlear cells that showed differential abundance in the murine snRNA-seq data post-cisplatin treatment. Evaluation of the association of these PGSs with CIO in our target CIO cohort revealed that PGS CIO demonstrated superior performance ( P = 5.54 × 10 − 5 ) relative to PGS HL ( P = 2.93 × 10 − 3 ). PGS CIO was also associated with CIO in our test cohort ( P = 0.04), while the PGS HL did not show a significant association with CIO ( P = 0.52). Conclusion This study developed the first PGS for CIO using a large-scale hearing loss dataset and a biologically informed filter generated from cisplatin-treated murine inner ear snRNA-seq data. This innovative approach offers new avenues for developing PGSs for pharmacogenomic traits, which could contribute to the implementation of tailored therapeutic interventions. Further, our approach facilitated the identification of specific cochlear cells that may play critical roles in CIO. These novel insights will guide future research aimed at developing targeted therapeutic strategies to prevent CIO.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".