Abstract 4357: Disparities in neurotoxicities in cisplatin-treated cancer patients: a population pharmacogenomics approach
Bibliographic record
Abstract
Abstract Background: Disparities in peripheral sensory neuropathy (PSN) have been studied in taxane-treated breast cancer and vincristine-treated leukemia survivors, but not addressed in cisplatin. Both platinum compounds and environmental heavy metals are associated with vascular toxicity, and there is genetic variation in single nucleotide polymorphisms (SNPs) associated with metal burden, which may be due to adaptation to exposures across geographies. We hypothesize that disparities may exist in cisplatin-induced neurotoxicities related to differences in population allele frequency and variants’ impact on gene expression. Methods: In a study of 1663 genotyped testicular cancer survivors, logistic regression between multidimensional scaling-calculated geographic ancestry and neurotoxicities (400-450 mg/m2 cisplatin) and clinical/lifestyle factors was assessed. SNPs with Fst > 0.25 in 1000 Genomes were filtered using GTEx and LDmatrix to find independent expression/splicing trait loci (eQTL/sQTL) in nerve/brain tissue or cisplatin-associated genes. Logistic regression was conducted between genotype and toxicity with age and 10 genetic principal components as covariates. Spearman correlation between gene expression (DepMap) and cisplatin sensitivity (GDSC) in cell lines was calculated for genes of interest from association analysis. Results: Survivors of African (AFR) ancestry had significantly higher PSN incidence versus European (EUR) (p=0.049) and Asian (ASN)-axis (p=0.034) ancestry and higher vertigo incidence versus EUR (p=5.2x10-3) and ASN-axis ancestry (p=0.04), with significant differences in self-reported health. Clinical factors did not show disparities, although ASN-axis survivors were significantly less likely to be on antihypertensives. 19,992 SNPs passed filtering. While genotype-toxicity associations did not hit Bonferroni threshold, six SNPs had suggestively significant p<1.0x10-4. One SNP had increased frequency of the risk allele in the AFR population for PSN and four SNPs for vertigo. rs34904346 (p=2x10-5) was a RNF24 eQTL in the nerve, with 3 other RNF24 eQTLs also associated with PSN (p<0.05). For vertigo, rs3777909 (p=3.1x10-5) was an eQTL for MFSD4B in the nerve and REV3L in the brain, with other independent eQTLs associated with vertigo (p<0.05) and MFSD4B (3) and REV3L (4). For vertigo, rs56819906 (p=7.6x10-5) and rs73626678 (p=1.8x10-4) were DDX25 eQTLs in the nerve. MFSD4B (p=0.0058) and REV3L (p=5.1x10-5) expression in cancer cell lines were significantly correlated with cisplatin sensitivity, matching direction of effect in toxicity association analyses. Conclusions: We show evidence for the contribution of differential risk allele frequencies to disparities in cisplatin-induced PSN and vertigo. If results are confirmed, genotyping risk alleles to identify at-risk patients may benefit cisplatin-treated populations. Citation Format: Swetha Nakshatri, Paul C. Dinh, Darren R. Feldman, Robert J. Hamilton, David J. Vaughn, Chunkit Fung, Christian Kollmannsberger, Robert Huddart, Lawrence H. Einhorn, Nancy J. Cox, Lois B. Travis, M. Eileen Dolan. Disparities in neurotoxicities in cisplatin-treated cancer patients: a population pharmacogenomics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4357.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".