Clinical and Genome-Wide Analysis of Cisplatin-Induced Peripheral Neuropathy in Survivors of Adult-Onset Cancer
Bibliographic record
Abstract
Abstract Purpose: Our purpose was to characterize the clinical influences, genetic risk factors, and gene mechanisms contributing to persistent cisplatin-induced peripheral neuropathy (CisIPN) in testicular cancer survivors (TCSs). Experimental Design: TCS given cisplatin-based therapy completed the validated EORTC QLQ-CIPN20 questionnaire. An ordinal CisIPN phenotype was derived, and associations with age, smoking, excess drinking, hypertension, body mass index, diabetes, hypercholesterolemia, cumulative cisplatin dose, and self-reported health were examined for 680 TCS. Genotyping was performed on the Illumina HumanOmniExpressExome chip. Following quality control and imputation, 5.1 million SNPs in 680 genetically European TCS formed the input set. GWAS and PrediXcan were used to identify genetic variation and genetically determined gene expression traits, respectively, contributing to CisIPN. We evaluated two independent datasets for replication: Vanderbilt's electronic health database (BioVU) and the CALGB 90401 trial. Results: Eight sensory items formed a subscale with good internal consistency (Cronbach α = 0.88). Variables significantly associated with CisIPN included age at diagnosis (OR per year, 1.06; P = 2 × 10−9), smoking (OR, 1.54; P = 0.004), excess drinking (OR, 1.83; P = 0.007), and hypertension (OR, 1.61; P = 0.03). CisIPN was correlated with lower self-reported health (OR, 0.56; P = 2.6 × 10−9) and weight gain adjusted for years since treatment (OR per Δkg/m2, 1.05; P = 0.004). PrediXcan identified lower expressions of MIDN and RPRD1B, and higher THEM5 expression as associated with CisIPN (P value for each < 5 × 10−6) with replication of RPRD1B meeting significance criteria (Fisher combined P = 0.0089). Conclusions: CisIPN is associated with age, modifiable risk factors, and genetically determined expression level of RPRD1B. Further study of implicated genes could elucidate the pathophysiologic underpinnings of CisIPN. Clin Cancer Res; 23(19); 5757–68. ©2017 AACR.
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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 | 0.000 |
| 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".