Integration of a polygenic risk score of kidney function with cumulative cisplatin dose and time variables for the prediction of serum platinum levels.
Bibliographic record
Abstract
12063 Background: Platinum levels are measurable in the serum for decades after cisplatin therapy and higher levels may be related to chemotherapy-induced toxicities. Since cisplatin is cleared exclusively by the kidney, we hypothesized that a genetic predictor of kidney function, an estimated glomerular filtration rate polygenic risk score (eGFR PRS), would significantly associate with serum platinum levels and could improve prediction models. Methods: Within a large well-characterized, multicenter clinical cohort of cisplatin-treated testicular cancer survivors (TCS), we conducted analyses on all patients with genetic data and serum platinum levels. Genotyping was performed on the HumanOmniExpressExome chip and standard QC measures were included. Serum platinum concentrations were quantified by inductively coupled plasma mass spectrometry. For all TCS, time since therapy (TIME) and cumulative cisplatin dose were collected. The eGFR PRS was developed from the Chronic Kidney Disease Genetics (CKDGen) consortium meta-analysis summary statistics using PRS-CS. Using principal component analysis, we restricted the analysis to TCS of genetically determined European ancestry, then calculated the genome-wide PRS for all participants. We performed Cox regression analyses to evaluate prediction models of serum platinum that included cumulative dose and TIME, as well as a model including eGFR PRS. Data are presented as median(interquartile range). Results: 901 patients were included in our analysis with a median diagnosis age of 31 (26 - 38) years, cumulative cisplatin dose of 400 (300-400) mg/m2, and time since first cisplatin dose of 4.6 (2.3-9.5) years. The median serum platinum level for all TCS was 305 (121-981) ng/L. When stratified into quartiles by eGFR PRS, TCS in the lowest quartile had a median serum platinum level of 316 (139-1014) ng/L while TCS in the highest had a median of 268 (106-731) ng/L. Comparison of two Cox regression models for serum platinum prediction, one including only cumulative dose and TIME as predictors and a second including dose, TIME, eGFR PRS, and an eGFR PRS*TIME interaction term, we determined the model including eGFR PRS had a lower AIC (14350 vs 16180) suggesting a more parsimonious model. Further, eGFR PRS was a significant independent predictor of serum platinum levels (p = 0.02) and the impact of eGFR PRS varies over time (eGFR PRS*TIME, p = 0.05). Conclusions: The genetic predictor of kidney function circumvents the use of renal function measures that may have been impaired by initial cisplatin administration. It is a significant independent predictor of serum platinum levels and consistent with expectation: TCS with higher genetically predicted kidney function had lower serum platinum levels. Our results suggest kidney function inferred by genetics may improve the prediction of serum platinum levels.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".