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Integration of a polygenic risk score of kidney function with cumulative cisplatin dose and time variables for the prediction of serum platinum levels.

2021· article· en· W3169739440 on OpenAlex
Megan M. Shuey, Annika Faucon, Matthew R. Trendowski, Mark J. Ratain, Paul C. Dinh, Darren R. Feldman, Robert J. Hamilton, David J. Vaughn, Chunkit Fung, Christian Kollmannsberger, Robert Huddart, Neil E. Martin, Robyn Hannigan, Lawrence H. Einhorn, Lois B. Travis, M. Eileen Dolan, Nancy J. Cox

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health Network
FundersNational Institutes of Health
KeywordsMedicineCisplatinInterquartile rangeRenal functionInternal medicineOncologyCumulative doseKidney diseaseCohortUrologyChemotherapy

Abstract

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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/m 2 , 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.396
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it