Germline Genetic Variation, Cancer Outcome, and Pharmacogenetics
Bibliographic record
Abstract
Studies of the role of germline or inherited genetic variation on cancer outcome can fall into three distinct categories. First, the impact of highly penetrant but lowly prevalent mutations of germline DNA on cancer prognosis has been studied extensively for BRCA1 and BRCA2 mutations as well as mutations related to hereditary nonpolyposis colorectal cancer syndrome. These mainly modest-sized analyses have produced conflicting results. Although some associations have been observed, they may not be independent of other known clinical or molecular prognostic factors. Second, the impact of germline polymorphisms on cancer prognosis is a burgeoning field of research. However, a deeper understanding of potentially confounding somatic changes and larger multi-institutional, multistage studies may be needed before consistent results are seen. Third, research examining the impact of germline genetic variation on differential treatment response or toxicity (pharmacogenetics) has produced some proof-of-principle results. Putative germline pharmacogenetic predictors of outcome include DPYD polymorphisms and fluorouracil toxicity, UGT1A1 variation and irinotecan toxicity, and CYP2D6 polymorphisms and tamoxifen efficacy, with emerging data on predictors of molecularly targeted or biologic drugs. Here we review data pertaining to these germline outcome and germline toxicity relationships.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".