Discovery of genetic profiles impacting response to chemotherapy: application to gemcitabine
Bibliographic record
Abstract
Chemotherapy is a major treatment modality for individuals affected by cancer. Currently, a number of genome-based technologies are being adopted to identify genes associated with drug response; however, large-scale genetic association applications are still limited. Here we describe a novel strategy based on the genetic and drug response data of the NCI60 cell lines to discover potential candidate genetic variants associated with variable response to chemotherapy. As an example we have applied this strategy to discover single genetic markers and haplotypes from candidate genes previously implicated in the pharmacobiology of gemcitabine. Single-marker association analyses have implicated the association of four SNPs within the gene loci of CDC5L, EPC2, POLS, and PARP1. We have also investigated the combined effect of SNPs using haplotype-based analysis. Accordingly, we have shown modest association of haplotypes in six genes, whereas the most significant associations included a haplotype of the POLS gene. The hypothesis-generating tool presented in this study can be applied to drugs profiled in the NCI60 cell line screen and provides an effective means for the identification of genes associated with drug response. The results obtained using this novel methodology can be used to better design the clinical trials for effective study of the chemotherapeutic agents and thus provide a basis for individualized chemotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".