Karyotypic divergence confounds cellular phenotypes in large pharmacogenomic studies
Bibliographic record
Abstract
ABSTRACT Background Somatic copy-number alterations that affect large genomic regions are a major source of genomic diversity in cancer and can impact cellular phenotypes. Clonal heterogeneity within cancer cell lines can affect phenotypic presentation, including drug response. Methods We aggregated and analyzed SNP and copy number profiles from six pharmacogenomic datasets encompassing 1,691 cell lines screened for 13 molecules. To look for sources of genotype and karyotype discordances, we compared SNP genotypes and segmental copy-ratios across 5 kb genomic bins. To assess the impact of genomic discordances on pharmacogenomic studies, we assessed gene expression and drug sensitivity data for compared discordant and concordant lines. Results We found 6/1,378 (0.4%) cell lines profiled in two studies to be discordant in both genotypic and karyotypic identity, 51 (3.7%) discordant in genotype, 97 (7.0%) discordant in karyotype, and 125 (9.1%) potential misidentifications. We highlight cell lines REH, NCI-H23 and PSN1 as having drug response discordances that may hinge on divergent copy-number q Conclusions Our study highlights the low level of misidentification as evidence of effective cell line authentication standards in recent pharmacogenomic studies. However, the proclivity of cell lines to acquire somatic copy-number variants can alter the cellular phenotype, resulting in a biological and predictable effects on drug sensitivity. These findings highlight the need for verification of cell line copy number profiles to inform interpretation of drug sensitivity data in biomedical studies.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".