Stability and Heterogeneity of Expression Profiles in Lung Cancer Specimens Harvested Following Surgical Resection
Bibliographic record
Abstract
One of the major concerns in microarray profiling studies of clinical samples is the effect of tissue sampling and RNA extraction on data. We analyzed gene expression in lung cancer specimens that were serially harvested from tumor mass and snap-frozen at several intervals up to 120 minutes after surgical resection. Global gene expression was profiled on cDNA microarrays, and selected stress and hypoxia-activated genes were evaluated using real-time reverse transcription polymerase chain reaction (RT-PCR). Remarkably, similar gene expression profiles were obtained for the majority of samples regardless of the time that had elapsed between resection and freezing. Real-time RT-PCR studies showed significant heterogeneity in the expression levels of stress and hypoxia-activated genes in samples obtained from different areas of a tumor specimen at one time point after resection. The variations between multiple samplings were significantly greater than those of elapsed time between sampling/freezing. Overall samples snap-frozen within 30 to 60 minutes of surgical resection are acceptable for gene expression studies, thus making sampling and snap-freezing of tumor samples in a routine surgical pathology laboratory setting feasible. However, sampling and pooling from multiple sites of each tumor may be necessary for expression profiling studies to overcome the molecular heterogeneity present in tumor specimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".