Influence of Evolution in Tumor Biobanking on the Interpretation of Translational Research
Bibliographic record
Abstract
PURPOSE: Translational cancer research increasingly relies on human tissue biospecimens and this has coincided with a shift in tissue biobanking approach. Newer biobanks (post year 2000) deploy standard operating procedures to reduce variability around biospecimen collection. Because current translational research is based on pre-2000 and post-2000 era biospecimens, we consider whether the collection era may influence gene expression data. DESIGN: We compared the range of breast tumor collection times from pre-2000 and post-2000 era biobanks and compared estrogen receptor (ER) protein expression with collection time. We then collected 10 breast tumor biospecimens under a standardized protocol and examined whether the expression of c-myc and ER was influenced by storage on ice < or = 24 hours. RESULTS: The range of collection times achieved at a pre-2000 versus post-2000 era biobank differed. Thirty-two percent of biospecimens were cryopreserved within 30 minutes at the pre-2000 era biobank versus 76% at the post-2000 era biobank. Collection time and ER protein level was inversely correlated (r = -0.19, P = 0.025; n = 137). We observed a wide range in initial c-myc and ER mRNA levels (50- versus 130-fold). Although mRNA levels of both genes declined with increasing collection time, the rate of change differed because c-myc was significantly reduced after 24 hours (mean reduction to 79% of initial) versus ER (94% of initial). CONCLUSION: The overall shift in biobanking around the year 2000 is reflected in the ranges of collection times associated with pre-2000 and post-2000 era biobanks. Because collection time can differentially alter gene expression, the biospecimen collection era should be considered in gene expression 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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".