Biospecimen Use Correlates with Emerging Techniques in Cancer Research: Impact on Planning Future Biobanks
Bibliographic record
Abstract
The average cohort size for tissue biospecimens used in cancer research studies has increased significantly over the last 20 years. To understand some of the factors behind changes in biospecimen use, we examined cancer research publications to characterize the relationship between specific assay techniques and biospecimen formats and products. We assessed a representative cross section of 378 publications in the journal Cancer Research that used tissue biospecimens, selected from 6 intervals between 1988 and 2010. Publications were categorized by biospecimen utilization, format type (Frozen, Formalin-Fixed Paraffin-Embedded, and Fresh), product type (RNA, DNA, Protein, Cells, and Metabolites), and types of research techniques performed. There was an increase in average biospecimen cohort size (p=0.001); relative use of Formalin-Fixed Paraffin-Embedded biospecimens (24%-68%, p<0.0001); and the proportion of techniques assaying RNA products from biospecimens (Frozen and Fresh formats, p<0.05), from 1988 to 2008. However, these trends have not continued and there has been no further increase from 2008 to 2010. While specific techniques such as 'tissue microarray' analysis appear to have driven some changes in format requirements, there is an overall trend towards techniques requiring RNA products across all formats of biospecimens in basic cancer research. Since pre-analytical variables influence gene expression (RNA levels) more than gene structure (DNA sequence), recognition of these research trends is important for biobanks when deciding priorities for the optimal preservation format and annotation of biospecimens.
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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 | 0.057 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".