Specimen Quality Evaluation in Canadian Biobanks Participating in the COEUR Repository
Bibliographic record
Abstract
Human biological specimens are important for translational research programs such as the Canadian Ovarian Experimental Unified Resource (COEUR) funded by the Terry Fox Research Institute. Sample quality is an important consideration, as it directly impacts the quality of ensuing research. The aim of the present study was to determine the quality of tissues collected from different sites contributing to the COEUR cohort. Samples from high-grade serous ovarian tumors (fresh frozen and corresponding paraffin-embedded tissues) were provided by nine participating Canadian biobanks. All samples were shipped to a central site using a Standard Operating Protocol (SOP). DNA and RNA extraction was conducted by the quality control division of the Canadian Tumor Repository Network (CTRNet). DNA quality was determined by ß-globin gene PCR amplification, and RNA quality by the RNA integrity number (RIN), as measured by the Agilent BioAnalyzer. DNA of acceptable quality had at least three bands of ß-globin amplified from DNA (n=115/135), and a RIN number ≥7 was considered very good for RNA (n=80/135). Sample preparation and storage time had little effect on RNA or DNA quality. Protein expression was assessed on tissue microarray by immunohistochemistry with antibodies against p53, WT1, E-cadherin, CK-7, and Ki67 from formalin fixed-paraffin embedded (FFPE) tissues. As seen with a nonhierarchical clustering statistical method, there was no significant difference in immunostaining of paraffin tissues among specimens from different biobanks. Interestingly, patients with worse outcome were highly positive for p53 and weak for WT1. In conclusion, while there was no common SOP for retrospectively collected material across Canadian biobanks, these results indicate that specimens collected at these multiple sites are of comparable quality, and can serve as an adequate resource to create a national cohort for the validation of molecular biomarkers in ovarian cancer.
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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 | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".