Factors that drive the increasing use of FFPE tissue in basic and translational cancer research
Bibliographic record
Abstract
The decision to use 10% neutral buffered formalin fixed, paraffin embedded (FFPE) archival pathology material may be dictated by the cancer research question or analytical technique, or may be governed by national ethical, legal and social implications (ELSI), biobank, and sample availability and access policy. Biobanked samples of common tumors are likely to be available, but not all samples will be annotated with treatment and outcomes data and this may limit their application. Tumors that are rare or very small exist mostly in FFPE pathology archives. Pathology departments worldwide contain millions of FFPE archival samples, but there are challenges to availability. Pathology departments lack resources for retrieving materials for research or for having pathologists select precise areas in paraffin blocks, a critical quality control step. When samples must be sourced from several pathology departments, different fixation and tissue processing approaches create variability in quality. Researchers must decide what sample quality and quality tolerance fit their specific purpose and whether sample enrichment is required. Recent publications report variable success with techniques modified to examine all common species of molecular targets in FFPE samples. Rigorous quality management may be particularly important in sample preparation for next generation sequencing and for optimizing the quality of extracted proteins for proteomics studies. Unpredictable failures, including unpublished ones, likely are related to pre-analytical factors, unstable molecular targets, biological and clinical sampling factors associated with specific tissue types or suboptimal quality management of pathology archives. Reproducible results depend on adherence to pre-analytical phase standards for molecular in vitro diagnostic analyses for DNA, RNA and in particular, extracted proteins. With continuing adaptations of techniques for application to FFPE, the potential to acquire much larger numbers of FFPE samples and the greater convenience of using FFPE in assays for precision medicine, the choice of material in the future will become increasingly biased toward FFPE samples from pathology archives. Recognition that FFPE samples may harbor greater variation in quality than frozen samples for several reasons, including variations in fixation and tissue processing, requires that FFPE results be validated provided a cohort of frozen tissue samples is available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".