Multiplexed color‐coded probe‐based gene expression assessment for clinical molecular diagnostics in formalin‐fixed paraffin‐embedded human renal allograft tissue
Bibliographic record
Abstract
Histopathologic diagnoses in transplantation can be improved with molecular testing. Preferably, molecular diagnostics should fit into standard-of-care workflows for transplant biopsies, that is, formalin-fixed paraffin-embedded (FFPE) processing. The NanoString(®) gene expression platform has recently been shown to work with FFPE samples. We aimed to evaluate its methodological robustness and feasibility for gene expression studies in human FFPE renal allograft samples. A literature-derived antibody-mediated rejection (ABMR) 34-gene set, comprised of endothelial, NK cell, and inflammation transcripts, was analyzed in different retrospective biopsy cohorts and showed potential to molecularly discriminate ABMR cases, including FFPE samples. NanoString(®) results were reproducible across a range of RNA input quantities (r = 0.998), with different operators (r = 0.998), and between different reagent lots (r = 0.983). There was moderate correlation between NanoString(®) with FFPE tissue and quantitative reverse transcription polymerase chain reaction (qRT-PCR) with corresponding dedicated fresh-stabilized tissue (r = 0.487). Better overall correlation with histology was observed with NanoString(®) (r = 0.354) than with qRT-PCR (r = 0.146). Our results demonstrate the feasibility of multiplexed gene expression quantification from FFPE renal allograft tissue. This represents a method for prospective and retrospective validation of molecular diagnostics and its adoption in clinical transplantation pathology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".