Diagnosing Rejection in Renal Transplants: A Comparison of Molecular- and Histopathology-Based Approaches
Bibliographic record
Abstract
The transcriptome has considerable potential for improving biopsy diagnoses. However, to realize this potential the relationship between the molecular phenotype of disease and histopathology must be established. We assessed 186 consecutive clinically indicated kidney transplant biopsies using microarrays, and built a classifier to distinguish rejection from nonrejection using predictive analysis of microarrays (PAM). Most genes selected by PAM were interferonγ—inducible or cytotoxic T-cell associated, for example, CXCL9, CXCL11, GBP1 and INDO. We then compared the PAM diagnoses to those from histopathology, which are based on the Banff diagnostic criteria. Disagreement occurred in approximately 20% of diagnoses, principally because of idiosyncratic limitations in the histopathology scoring system. The problematic diagnosis of ‘borderline rejection’ was resolved by PAM into two distinct classes, rejection and nonrejection. The diagnostic discrepancies between Banff and PAM in these cases were largely due to the Banff system's requirement for a tubulitis threshold in defining rejection. By examining the discrepancies between gene expression and histopathology, we provide external validation of the main features of the histopathology diagnostic criteria (the Banff consensus system), recommend improvements and outline a pathway for introducing molecular measurements. The transcriptome has considerable potential for improving biopsy diagnoses. However, to realize this potential the relationship between the molecular phenotype of disease and histopathology must be established. We assessed 186 consecutive clinically indicated kidney transplant biopsies using microarrays, and built a classifier to distinguish rejection from nonrejection using predictive analysis of microarrays (PAM). Most genes selected by PAM were interferonγ—inducible or cytotoxic T-cell associated, for example, CXCL9, CXCL11, GBP1 and INDO. We then compared the PAM diagnoses to those from histopathology, which are based on the Banff diagnostic criteria. Disagreement occurred in approximately 20% of diagnoses, principally because of idiosyncratic limitations in the histopathology scoring system. The problematic diagnosis of ‘borderline rejection’ was resolved by PAM into two distinct classes, rejection and nonrejection. The diagnostic discrepancies between Banff and PAM in these cases were largely due to the Banff system's requirement for a tubulitis threshold in defining rejection. By examining the discrepancies between gene expression and histopathology, we provide external validation of the main features of the histopathology diagnostic criteria (the Banff consensus system), recommend improvements and outline a pathway for introducing molecular measurements.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".