Donor-Specific Antibody Is Associated with Increased Expression of Rejection Transcripts in Renal Transplant Biopsies Classified as No Rejection
Bibliographic record
Abstract
Significance Statement Many kidney transplant patients in INTERCOMEX whose biopsy specimens are diagnosed molecularly or histologically as no rejection have donor-specific HLA antibodies (DSAs, 32%). Although the significance of DSA in no rejection has been unclear, we hypothesized that current diagnostic thresholds miss some DSA-positive patients who may have subtle antibody-mediated rejection (ABMR)–related stress, with potential effect on outcomes. To search for subtle ABMR-related gene expression in “no rejection” biopsy samples, we developed a “DSA-probability” classifier (trained on DSA positivity) in microarray results from 1679 biopsy samples that detected ABMR-related transcripts ( e.g., NK cell and IFNG-inducible). Many no rejection biopsy samples had mildly increased expression of ABMR-related transcripts, associated with DSA positivity, and these kidneys had increased risk of failure. Thus, mild ABMR-related stress is more common than previously thought. Background Donor -specific HLA antibody (DSA) is present in many kidney transplant patients whose biopsies are classified as no rejection (NR). We explored whether in some NR kidneys DSA has subtle effects not currently being recognized. Methods We used microarrays to examine the relationship between standard-of-care DSA and rejection-related transcript increases in 1679 kidney transplant indication biopsies in the INTERCOMEX study (ClinicalTrials.gov NCT01299168), focusing on biopsies classified as NR by automatically assigned archetypal clustering. DSA testing results were available for 835 NR biopsies and were positive in 271 (32%). Results DSA positivity in NR biopsies was associated with mildly increased expression of antibody-mediated rejection (ABMR)–related transcripts, particularly IFNG-inducible and NK cell transcripts. We developed a machine learning DSA probability (DSA Prob ) classifier based on transcript expression in biopsies from DSA-positive versus DSA-negative patients, assigning scores using 10-fold cross-validation. This DSA Prob classifier was very similar to a previously described “ABMR probability” classifier trained on histologic ABMR in transcript associations and prediction of molecular or histologic ABMR. Plotting the biopsies using Uniform Manifold Approximation and Projection revealed a gradient of increasing molecular ABMR-like transcript expression in NR biopsies, associated with increased DSA ( P <2 × 10 −16 ). In biopsies with no molecular or histologic rejection, increased DSA Prob or ABMR probability scores were associated with increased risk of kidney failure over 3 years. Conclusions Many biopsies currently considered to have no molecular or histologic rejection have mild increases in expression of ABMR-related transcripts, associated with increasing frequency of DSA. Thus, mild molecular ABMR-related pathology is more common than previously realized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".