Letter to the Editor Re: Letter by Naesens et al. Microvascular inflammation: Gene expression changes do not necessarily reflect pathogenesis
Bibliographic record
Abstract
We thank the authors for this useful commentary1Naesens M Thaunat O Mengel M Microvascular inflammation: gene expression changes do not necessarily reflect pathogenesis.Am J Transplant. 2022; (doi:10.1111/ajt.17136)Abstract Full Text Full Text PDF Scopus (1) Google Scholar on our paper analyzing DSA-negative molecular ABMR,2Halloran PF, Madill-Thomsen KS, Pon S, et al. Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: differences in timing and intensity but similar mechanisms and outcomes [published online ahead of print May 16, 2022]. Am J Transplant. doi:10.1111/ajt.17092Google Scholar although we disagree to some extent with the conclusions. Our data found that in the INTERCOMEX population with molecular ABMR, DSA negativity is very common, particularly in early-stage ABMR but also in late-stage ABMR. On average, DSA-negative ABMR was earlier, less intense (i.e. lower ABMR-associated gene expression), and more often C4d-negative, but was virtually identical to DSA-positive ABMR in the top differentially expressed ABMR-associated transcripts. We could not find distinct molecular differences between DSA-negative and DSA-positive ABMR, other than some small increases in injury-related transcript expression, which may be explained by the earlier mean time posttransplant. We have recently confirmed the main points in the new Trifecta-Kidney analysis, where we show that both molecular and histologic ABMR are approximately 50% DSA-negative.3Halloran PF, Reeve J, Madill-Thomsen KS, et al. Antibody-mediated rejection without detectable donor-specific antibody releases donor-derived cell-free DNA: results from the Trifecta study. Transplantation. In press.Google Scholar In Trifecta-Kidney, DSA-negative ABMR is slightly less molecularly active, but still releases donor-derived cell-free DNA at levels similar to that seen in DSA-positive ABMR.3Halloran PF, Reeve J, Madill-Thomsen KS, et al. Antibody-mediated rejection without detectable donor-specific antibody releases donor-derived cell-free DNA: results from the Trifecta study. Transplantation. In press.Google Scholar The main point is that within all ABMR—DSA-negative or DSA-positive—there is extensive heterogeneity in intensity and stage. Type 1 ABMR (in patients with DSA before transplantation) behaves differently from type 2 ABMR with de novo DSA.4Aubert O Loupy A Hidalgo L et al.Antibody-mediated rejection due to preexisting versus de novo donor-specific antibodies in kidney allograft recipients.J Am Soc Nephrol. 2017; 28: 1912-1923Crossref PubMed Scopus (158) Google Scholar In addition, there is a subtle minor ABMR-related process in many biopsies that we have been calling negative for ABMR because they fall below the arbitrary thresholds for ABMR established both in MMDx and histology.5Madill-Thomsen KS Bohmig GA Bromberg J et al.Donor-specific antibody is associated with increased expression of rejection transcripts in renal transplant biopsies classified as no rejection.J Am Soc Nephrol. 2021; 32: 2743-2758Crossref PubMed Scopus (16) Google Scholar We must start thinking of ABMR-related molecular and histologic changes as a broad spectrum. At the moment, it is unlikely that distinct disease mechanisms operate in DSA-negative versus DSA-positive ABMR. These labels can be retained as reminders of one aspect of the heterogeneity within ABMR, as long as the much greater heterogeneity in intensity, stage, and duration is recognized. We are in a new era of appreciating the diversity within the ABMR phenotype and its relationship to pathogenic mechanisms. More importantly, management issues are critical: we do not really know how to manage DSA-positive ABMR or DSA-negative ABMR, or for that matter how to adjust management based on other aspects of heterogeneity in the ABMR spectrum. We should keep an open mind on how we should ultimately classify these common, important, and heterogeneous disease states. The authors of this manuscript have conflicts of interest to disclose as described by the American Journal of Transplantation. P.F. Halloran holds shares in Transcriptome Sciences Inc., a University of Alberta research company dedicated to developing molecular diagnostics, supported in part by a licensing agreement between TSI and Thermo Fisher, and by a research grant from Natera. P.F. Halloran is a consultant to Natera. The other author has declared no conflict of interest exists.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".