High-Resolution HLA Typing for Sensitized Patients: Advances in Medicine and Science Require Us to Challenge Existing Paradigms
Bibliographic record
Abstract
To the Editor: In the April issue of AJT, an editorial by Cecka et al (1.Cecka JM Reed EF Zachary AA. HLA high-resolution typing for sensitized patients: A solution in search of a problem?.Am J Transplant. 2015; 15: 855-856Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar) commented on our personal viewpoint article wherein we proposed that HLA mismatch acceptability for sensitized transplant candidates should be determined at high-resolution levels (2.Duquesnoy RJ Kamoun M Baxter-Lowe LA Should HLA mismatch acceptability for sensitized transplant candidates be determined at the high-resolution rather than the antigen level?.Am J Transplant. 2015; 15 (et al): 923-930Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar). This editorial seems to express the view that HLA antigen-based testing be maintained as is despite its inherent deficiencies. We are perplexed by conflicting comments that more HLA complexity “should be pretty low on the list of priorities” while simultaneously mentioning a need for HLA-DQA and DP typing and “better” resolution of HLA-DRB3/4/5 types. Furthermore, the statement that “a whole bunch of antibodies, each recognizing a single HLA antigen or allele” places little emphasis on the concept that those antibodies are epitope specific. The editorial states that epitopes are theoretically based on “nucleotide sequence similarities between HLA alleles” and “only a few have been documented with antibodies.” During the past 2 decades, many investigators, notably Paul Terasaki’s group, have confirmed unique HLA epitopes defined by antibodies and three recent publications list 97 HLA-ABC, 50 HLA-DR, -DQ, -DP and 21 MICA antibody-verified epitopes recorded so far in the International Registry of HLA Epitopes at http://www.epregistry.com.br (3.Duquesnoy RJ Marrari M Tambur A First report on the antibody verification of HLA-DR, HLA-DQ and HLA-DP epitopes recorded in the HLA epitope registry.Hum Immunol. 2014; 75 (et al): 1097-1103Crossref PubMed Scopus (69) Google Scholar, 4.Duquesnoy RJ Marrari M Mulder A da Mata Sousa LCD Da Silva AS do Monte SJH. First report on the antibody verification of HLA-ABC epitopes recorded in the HLA epitope registry.Tissue Antigens. 2014; 83: 391-400Crossref PubMed Scopus (50) Google Scholar, 5.Duquesnoy RJ Marrari M Mostecki J da Mata Sousa LCD do Monte SJH. First report on the antibody verification of MICA epitopes recorded in the HLA epitope registry.Int J Immunogenetics. 2014; 41: 370-377Crossref PubMed Scopus (14) Google Scholar). The editorial expresses the opinion that “more information is almost always better than less when making clinical decisions, except when it is not directly applicable to the problem at hand.” Clearly, it does not consider the limitations of antigen-based matching as problematic and that “it is likely that one or two patients on a waiting list might benefit from knowing which HLA alleles are expressed by the donor.” How these numbers were determined is itself an interesting question. An informal survey among viewpoint article authors revealed that the number of highly sensitized patients with specific allele-reactive antibodies was >10%. Accordingly, hundreds of such patients on the US national waiting list would benefit from high-resolution typing. Most transplant programs are government funded and fairness and equity in the organ allocation process is of considerable importance even if it only impacts a small percentage of transplant candidates. We acknowledge that “it is difficult to tease out the precise impact of allele-level typing and identifying epitopes on the chance of a transplant for a sensitized patient.” This editorial has a calculation for a single allele with a 0.5% frequency and for a 10% admixture population concluding that only 1 in 2000 donors would have this allele. However, this calculation fails to acknowledge that (1.Cecka JM Reed EF Zachary AA. HLA high-resolution typing for sensitized patients: A solution in search of a problem?.Am J Transplant. 2015; 15: 855-856Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar) a given population may have multiple alleles corresponding to a given antigen, (2.Duquesnoy RJ Kamoun M Baxter-Lowe LA Should HLA mismatch acceptability for sensitized transplant candidates be determined at the high-resolution rather than the antigen level?.Am J Transplant. 2015; 15 (et al): 923-930Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar) many alleles have frequencies well above the cut-off point of 0.5%, and (3.Duquesnoy RJ Marrari M Tambur A First report on the antibody verification of HLA-DR, HLA-DQ and HLA-DP epitopes recorded in the HLA epitope registry.Hum Immunol. 2014; 75 (et al): 1097-1103Crossref PubMed Scopus (69) Google Scholar) admixtures may have multiple population and ethnic groups each with their own distinct alleles. These factors all contribute to the allelic diversities of donors and recipients especially when worldwide transplant programs are considered. Rather than clutching to old paradigms, we must apply the newest scientific concepts and the most precise technologies to define humoral barriers to successful transplantation. Let’s move forward. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".