Editorial: HLA in personalized medicine
Bibliographic record
Abstract
Editorial on the Research Topic HLA in personalized medicine HLA traveled a long distance from initially being regarded as a transplant antigen to becoming a molecule of interest in 21st century for the development of novel diagnostics and therapeutics in the treatment of human diseases, vaccine design, pharmacogenetics, and genomics.The proteins encoded by genes in the Human Leukocyte Antigen (HLA) complex regulate the innate and adaptive arms of human immune responsiveness through antigen recognition and presentation, inflammation regulation, and the complement system, and the impact of this region is well documented in solid organ and hematopoietic stem cell transplantation and various immunemediated conditions.In the 21st century, the role of HLA in personalized medicine is tailored with the advent of massive parallel sequencing technologies such as targeted sequencing by nextgeneration sequencing (NGS) and third-generation sequencing (TGS), which opens new frontiers in the development of predictive and preventive medicine.The goal of our Research Topic was to collect articles that provide the most cutting-edge research on the application of the concept of HLA in personalized medicine in the context of clinical allotransplantation and cancer.The Research Topic of articles in Frontiers in Genetics under the specialty Research Topic of "HLA in Personalized Medicine" presents three original research papers, one case report, and one perspective article.The Research Topic was open between 9 September 2023, and 13 February 2024, and five manuscripts were accepted for publication.The Research Topic of this Research Topic provides an important insight into the application of the concept of HLA in personalized medicine by presenting articles that demonstrate the significance of heterozygous advantages in cancer, the application of vaccination with a cocktail of HLA class I and II peptides in treating metastatic cancer, HLA-B and TAP transcriptional silencing in pediatric solid cancers, the utility of race, ethnicity, and ancestry in transplant donor registries, and the clinical relevance of HLA-DQ eplet mismatches in the management of immunosuppression in retransplant candidates.The original research study performed by Tsai et al. tested the hypothesis that the increased heterozygosity at HLA loci is correlated with a diminished risk of developing colorectal cancer (CRC).This study found that individuals with all heterozygous genotypes at HLA class I and class II genes had a decreased risk of CRC.Similarly, individuals with combined HLA class I and class II heterozygous genotypes had significantly reduced odds of developing CRC as compared to those with 0 or one heterozygous genotype.This study further examined whether germline HLA diversity is linked to tumor T cell landscape.Although the differences were not statistically significant, HLA class I and/or II diversity was correlated with higher T cell receptor (TCR) abundance and reduced TCR clonality.This research study highlighted the heterozygote
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".