Predicting Toxicity and Response to Pembrolizumab Through Germline Genomic HLA Class 1 Analysis
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Bibliographic record
Abstract
Abstract Background Human leukocyte antigen class 1 (HLA-1)–dependent immune activity is linked to autoimmune diseases. HLA-1–dependent CD8+ T cells are required for immune checkpoint blockade antitumor activity. It is unknown if HLA-1 genotype is predictive of toxicity to immune checkpoint blockade. Methods Patients with advanced solid tumors stratified into 5 cohorts received single agent pembrolizumab (anti-programmed cell death-1) 200 mg intravenously every 3 weeks in an investigator-initiated phase II trial (Investigator-Initiated Phase II Study of Pembrolizumab Immunological Response Evaluation study, NCT02644369). Germline whole-exome sequencing of peripheral blood mononuclear cells was performed using the Illumina HiSeq2500 platform. HLA-1 haplotypes were predicted from whole-exome sequencing using HLAminer and HLAVBSeq. Heterozygosity of HLA-A, -B, and -C, individual HLA-1 alleles, and HLA haplotype dimorphism at positions −21 M and −21 T of the HLA-A and -B leader sequence were analyzed as predictors of toxicity defined as grade 2 or greater immune-related adverse events and clinical benefit defined as complete or partial response, or stable disease for 6 or more cycles of pembrolizumab. Statistical significance tests were 2-sided. Results In the overall cohort of 101 patients, the frequency of toxicity and clinical benefit from pembrolizumab was 22.8% and 25.7%, respectively. There was no association between any of the HLA-1 loci or alleles with toxicity. HLA-C heterozygosity had an association with decreased clinical benefit relative to HLA-C homozygosity when controlling for cohort (odds ratio = 0.28, 95% confidence interval = 0.09 to 0.91, P = .04). HLA-A and -B haplotype −21 M/T dimorphism and heterozygosity of HLA-A, -B, and -C were not predictive of outcomes. Conclusions HLA-C heterozygosity may predict decreased response to pembrolizumab. Prospective validation is required.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it