A new HLA genotyping algorithm to show accuracy and concordance between tumor and normal samples.

A Ariane Lozac'hmeur (Tempus AI, Inc., Chicago, IL) M Mansen Yu (2Stanford University Cancer Center, Palo Alto, United States) Q Qidi Yang (Tempus AI, Inc., Chicago, IL) T Taylor Harding (Tempus AI, Inc., Chicago, IL) W Wenjin Gu (Tempus AI, Inc., Chicago, IL) M Michael Mumphrey (Tempus AI, Inc., Chicago, IL) Y Yingyue Li (Tempus AI, Inc., Chicago, IL) P Pauline Funchain

Abstract

2584 Background: HLA genotyping, specifically HLA-A*02:01 is an actionable marker in uveal melanoma for Tebentafusp, a bispecific gp100 peptide-HLA-directed CD3 T cell engager, with ongoing clinical trials investigating its use in HLA-A*02:01 positive cutaneous and mucosal melanoma. However, prior work has shown a discrepancy in this marker between blood and somatic tumor testing (Seedor R et al ASCO 2023). Given therapeutic implications of these discordances, a better understanding of tumor versus normal HLA testing concordance is critical. Methods: The Tempus HLA genotyping algorithm was run on a cohort of 191 patients, each with a tumor and a normal sample. The reference HLA genotype was determined by an independent lab using the GenDx HLA genotyping kit and long read sequencing of the normal sample. Samples that failed sequencing in either lab were excluded from the analysis. Concordance between tumor and normal samples was additionally evaluated on a cohort of 7,821 solid-tumor samples from a variety of sites, selected from the Tempus multimodal database. Results: The Tempus HLA genotyping algorithm is highly accurate (>98.9% on all HLA genes except DQA1). Specifically, it has an accuracy of 100% on the HLA-A gene, using either the tumor or normal sample. On the larger tumor/normal comparison cohort (7,821 samples), the Tempus HLA genotyping algorithm is highly concordant (>99% for all genes except HLA-DQA1 [94.3%]). For the 183 melanoma samples in this cohort, concordance was similar with 100% for HLA-A and HLA-B and 99.5% (182/183) for HLA-C, further confirming that HLA genotyping can be performed accurately on melanoma tumor samples. Investigation of all discordances on HLA genes for which HLA LOH results were available (HLA-A, -B and -C) shows that 84% (88/105) of those discordant samples have somatically lost the allele responsible for the discordance. Of LOH positive discordant samples, 73% (64/88) had a tumor purity ≥70%. Thus, over half (64/105) of the discordances between tumor- and normal-HLA genotyping are caused by high tumor purity HLA LOH positive samples. On samples meeting these criteria, concordance drops to 86%-89%. Conclusions: The Tempus HLA genotyping algorithm is an extremely accurate laboratory developed test that provides accurate results, regardless of whether the normal or tumor sample is used. Concordance between normal- and tumor-based results is above 99.5% in a large-scale dataset, provided the sample does not have HLA LOH and a tumor purity over 70%. These findings suggest that this test can rapidly screen patients for HLA genotype and match them to appropriate clinical trials but emphasize the importance of a normal sample for high tumor purity specimens. Accuracy (normal) N (normal) Accuracy (tumor) N (tumor) HLA-A 100.0% 189 100.0% 189 HLA-B 99.5% 187 99.0% 189 HLA-C 100.0% 185 98.9% 185 HLA-DQA1 99.5% 182 97.9% 189 HLA-DQB1 100.0% 186 99.5% 187 HLA-DRB1 98.9% 182 98.9% 182

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 2584-2584
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (8)

A

Ariane Lozac'hmeur

Tempus AI, Inc., Chicago, IL

M

Mansen Yu

2Stanford University Cancer Center, Palo Alto, United States

Q

Qidi Yang

Tempus AI, Inc., Chicago, IL

T

Taylor Harding

Tempus AI, Inc., Chicago, IL

W

Wenjin Gu

Tempus AI, Inc., Chicago, IL

M

Michael Mumphrey

Tempus AI, Inc., Chicago, IL

Y

Yingyue Li

Tempus AI, Inc., Chicago, IL

P

Pauline Funchain