Evidence map›Paper›PMID 40830750›Full record

ArticleBMC bioinformatics2025

consHLA: a next generation sequencing consensus-based HLA typing workflow.

Rachel Bowen-James, Weilin Wu, Marie Wong-Erasmus, Julian M W Quinn, Chelsea Mayoh, Mark J Cowley

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Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rachel Bowen-James *Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
Weilin Wu *Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
Marie Wong-ErasmusChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
Julian M W QuinnChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
Chelsea MayohChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
Mark J CowleyChildren's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia. mcowley@ccia.org.au.

Funding

Australian Medical Research Future Fund NHMRC MRF9500002Cancer Australia 1165556
6 · The paper itself

Abstract

backgroundHuman Leukocyte Antigens (HLA) play central roles in histocompatibility and immune system functions, including antigen presentation. Accurate typing of Class I and II HLA genes is crucial for transplant tissue matching, characterising autoimmune diseases and informing cancer immunotherapy. Clinical serology and PCR-based testing are the gold standards for HLA typing, but offer only single-field resolution (e.g., HLA-A*11). Whole genome sequencing (WGS) and RNA sequencing (RNA-seq) can achieve higher, three-field resolution (e.g., HLA-A∗11:01:01), although some HLA genes can be challenging to type from sequencing data. With the increasing use of germline WGS, tumour WGS and tumour RNA-seq in cancer patient care, there is an opportunity to combine these three dataset types to improve HLA typing accuracy and confidence, and to identify clinically relevant HLA type changes in tumours. To achieve this, we developed consHLA, a tool that employs this consensus HLA typing approach.

resultsWe obtained matched germline and tumour WGS and RNA-seq data from 86 high-risk paediatric cancer patients (76 brain cancers, 10 leukaemias) from the ZERO Childhood Cancer precision medicine program. We examined 10 HLA typing packages, selecting HLA-HD to develop our consHLA workflow as HLA-HD can employ all three dataset types, analysing both Class I and II HLA genes at three field resolution. Using consHLA we achieved 97.9% concordance with gold standard HLA test results. We observed 90.5% allele consistency across the three sequencing NGS inputs. Typing inconsistencies in at least one of 12 clinically relevant HLA genes were observed in 29 of the brain tumour cases. 32% of these had clinically relevant explanations. To assist clinically, we implemented consHLA as a fully automated workflow producing a clinician-friendly HLA-typing report.

conclusionsTo leverage cancer patient germline and tumour WGS and tumour RNA-seq data we developed an automated workflow, consHLA, that produces consensus typing of HLA genes in a clinically relevant timeframe. This workflow provides higher resolution patient HLA-typing than current gold standard approaches, identifies HLA alterations arising in patient tumours and generates clear, simple reports.

Indexed as

High-Throughput Nucleotide SequencingHistocompatibility TestingHLA AntigensSoftwareBrain NeoplasmsConsensus SequenceHumansNeoplasmsWhole Genome SequencingWorkflowHLA AntigensBioinformaticsComputational BiologyConsensus algorithmHuman leukocyte antigenMajor histocompatibility complexTissue typingTranscriptome sequencingWhole genome sequencing

Identifiers

PMID40830750
PMCPMC12363109

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.