PO.MCB08.05 · 分子与细胞生物学
超越HLA LOH:HLA丢失的替代模式常见且因癌症类型而异
Beyond HLA LOH: Alternative modes of HLA loss are common and vary by cancer type
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
人类白细胞抗原(HLA)基因通过向细胞毒性T细胞呈递肿瘤来源抗原,在肿瘤免疫监视中发挥关键作用,使得恶性细胞得以被免疫识别和清除。HLA位点的杂合性缺失(LOH)、体细胞突变、表观遗传沉默和结构变异是肿瘤逃避免疫检测的充分表征机制,促进疾病进展和对免疫治疗的耐药。然而,这些机制很少被联合考虑。全面理解HLA丢失的这些多样化模式,对于阐明肿瘤免疫逃逸和优化免疫治疗策略至关重要。
我们开发了一个整合工作流程,利用Tempus xT检测法来检测HLA基因中的体细胞改变。首先使用经验证的专有检测法对胚系HLA等位基因进行基因分型。使用患者特异性HLA等位基因和诱饵序列对测序读段进行重新比对。重新比对的读段被输入标准变异检出和RNA表达定量工作流程,以确定HLA变异和HLA表达。对HLA变异进行过滤以去除伪影。使用在超过2,000个样本上训练的机器学习模型对HLA表达进行归一化和偏差校正。总HLA表达丢失定义为M值截尾均值(TMM)低于按基因和肿瘤纯度分层的均值2个标准差以上。使用经验证的Tempus HLA LOH设备确定HLA LOH。我们将该工作流程应用于11,000个癌症样本,评估HLA基因型、LOH、体细胞变异和表达。
我们的HLA丢失检测方法显示出高度一致性,基于DNA的改变在RNA水平上显现。61%具有LOH和/或功能缺失(LOF)变异的HLA等位基因也表现出HLA RNA表达的丢失,包括等位基因特异性表达(ASE)和两个HLA等位基因的总表达丢失。12%没有任何DNA丢失事件的肿瘤表现出RNA水平的HLA丢失,可能是由于表观遗传调控或其他原因。HLA丢失的患病率和分子机制因癌症类型而异。LOH是大多数癌症中HLA丢失的主导机制,在头颈部鳞状细胞癌(46%,占丢失事件的92%)和肺鳞状细胞癌(32%,占丢失事件的87%)中LOH发生率异常高。相反,体细胞LOF变异是MSI-H结直肠癌中的主导机制(22%,占丢失事件的51%),而表达丢失在前列腺癌中占主导(21%的病例,占丢失事件的84%)。
随着HLA限制性免疫治疗的扩展,准确、全面地表征肿瘤中HLA基因改变日益重要。我们的发现突显,除LOH外,LOF变异和转录沉默是某些癌症中HLA丢失的重要促成因素,应常规评估以指导免疫治疗决策。
查看英文原文 English abstract
The human leukocyte antigen (HLA) genes play a pivotal role in immune surveillance of tumors by presenting tumor-derived antigens to cytotoxic T cells, enabling immune recognition and elimination of malignant cells. Loss of heterozygosity (LOH), somatic mutations, epigenetic silencing and structural variations at the HLA locus are well-characterized mechanisms by which tumors evade immune detection, contributing to disease progression and resistance to immunotherapies. However, those mechanisms are rarely considered in conjunction. A comprehensive understanding of these diverse modes of HLA loss is critical for elucidating tumor-immune escape and optimizing immunotherapeutic strategies.
We developed an integrated workflow leveraging the Tempus xT assay to detect somatic alterations in HLA genes. Germline HLA alleles were first genotyped using a validated proprietary assay. Patient-specific HLA alleles and decoy sequences were used to realign sequencing reads. The realigned reads were fed into standard variant calling and RNA expression quantification workflows to establish HLA variants and HLA expression. HLA variants were filtered to remove artifacts. HLA expression was normalized and bias corrected using machine learning models trained on over 2,000 samples. Total HLA expression loss was defined as the trimmed mean of M values (TMM) being more than 2 standard deviations below the gene- and tumor-purity-stratified mean. HLA LOH was determined using the validated Tempus HLA LOH device. We applied our workflow to 11,000 cancer samples, assessing HLA genotypes, LOH, somatic variants, and expression.
Our HLA loss detection methods showed a high level of concordance, with DNA-based alterations evident at the RNA level. 61% of HLA alleles with LOH and/or loss of function (LOF) variants also displayed loss of HLA RNA expression, which included both allele-specific expression (ASE) and total expression loss from both HLA alleles. 12% of tumors without any DNA loss events displayed RNA-level HLA loss, potentially due to epigenetic regulation, or other causes. The prevalence and molecular mechanisms of HLA loss varied by cancer type. LOH was the dominant mechanism for HLA loss in most cancers, with exceptionally high rates of LOH in head and neck squamous cell carcinoma (46%, 92% of loss events) and lung squamous cell carcinoma (32%, 87% of loss events). Conversely, somatic LOF variants were the dominant mechanism in MSI-H colorectal cancer (22%, 51% of loss events), and expression loss dominated in prostate cancers (21% of cases, 84% of loss events).
As HLA-restricted immunotherapies expand, accurate and comprehensive characterization of HLA gene alterations in tumors is increasingly important. Our findings highlight that, beyond LOH, LOF variants and transcriptional silencing are significant contributors to HLA loss in certain cancers and should be routinely assessed to inform immunotherapeutic decision-making.
利益披露 Disclosure
Q. Yang,
Tempus AI Employment, Stock, Stock Option, Travel.
M. Mumphrey,
Tempus AI Employment, Stock, Stock Option, Travel.
W. Gu,
Tempus AI Employment, Stock, Stock Option, Travel.
T. Harding,
Tempus AI Employment, Stock, Stock Option, Travel.
A. Lozac'hmeur,
Tempus AI Employment, Stock, Stock Option, Patent.