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全基因组关联分析确定实体瘤中免疫浸润的遗传相关性。

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors.

作者信息

Siemers Nathan O, Holloway James L, Chang Han, Chasalow Scott D, Ross-MacDonald Petra B, Voliva Charles F, Szustakowski Joseph D

机构信息

Translational Bioinformatics, Bristol-Myers Squibb, Redwood City, California, United States of America.

Translational Bioinformatics, Bristol-Myers Squibb, Hopewell, New Jersey, United States of America.

出版信息

PLoS One. 2017 Jul 27;12(7):e0179726. doi: 10.1371/journal.pone.0179726. eCollection 2017.

DOI:10.1371/journal.pone.0179726
PMID:28749946
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5531551/
Abstract

Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

摘要

一类新的药物(通常称为检查点阻断剂)的引入扩大了治疗越来越多癌症的治疗选择,这类药物靶向宿主免疫系统以正向调节抗肿瘤免疫反应。尽管这些药物的疗效与预先存在的肿瘤免疫浸润水平有关,但仍不清楚为什么有些患者对这些药物表现出深度且持久的反应,而其他患者却没有受益。为了研究肿瘤遗传学对肿瘤免疫状态的影响,我们在癌症基因组图谱中调查了40个肿瘤队列中7种免疫细胞类型的浸润水平与体细胞突变和拷贝数改变之间的关系。使用一组新设计的转录特征来估计细胞毒性T细胞、调节性T细胞、总T细胞、自然杀伤细胞和B细胞以及单核细胞和M2巨噬细胞的水平,这些转录特征旨在抵抗肿瘤细胞异质性的干扰。我们的关联模型中纳入了肿瘤突变负荷和肿瘤纯度估计值,以调整多模态基因组数据中的偏差。评估了拷贝数改变、基因水平汇总的突变以及特定位置的突变与肿瘤免疫浸润的关联。我们观察到9号染色体短臂大片段的拷贝数缺失与黑色素瘤、胰腺癌和头颈癌中淋巴细胞估计值降低之间存在密切关系。癌基因PIK3CA、FGFR3和RAS/RAF家族成员以及肿瘤抑制基因TP53中的突变与免疫浸润的变化有关,通常仅限于特定的肿瘤类型。特定的WNT/β-连环蛋白通路基因变化与免疫状态的关联有限,但我们注意到9p缺失与WNT受体FZD3的表达之间存在联系,这表明9p改变与WNT通路之间存在相互作用。最后,在淋巴细胞浸润较高的头颈肿瘤中,两种不同的细胞死亡调节因子CASP8和DIDO1经常发生突变。总之,我们的研究支持肿瘤遗传学与检查点阻断疗法的疗效和耐药性问题相关。它还强调了在探索任何假设与治疗反应相关的特定肿瘤通路时评估全基因组影响的必要性。一些观察到的与免疫状态的遗传联系,如9p缺失,可能会影响对癌症免疫疗法的反应。其他的,如细胞死亡通路中的突变,可能有助于指导联合治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/5531551/911aa213b5a4/pone.0179726.g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/5531551/2d51b4b08d0e/pone.0179726.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2456/5531551/d0008297b021/pone.0179726.g002.jpg
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