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Leveraging big data of immune checkpoint blockade response identifies novel potential targets

免疫检查点 封锁 医学 转录组 基因签名 肿瘤科 生物标志物 基因表达谱 免疫系统 免疫疗法 计算生物学 癌症研究 内科学 基因 生物信息学 免疫学 基因表达 生物 遗传学 受体
作者
Yacine Barèche,Deirdre Kelly,Farnoosh Abbas‐Aghababazadeh,Masahiro Nakano,Parinaz Nasr Esfahani,Douglas Tkachuk,Helai P. Mohammad,Robert M. Samstein,Chung‐Han Lee,Luc G.T. Morris,Philippe L. Bédard,Benjamin Haibe‐Kains,John Stagg
出处
期刊:Annals of Oncology [Elsevier BV]
卷期号:33 (12): 1304-1317 被引量:49
标识
DOI:10.1016/j.annonc.2022.08.084
摘要

Background

The development of immune checkpoint blockade (ICB) has changed the way we treat various cancers. While ICB produces durable survival benefits in a number of malignancies, a large proportion of treated patients do not derive clinical benefit. Recent clinical profiling studies have shed light on molecular features and mechanisms that modulate response to ICB. Nevertheless, none of these identified molecular features were investigated in large enough cohorts to be of clinical value.

Materials and methods

Literature review was carried out to identify relevant studies including clinical dataset of patients treated with ICB [anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1), anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) or the combination] and available sequencing data. Tumor mutational burden (TMB) and 37 previously reported gene expression (GE) signatures were computed with respect to the original publication. Biomarker association with ICB response (IR) and survival (progression-free survival/overall survival) was investigated separately within each study and combined together for meta-analysis.

Results

We carried out a comparative meta-analysis of genomic and transcriptomic biomarkers of IRs in over 3600 patients across 12 tumor types and implemented an open-source web application (predictIO.ca) for exploration. TMB and 21/37 gene signatures were predictive of IRs across tumor types. We next developed a de novo GE signature (PredictIO) from our pan-cancer analysis and demonstrated its superior predictive value over other biomarkers. To identify novel targets, we computed the T-cell dysfunction score for each gene within PredictIO and their ability to predict dual PD-1/CTLA-4 blockade in mice. Two genes, F2RL1 (encoding protease-activated receptor-2) and RBFOX2 (encoding RNA-binding motif protein 9), were concurrently associated with worse ICB clinical outcomes, T-cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical models.

Conclusion

Our study highlights the potential of large-scale meta-analyses in identifying novel biomarkers and potential therapeutic targets for cancer immunotherapy.
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