Department of Pulmonary Diseases, University of Groningen, University Medical Centre Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands.
Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.
Sci Rep. 2023 Apr 21;13(1):6581. doi: 10.1038/s41598-023-32499-3.
In advanced non-small cell lung cancer (NSCLC), response to immunotherapy is difficult to predict from pre-treatment information. Given the toxicity of immunotherapy and its financial burden on the healthcare system, we set out to identify patients for whom treatment is effective. To this end, we used mutational signatures from DNA mutations in pre-treatment tissue. Single base substitutions, doublet base substitutions, indels, and copy number alteration signatures were analysed in [Formula: see text] patients (the discovery set). We found that tobacco smoking signature (SBS4) and thiopurine chemotherapy exposure-associated signature (SBS87) were linked to durable benefit. Combining both signatures in a machine learning model separated patients with a progression-free survival hazard ratio of 0.40[Formula: see text] on the cross-validated discovery set and 0.24[Formula: see text] on an independent external validation set ([Formula: see text]). This paper demonstrates that the fingerprints of mutagenesis, codified through mutational signatures, select advanced NSCLC patients who may benefit from immunotherapy, thus potentially reducing unnecessary patient burden.
在晚期非小细胞肺癌（NSCLC）中，从治疗前的信息预测免疫治疗的反应是困难的。鉴于免疫治疗的毒性及其对医疗保健系统的经济负担，我们着手确定治疗有效的患者。为此，我们使用了治疗前组织中 DNA 突变的突变特征。在[公式：见文本]例患者（发现集）中分析了单碱基替换、双碱基替换、插入缺失和拷贝数改变特征。我们发现，烟草吸烟特征（SBS4）和硫嘌呤化疗暴露相关特征（SBS87）与持久获益有关。在交叉验证的发现集中，将这两个特征结合在机器学习模型中，将无进展生存风险比为 0.40[公式：见文本]的患者分开，在独立的外部验证集中为 0.24[公式：见文本]。本文证明，通过突变特征编码的突变指纹选择了可能从免疫治疗中获益的晚期 NSCLC 患者，从而可能减轻不必要的患者负担。
