Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
J Transl Med. 2024 Jun 18;22(1):576. doi: 10.1186/s12967-024-05400-7.
Identifying new biomarkers for predicting immune checkpoint inhibitors (ICIs) response in non-small cell lung cancer (NSCLC) is crucial. We aimed to assess the variant allele frequency (VAF)-related profile as a novel biomarker for NSCLC personalized therapy.
We utilized genomic data of 915 NSCLC patients via cBioPortal and a local cohort of 23 patients for model construction and mutational analysis. Genomic, transcriptomic data from 952 TCGA NSCLC patients, and immunofluorescence (IF) assessment with the local cohort supported mechanism analysis.
Utilizing the random forest algorithm, a 15-gene VAF-related model was established, differentiating patients with durable clinical benefit (DCB) from no durable benefit (NDB). The model demonstrated robust performance, with ROC-AUC values of 0.905, 0.737, and 0.711 across training (n = 313), internal validation (n = 133), and external validation (n = 157) cohorts. Stratification by the model into high- and low-score groups correlated significantly with both progression-free survival (PFS) (training: P < 0.0001, internal validation: P < 0.0001, external validation: P = 0.0066) and overall survival (OS) (n = 341) (P < 0.0001). Notably, the stratification system was independent of PD-L1 (P < 0.0001) and TMB (P < 0.0001). High-score patients exhibited an increased DCB ratio and longer PFS across both PD-L1 and TMB subgroups. Additionally, the high-score group appeared influenced by tobacco exposure, with activated DNA damage response pathways. Whereas, immune/inflammation-related pathways were enriched in the low-score group. Tumor immune microenvironment analyses revealed higher proportions of exhausted/effector memory CD8 + T cells in the high-score group.
The mutational VAF profile is a promising biomarker for ICI therapy in NSCLC, with enhanced therapeutic stratification and management as a supplement to PD-L1 or TMB.
鉴定新的生物标志物以预测非小细胞肺癌（NSCLC）的免疫检查点抑制剂（ICI）反应至关重要。我们旨在评估变异等位基因频率（VAF）相关谱作为 NSCLC 个体化治疗的新生物标志物。
我们通过 cBioPortal 利用 915 例 NSCLC 患者的基因组数据和 23 例患者的本地队列进行模型构建和突变分析。利用 952 例 TCGA NSCLC 患者的基因组和转录组数据以及与本地队列的免疫荧光（IF）评估支持机制分析。
利用随机森林算法，建立了一个 15 个基因 VAF 相关模型，可区分具有持久临床获益（DCB）和无持久获益（NDB）的患者。该模型具有稳健的性能，在训练集（n=313）、内部验证集（n=133）和外部验证集（n=157）中，ROC-AUC 值分别为 0.905、0.737 和 0.711。根据模型将高、低评分组进行分层，与无进展生存期（PFS）（训练：P<0.0001，内部验证：P<0.0001，外部验证：P=0.0066）和总生存期（OS）（n=341）显著相关（P<0.0001）。值得注意的是，分层系统与 PD-L1（P<0.0001）和 TMB（P<0.0001）无关。高评分患者在 PD-L1 和 TMB 亚组中均表现出更高的 DCB 比例和更长的 PFS。此外，高评分组似乎受到烟草暴露的影响，存在激活的 DNA 损伤反应途径。而低评分组则富集了与免疫/炎症相关的途径。肿瘤免疫微环境分析显示，高评分组中耗尽/效应记忆 CD8+T 细胞的比例更高。
突变 VAF 谱是 NSCLC ICI 治疗的有前途的生物标志物，可增强治疗分层和管理，作为 PD-L1 或 TMB 的补充。
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