合成活力可诱导癌细胞对免疫检查点抑制剂产生耐药性。
Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells.
机构信息
Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
出版信息
Br J Cancer. 2023 Oct;129(8):1339-1349. doi: 10.1038/s41416-023-02404-w. Epub 2023 Aug 24.
BACKGROUND
Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma.
METHODS
We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients.
RESULTS
This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8 T cells infiltration.
CONCLUSIONS
In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.
背景
免疫检查点抑制剂（ICI）已经彻底改变了多种癌症的治疗方法。然而，大多数患者会产生耐药性。基因之间的合成生存力（SV）可能会诱导耐药性。在这项研究中，我们建立了 SV 特征来预测 ICI 治疗黑色素瘤的疗效。
方法
我们通过随机森林分类器收集特征并预测 SV 基因对。这项工作基于 CRISPR/Cas9 筛选对 SV 基因对进行优先级排序。构建 SV 基因对特征来预测黑色素瘤患者对 ICI 的反应。
结果
本研究基于 14 个特征预测了稳健的 SV 基因对。通过 CRISPR/Cas9 筛选过滤后，我们鉴定了 1861 个与多种癌症类型的预后相关的 SV 基因对。接下来，我们构建了六个 SV 对特征来预测黑色素瘤患者对 ICI 的耐药性。本研究应用六个 SV 对特征将黑色素瘤患者分为高危和低危组。高危黑色素瘤患者在 ICI 治疗后反应较差。免疫景观分析显示，高危黑色素瘤患者的自然杀伤细胞和 CD8+T 细胞浸润水平较低。
结论
总之，14 个特征分类器准确地预测了癌症中稳健的 SV 基因对。六个 SV 对特征可以预测对 ICI 的耐药性。
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