Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
Nat Cancer. 2024 Jun;5(6):938-952. doi: 10.1038/s43018-024-00756-7. Epub 2024 Apr 18.
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
为个体癌症患者量身定制最佳治疗方案仍然是一项重大挑战。为了解决这个问题，我们开发了 PERCEPTION（基于个性化单细胞表达的肿瘤治疗个体化规划系统），这是一种精准肿瘤计算管道。我们的方法使用了来自大规模细胞系药物筛选的公开可用的匹配批量和单细胞（sc）表达谱。这些谱有助于根据患者的 sc 肿瘤转录组学构建治疗反应模型。PERCEPTION 成功地预测了培养的和源自患者肿瘤的原代细胞对靶向治疗的反应，以及在多发性骨髓瘤和乳腺癌的两项临床试验中也取得了成功。它还捕捉到了接受酪氨酸激酶抑制剂治疗的肺癌患者的耐药性发展。PERCEPTION 在所有临床队列中都优于已发表的基于 sc 和基于批量的最先进预测器。PERCEPTION 可在 https://github.com/ruppinlab/PERCEPTION 上获得。我们的工作展示了使用肿瘤的 sc 表达谱进行患者分层，将鼓励在临床环境中采用 sc-omics 分析，增强基于 sc-omics 的精准肿瘤学工具。
