PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

IF 23.5 1区 医学 Q1 ONCOLOGY Nature cancer Pub Date : 2024-04-18 DOI:10.1038/s43018-024-00756-7
Sanju Sinha, Rahulsimham Vegesna, Sumit Mukherjee, Ashwin V. Kammula, Saugato Rahman Dhruba, Wei Wu, D. Lucas Kerr, Nishanth Ulhas Nair, Matthew G. Jones, Nir Yosef, Oleg V. Stroganov, Ivan Grishagin, Kenneth D. Aldape, Collin M. Blakely, Peng Jiang, Craig J. Thomas, Cyril H. Benes, Trever G. Bivona, Alejandro A. Schäffer, Eytan Ruppin
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Abstract

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. Sinha and colleagues present PERCEPTION, a precision oncology computational pipeline that can predict the response and resistance of patients by analyzing single-cell transcriptomic data from their tumor samples.

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PERCEPTION 利用肿瘤单细胞转录组学预测患者对治疗的反应和耐药性
为癌症患者量身定制最佳治疗方案仍是一项重大挑战。为了解决这个问题,我们开发了 PERCEPTION(PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology,基于单细胞表达的肿瘤精准治疗规划)--一个肿瘤精准治疗计算管道。我们的方法使用从大规模细胞系药物筛选中公开获得的匹配批量和单细胞(sc)表达谱。这些图谱有助于根据患者的肿瘤转录组学建立治疗反应模型。PERCEPTION 成功预测了培养细胞和患者肿瘤原代细胞对靶向疗法的反应,并在多发性骨髓瘤和乳腺癌的两项临床试验中得到了验证。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情况。在所有临床队列中,PERCEPTION 都优于已发表的最先进的基于 sc 和基于 bulk 的预测指标。PERCEPTION可在https://github.com/ruppinlab/PERCEPTION。我们的工作展示了利用肿瘤的sc表达谱对患者进行分层,这将鼓励在临床环境中采用sc组学分析,增强基于sc组学的精准肿瘤学工具。
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来源期刊
Nature cancer
Nature cancer Medicine-Oncology
CiteScore
31.10
自引率
1.80%
发文量
129
期刊介绍: Cancer is a devastating disease responsible for millions of deaths worldwide. However, many of these deaths could be prevented with improved prevention and treatment strategies. To achieve this, it is crucial to focus on accurate diagnosis, effective treatment methods, and understanding the socioeconomic factors that influence cancer rates. Nature Cancer aims to serve as a unique platform for sharing the latest advancements in cancer research across various scientific fields, encompassing life sciences, physical sciences, applied sciences, and social sciences. The journal is particularly interested in fundamental research that enhances our understanding of tumor development and progression, as well as research that translates this knowledge into clinical applications through innovative diagnostic and therapeutic approaches. Additionally, Nature Cancer welcomes clinical studies that inform cancer diagnosis, treatment, and prevention, along with contributions exploring the societal impact of cancer on a global scale. In addition to publishing original research, Nature Cancer will feature Comments, Reviews, News & Views, Features, and Correspondence that hold significant value for the diverse field of cancer research.
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