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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalNature Cancer
Early online date18 Apr 2024
DOIs
StateE-pub ahead of print - 18 Apr 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.

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