AI Article Synopsis
- - We created PERCEPTION, a computational pipeline designed for personalized cancer treatment by analyzing expression profiles from cancer cells and matching them with therapeutic responses.
- - PERCEPTION effectively predicts treatment responses in various cancers, including multiple myeloma and breast cancer, while also tracking resistance to therapies like tyrosine kinase inhibitors in lung cancer patients.
- - It outperforms existing prediction models and promotes the use of single-cell expression data in clinical practice to improve precision oncology outcomes.
Article 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.
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| http://dx.doi.org/10.1038/s43018-024-00756-7 | DOI Listing |
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