ArticleNature cancer2024
PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.
Article in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Artificial intelligence in genitourinary oncology: publication trends and systematic review.BJU international · 2026Pooled it
- Medical AI and Human Dignity: A Catholic Normative Ethical Framework in Light of Magnifica Humanitas.Journal of religion and health · 2026Article
- Cuproptosis and ferroptosis: signal pathways, diseases and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- Targeting a Myeloid-Regulatory B Cell Network Reverses Immune Paralysis in Periprosthetic Joint Infections.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ANGPTL4 contributes to osimertinib resistance through NDRG1-associated extracellular matrix remodeling in EGFR-mutant NSCLC organoid models.Cancer cell international · 2026Article
- A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Multidrug resistance in cancer: current understandings and future perspective.Molecular biomedicine · 2026Review
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- Histone deacetylase enzyme activity is not the universal anticancer target of HDAC inhibitors.Signal transduction and targeted therapy · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Artificial intelligence for predicting transient hypocalcemia after total thyroidectomy.Scientific reports · 2026Article
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Surgical Oncologists for Sustainability: A Statement from the SSO Surgical Oncologists for Sustainability Committee.Annals of surgical oncology · 2026Review
- CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.Cancer research · 2026Article
- TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery.Genome medicine · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations.Nature communications · 2026Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
20 authors at 8 institutions in 1 country.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.