ArticleNature communications2022
Gene expression based inference of cancer drug sensitivity.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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Who cites it
91 citing papers in PubMed, 1 synthesis or guideline pooled it, 140 citations in OpenAlex.
- Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.Frontiers in oncology · 2025Pooled it
- DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- BAYESIAN EMPIRICAL LIKELIHOOD FOR ULTRA-HIGH DIMENSIONAL VARIABLE SELECTION WITH APPLICATION TO DRUG SENSITIVITY.The annals of applied statistics · 2026Article
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- AnGenes · 2026Article
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- FourierDrug: a domain generalization framework for robust drug response prediction via frequency-space asymmetric attention.Bioinformatics (Oxford, England) · 2026Article
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors.Briefings in bioinformatics · 2026Article
- Thermoplastic elastomer based microfluidic gradient generator for cell culture and drug testing.Biomedical microdevices · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- Novel explainable deep learning based drug sensitivity prediction for early treatment of breast cancer.Scientific reports · 2026Article
- Sanguinarine and Doxorubicin Inhibit Heat Shock Protein HSPA5 and Trigger ER Stress in Breast Cancer.Cell biochemistry and biophysics · 2026Article
- Identifying myosin heavy chain 11 as a predictive biomarker of prostate cancer progression and antiandrogen resistance.Oncology letters · 2026Article
- Benchmarking deep learning models for predicting anticancer drug potency (ICCommunications chemistry · 2026Article
- Machine Learning-Based Analysis of Large-Scale Transcriptomic Data Identifies Core Genes Associated with Multi-Drug Resistance.International journal of molecular sciences · 2026Article
- A RAIR-ATC transcriptional axis and multimodal drug-response modelling reveal class-level vulnerabilities in thyroid cancer.Frontiers in pharmacology · 2026Article
- A 12-gene immune signature predicts prognosis and identifies KRT6B as a therapeutic target in lung adenocarcinoma.Frontiers in immunology · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.
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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.