ArticleProceedings of the National Academy of Sciences of the United States of America2021
Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Systems biology framework for the rational design of operational conditions for in vitro/in vivo translation of tissue models.Science advances · 2026Article
- Decoding cellular vulnerabilities for interpretable drug response prediction.Briefings in bioinformatics · 2026Article
- TMEDRP: decoding tumor-intrinsic and microenvironmental signatures for clinical drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- Patient-derived xenograft models: Current status, challenges, and innovations in cancer research.Genes & diseases · 2025Review
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- A disentangled generative model for improved drug response prediction in patients via sample synthesis.Journal of pharmaceutical analysis · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Two-Step Transfer Learning Improves Deep Learning-Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma.Bioinformatics and biology insights · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Best holdout assessment is sufficient for cancer transcriptomic model selection.Patterns (New York, N.Y.) · 2024Article
- Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.Statistics in biosciences · 2024Article
- Machine learning of brain-specific biomarkers from EEG.EBioMedicine · 2024Article
- Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3DBiophysics reviews · 2024Review
- Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- 3D Cell Models in Radiobiology: Improving the Predictive Value of In Vitro Research.International journal of molecular sciences · 2023Review
- Patient-derived xenograft models in cancer therapy: technologies and applications.Signal transduction and targeted therapy · 2023Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.
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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.