Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients
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Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).
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Meta-learning has been applied to learn interactions between cell-line phenotypes with drug responses, and further transferred them into clinical context, enabling prediction on a limited number of human tumor samples.
“Furthermore, meta-learning has been applied to learn the interactions between cell-line phenotypes with the drug responses, and further transferred them into clinical context, enabling prediction on a limited number of human tumor samples [184].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [184]
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- Authors
- Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., & Ideker, T.
- Affiliations
- Purdue University West Lafayette (US)
- University of California San Diego (US)
- University of Illinois Urbana-Champaign (US)
- University of Pittsburgh (US)
- The University of Texas MD Anderson Cancer Center (US)
- The Netherlands Cancer Institute (NL)
- Genentech (US)
- Tel Aviv University (IL)
- Corresponding author
- Ideker, Trey
- ORCID
- Venue
- Nature Cancer
- Published
- 2021-01-25
- Volume
- 2
- Issue
- 2
- Pages
- 233-244
- ISSN
- 2662-1347 (ISSN-L)
- PMC
- PMC8248912
- OpenAlex
- W3124481204
- Cited by
- 180
- References
- 56
- Citations by year
- 2026: 27 · 2025: 33 · 2024: 37 · 2023: 37 · 2022: 24 · 2021: 22
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Cancer (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01CA204173, K22CA234406, U54CA209891)
- Cancer Prevention and Research Institute of Texas (CPRIT RR180035)
- ZonMw (40-00812-98-16012)
- Israel Science Foundation (1652815)
- National Science Foundation (1652815)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (P41GM103504)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG009979)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- few-shot learning, drug response prediction, patient-derived tumor cells, patient-derived xenografts, RB1, SMAD4, CDK inhibition, RNF8, CHD4, ATM inhibition, high-throughput screening
- MeSH terms
- Machine Learning, DNA-Binding Proteins, Humans, Neural Networks, Computer, Ubiquitin-Protein Ligases
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