Cited by 186Computational Drug Discovery MethodsDomain Adaptation and Few-Shot LearningMachine Learning in Healthcare

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Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

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Ma, Jianzhu, Fong, Samson H., Luo, Yunan, Bakkenist, Christopher J., Shen, John Paul, Mourragui, Soufiane, Wessels, Lodewyk F. A., Hafner, Marc, Sharan, Roded, Peng, Jian, Ideker, Trey·2021·Nature Cancer

DOI 10.1038/s43018-020-00169-2PMC PMC8248912

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Abstract

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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Additional file 3 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
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Additional file 4 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
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Additional file 7 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
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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: Ma, Jianzhu
Fong, Samson H.
Luo, Yunan
Bakkenist, Christopher J.
Shen, John Paul
Mourragui, Soufiane
Wessels, Lodewyk F. A.
Hafner, Marc
Sharan, Roded
Peng, Jian
Ideker, Trey
Venue: Nature Cancer
Publisher: Springer Science and Business Media LLC
Published: 2021-01-25
Volume: 2
Issue: 2
Pages: 233-244
ISSN: 2662-1347 (ISSN-L)
DOI: 10.1038/s43018-020-00169-2
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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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Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

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