@article{43e0a6583cf9470bb4aee465425068e1,
title = "Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients",
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).",
author = "Jianzhu Ma and Fong, {Samson H.} and Yunan Luo and Bakkenist, {Christopher J.} and Shen, {John Paul} and Soufiane Mourragui and Wessels, {Lodewyk F.A.} and Marc Hafner and Roded Sharan and Jian Peng and Trey Ideker",
note = "Funding Information: We thank the following for their support for the present study: the National Cancer Institute for grants (nos. U54CA209891 to T.I., R01CA204173 to C.B. and K22CA234406 to J.S.), the National Institute of General Medical Sciences for a grant (no. P41GM103504 to T.I.) and the National Human Genome Research Institute for a grant (no. R01HG009979 to T.I.). R.S. was supported by a research grant from the Israel Science Foundation (grant no. 715/18). J.P. was supported by a grant from the National Science Foundation (grant no. 1652815). L.W. and S.M. were supported by the ZonMw TOP grant COMPUTE CANCER (40-00812-98-16012). J.S. was supported by the Cancer Prevention and Research Institute of Texas (CPRIT RR180035). Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. part of Springer Nature.",
year = "2021",
month = feb,
doi = "10.1038/s43018-020-00169-2",
language = "English (US)",
volume = "2",
pages = "233--244",
journal = "Nature Cancer",
issn = "2662-1347",
publisher = "Nature Research",
number = "2",
}