Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.
Journal
Nature cancer
ISSN: 2662-1347
Titre abrégé: Nat Cancer
Pays: England
ID NLM: 101761119
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
entrez:
5
7
2021
pubmed:
6
7
2021
medline:
6
7
2021
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 34223192
doi: 10.1038/s43018-020-00169-2
pmc: PMC8248912
mid: NIHMS1716449
pii: 10.1038/s43018-020-00169-2
doi:
Substances chimiques
DNA-Binding Proteins
0
RNF8 protein, human
0
Ubiquitin-Protein Ligases
EC 2.3.2.27
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
233-244Subventions
Organisme : NIGMS NIH HHS
ID : P41 GM103504
Pays : United States
Organisme : NCI NIH HHS
ID : K22 CA234406
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG009979
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA209891
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA236367
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA204173
Pays : United States
Références
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
doi: 10.1038/ng.3984
pubmed: 29083409
pmcid: 5709193
Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).
doi: 10.1016/j.cell.2016.06.017
pubmed: 27397505
pmcid: 4967469
Brabetz, S. et al. A biobank of patient-derived pediatric brain tumor models. Nat. Med. 24, 1752–1761 (2018).
doi: 10.1038/s41591-018-0207-3
pubmed: 30349086
Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).
doi: 10.1016/j.cell.2016.08.041
pubmed: 27641504
pmcid: 5037319
Butler, D. Translational research: crossing the valley of death. Nature 453, 840–842 (2008).
doi: 10.1038/453840a
pubmed: 18548043
Lieu, C. H., Tan, A.-C., Leong, S., Diamond, J. R. & Eckhardt, S. G. From bench to bedside: lessons learned in translating preclinical studies in cancer drug development. J. Natl Cancer Inst. 105, 1441–1456 (2013).
doi: 10.1093/jnci/djt209
pubmed: 24052618
pmcid: 3787906
Seyhan, A. A. Lost in translation: the valley of death across preclinical and clinical divide—identification of problems and overcoming obstacles. Trans. Med. Commun. https://doi.org/10.1186/s41231-019-0050-7 (2019).
Naumov, G. N. et al. Combined vascular endothelial growth factor receptor and epidermal growth factor receptor (EGFR) blockade inhibits tumor growth in xenograft models of EGFR inhibitor resistance. Clin. Cancer Res. 15, 3484–3494 (2009).
doi: 10.1158/1078-0432.CCR-08-2904
pubmed: 19447865
pmcid: 2893040
Lee, J. S. et al. Vandetanib versus placebo in patients with advanced non-small-cell lung cancer after prior therapy with an epidermal growth factor receptor tyrosine kinase inhibitor: a randomized, double-blind phase III trial (ZEPHYR). J. Clin. Oncol. 30, 1114–1121 (2012).
doi: 10.1200/JCO.2011.36.1709
pubmed: 22370318
Parisot, J. P., Hu, X. F., DeLuise, M. & Zalcberg, J. R. Altered expression of the IGF-1 receptor in a tamoxifen-resistant human breast cancer cell line. Br. J. Cancer 79, 693–700 (1999).
doi: 10.1038/sj.bjc.6690112
pubmed: 10070856
pmcid: 2362670
Drury, S. C. et al. Changes in breast cancer biomarkers in the IGF1R/PI3K pathway in recurrent breast cancer after tamoxifen treatment. Endocr. Relat. Cancer 18, 565–577 (2011).
doi: 10.1530/ERC-10-0046
pubmed: 21734071
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. Human-level concept learning through probabilistic program induction. Science 350, 1332–1338 (2015).
doi: 10.1126/science.aab3050
pubmed: 26659050
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D. & Lillicrap, T. Meta-learning with memory-augmented neural networks. in Proc. 33rd International Conference on Machine Learning Vol. 48 (eds Balcan, M. F. & Weinberger, K. Q.) 1842–1850 (PMLR, 2016).
Dai, W., Yang, Q., Xue, G.-R. & Yu, Y. Boosting for transfer learning. in Proc. 24th International Conference on Machine Learning 193–200 (Association for Computing Machinery, 2007).
Blitzer, J., McDonald, R. & Pereira, F. Domain adaptation with structural correspondence learning. in Proc. 2006 Conference on Empirical Methods in Natural Language Processing 120–128 (EMNLP, 2006).
Argyriou, A., Evgeniou, T. & Pontil, M. Multi-task feature learning. in Advances in Neural Information Processing Systems Vol. 19 (eds Schölkopf, B. et al.) 41–48 (MIT Press, 2007).
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. The Omniglot challenge: a 3-year progress report. Curr. Opin. Behav. Sci. 29, 97–104 (2019).
doi: 10.1016/j.cobeha.2019.04.007
Altae-Tran, H., Ramsundar, B., Pappu, A. S. & Pande, V. Low data drug discovery with one-shot learning. ACS Cent. Sci. 3, 283–293 (2017).
doi: 10.1021/acscentsci.6b00367
pubmed: 28470045
pmcid: 5408335
Medela, A. et al. Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. in Proc. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI, 2019); https://doi.org/10.1109/isbi.2019.8759182
Snell, J. et al. Prototypical Networks for Few-shot Learning. in Advances in Neural Information Processing Systems 4077–4087 (Curran Associates, 2017); https://proceedings.neurips.cc/paper/2017/hash/cb8da6767461f2812ae4290eac7cbc42-Abstract.html
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K. & Wierstra, D. Matching networks for one shot learning. in Advances in Neural Information Processing Systems Vol. 29 (eds Lee, D. D. et al.) 3630–3638 (Curran Associates, 2016).
Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the 34th International Conference on Machine Learning 70, 1126–1135 (2017).
Preuer, K. et al. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 34, 1538–1546 (2018).
doi: 10.1093/bioinformatics/btx806
pubmed: 29253077
Yu, D.-D., Guo, S.-W., Jing, Y.-Y., Dong, Y.-L. & Wei, L.-X. A review on hepatocyte nuclear factor-1beta and tumor. Cell Biosci. 5, 58 (2015).
doi: 10.1186/s13578-015-0049-3
pubmed: 26464794
pmcid: 4603907
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).
doi: 10.1038/nm.3954
pubmed: 26479923
Lipton, Z. C. The mythos of model interpretability. ACM Queue https://doi.org/10.1145/3236386.3241340 (2018).
Ma, J. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods https://doi.org/10.1038/nmeth.4627 (2018).
Liu, F. & Matsuura, I. Inhibition of Smad antiproliferative function by CDK phosphorylation. Cell Cycle 4, 63–66 (2005).
doi: 10.4161/cc.4.1.1366
pubmed: 15611645
Zhao, M., Mishra, L. & Deng, C.-X. The role of TGF-β/SMAD4 signaling in cancer. Int. J. Biol. Sci. 14, 111–123 (2018).
doi: 10.7150/ijbs.23230
pubmed: 29483830
pmcid: 5821033
Zhang, F., Bick, G., Park, J.-Y. & Andreassen, P. R. MDC1 and RNF8 function in a pathway that directs BRCA1-dependent localization of PALB2 required for homologous recombination. J. Cell Sci. 125, 6049–6057 (2012).
doi: 10.1242/jcs.111872
pubmed: 23038782
pmcid: 3585519
Lu, C.-S. et al. The RING finger protein RNF8 ubiquitinates Nbs1 to promote DNA double-strand break repair by homologous recombination. J. Biol. Chem. 287, 43984–43994 (2012).
doi: 10.1074/jbc.M112.421545
pubmed: 23115235
pmcid: 3527981
Kobayashi, S. et al. Rad18 and Rnf8 facilitate homologous recombination by two distinct mechanisms, promoting Rad51 focus formation and suppressing the toxic effect of nonhomologous end joining. Oncogene 34, 4403–4411 (2015).
doi: 10.1038/onc.2014.371
pubmed: 25417706
Smith, R., Sellou, H., Chapuis, C., Huet, S. & Timinszky, G. CHD3 and CHD4 recruitment and chromatin remodeling activity at DNA breaks is promoted by early poly(ADP-ribose)-dependent chromatin relaxation. Nucleic Acids Res. 46, 6087–6098 (2018).
doi: 10.1093/nar/gky334
pubmed: 29733391
pmcid: 6158744
Larsen, D. H. et al. The chromatin-remodeling factor CHD4 coordinates signaling and repair after DNA damage. J. Cell Biol. 190, 731–740 (2010).
doi: 10.1083/jcb.200912135
pubmed: 20805324
pmcid: 2935572
Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).
doi: 10.1038/nature10868
pubmed: 22281684
Young, L. C. et al. SHOC2–MRAS–PP1 complex positively regulates RAF activity and contributes to Noonan syndrome pathogenesis. Proc. Natl Acad. Sci. USA 115, E10576–E10585 (2018).
doi: 10.1073/pnas.1720352115
pubmed: 30348783
pmcid: 6233131
Tzivion, G., Luo, Z. & Avruch, J. A dimeric 14-3-3 protein is an essential cofactor for Raf kinase activity. Nature 394, 88–92 (1998).
doi: 10.1038/27938
pubmed: 9665134
Schwartz, L. H. et al. RECIST 1.1—update and clarification: from the RECIST committee. Eur. J. Cancer 62, 132–137 (2016).
doi: 10.1016/j.ejca.2016.03.081
pubmed: 27189322
pmcid: 5737828
Yu, K. et al. Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types. Nat. Commun. https://doi.org/10.1038/s41467-019-11415-2 (2019).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
doi: 10.1038/s41586-019-1186-3
pubmed: 31068700
pmcid: 6697103
Li, T. et al. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).
doi: 10.1038/nmeth.4083
pubmed: 27892958
Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).
doi: 10.1093/nar/gkq1039
pubmed: 21071392
Giurgiu, M. et al. CORUM: the comprehensive resource of mammalian protein complexes—2019. Nucleic Acids Res. 47, D559–D563 (2019).
doi: 10.1093/nar/gky973
pubmed: 30357367
Meyers, R. M. et al. Computational correction of copy-number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Kim, Y., Bismeijer, T., Zwart, W., Wessels, L. F. A. & Vis, D. J. Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo. Nat. Commun. 10, 5034 (2019).
doi: 10.1038/s41467-019-13027-2
pubmed: 31695042
pmcid: 6834616
Harakalova, M. et al. Multiplexed array-based and in-solution genomic enrichment for flexible and cost-effective targeted next-generation sequencing. Nat. Protoc. 6, 1870–1886 (2011).
doi: 10.1038/nprot.2011.396
pubmed: 22051800
Glorot, X., Bordes, A. & Bengio, Y. Deep sparse rectifier neural networks. in Proc. Fourteenth International Conference on Artificial Intelligence and Statistics 15, 315–323 (2011).
Kingma, D. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv https://arxiv.org/abs/1412.6980 (2014).
Baumann, D. & Baumann, K. Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation. J. Cheminform. 6, 47 (2014).
doi: 10.1186/s13321-014-0047-1
pubmed: 25506400
pmcid: 4260165
Ribeiro, M. T., Singh, S. & Guestrin, C. Why should I trust you?: explaining the predictions of any classifier. in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (2016).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol. 67, 301–320 (2005).
doi: 10.1111/j.1467-9868.2005.00503.x
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288 (1996).
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R. & Samek, W. Layer-wise relevance propagation for neural networks with local renormalization layers. in Artificial Neural Networks and Machine Learning—ICANN 2016 (eds Villa, A. et al.) 63–71 (Springer, 2016).
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. International Conference on Learning Representations https://openreview.net/forum?id=cO4ycnpqxKcS9 (2014).