Big data in basic and translational cancer research

Hallmarks of Cancer: The Next Generation

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PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer

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The Elements of Statistical Learning

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The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data

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Integrated analysis of multimodal single-cell data

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Comprehensive Integration of Single-Cell Data

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Tissue-based map of the human proteome

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Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

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Robust enumeration of cell subsets from tissue expression profiles

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Learning Deep Features for Discriminative Localization

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Signatures of mutational processes in human cancer

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Gene expression profiling predicts clinical outcome of breast cancer

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The cancer genome atlas pan-cancer analysis project

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Adjusting batch effects in microarray expression data using empirical Bayes methods

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Radiomics: Images Are More than Pictures, They Are Data

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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works

Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer

Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works

Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials

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Massively parallel digital transcriptional profiling of single cells

Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works

Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.

Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works

Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0136-1 · Cited by 5,179 · see citing works

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,737 · see citing works

Drug repurposing: progress, challenges and recommendations

Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.168 · Cited by 4,718 · see citing works

Visualizing and interpreting cancer genomics data via the Xena platform

Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., Banerjee, A., Luo, Y., Rogers, D., Brooks, A. N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0546-8 · Cited by 4,481 · see citing works

Determining cell type abundance and expression from bulk tissues with digital cytometry

Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,427 · see citing works

Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma

Snyder, A., Makarov, V., Merghoub, T., Yuan, J., Zaretsky, J. M., Desrichard, A., Walsh, L. A., Postow, M. A., Wong, P., Ho, T. S., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1406498 · Cited by 4,286 · see citing works

Tumour heterogeneity and resistance to cancer therapies

Dagogo-Jack, I., & Shaw, A. T. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.166 · Cited by 3,846 · see citing works

Recurrent Fusion of <i>TMPRSS2</i> and ETS Transcription Factor Genes in Prostate Cancer

Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X.-W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al. (2005) · Science · doi:10.1126/science.1117679 · Cited by 3,846 · see citing works

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works

Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works

Next-generation characterization of the Cancer Cell Line Encyclopedia

Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., Barretina, J., Gelfand, E. T., Bielski, C. M., Li, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1186-3 · Cited by 3,394 · see citing works

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Zehir, A., Benayed, R., Shah, R. H., Syed, A., Middha, S., Kim, H. R., Srinivasan, P., Gao, J., Chakravarty, D., Devlin, S. M., et al. (2017) · Nature Medicine · doi:10.1038/nm.4333 · Cited by 3,380 · cited 2× · see citing works

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works

Defining a Cancer Dependency Map

Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276 · see citing works

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works

The prognostic landscape of genes and infiltrating immune cells across human cancers

Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., et al. (2015) · Nature Medicine · doi:10.1038/nm.3909 · Cited by 3,097 · see citing works

Pan-cancer analysis of whole genomes

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen, L. A., Abascal, F., Abeshouse, A., Aburatani, H., Adams, D. J., Agrawal, N., Ahn, K. S., Ahn, S.-M., Aikata, H., et al. (2020) · Nature · doi:10.1038/s41586-020-1969-6 · Cited by 3,064 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Genomic correlates of response to CTLA-4 blockade in metastatic melanoma

Van Allen, E. M., Miao, D., Schilling, B., Shukla, S. A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Geukes Foppen, M. H., Goldinger, S. M., et al. (2015) · Science · doi:10.1126/science.aad0095 · Cited by 2,839 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation

Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.029 · Cited by 2,728 · see citing works

ImageNet: A large-scale hierarchical image database

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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424 · see citing works

Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer

Sparano, J. A., Gray, R. J., Makower, D. F., Pritchard, K. I., Albain, K. S., Hayes, D. F., Geyer, C. E., Dees, E. C., Goetz, M. P., Olson, J. A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1804710 · Cited by 2,397 · see citing works

Single-cell chromatin accessibility reveals principles of regulatory variation

Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works

Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works

An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage

Newman, A. M., Bratman, S. V., To, J., Wynne, J. F., Eclov, N. C. W., Modlin, L. A., Liu, C. L., Neal, J. W., Wakelee, H. A., Merritt, R. E., et al. (2014) · Nature Medicine · doi:10.1038/nm.3519 · Cited by 2,220 · see citing works

Tackling the widespread and critical impact of batch effects in high-throughput data

Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2825 · Cited by 2,196 · see citing works

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167 · see citing works

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Beckonert, O. P., Keun, H. C., Ebbels, T. M. D., Bundy, J. G., Holmes, E. C., Lindon, J. C., & Nicholson, J. K. (2007) · Nature Protocols · doi:10.1038/nprot.2007.376 · Cited by 2,112 · see citing works

Absolute quantification of somatic DNA alterations in human cancer

Carter, S. L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T. I., Laird, P. W., Onofrio, R. C., Winckler, W., Weir, B. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2203 · Cited by 2,003 · see citing works

70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer

Cardoso, F., Veer, L. J. V., Bogaerts, J., Slaets, L., Viale, G., Delaloge, S., Pierga, J., Brain, E. G. C., Causeret, S., Delorenzi, M., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1602253 · Cited by 1,998 · see citing works

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0482-1 · Cited by 1,995 · see citing works

Interactome Networks and Human Disease

Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771 · see citing works

Exploring tissue architecture using spatial transcriptomics

Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works

Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · see citing works

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

Current and future perspectives of liquid biopsies in genomics-driven oncology

Heitzer, E., Haque, I. S., Roberts, C. E. S., & Speicher, M. R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0071-5 · Cited by 1,557 · see citing works

Integrative single-cell analysis

Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works

Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., et al. (2007) · Nature Methods · doi:10.1038/nmeth1068 · Cited by 1,454 · see citing works

Bayesian approach to single-cell differential expression analysis

Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works

Single-cell genome sequencing: current state of the science

Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · see citing works

High-throughput functional genomics using CRISPR-Cas9

Shalem, O., Sanjana, N. E., & Zhang, F. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3899 · Cited by 1,233 · see citing works

The chromatin accessibility landscape of primary human cancers

Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., et al. (2018) · Science · doi:10.1126/science.aav1898 · Cited by 1,230 · see citing works

Saliency, Scale and Image Description

Kadir, T., & Brady, M. (2001) · International Journal of Computer Vision · doi:10.1023/a:1012460413855 · Cited by 1,195 · see citing works

A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging

Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S.-R., Kurian, A., Van Valen, D., West, R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.039 · Cited by 1,183 · see citing works

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works

Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity

Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · see citing works

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma

Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117 · see citing works

Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials

Cheng, Y., He, C., Wang, M., Ma, X., Mo, F., Yang, S., Han, J., & Wei, X. (2019) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-019-0095-0 · Cited by 1,097 · see citing works

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Sun, D., Wang, J., Han, Y., Dong, X., Ge, J., Zheng, R., Shi, X., Wang, B., Li, Z., Ren, P., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1020 · Cited by 1,076 · see citing works

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front

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PyClone: statistical inference of clonal population structure in cancer

Roth, A. J. L., Khattra, J. S., Yap, D., Wan, A. N. C., Laks, E., Biele, J., Ha, G., Aparicio, S. A. J. R., Bouchard‐Côté, A., & Shah, S. P. (2014) · Nature Methods · doi:10.1038/nmeth.2883 · Cited by 1,002 · see citing works

21-Gene Assay to Inform Chemotherapy Benefit in Node-Positive Breast Cancer

Kalinsky, K. M., Barlow, W. E., Gralow, J. R., Meric-Bernstam, F., Albain, K. S., Hayes, D. F., Lin, N., Perez, E. A., Goldstein, L. J., Chia, S. K. L., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2108873 · Cited by 919 · see citing works

Deep learning in histopathology: the path to the clinic

van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works

Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition

Litchfield, K., Reading, J. L., Puttick, C., Thakkar, K., Abbosh, C., Bentham, R., Watkins, T. B. K., Rosenthal, R., Biswas, D., Rowan, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.002 · Cited by 893 · see citing works

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