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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Robust enumeration of cell subsets from tissue expression profiles

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

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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

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

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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

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

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Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

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Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

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Drug repurposing: progress, challenges and recommendations

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

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Visualizing and interpreting cancer genomics data via the Xena platform

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

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Tumour heterogeneity and resistance to cancer therapies

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Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer

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

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Artificial intelligence in radiology

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

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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

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×

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

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

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

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Simultaneous epitope and transcriptome measurement in single cells

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Pan-cancer analysis of whole genomes

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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

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

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

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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

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

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

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Single-cell chromatin accessibility reveals principles of regulatory variation

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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

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

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

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

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

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

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Absolute quantification of somatic DNA alterations in human cancer

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

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Interactome Networks and Human Disease

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Exploring tissue architecture using spatial transcriptomics

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

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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

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Integrative single-cell analysis

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

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

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

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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

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

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

The chromatin accessibility landscape of primary human cancers

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Saliency, Scale and Image Description

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

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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

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

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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

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 · cited 1×

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 1,090

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

Schürch, C. M., Bhate, S. S., Barlow, G. L., Phillips, D. J., Noti, L., Zlobec, I., Chu, P., Black, S., Demeter, J., McIlwain, D. R., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.005 · Cited by 1,084

PyClone: statistical inference of clonal population structure in cancer

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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 950

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

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

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The single-cell pathology landscape of breast cancer

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Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput

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ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions

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Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

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