Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

CAS  PubMed  Article  Google Scholar 

Weinstein, J. N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–110 (2013).

PubMed  PubMed Central  Article  CAS  Google Scholar 

Edgar, R., Domrachev, M. & Lash, A. E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Deng, J. et al. ImageNet: a large-scale hierarchical image database. 2009 IEEE Conf. Computer Vis. Pattern Recognit. https://doi.org/10.1109/cvprw.2009.5206848 (2009).

Article  Google Scholar 

Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

CAS  PubMed  Article  Google Scholar 

Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 1661–1662 (2020).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).

PubMed  PubMed Central  Article  Google Scholar 

Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Miller, C. A. et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 10, e1003665 (2014).

PubMed  PubMed Central  Article  CAS  Google Scholar 

Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Minussi, D. C. et al. Breast tumours maintain a reservoir of subclonal diversity during expansion. Nature 592, 302–308 (2021).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).

CAS  PubMed  Article  Google Scholar 

Przybyla, L. & Gilbert, L. A. A new era in functional genomics screens. Nat. Rev. Genet. 23, 89–103 (2022).

CAS  PubMed  Article  Google Scholar 

Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Johannessen, C. M. et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

CAS  PubMed  Article  Google Scholar 

Hafner, M. et al. CLIP and complementary methods. Nat. Rev. Methods Prim. 1, 20 (2021).

CAS  Article  Google Scholar 

Vidal, M., Cusick, M. E. & Barabási, A.-L. Interactome networks and human disease. Cell 144, 986–998 (2011).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21, 207–226 (2020).

CAS  PubMed  Article  Google Scholar 

Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).

CAS  PubMed  PubMed Central  Article  Google Scholar 

van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021).

PubMed  Article  CAS  Google Scholar 

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Hjwl, A. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

PubMed  Article  Google Scholar 

Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018). This integrative study of tumour immune evasion across many clinical datasets reveals that SERPINB9 expression consistently correlates with intratumoural T cell dysfunction and resistance to immune checkpoint blockade.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Parkinson, H. et al. ArrayExpress — a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 35, D747–D750 (2007).

CAS  PubMed  Article  Google Scholar 

Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005). This compendium analysis across 132 gene expression datasets representing 10,486 microarray experiments identifies ERG and ETV1 fused with TMPRSS2 as highly expressed genes in six independent prostate cancer cohorts.

CAS  PubMed  Article  Google Scholar 

Jiang, L. et al. Direct tumor killing and immunotherapy through anti-serpinB9 therapy. Cell 183, 1219–1233.e18 (2020).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Jiang, P. et al. Systematic investigation of cytokine signaling activity at the tissue and single-cell levels. Nat. Methods 18, 1181–1191 (2021). This study describes a transcriptomic data atlas collected from cytokine treatments in bulk cell cultures, which enables the inference of signalling activities in bulk and single-cell transcriptomics data to study human inflammatory diseases.

CAS  PubMed  PubMed Central  Article  Google Scholar 

Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

CAS  PubMed  Article  Google Scholar 

Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

PubMed  Article  Google Scholar 

Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

CAS  PubMed  PubMed Central  Article  Google Scholar 

Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

CAS  PubMed