Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of ‘big data’ in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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References
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Weinstein, J. N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–110 (2013).
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).
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).
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).
Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 1661–1662 (2020).
Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).
Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).
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).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
Minussi, D. C. et al. Breast tumours maintain a reservoir of subclonal diversity during expansion. Nature 592, 302–308 (2021).
Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).
Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).
Przybyla, L. & Gilbert, L. A. A new era in functional genomics screens. Nat. Rev. Genet. 23, 89–103 (2022).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
Johannessen, C. M. et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013).
Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).
Hafner, M. et al. CLIP and complementary methods. Nat. Rev. Methods Prim. 1, 20 (2021).
Vidal, M., Cusick, M. E. & Barabási, A.-L. Interactome networks and human disease. Cell 144, 986–998 (2011).
Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21, 207–226 (2020).
Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).
van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021).
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Hjwl, A. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018).
Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).
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.
Parkinson, H. et al. ArrayExpress — a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 35, D747–D750 (2007).
Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).
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.
Jiang, L. et al. Direct tumor killing and immunotherapy through anti-serpinB9 therapy. Cell 183, 1219–1233.e18 (2020).
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.
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).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
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).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Nygaard, V., Rødland, E. A. & Hovig, E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17, 29–39 (2016).
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22, 114–126 (2022).
Huang, C. et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 39, 361–379.e16 (2021).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). This study integrates multiple single-cell data modalities, such as gene expression, cell-surface protein levels and chromatin accessibilities, to increase the accuracy of cell lineage clustering.
Klein, M. I. et al. Identifying modules of cooperating cancer drivers. Mol. Syst. Biol. 17, e9810 (2021).
Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).
Reyna, M. A. et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 11, 729 (2020).
Zheng, F. et al. Interpretation of cancer mutations using a multiscale map of protein systems. Science 374, eabf3067 (2021).
Paull, E. O. et al. A modular master regulator landscape controls cancer transcriptional identity. Cell 184, 334–351 (2021).
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P. & De Preter, K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat. Commun. 11, 5650 (2020).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Wang, K. et al. Deconvolving clinically relevant cellular immune cross-talk from bulk gene expression using CODEFACS and LIRICS stratifies patients with melanoma to anti-PD-1 therapy. Cancer Discov. 12, 1088–1105 (2022). Together with Newman et al. (2019), this study demonstrates that assembling gene expression profiles of diverse cell types from existing datasets can enable deconvolution of cell fractions and lineage-specific expression in a bulk-tumour expression profile.
Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).
Suvà, M. L. & Tirosh, I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol. Cell 75, 7–12 (2019).
Zhang, Y. et al. A T cell resilience model associated with response to immunotherapy in multiple tumor types. Nat. Med. https://doi.org/10.1038/s41591-022-01799-y (2022). This study uses a computational model to repurpose a vast amount of single-cell transcriptomics data and identify biomarkers of tumour-resilient T cells and new therapeutic targets, such as FIBP, to potentiate cellular immunotherapies.
Gopalan, V. et al. A transcriptionally distinct subpopulation of healthy acinar cells exhibit features of pancreatic progenitors and PDAC. Cancer Res. 81, 3958–3970 (2021).
Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).
Heitzer, E., Haque, I. S., Roberts, C. E. S. & Speicher, M. R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 20, 71–88 (2019).
Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001).
Ma, J. et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat. Cancer 2, 233–244 (2021).
Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. Adv. Neural Inf. Process. Syst. 33, 3347–3357 (2019).
Zoph, B. et al. Rethinking pre-training and self-training. Adv. Neural Inf. Process. Syst. 34, 3833–3845 (2020).
Meier, F. A., Varney, R. C. & Zarbo, R. J. Study of amended reports to evaluate and improve surgical pathology processes. Adv. Anat. Pathol. 18, 406–413 (2011).
Nakhleh, R. E. Error reduction in surgical pathology. Arch. Pathol. Lab. Med. 130, 630–632 (2006).
Nakhleh, R. E. et al. Interpretive diagnostic error reduction in surgical pathology and cytology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center and the Association of Directors of Anatomic and Surgical Pathology. Arch. Pathol. Lab. Med. 140, 29–40 (2016).
Raab, S. S. et al. The ‘Big Dog’ effect: variability assessing the causes of error in diagnoses of patients with lung cancer. J. Clin. Oncol. 24, 2808–2814 (2006).
Jiang, P., Sellers, W. R. & Liu, X. S. Big data approaches for modeling response and resistance to cancer drugs. Annu. Rev. Biomed. Data Sci. 1, 1–27 (2018).
van’t Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).
Sparano, J. A. et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N. Engl. J. Med. 379, 111–121 (2018).
Kalinsky, K. et al. 21-gene assay to inform chemotherapy benefit in node-positive breast cancer. N. Engl. J. Med. 385, 2336–2347 (2021).
Cardoso, F. et al. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375, 717–729 (2016).
Filipits, M. et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. 17, 6012–6020 (2011).
Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365, 1687–1717 (2005).
You, Y. N., Rustin, R. B. & Sullivan, J. D. Oncotype DX® colon cancer assay for prediction of recurrence risk in patients with stage II and III colon cancer: a review of the evidence. Surg. Oncol. 24, 61–66 (2015).
Klein, E. A. et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur. Urol. 66, 550–560 (2014).
Kratz, J. R. et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet 379, 823–832 (2012).
Beaubier, N. et al. Integrated genomic profiling expands clinical options for patients with cancer. Nat. Biotechnol. 37, 1351–1360 (2019).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Li, M. Statistical methods for clinical validation of follow-on companion diagnostic devices via an external concordance study. Stat. Biopharm. Res. 8, 355–363 (2016).
Litchfield, K. et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell 184, 596–614.e14 (2021).
Bielski, C. M. et al. Widespread selection for oncogenic mutant allele imbalance in cancer. Cancer Cell 34, 852–862.e4 (2018).
El Tekle, G. et al. Co-occurrence and mutual exclusivity: what cross-cancer mutation patterns can tell us. Trends Cancer Res. 7, 823–836 (2021).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Cheng, Y. et al. Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials. Signal Transduct. Target. Ther. 4, 62 (2019).
Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019). This study describes the WINTHER trial, which prospectively matched patients with advanced cancer to therapy on the basis of DNA sequencing or RNA expression data from tumour biopsies and concluded that both data types were of value for improving therapy recommendations.
Pleasance, E. et al. Whole genome and transcriptome analysis enhances precision cancer treatment options. Ann. Oncol. https://doi.org/10.1016/j.annonc.2022.05.522 (2022).
Massard, C. et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 7, 586–595 (2017).
Tuxen, I. V. et al. Copenhagen Prospective Personalized Oncology (CoPPO) — clinical utility of using molecular profiling to select patients to phase I trials. Clin. Cancer Res. 25, 1239–1247 (2019).
Horak, P. et al. Comprehensive genomic and transcriptomic analysis for guiding therapeutic decisions in patients with rare cancers. Cancer Discov. 11, 2780–2795 (2021).
Von Hoff, D. D. et al. Pilot study using molecular profiling of patients’ tumors to find potential targets and select treatments for their refractory cancers. J. Clin. Oncol. 28, 4877–4883 (2010).
Kato, S. et al. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-one strategy. Nat. Commun. 11, 4965 (2020).
Hoefflin, R. et al. Personalized clinical decision making through implementation of a molecular tumor board: a German single-center experience. JCO Precis. Oncol. 1–16 https://doi.org/10.1200/po.18.00105 (2018).
Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021).
Cohen, Y. C. et al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing. Nat. Med. 27, 491–503 (2021).
Lee, J. S. et al. Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 184, 2487–2502.e13 (2021). This study demonstrates that integrating information regarding synthetic lethal interactions with tumour transcriptomics profiles can accurately score drug-target importance and predict clinical outcomes for a broad category of anticancer treatments.
Zhang, B. et al. The tumor therapy landscape of synthetic lethality. Nat. Commun. 12, 1275 (2021).
Pathria, G. et al. Translational reprogramming marks adaptation to asparagine restriction in cancer. Nat. Cell Biol. 21, 1590–1603 (2019).
Feng, X. et al. A platform of synthetic lethal gene interaction networks reveals that the GNAQ uveal melanoma oncogene controls the Hippo pathway through FAK. Cancer Cell 35, (2019).
Lee, J. S. et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat. Commun. 9, 2546 (2018).
Cheng, K., Nair, N. U., Lee, J. S. & Ruppin, E. Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity. Sci. Adv. 7, eabc2100 (2021).
Sahu, A. D. et al. Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy. Mol. Syst. Biol. 15, e8323 (2019).
Elemento, O., Leslie, C., Lundin, J. & Tourassi, G. Artificial intelligence in cancer research, diagnosis and therapy. Nat. Rev. Cancer 21, 747–752 (2021).
Raciti, P. et al. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Mod. Pathol. 33, 2058–2066 (2020).
Office of the Commissioner. FDA authorizes software that can help identify prostate cancer. https://www.fda.gov/news-events/press-announcements/fda-authorizes-software-can-help-identify-prostate-cancer (2021).
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
Litjens, G. et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. GigaScience 7, giy065 (2018).
Ehteshami Bejnordi, B. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).
Wulczyn, E. et al. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS ONE 15, e0233678 (2020).
Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).
Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).
Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954–961 (2019).
Hosny, A. et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 15, e1002711 (2018).
Zviran, A. et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat. Med. 26, 1114–1124 (2020).
Mathios, D. et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat. Commun. 12, 5060 (2021).
Beshnova, D. et al. De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection. Sci. Transl. Med. 12, eaaz3738 (2020).
Katzman, J. L. et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 24 (2018).
Ching, T., Zhu, X. & Garmire, L. X. Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput. Biol. 14, e1006076 (2018).
Kann, B. H., Hosny, A. & Hjwl, A. Artificial intelligence for clinical oncology. Cancer Cell 39, 916–927 (2021).
Kadir, T. & Brady, M. Saliency, scale and image description. Int. J. Comput. Vis. 45, 83–105 (2001).
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) https://doi.org/10.1109/cvpr.2016.319https://www.computer.org/csdl/proceedings/cvpr/2016/12OmNqH9hnp (2016).
Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit. Med. 4, 71 (2020). This study clusters similar image patches related to colorectal cancer survival prediction to reveal that high-risk survival predictions are associated with a tumour–adipose feature, characterized by poorly differentiated tumour cells adjacent to adipose tissue.
Buel, G. R. & Walters, K. J. Can AlphaFold2 predict the impact of missense mutations on structure? Nat. Struct. Mol. Biol. 29, 1–2 (2022).
US Food and Drug Administration. Evaluation of automatic class III designation for Paige Prostate. https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN200080.pdf (2021).
Calcoen, D., Elias, L. & Yu, X. What does it take to produce a breakthrough drug? Nat. Rev. Drug Discov. 14, 161–162 (2015).
Jayatunga, M. K. P., Xie, W., Ruder, L., Schulze, U. & Meier, C. AI in small-molecule drug discovery: a coming wave? Nat. Rev. Drug Discov. 21, 175–176 (2022).
Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2019).
Jahchan, N. S. et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov. 3, 1364–1377 (2013).
Kuenzi, B. M. et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38, 672–684.e6 (2020).
Ling, A. & Huang, R. S. Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action. Nat. Commun. 11, 5848 (2020).
Aissa, A. F. et al. Single-cell transcriptional changes associated with drug tolerance and response to combination therapies in cancer. Nat. Commun. 12, 1628 (2021).
Menden, M. P. et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat. Commun. 10, 2674 (2019).
Carvalho, D. M. et al. Repurposing vandetanib plus everolimus for the treatment of ACVR1-mutant diffuse intrinsic pontine glioma. Cancer Discov. https://doi.org/10.1158/2159-8290.CD-20-1201 (2021).
Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019). This study describes a deep generative AI model, which enabled the design of new inhibitors of the receptor tyrosine kinase DDR1 by modelling molecule structures from a compound library, existing DDR1 inhibitors, non-kinase inhibitors and patented drugs.
Ruthotto, L. & Haber, E. An introduction to deep generative modeling. GAMM-Mitteilungen 44, e202100008 (2021).
Wallach, I., Dzamba, M. & Heifets, A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Preprint at https://arxiv.org/abs/1510.02855 (2015).
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a method for quantitative structure–activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015).
Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).
Bansal, M. et al. A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol. 32, 1213–1222 (2014).
Ahmadi, S. et al. The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective. Nat. Commun. 13, 1613 (2022).
Eduati, F. et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nat. Biotechnol. 33, 933–940 (2015).
Gayvert, K. M., Madhukar, N. S. & Elemento, O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biol. 23, 1294–1301 (2016).
McDermott, M. B. A. et al. Reproducibility in machine learning for health research: still a ways to go. Sci. Transl. Med. 13, eabb1655 (2021).
AP News. Caris Precision Oncology Alliance partners with the National Cancer Institute, part of the National Institutes of Health, to expand collaborative clinical research efforts. Associated Press https://apnews.com/press-release/pr-newswire/technology-science-business-health-cancer-221e9238956a7a4835be75cb65832573 (2021).
Alvi, M. A., Wilson, R. H. & Salto-Tellez, M. Rare cancers: the greatest inequality in cancer research and oncology treatment. Br. J. Cancer 117, 1255–1257 (2017).
Park, K. H. et al. Genomic landscape and clinical utility in Korean advanced pan-cancer patients from prospective clinical sequencing: K-MASTER program. Cancer Discov. 12, 938–948 (2022).
Bailey, M. H. et al. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. Nat. Commun. 11, 4748 (2020).
Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281.e7 (2018).
Zare, F., Dow, M., Monteleone, N., Hosny, A. & Nabavi, S. An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinforma. 18, 286 (2017).
Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).
Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
Furey, T. S. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nat. Rev. Genet. 13, 840–852 (2012).
Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).
Papanicolau-Sengos, A. & Aldape, K. DNA methylation profiling: an emerging paradigm for cancer diagnosis. Annu. Rev. Pathol. 17, 295–321 (2022).
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
Cieślik, M. & Chinnaiyan, A. M. Cancer transcriptome profiling at the juncture of clinical translation. Nat. Rev. Genet. 19, 93–109 (2018).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).
Ellis, M. J. et al. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov. 3, 1108–1112 (2013).
Li, J. et al. TCPA: a resource for cancer functional proteomics data. Nat. Methods 10, 1046–1047 (2013).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 183, 838 (2020).
Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).
Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Fedorov, A. et al. NCI Imaging Data Commons. Cancer Res 81, 4188–4193 (2021).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).
Jiang, P., Freedman, M. L., Liu, J. S. & Liu, X. S. Inference of transcriptional regulation in cancers. Proc. Natl Acad. Sci. USA 112, 7731–7736 (2015).
Sun, D. et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 49, D1420–D1430 (2021).
Kristiansen, G. Markers of clinical utility in the differential diagnosis and prognosis of prostate cancer. Mod. Pathol. 31, S143–S155 (2018).
Acknowledgements
The authors are supported by the intramural research budget of the US National Cancer Institute.
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P.J. and E.R. designed the scope and structure of the Review, assembled write-up components and finalized the manuscript. C.S. wrote the text on tumour evolution and heterogeneity. S.H. wrote the text on transcriptional dysregulation. P.J. wrote the sections related to spatial genomics and artificial intelligence. P.J., E.R. and K.A. wrote the section on cancer diagnosis and treatment decisions. S.S. and P.J. prepared Tables 1–4.
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Nature Reviews Cancer thanks Itai Yanai, Anjali Rao and the other, anonymous, reviewers for their contribution to the peer review of this work.
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Related links
Array Express: https://www.ebi.ac.uk/arrayexpress/
CAMELYON: https://camelyon17.grand-challenge.org/
cBioportal: https://www.cbioportal.org/
CCLE: https://depmap.org/portal/ccle/
CPTAC: https://proteomics.cancer.gov/data-portal
CytoSig: https://cytosig.ccr.cancer.gov/
DepMap: https://depmap.org/portal
DNA sequencing costs: https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
DrugCombDB: http://drugcombdb.denglab.org/
FDC: https://curate.ccr.cancer.gov/
GENIE: https://www.aacr.org/professionals/research/aacr-project-genie
GEO: https://www.ncbi.nlm.nih.gov/geo
Human Protein Atlas: https://www.proteinatlas.org/humanproteome/pathology
ICGC: https://dcc.icgc.org/
IDC: https://datacommons.cancer.gov/repository/imaging-data-commons
LINCS: https://clue.io/
PCAWG: https://dcc.icgc.org/pcawg
PRECOG: https://precog.stanford.edu/
RABIT: http://rabit.dfci.harvard.edu/
TARGET: https://ocg.cancer.gov/programs/target/data-matrix
TCIA: https://www.cancerimagingarchive.net/
TCGA: https://gdc.cancer.gov/
TIDE: http://tide.dfci.harvard.edu/
TISCH: http://tisch.comp-genomics.org/
Tres: https://resilience.ccr.cancer.gov/
UCSC Xena: https://xena.ucsc.edu/
Glossary
- Few-shot learning
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A machine learning method that classifies new data using only a few training samples by transferring knowledge from large, related datasets.
- Saliency map
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A map of important image locations that support machine learning outputs.
- Class activation map
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A coarse-resolution map of important image regions for predicting a specific class using activations and gradients in the final convolutional layer.
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Jiang, P., Sinha, S., Aldape, K. et al. Big data in basic and translational cancer research. Nat Rev Cancer 22, 625–639 (2022). https://doi.org/10.1038/s41568-022-00502-0
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DOI: https://doi.org/10.1038/s41568-022-00502-0
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