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Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment
Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019).
Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021).
Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021).
Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).
Lo Vercio, L. et al. Supervised machine learning tools: a tutorial for clinicians. J. Neural Eng. 17, 062001 (2020).
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).
Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374, 20150202 (2016).
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P. & Green, R. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad. Pathol. 6, 2374289519873088 (2019).
Quinn, T. P., Nguyen, T., Lee, S. C. & Venkatesh, S. Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data. Front. Genet. 10, 599 (2019).
Yakimovich, A., Beaugnon, A., Huang, Y. & Ozkirimli, E. Labels in a haystack: approaches beyond supervised learning in biomedical applications. Patterns 2, 100383 (2021).
Eckardt, J. N., Bornhauser, M., Wendt, K. & Middeke, J. M. Semi-supervised learning in cancer diagnostics. Front. Oncol. 12, 960984 (2022).
Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020).
Marini, N., Otalora, S., Muller, H. & Atzori, M. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on prostate histopathology image classification. Med. Image Anal. 73, 102165 (2021).
Doan, S., Conway, M., Phuong, T. M. & Ohno-Machado, L. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol. Biol. 1168, 275–294 (2014).
Finne, P. et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 56, 418–422 (2000).
Remzi, M. et al. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology 62, 456–460 (2003).
Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022).
Chen, A. B. et al. Artificial intelligence applications in urology: reporting standards to achieve fluency for urologists. Urol. Clin. North. Am. 49, 65–117 (2022).
Thykjaer, T. et al. Identification of gene expression patterns in superficial and invasive human bladder cancer. Cancer Res. 61, 2492–2499 (2001).
Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822–826 (2001).
Luo, J. et al. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res. 61, 4683–4688 (2001).
Singh, D. et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002).
Dyrskjot, L. et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat. Genet. 33, 90–96 (2003).
Blaveri, E. et al. Bladder cancer outcome and subtype classification by gene expression. Clin. Cancer Res. 11, 4044–4055 (2005).
Rhodes, D. R. et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1–6 (2004).
The Cancer Genome Atlas Research Network Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).
The Cancer Genome Atlas Research Network The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).
Shen, H. et al. Integrated molecular characterization of testicular germ cell tumors. Cell Rep. 23, 3392–3406 (2018).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2016).
Veldman-Jones, M. H. et al. Evaluating robustness and sensitivity of the NanoString Technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res. 75, 2587–2593 (2015).
Zheng, H. et al. Comprehensive review of web servers and bioinformatics tools for cancer prognosis analysis. Front. Oncol. 10, 68 (2020).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Chandrashekar, D. S. et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia 25, 18–27 (2022).
Chen, M. M. et al. TCPA v3.0: an integrative platform to explore the pan-cancer analysis of functional proteomic data. Mol. Cell Proteom. 18, S15–S25 (2019).
Navin, N. E. The first five years of single-cell cancer genomics and beyond. Genome Res. 25, 1499–1507 (2015).
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
Su, F. et al. Multimodal single-cell analyses outline the immune microenvironment and therapeutic effectors of interstitial cystitis/bladder pain syndrome. Adv. Sci. 9, e2106063 (2022).
Peng, L. et al. Integrating single-cell RNA sequencing with spatial transcriptomics reveals immune landscape for interstitial cystitis. Signal Transduct. Target. Ther. 7, 161 (2022).
Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542.e5 (2018).
Karthaus, W. R. et al. Regenerative potential of prostate luminal cells revealed by single-cell analysis. Science 368, 497–505 (2020).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Zurauskiene, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinforma. 17, 140 (2016).
Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).
Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).
Clarke, Z. A. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 16, 2749–2764 (2021).
Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).
Zhang, Y. et al. Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response. Proc. Natl Acad. Sci. USA 118, e2103240118 (2021).
Dong, B. et al. Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer. Commun. Biol. 3, 778 (2020).
Song, H. et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nat. Commun. 13, 141 (2022).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).
Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e2 (2019).
Chen, S. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021).
Chen, Z. et al. Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nat. Commun. 11, 5077 (2020).
Wu, T., Wu, X., Wang, H. Y. & Chen, L. Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer. Cancer Commun. 39, 21 (2019).
Tuong, Z. K. et al. Resolving the immune landscape of human prostate at a single-cell level in health and cancer. Cell Rep. 37, 110132 (2021).
Chen, W. J. et al. Heterogeneity of tumor microenvironment is associated with clinical prognosis of non-clear cell renal cell carcinoma: a single-cell genomics study. Cell Death Dis. 13, 50 (2022).
Bi, K. et al. Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma. Cancer Cell 39, 649–661.e5 (2021).
Wang, L. et al. Myeloid cell-associated resistance to PD-1/PD-L1 blockade in urothelial cancer revealed through bulk and single-cell RNA sequencing. Clin. Cancer Res. 27, 4287–4300 (2021).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68 (2022).
Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).
Rao, A., Barkley, D., Franca, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Dries, R. et al. Advances in spatial transcriptomic data analysis. Genome Res. 31, 1706–1718 (2021).
Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 36, 2293–2294 (2020).
Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).
Edsgard, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2021).
Bergenstrahle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).
Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).
Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).
Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).
Wang, Y., Ma, S. & Ruzzo, W. L. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci. Rep. 10, 3490 (2020).
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).
Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Lu, K. et al. Identification of novel biomarkers in Hunner’s interstitial cystitis using the CIBERSORT, an algorithm based on machine learning. BMC Urol. 21, 109 (2021).
Gamper, M. et al. Gene expression profile of bladder tissue of patients with ulcerative interstitial cystitis. BMC Genom. 10, 199 (2009).
Colaco, M. et al. Correlation of gene expression with bladder capacity in interstitial cystitis/bladder pain syndrome. J. Urol. 192, 1123–1129 (2014).
Lindskrog, S. V. et al. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nat. Commun. 12, 2301 (2021).
Yu, L. et al. Prognostic significance of lineage diversity in bladder cancer revealed by single-cell sequencing. Front. Genet. 13, 862634 (2022).
Lopez, A. & Liao, J. C. Emerging endoscopic imaging technologies for bladder cancer detection. Curr. Urol. Rep. 15, 406 (2014).
Shkolyar, E. et al. Augmented bladder tumor detection using deep learning. Eur. Urol. 76, 714–718 (2019).
Ali, N. et al. Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors. Sci. Rep. 11, 11629 (2021).
Wu, S. et al. An artificial intelligence system for the detection of bladder cancer via cystoscopy: a multicenter diagnostic study. J. Natl Cancer Inst. 114, 220–227 (2022).
Negassi, M., Suarez-Ibarrola, R., Hein, S., Miernik, A. & Reiterer, A. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J. Urol. 38, 2349–2358 (2020).
Chan, E. O., Pradere, B. & Teoh, J. Y., European Association of Urology – Young Academic Urologists Urothelial Carcinoma Working Group The use of artificial intelligence for the diagnosis of bladder cancer: a review and perspectives. Curr. Opin. Urol. 31, 397–403 (2021).
Lenis, A. T. & Litwin, M. S. Does artificial intelligence meaningfully enhance cystoscopy. J. Natl Cancer Inst. 114, 174–175 (2022).
Sanghvi, A. B., Allen, E. Z., Callenberg, K. M. & Pantanowitz, L. Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 127, 658–666 (2019).
Nojima, S. et al. A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Cancer Cytopathol. 129, 984–995 (2021).
Lebret, T. et al. Artificial intelligence to improve cytology performances in bladder carcinoma detection: results of the VisioCyt test. BJU Int. 129, 356–363 (2022).
Sanchez-Carbayo, M., Socci, N. D., Lozano, J., Saint, F. & Cordon-Cardo, C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J. Clin. Oncol. 24, 778–789 (2006).
Kim, W. J. et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol. Cancer 9, 3 (2010).
Lindgren, D. et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 70, 3463–3472 (2010).
Sjodahl, G. et al. A molecular taxonomy for urothelial carcinoma. Clin. Cancer Res. 18, 3377–3386 (2012).
Damrauer, J. S. et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc. Natl Acad. Sci. USA 111, 3110–3115 (2014).
Choi, W. et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25, 152–165 (2014).
Kardos, J. et al. Development and validation of a NanoString BASE47 bladder cancer gene classifier. PLoS ONE 15, e0243935 (2020).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e25 (2017).
Sjodahl, G., Eriksson, P., Liedberg, F. & Hoglund, M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J. Pathol. 242, 113–125 (2017).
Batista da Costa, J. et al. Molecular characterization of neuroendocrine-like bladder cancer. Clin. Cancer Res. 25, 3908–3920 (2019).
de Jong, J. J. et al. Long non-coding RNAs identify a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis. Genome Med. 11, 60 (2019).
Grivas, P. et al. Validation of a neuroendocrine-like classifier confirms poor outcomes in patients with bladder cancer treated with cisplatin-based neoadjuvant chemotherapy. Urol. Oncol. 38, 262–268 (2020).
Hedegaard, J. et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer Cell 30, 27–42 (2016).
Hurst, C. D. et al. Genomic subtypes of non-invasive bladder cancer with distinct metabolic profile and female gender bias in KDM6A mutation frequency. Cancer Cell 32, 701–715.e7 (2017).
Kates, M. et al. Adaptive immune resistance to intravesical BCG in non-muscle invasive bladder cancer: implications for prospective BCG-unresponsive trials. Clin. Cancer Res. 26, 882–891 (2020).
Strandgaard, T. et al. Elevated T-cell exhaustion and urinary tumor DNA levels are associated with bacillus Calmette-Guérin failure in patients with non-muscle-invasive bladder cancer. Eur. Urol. 82, 646–656 (2022).
Kamoun, A. et al. A consensus molecular classification of muscle-invasive bladder cancer. Eur. Urol. 77, 420–433 (2020).
de Jong, J. J. et al. Gene expression profiling of muscle-invasive bladder cancer with secondary variant histology. Am. J. Clin. Pathol. 156, 895–905 (2021).
Lotan, Y. et al. Patients with muscle-invasive bladder cancer with nonluminal subtype derive greatest benefit from platinum based neoadjuvant chemotherapy. J. Urol. 207, 541–550 (2022).
Morera, D. S. et al. Clinical parameters outperform molecular subtypes for predicting outcome in bladder cancer: results from multiple cohorts, including TCGA. J. Urol. 203, 62–72 (2020).
Woerl, A. C. et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur. Urol. 78, 256–264 (2020).
Roubal, K., Myint, Z. W. & Kolesar, J. M. Erdafitinib: a novel therapy for FGFR-mutated urothelial cancer. Am. J. Health Syst. Pharm. 77, 346–351 (2020).
Loeffler, C. M. L. et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing? Eur. Urol. Focus. 8, 472–479 (2021).
McConkey, D. J. et al. Therapeutic opportunities in the intrinsic subtypes of muscle-invasive bladder cancer. Hematol. Oncol. Clin. North. Am. 29, 377–394 (2015).
Motterle, G., Andrews, J. R., Morlacco, A. & Karnes, R. J. Predicting response to neoadjuvant chemotherapy in bladder cancer. Eur. Urol. Focus. 6, 642–649 (2020).
Takata, R. et al. Predicting response to methotrexate, vinblastine, doxorubicin, and cisplatin neoadjuvant chemotherapy for bladder cancers through genome-wide gene expression profiling. Clin. Cancer Res. 11, 2625–2636 (2005).
Kato, Y. et al. Predicting response of bladder cancers to gemcitabine and carboplatin neoadjuvant chemotherapy through genome-wide gene expression profiling. Exp. Ther. Med. 2, 47–56 (2011).
Als, A. B. et al. Emmprin and survivin predict response and survival following cisplatin-containing chemotherapy in patients with advanced bladder cancer. Clin. Cancer Res. 13, 4407–4414 (2007).
Kato, Y. et al. A prospective study to examine the accuracies and efficacies of prediction systems for response to neoadjuvant chemotherapy for muscle invasive bladder cancer. Oncol. Lett. 16, 5775–5784 (2018).
McConkey, D. J. et al. A prognostic gene expression signature in the molecular classification of chemotherapy-naive urothelial cancer is predictive of clinical outcomes from neoadjuvant chemotherapy: a phase 2 trial of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with bevacizumab in urothelial cancer. Eur. Urol. 69, 855–862 (2016).
Seiler, R. et al. Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur. Urol. 72, 544–554 (2017).
Moschini, M. et al. Characteristics and clinical significance of histological variants of bladder cancer. Nat. Rev. Urol. 14, 651–668 (2017).
Warrick, J. I. et al. Intratumoral heterogeneity of bladder cancer by molecular subtypes and histologic variants. Eur. Urol. 75, 18–22 (2019).
Sjodahl, G. et al. Molecular subtypes as a basis for stratified use of neoadjuvant chemotherapy for muscle-invasive bladder cancer – a narrative review. Cancers 14, 1692 (2022).
Bellmunt, J., Powles, T. & Vogelzang, N. J. A review on the evolution of PD-1/PD-L1 immunotherapy for bladder cancer: the future is now. Cancer Treat. Rev. 54, 58–67 (2017).
Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).
Kim, J. et al. The Cancer Genome Atlas expression subtypes stratify response to checkpoint inhibition in advanced urothelial cancer and identify a subset of patients with high survival probability. Eur. Urol. 75, 961–964 (2019).
Korpal, M. et al. Evasion of immunosurveillance by genomic alterations of PPARγ/RXRα in bladder cancer. Nat. Commun. 8, 103 (2017).
Necchi, A. et al. Impact of molecular subtyping and immune infiltration on pathological response and outcome following neoadjuvant pembrolizumab in muscle-invasive bladder cancer. Eur. Urol. 77, 701–710 (2020).
Havaleshko, D. M. et al. Prediction of drug combination chemosensitivity in human bladder cancer. Mol. Cancer Ther. 6, 578–586 (2007).
Lee, J. K. et al. A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proc. Natl Acad. Sci. USA 104, 13086–13091 (2007).
Smith, S. C., Baras, A. S., Lee, J. K. & Theodorescu, D. The COXEN principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer. Cancer Res. 70, 1753–1758 (2010).
Flaig, T. W. et al. A randomized phase II study of coexpression extrapolation (COXEN) with neoadjuvant chemotherapy for bladder cancer (SWOG S1314; NCT02177695). Clin. Cancer Res. 27, 2435–2441 (2021).
Kong, J. et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun. 11, 5485 (2020).
Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Menche, J. et al. Disease networks. Uncovering disease–disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide datasets. Nucleic Acids Res. 47, D607–D613 (2019).
Cha, K. H. et al. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Sci. Rep. 7, 8738 (2017).
Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).
Pettersson, A. et al. The TMPRSS2:ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and meta-analysis. Cancer Epidemiol. Biomark. Prev. 21, 1497–1509 (2012).
Adamo, P. & Ladomery, M. R. The oncogene ERG: a key factor in prostate cancer. Oncogene 35, 403–414 (2016).
Rosen, P. et al. Clinical potential of the ERG oncoprotein in prostate cancer. Nat. Rev. Urol. 9, 131–137 (2012).
Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).
You, S. et al. Integrated classification of prostate cancer reveals a novel luminal subtype with poor outcome. Cancer Res. 76, 4948–4958 (2016).
Zhao, S. G. et al. Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol. 3, 1663–1672 (2017).
Signoretti, S. et al. p63 is a prostate basal cell marker and is required for prostate development. Am. J. Pathol. 157, 1769–1775 (2000).
Stoyanova, T. et al. Prostate cancer originating in basal cells progresses to adenocarcinoma propagated by luminal-like cells. Proc. Natl Acad. Sci. USA 110, 20111–20116 (2013).
Yoon, J. et al. A comparative study of PCS and PAM50 prostate cancer classification schemes. Prostate Cancer Prostatic Dis. 24, 733–742 (2021).
Mosley, J. D. & Keri, R. A. Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med. Genom. 1, 11 (2008).
Cuzick, J. et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. 12, 245–255 (2011).
Whitfield, M. L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).
Cuzick, J. et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br. J. Cancer 106, 1095–1099 (2012).
Erho, N. et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8, e66855 (2013).
Karnes, R. J. et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J. Urol. 190, 2047–2053 (2013).
Klein, E. A. et al. Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology 90, 148–152 (2016).
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).
Van Den Eeden, S. K. et al. A biopsy-based 17-gene genomic prostate score as a predictor of metastases and prostate cancer death in surgically treated men with clinically localized disease. Eur. Urol. 73, 129–138 (2018).
Brooks, M. A. et al. GPS assay association with long-term cancer outcomes: twenty-year risk of distant metastasis and prostate cancer-specific mortality. JCO Precis. Oncol. 5, 325 (2021).
Hu, J. C. et al. Clinical utility of gene expression classifiers in men with newly diagnosed prostate cancer. JCO Precis. Oncol. 2, 163 (2018).
Fine, N. D., LaPolla, F., Epstein, M., Loeb, S. & Dani, H. Genomic classifiers for treatment selection in newly diagnosed prostate cancer. BJU Int. 124, 578–586 (2019).
Karnes, R. J. et al. Development and validation of a prostate cancer genomic signature that predicts early ADT treatment response following radical prostatectomy. Clin. Cancer Res. 24, 3908–3916 (2018).
Feng, F. Y. et al. Association of molecular subtypes with differential outcome to apalutamide treatment in nonmetastatic castration-resistant prostate cancer. JAMA Oncol. 7, 1005–1014 (2021).
Smith, M. R. et al. Apalutamide treatment and metastasis-free survival in prostate cancer. N. Engl. J. Med. 378, 1408–1418 (2018).
Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).
Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).
Snow, P. B., Smith, D. S. & Catalona, W. J. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J. Urol. 152, 1923–1926 (1994).
Mosquera-Lopez, C., Agaian, S., Velez-Hoyos, A. & Thompson, I. Computer-aided prostate cancer diagnosis from digitized histopathology: a review on texture-based systems. IEEE Rev. Biomed. Eng. 8, 98–113 (2015).
Turkbey, B. & Haider, M. A. Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br. J. Radiol. 95, 20210563 (2022).
Suarez-Ibarrola, R. et al. Artificial intelligence in magnetic resonance imaging-based prostate cancer diagnosis: where do we stand in 2021? Eur. Urol. Focus. 8, 409–417 (2022).
Ferro, M. et al. Radiomics in prostate cancer: an up-to-date review. Ther. Adv. Urol. 14, 17562872221109020 (2022).
Baydoun, A. et al. Artificial intelligence applications in prostate cancer. Prostate Cancer Prostatic Dis. https://doi.org/10.1038/s41391-023-00684-0 (2023).
Doyle, S., Feldman, M., Tomaszewski, J. & Madabhushi, A. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Eng. 59, 1205–1218 (2012).
Berney, D. M. et al. The reasons behind variation in Gleason grading of prostatic biopsies: areas of agreement and misconception among 266 European pathologists. Histopathology 64, 405–411 (2014).
Nir, G. et al. Automatic grading of prostate cancer in digitized histopathology images: learning from multiple experts. Med. Image Anal. 50, 167–180 (2018).
Strom, P. et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21, 222–232 (2020).
Pantanowitz, L. et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit. Health 2, e407–e416 (2020).
Nagpal, K. et al. Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol. 6, 1372–1380 (2020).
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
Huang, W. et al. Development and validation of an artificial intelligence-powered platform for prostate cancer grading and quantification. JAMA Netw. Open. 4, e2132554 (2021).
da Silva, L. M. et al. Independent real-world application of a clinical-grade automated prostate cancer detection system. J. Pathol. 254, 147–158 (2021).
Pound, C. R. et al. Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281, 1591–1597 (1999).
Wong, N. C., Lam, C., Patterson, L. & Shayegan, B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int. 123, 51–57 (2019).
Eksi, M. et al. Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical prostatectomy. Prostate 81, 913–920 (2021).
Tan, Y. G. et al. Incorporating artificial intelligence in urology: supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy. Prostate 82, 298–305 (2022).
Cheng, L. et al. Risk of prostate carcinoma death in patients with lymph node metastasis. Cancer 91, 66–73 (2001).
Gandaglia, G. et al. A novel nomogram to identify candidates for extended pelvic lymph node dissection among patients with clinically localized prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies. Eur. Urol. 75, 506–514 (2019).
Luzzago, S. et al. A novel nomogram to identify candidates for active surveillance amongst patients with International Society of Urological Pathology (ISUP) grade group (GG) 1 or ISUP GG2 prostate cancer, according to multiparametric magnetic resonance imaging findings. BJU Int. 126, 104–113 (2020).
Wessels, F. et al. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int. 128, 352–360 (2021).
Davis, C. F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).
Ricketts, C. J. et al. The Cancer Genome Atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep. 23, 313–326.e5 (2018).
Linehan, W. M. & Ricketts, C. J. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nat. Rev. Urol. 16, 539–552 (2019).
Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).
Rini, B. et al. A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies. Lancet Oncol. 16, 676–685 (2015).
Motzer, R. J. et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell 38, 803–817.e4 (2020).
Buttner, F. A. et al. A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy. Genome Med. 14, 105 (2022).
Motzer, R. J. et al. Molecular characterization of renal cell carcinoma tumors from a phase III anti-angiogenic adjuvant therapy trial. Nat. Commun. 13, 5959 (2022).
Rini, B. I. et al. Validation of the 16-gene recurrence score in patients with locoregional, high-risk renal cell carcinoma from a phase III trial of adjuvant sunitinib. Clin. Cancer Res. 24, 4407–4415 (2018).
Rini, B. I. et al. Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet 393, 2404–2415 (2019).
McDaniel, A. S. et al. Genomic profiling of penile squamous cell carcinoma reveals new opportunities for targeted therapy. Cancer Res. 75, 5219–5227 (2015).
Necchi, A. et al. Gene expression profiling of advanced penile squamous cell carcinoma receiving cisplatin-based chemotherapy improves prognostication and identifies potential therapeutic targets. Eur. Urol. Focus. 4, 733–736 (2018).
Macedo, J. et al. Genomic profiling reveals the pivotal role of hrHPV driving copy number and gene expression alterations, including mRNA downregulation of TP53 and RB1 in penile cancer. Mol. Carcinog. 59, 604–617 (2020).
Chahoud, J. et al. Whole-exome sequencing in penile squamous cell carcinoma uncovers novel prognostic categorization and drug targets similar to head and neck squamous cell carcinoma. Clin. Cancer Res. 27, 2560–2570 (2021).
Jacob, J. et al. Comprehensive genomic profiling of histologic subtypes of urethral carcinomas. Urol. Oncol. 39, 731.e1–731.e15 (2021).
Hovelson, D. H. et al. Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors. Neoplasia 17, 385–399 (2015).
Sambandam, V. et al. PDK1 mediates NOTCH1-mutated head and neck squamous carcinoma vulnerability to therapeutic PI3K/mTOR inhibition. Clin. Cancer Res. 25, 3329–3340 (2019).
Hashemi Gheinani, A., Bigger-Allen, A., Wacker, A. & Adam, R. M. Systems analysis of benign bladder disorders: insights from omics analysis. Am. J. Physiol. Ren. Physiol. 318, F901–F910 (2020).
Gheinani, A. H. et al. Integrated mRNA-miRNA transcriptome analysis of bladder biopsies from patients with bladder pain syndrome identifies signaling alterations contributing to the disease pathogenesis. BMC Urol. 21, 172 (2021).
Cheng, X. F. et al. Integrated analysis of microarray studies to identify novel diagnostic markers in bladder pain syndrome/interstitial cystitis with Hunner lesion. Int. J. Gen. Med. 15, 3143–3154 (2022).
Joseph, D. B. et al. Single-cell analysis of mouse and human prostate reveals novel fibroblasts with specialized distribution and microenvironment interactions. J. Pathol. 255, 141–154 (2021).
Middleton, L. W. et al. Genomic analysis of benign prostatic hyperplasia implicates cellular re-landscaping in disease pathogenesis. JCI Insight 5, e129749 (2019).
Liu, D. et al. Integrative multiplatform molecular profiling of benign prostatic hyperplasia identifies distinct subtypes. Nat. Commun. 11, 1987 (2020).
Yang, B., Veneziano, D. & Somani, B. K. Artificial intelligence in the diagnosis, treatment and prevention of urinary stones. Curr. Opin. Urol. 30, 782–787 (2020).
Michaels, E. K. et al. Use of a neural network to predict stone growth after shock wave lithotripsy. Urology 51, 335–338 (1998).
Black, K. M., Law, H., Aldoukhi, A., Deng, J. & Ghani, K. R. Deep learning computer vision algorithm for detecting kidney stone composition. BJU Int. 125, 920–924 (2020).
Aminsharifi, A. et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J. Endourol. 31, 461–467 (2017).
Ganesan, V. & Pearle, M. S. Artificial intelligence in stone disease. Curr. Opin. Urol. 31, 391–396 (2021).
Muller, S. et al. Can a dinosaur think? Implementation of artificial intelligence in extracorporeal shock wave lithotripsy. Eur. Urol. Open. Sci. 27, 33–42 (2021).
Aminsharifi, A. et al. Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with Guy’s stone score and the CROES nomogram. J. Endourol. 34, 692–699 (2020).
Venhola, M., Reunanen, M., Taskinen, S., Lahdes-Vasama, T. & Uhari, M. Interobserver and intra-observer agreement in interpreting urodynamic measurements in children. J. Urol. 169, 2344–2346 (2003).
Dudley, A. G. et al. Interrater reliability in pediatric urodynamic tracings: a pilot study. J. Urol. 197, 865–870 (2017).
Wang, H. S. et al. Pattern recognition algorithm to identify detrusor overactivity on urodynamics. Neurourol. Urodyn. 40, 428–434 (2021).
Hobbs, K. T. et al. Machine learning for urodynamic detection of detrusor overactivity. Urology 159, 247–254 (2022).
Doern, C. D. & Richardson, S. E. Diagnosis of urinary tract infections in children. J. Clin. Microbiol. 54, 2233–2242 (2016).
Medina, M. & Castillo-Pino, E. An introduction to the epidemiology and burden of urinary tract infections. Ther. Adv. Urol. 11, 1756287219832172 (2019).
Nitzan, O., Elias, M., Chazan, B. & Saliba, W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management. Diabetes Metab. Syndr. Obes. 8, 129–136 (2015).
Pannek, J. & Wollner, J. Management of urinary tract infections in patients with neurogenic bladder: challenges and solutions. Res. Rep. Urol. 9, 121–127 (2017).
Ripa, F. et al. Association of kidney stones and recurrent UTIs: the chicken and egg situation. A systematic review of literature. Curr. Urol. Rep. 23, 165–174 (2022).
Taylor, R. A., Moore, C. L., Cheung, K. H. & Brandt, C. Predicting urinary tract infections in the emergency department with machine learning. PLoS ONE 13, e0194085 (2018).
Ozkan, I. A., Koklu, M. & Sert, I. U. Diagnosis of urinary tract infection based on artificial intelligence methods. Comput. Meth Prog. Bio 166, 51–59 (2018).
Price, T. K. et al. The clinical urine culture: enhanced techniques improve detection of clinically relevant microorganisms. J. Clin. Microbiol. 54, 1216–1222 (2016).
Szlachta-McGinn, A. et al. Molecular diagnostic methods versus conventional urine culture for diagnosis and treatment of urinary tract infection: a systematic review and meta-analysis. Eur. Urol. Open. Sci. 44, 113–124 (2022).
Roux-Dalvai, F. et al. Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning. Mol. Cell Proteom. 18, 2492–2505 (2019).
Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group Targeted workup after initial febrile urinary tract infection: using a novel machine learning model to identify children most likely to benefit from voiding cystourethrogram. J. Urol. 202, 144–152 (2019).
Bagli, D. J. et al. Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. J. Urol. 160, 980–983 (1998).
Seckiner, I., Seckiner, S. U., Bayrak, O. & Erturhan, S. Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction. Can. Urol. Assoc. J. 5, E152–E155 (2011).
Drysdale, E. et al. Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty. World J. Urol. 40, 593–599 (2022).
Rademakers, K. et al. Male bladder outlet obstruction: time to re-evaluate the definition and reconsider our diagnostic pathway? ICI-RS 2015. Neurourol. Urodyn. 36, 894–901 (2017).
Sonke, G. S., Heskes, T., Verbeek, A. L., de la Rosette, J. J. & Kiemeney, L. A. Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks. J. Urol. 163, 300–305 (2000).
Abdovic, S. et al. Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network. World J. Urol. 37, 1973–1979 (2019).
Yin, S. et al. Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology 142, 183–189 (2020).
Kwong, J. C. et al. Posterior urethral valves outcomes prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves. Pediatr. Nephrol. 37, 1067–1084 (2021).
Thomas, A. A. et al. Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results. World J. Urol. 32, 99–103 (2014).
Odisho, A. Y. et al. Automating the capture of structured pathology data for prostate cancer clinical care and research. JCO Clin. Cancer Inf. 3, 1–8 (2019).
Schroeck, F. R. et al. Development of a natural language processing engine to generate bladder cancer pathology data for health services research. Urology 110, 84–91 (2017).
Glaser, A. P. et al. Automated extraction of grade, stage, and quality information from transurethral resection of bladder tumor pathology reports using natural language processing. JCO Clin. Cancer Inf. 2, 1–8 (2018).
Bashashati, A. & Goldenberg, S. L. AI for prostate cancer diagnosis – hype or today’s reality? Nat. Rev. Urol. 19, 261–262 (2022).
Yang, C. et al. Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review. J. Am. Med. Inf. Assoc. 29, 983–989 (2022).
Reinke, A., Tizabi, M. D., Eisenmann, M. & Maier-Hein, L. Common pitfalls and recommendations for grand challenges in medical artificial intelligence. Eur. Urol. Focus. 7, 710–712 (2021).
Bulten, W. et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat. Med. 28, 154–163 (2022).
Maier-Hein, L. et al. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9, 5217 (2018).
Zhou, Q., Chen, Z. H., Cao, Y. H. & Peng, S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit. Med. 4, 154 (2021).
Dhiman, P. et al. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med. Res. Methodol. 22, 101 (2022).
Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).
Calogero, A. E., Burgio, G., Condorelli, R. A., Cannarella, R. & La Vignera, S. Epidemiology and risk factors of lower urinary tract symptoms/benign prostatic hyperplasia and erectile dysfunction. Aging Male 22, 12–19 (2019).
Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
Regev, A. et al. The Human Cell Atlas. Elife 6, e27041 (2017).
Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).
Papatheodorou, I. et al. Expression Atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 46, D246–D251 (2018).
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Hu, X. et al. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res. 46, D1144–D1149 (2018).
Omar, M. I. et al. Introducing PIONEER: a project to harness big data in prostate cancer research. Nat. Rev. Urol. 17, 351–362 (2020).
Dunning, M. J. et al. Mining human prostate cancer datasets: the “camcAPP” shiny app. EBioMedicine 17, 5–6 (2017).
Hu, Z. et al. Genomic characterization of genes encoding histone acetylation modulator proteins identifies therapeutic targets for cancer treatment. Nat. Commun. 10, 733 (2019).
Ghoshdastider, U. et al. Pan-cancer analysis of ligand–receptor cross-talk in the tumor microenvironment. Cancer Res. 81, 1802–1812 (2021).
Rohatgi, N., Ghoshdastider, U., Baruah, P., Kulshrestha, T. & Skanderup, A. J. A pan-cancer metabolic atlas of the tumor microenvironment. Cell Rep. 39, 110800 (2022).
Stewart, B. J. et al. Spatiotemporal immune zonation of the human kidney. Science 365, 1461–1466 (2019).
McMahon, A. P. et al. GUDMAP: the genitourinary developmental molecular anatomy project. J. Am. Soc. Nephrol. 19, 667–671 (2008).
Rigden, D. J. & Fernandez, X. M. The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 46, D1–D7 (2018).
van der Wijst, M. et al. Science Forum: the single-cell eQTLGen consortium. Elife 9, e52155 (2020).
Abugessaisa, I. et al. SCPortalen: human and mouse single-cell centric database. Nucleic Acids Res. 46, D781–D787 (2018).
Ner-Gaon, H., Melchior, A., Golan, N., Ben-Haim, Y. & Shay, T. JingleBells: a repository of immune-related single-cell RNA-sequencing datasets. J. Immunol. 198, 3375–3379 (2017).
Cao, Y., Zhu, J., Jia, P. & Zhao, Z. scRNASeqDB: a database for RNA-seq based gene expression profiles in human single cells. Genes 8, 368 (2017).
Cao, Z. J., Wei, L., Lu, S., Yang, D. C. & Gao, G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST. Nat. Commun. 11, 3458 (2020).
Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).
Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database 2020, baaa073 (2020).
Li, M. et al. DISCO: a database of Deeply Integrated human Single-Cell Omics data. Nucleic Acids Res. 50, D596–D602 (2022).
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