Gambardella, G. et al. A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response. Nat. Commun. 13, 1714 (2022).
Google Scholar
Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).
Google Scholar
Mohammadi, S., Davila-Velderrain, J. & Kellis, M. A multiresolution framework to characterize single-cell state landscapes. Nat. Commun. 11, 5399 (2020).
Google Scholar
Lawson, D. A. et al. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 20, 1349–1360 (2018).
Google Scholar
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).
Google Scholar
França, G. S. et al. Cellular adaptation to cancer therapy along a resistance continuum. Nature 631, 876–883 (2024).
Google Scholar
Yan, Y. et al. Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer. Nat. Genet 57, 126–139 (2025).
Google Scholar
Wu, Z. et al. Single-cell techniques and deep learning in predicting drug response. Trends Pharm. Sci. 41, 1050–1065 (2020).
Google Scholar
Zhang, P. et al. A deep learning framework for in silico screening of anticancer drugs at the single-cell level. Natl. Sci. Rev. 12, nwae451 (2025).
Google Scholar
Suphavilai, C. et al. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures. Genome Med 13, 189 (2021).
Google Scholar
Fustero-Torre, C. et al. Beyondcell: Targeting cancer therapeutic heterogeneity in single-cell RNA-seq data. Genome Med 13, 187 (2021).
Google Scholar
Pellecchia, S. et al. Predicting drug response from single-cell expression profiles of tumours. BMC Med 21, 476 (2023).
Google Scholar
Chen, J. et al. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nat. Commun. 13, 6494 (2022).
Google Scholar
Zheng, Z. et al. Enabling single-cell drug response annotations from bulk RNA-seq using SCAD. Adv. Sci. 10, e2204113 (2023).
Google Scholar
Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e634 (2017).
Google Scholar
Shin, D. et al. Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. Sci. Adv. 5, eaav2249 (2019).
Google Scholar
Li, J. et al. Higher-order attribute-enhancing heterogeneous graph neural networks. Ieee Trans. Knowl. Data Eng. 35, 560–574 (2023).
Google Scholar
Sharma, A. et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat. Commun. 9, 4931 (2018).
Google Scholar
Ho, Y. J. et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res 28, 1353–1363 (2018).
Google Scholar
Chi, F. et al. A ‘one-two punch’ therapy strategy to target chemoresistance in estrogen receptor positive breast cancer. Transl. Oncol. 14, 100946 (2021).
Google Scholar
Kim, Y. S. et al. Single-cell RNA sequencing reveals the existence of pro-metastatic subpopulation within a parental b16 murine melanoma cell line. Biochem Biophys. Res Commun. 613, 120–126 (2022).
Google Scholar
Rappaport, N. et al. Malacards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res 45, D877–D887 (2017).
Google Scholar
Slamon, D. J. et al. Phase III randomized study of ribociclib and fulvestrant in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer: Monaleesa-3. J. Clin. Oncol. 36, 2465–2472 (2018).
Google Scholar
Rae J. M., Lippman M. E. The role of estrogen receptor signaling in suppressing the immune response to cancer comment. J. Clin. Investig. 131, e155476 (2021).
Carrara, G. F. A. et al. Analysis of RPL37A, MTSS1, and HTRA1 expression as potential markers for pathologic complete response and survival. Breast Cancer 28, 307–320 (2021).
Google Scholar
Hoof, T., Fislage, R. & Tummler, B. Primary sequence of the human ribosomal-protein L37a. Nucleic Acids Res 20, 5475–5475 (1992).
Google Scholar
Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet 52, 1208–1220 (2020).
Google Scholar
Galanski, M. Recent developments in the field of anticancer platinum complexes. Recent Pat. Anti-Cancer Drug Discov. 1, 285–295 (2006).
Google Scholar
Mossé, Y. P. et al. Safety and activity of crizotinib for paediatric patients with refractory solid tumours or anaplastic large-cell lymphoma: A children’s oncology group phase 1 consortium study. Lancet Oncol. 14, 472–480 (2013).
Google Scholar
Wee P. & Wang Z. Epidermal growth factor receptor cell proliferation signaling pathways. Cancers 9, 52 (2017).
Davies, C. et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: Patient-level meta-analysis of randomised trials. Lancet 378, 771–784 (2011).
Google Scholar
Planchard, D. et al. Dabrafenib plus Trametinib in patients with previously untreated BRAF(V600E)-mutant metastatic non-small-cell lung cancer: An open-label, phase 2 trial. Lancet Oncol. 18, 1307–1316 (2017).
Google Scholar
Cheng, S. et al. Chemotherapy for relapsed small cell lung cancer: A systematic review and practice guideline. J. Thorac. Oncol. 2, 348–354 (2007).
Google Scholar
Tesauro, C. et al. Topoisomerase i activity and sensitivity to camptothecin in breast cancer-derived cells: A comparative study. BMC Cancer 19, 1158 (2019).
Google Scholar
Weitzman, J. B. et al. Jund protects cells from p53-dependent senescence and apoptosis. Mol. Cell 6, 1109–1119 (2000).
Google Scholar
Polanski, R. et al. Senescence induction in renal carcinoma cells by nutlin-3: A potential therapeutic strategy based on mdm2 antagonism. Cancer Lett. 353, 211–219 (2014).
Google Scholar
Zeng, S. X. et al. The phosphatidylinositol 3-kinase pathway as a potential therapeutic target in bladder cancer. Clin. Cancer Res 23, 6580–6591 (2017).
Google Scholar
Abraham, A. G. & O’Neill, E. Pi3k/akt-mediated regulation of p53 in cancer. Biochem Soc. Trans. 42, 798–803 (2014).
Google Scholar
Leo, C. P., Leo, C. & Szucs, T. D. Breast cancer drug approvals by the us fda from 1949 to 2018. Nat. Rev. Drug Discov. 19, 11 (2020).
Google Scholar
Asiimwe, I. G. & Rumona, D. Publication proportions for registered breast cancer trials: Before and following the introduction of the clinicaltrials.Gov results database. Res. Integr. peer Rev. 1, 10–20 (2016).
Google Scholar
Pujade-Lauraine, E. et al. Pegylated liposomal doxorubicin and carboplatin compared with paclitaxel and carboplatin for patients with platinum-sensitive ovarian cancer in late relapse. J. Clin. Oncol. 28, 3323–3329 (2010).
Google Scholar
Hoa Thi Nhu T. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Zhang, F. et al. A novel heterogeneous network-based method for drug response prediction in cancer cell lines. Sci. Rep. 8, 3355 (2018).
Google Scholar
Güvenç Paltun, B., Mamitsuka, H. & Kaski, S. Improving drug response prediction by integrating multiple data sources: Matrix factorization, kernel and network-based approaches. Brief. Bioinform 22, 346–359 (2021).
Google Scholar
Sun, C. et al. Reversible and adaptive resistance to BRAF(v600e) inhibition in melanoma. Nature 508, 118–128 (2014).
Google Scholar
Ma, J. et al. Rna binding protein: Coordinated expression between the nuclear and mitochondrial genomes in tumors. J. Transl. Med 21, 512 (2023).
Google Scholar
Qi, J. et al. TUBA1B as a novel prognostic biomarker correlated with immunosuppressive tumor microenvironment and immunotherapy response. Front Pharm. 16, 1517887 (2025).
Google Scholar
Chakraborty B. et al. Inhibition of estrogen signaling in myeloid cells increases tumor immunity in melanoma. J. Clin. Invest. 131, e151347 (2021).
Catez, F. et al. Ribosome biogenesis: An emerging druggable pathway for cancer therapeutics. Biochem Pharm. 159, 74–81 (2019).
Google Scholar
Kuwahara, H. & Gao, X. Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach. J. Cheminform 13, 27 (2021).
Google Scholar
Govindan G., Nair A. S. Composition, transition and distribution (CTD) – a dynamic feature for predictions based on hierarchical structure of cellular sorting. In: Annual IEEE India Conference – Engineering Sustainable Solutions) (2011).
Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020).
Google Scholar
Hao Z. et al. A selection-pattern-aware recommendation model with colored-motif attention network. Neurocomputing 538, 756–767 (2023).
Wang, Y., Ding, Y. & Shahrampour, S. Takde: Temporal adaptive kernel density estimator for real-time dynamic density estimation. IEEE Trans. Pattern Anal. Mach. Intell. 45, 13831–13843 (2023).
Google Scholar
Tang, J., Deng, C. & Huang, G.-B. Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27, 809–821 (2016).
Google Scholar
Bantis, L. E., Nakas, C. T. & Reiser, B. Construction of confidence regions in the roc space after the estimation of the optimal youden index-based cut-off point. Biometrics 70, 212–223 (2014).
Google Scholar
Barretina, J. et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Google Scholar
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).
Google Scholar
Lopez, R. et al. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1068 (2018).
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).
Google Scholar
Xu, Y. et al. Maca: Marker-based automatic cell-type annotation for single-cell expression data. Bioinformatics 38, 1756–1760 (2022).
Google Scholar
Zhong Z. et al. Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics. Nat Commun 15, 1929 (2024).
Qi, G. scXDR. https://doi.org/10.5281/zenodo.17857697 (2025).