GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks

References

• Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

Google Scholar

• Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955–D961 (2013).

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

• Adam, G. et al. Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis. Oncol. 4, 19 (2020).

Google Scholar

• Firoozbakht, F., Yousefi, B. & Schwikowski, B. An overview of machine learning methods for monotherapy drug response prediction. Brief. Bioinforma. 23, bbab408 (2022).

Google Scholar

• Shen, B. et al. A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications. Brief Bioinform 24, https://doi.org/10.1093/bib/bbac605 (2023).
• Partin, A. et al. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 10, 1086097 (2023).

Google Scholar

• Liu, P., Li, H., Li, S. & Leung, K. S. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinforma. 20, 408 (2019).

Google Scholar

• Zhou, J. et al. Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020).

Google Scholar

• Nguyen, T., Nguyen, G. T. T., Nguyen, T. & Le, D. H. Graph Convolutional Networks for Drug Response Prediction. IEEE/ACM Trans. Comput Biol. Bioinform 19, 146–154 (2022).

Google Scholar

• Zhu, Y. et al. TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation. Bioinformatics 38, 461–468 (2022).

Google Scholar

• Shin, J., Piao, Y., Bang, D., Kim, S. & Jo, K. DRPreter: Interpretable anticancer drug response prediction using knowledge-guided graph neural networks and transformer. Int. J. Mol. Sci. 23, https://doi.org/10.3390/ijms232213919 (2022).
• Manica, M. et al. Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders. Mol. Pharm. 16, 4797–4806 (2019).

Google Scholar

• Jin, I. & Nam, H. HiDRA: Hierarchical network for drug response prediction with attention. J. Chem. Inf. Model 61, 3858–3867 (2021).

Google Scholar

• Zhan, Y., Guo, J., Philip Chen, C. & Meng, X.-B. iBT-Net: An incremental broad transformer network for cancer drug response prediction. Brief. Bioinforma. 24, bbad256 (2023).

Google Scholar

• Xia, X., Zhu, C., Zhong, F. & Liu, L. TransCDR: A deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion. BMC Biol. 22, 227 (2024).

Google Scholar

• Gaulton, A. et al. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res 40, D1100–D1107 (2012).

Google Scholar

• Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C. & Ester, M. MOLI: Multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35, i501–i509 (2019).

Google Scholar

• Koras, K. et al. Feature selection strategies for drug sensitivity prediction. Sci. Rep. 10, 9377 (2020).

Google Scholar

• Chawla, S. et al. Gene expression based inference of cancer drug sensitivity. Nat. Commun. 13, 5680 (2022).

Google Scholar

• Tang, Y.C. & Gottlieb, A. Explainable drug sensitivity prediction through cancer pathway enrichment. Sci. Rep. 11, 3128 (2021).

Google Scholar

• Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinforma. 14, 7 (2013).

Google Scholar

• Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

Google Scholar

• Schlichtkrull, M. et al. in The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. 593-607 (Springer).
• Bento, A. P. et al. An open source chemical structure curation pipeline using RDKit. J. Cheminformatics 12, 1–16 (2020).

Google Scholar

• Öztürk, H., Ozkirimli, E. & Özgür, A. A novel methodology on distributed representations of proteins using their interacting ligands. Bioinformatics 34, i295–i303 (2018).

Google Scholar

• Cortes-Ciriano, I. et al. Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics 32, 85–95 (2016).

Google Scholar

• Costello, J. C. et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202–1212 (2014).

Google Scholar

• Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

Google Scholar

• Foroutan, M. et al. Single sample scoring of molecular phenotypes. BMC Bioinforma. 19, 404 (2018).

Google Scholar

• Bhuva, D. D., Cursons, J. & Davis, M. J. Stable gene expression for normalisation and single-sample scoring. Nucleic Acids Res. 48, e113–e113 (2020).

Google Scholar

• Zhang, Y., Jenkins, D. F., Manimaran, S. & Johnson, W. E. Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC Bioinforma. 19, 1–15 (2018).

Google Scholar

• Dang, Q. et al. Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges. Cancer Med. 13, e70041 (2024).

Google Scholar

• Dai, X., Cheng, H., Bai, Z. & Li, J. Breast cancer cell line classification and its relevance with breast tumor subtyping. J. Cancer 8, 3131 (2017).

Google Scholar

• Megyesfalvi, Z. et al. Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions. CA Cancer J. Clin. 73, 620–652 (2023).
• Liu, Q. et al. Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies. Cell 187, 184–203. e128 (2024).

Google Scholar

• Pardo, O. E. et al. Fibroblast growth factor-2 induces translational regulation of Bcl-XL and Bcl-2 via a MEK-dependent pathway: Correlation with resistance to etoposide-induced apoptosis. J. Biol. Chem. 277, 12040–12046 (2002).

Google Scholar

• Yang, X., Tang, C., Luo, H., Wang, H. & Zhou, X. Shp2 confers cisplatin resistance in small cell lung cancer via an AKT-mediated increase in CA916798. Oncotarget 8, 23664 (2017).

Google Scholar

• Nguyen, A., Lee, N., Moroney, J., Fleming, G. & Challa, S. Exploring the mechanisms of resistance to trastuzumab-deruxtecan in endometrial cancer cells. Gynecol. Oncol. 190, S244 (2024).

Google Scholar

• Marengo, B. et al. p38MAPK inhibition: a new combined approach to reduce neuroblastoma resistance under etoposide treatment. Cell Death Dis. 4, e589–e589 (2013).

Google Scholar

• Tang, X. -l et al. Salvianolic acid A reverses cisplatin resistance in lung cancer A549 cells by targeting c-met and attenuating Akt/mTOR pathway. J. Pharmacol. Sci. 135, 1–7 (2017).

Google Scholar

• Seubwai, W. et al. Inhibition of NF-κB activity enhances sensitivity to anticancer drugs in cholangiocarcinoma cells. Oncol. Res. 23, 21 (2016).

Google Scholar

• Megyesfalvi, Z. et al. Expression patterns and prognostic relevance of subtype-specific transcription factors in surgically resected small-cell lung cancer: an international multicenter study. J. Pathol. 257, 674–686 (2022).

Google Scholar

• Gay, C. M. et al. Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities. Cancer cell 39, 346–360. e347 (2021).

Google Scholar

• Greenbaum, D., Colangelo, C., Williams, K. & Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 1–8 (2003).

Google Scholar

• Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

Google Scholar

• Pak, M., Lee, S., Sung, I., Koo, B. & Kim, S. Improved drug response prediction by drug target data integration via network-based profiling. Brief. Bioinforma. 24, bbad034 (2023).

Google Scholar

• Li, P. et al. Improving drug response prediction via integrating gene relationships with deep learning. Brief. Bioinforma. 25, bbae153 (2024).

Google Scholar

• Zhu, Y. et al. Ensemble transfer learning for the prediction of anti-cancer drug response. Sci. Rep. 10, 18040 (2020).

Google Scholar

• Bang, D., Koo, B. & Kim, S. Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction. Bioinformatics 40, i130–i139 (2024).

Google Scholar

• Sinha, S. et al. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat. Cancer 5, 938–952 (2024).

Google Scholar

• Szałata, A. et al. A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types. Adv. Neural Inf. Process. Syst. 37, 20566–20616 (2024).

Google Scholar

• van der Meer, D. et al. Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models. Nucleic Acids Res. 47, D923–D929 (2019).

Google Scholar

• Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

Google Scholar

• Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

Google Scholar

• Liu, Z. P., Wu, C., Miao, H. & Wu, H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford) 2015, https://doi.org/10.1093/database/bav095 (2015).
• Unsal-Beyge, S. & Tuncbag, N. Functional stratification of cancer drugs through integrated network similarity. NPJ Syst. Biol. Appl. 8, 11 (2022).

Google Scholar

• Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, complex Syst. 1695, 1–9 (2006).

Google Scholar

• Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

Google Scholar

• Fey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 (2019).
• Kim, S. et al. PubChem 2023 update. Nucleic Acids Res 51, D1373–D1380 (2023).

Google Scholar

• Szöcs, E., Stirling, T., Scott, E. R., Scharmüller, A. & Schäfer, R. B. webchem: an R package to retrieve chemical information from the web. J. Stat. Softw. 93, 1–17 (2020).

Google Scholar

• Keenan, A. B. et al. The library of integrated network-based cellular signatures NIH program: System-level cataloging of human cells response to perturbations. Cell Syst. 6, 13–24 (2018).

Google Scholar

• Xie, Z. et al. Getting started with LINCS datasets and tools. Curr. Protoc. 2, e487 (2022).

Google Scholar

• Li, M. et al. DGL-LifeSci: An open-source toolkit for deep learning on graphs in life science. ACS Omega 6, 27233–27238 (2021).

Google Scholar

• Cancer Genome Atlas Research, N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet 45, 1113–1120 (2013).

Google Scholar

• Colaprico, A. et al. TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44, e71 (2016).

Google Scholar

• Spainhour, J. C. G., Lim, J. & Qiu, P. GDISC: A web portal for integrative analysis of gene-drug interaction for survival in cancer. Bioinformatics 33, 1426–1428 (2017).

Google Scholar

• Brody, S., Alon, U. & Yahav, E. How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021).
• Li, G., Muller, M., Thabet, A. & Ghanem, B. In Proceedings of the IEEE/CVF international conference on computer vision. 9267-9276.
• Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700-4708.
• Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).

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