Correction to: Nature Biomedical Engineering https://doi.org/10.1038/s41551-024-01297-1, published online 6 December 2024.
Correction to: Nature Biomedical Engineering https://doi.org/10.1038/s41551-024-01297-1, published online 6 December 2024.
The utility of urinary tests for the monitoring of the treatment efficacy and adverse events of anticancer therapies is constrained by the low concentration of relevant urinary biomarkers. Here we report, using mice with lung cancer and treated with chemotherapy, of a urinary fluorescence test for the concurrent monitoring of the levels of a tumour biomarker (cathepsin B) and of a biomarker of chemotherapy-induced kidney injury (N-acetyl-β-d-glucosaminidase). The test involves two intratracheally administered urinary reporters leveraging caged bioorthogonal click handles for the biomarker-dependent activation of ‘clickability’ and renal clearance, and the bioorthogonal click reaction of each renally cleared reporter with paired fluorescence indicators in the collected urine. In mouse models of chemotherapy-treated orthotopic lung cancer and of cisplatin-induced kidney injury, lower urinary fluorescence signals (which can be measured by a smartphone camera) for tumour and kidney injury levels positively correlated with animal weight gain and survival time. Biomarker-activated bioorthogonal click reactivity and renal clearance combined with bioorthogonally triggered fluorescence in vitro may enable specific, sensitive and rapid urinary assays for the monitoring of other physiopathological processes.
Correction to: Nature Biomedical Engineering https://doi.org/10.1038/s41551-024-01281-9, published online 11 November 2024.
Correction to: Nature Biomedical Engineering https://doi.org/10.1038/s41551-024-01313-4, published online 4 December 2024.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions. The model allows for the interpretation of the respective importance of multi-omic and higher-order structural features, achieved state-of-the-art performance in the prediction of cancer genes across biological networks (including networks of interactions between miRNA and proteins, transcription factors and proteins, and transcription factors and miRNA) in pan-cancer and cancer-specific scenarios, and predicted 57 cancer-gene candidates (including three genes that had not been identified by other models) among 4,729 unlabelled genes across 8 pan-cancer datasets. The model’s interpretability and generalization may facilitate the understanding of gene-related regulatory mechanisms and the discovery of new cancer genes.
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