Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperforms other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captures occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have metastatic disease. Notably, EmitGCL identifies HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which we validate by in-vitro pharmacological inhibition of HSP90 that reduced breast cancer cell migration and further support across five independent cohorts of patients (n = 420). Furthermore, we demonstrate YY1 transcription factor as a key driver of breast cancer metastasis, which we corroborate with in-silico, CRISPR-based migration assays, and in vivo mouse lung colonization experiments, suggesting that YY1 is a potential therapeutic target for further investigation. Predicting future metastases remains a major clinical challenge. Here, the authors develop EmitGCL, a deep-learning framework to predict metastasis and related biomarkers using cancer single-cell sequencing data, enabling and validating the discovery of occult metastases and breast cancer metastasis biomarkers.
Xiaoying Wang, Maoteng Duan, Anthony J. Snyder et al.· Nature Communications· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperformed other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captured occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have a metastatic disease. Notably, EmitGCL identified HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which was validated across five independent cohorts of patients (n=420). Furthermore, we demonstrated YY1 transcription factor as a key driver of breast cancer metastasis which was validated through in-silico and CRISPR-based migration assays, suggesting that YY1 is a potential therapeutic target for deterring metastasis.
Xiaoying Wang, Maoteng Duan, Po-Lan Su et al.· bioRxiv· 0 citations
Ferroptosis is a unique type of programmed cell death caused by excessive lipid peroxidation and represents a vulnerability in certain types of cancer. However, the signaling mechanisms that modulate ferroptosis and its functional consequence on the tumor microenvironment are poorly understood. Here, we demonstrate an inhibitory effect of mitochondrial calcium uniporter (MCU) on ferroptosis during embryogenesis and tumor development. MCU-dependent production of metabolite acetyl-coenzyme A (acetyl-CoA) supports the normal function of glutathione peroxidase 4 (GPX4), a critical gatekeeper of ferroptosis. Mechanistically, acetylation of GPX4 on lysine 90 (K90) prevents the formation of a detrimental salt bridge between K90 and aspartate 23, therefore protecting GPX4 enzymatic activity and avoiding ferroptosis. Deletion of MCU in cancer cells caused a robust antitumor T cell response and significantly blunted tumor growth. Thus, our findings indicate MCU-mediated acetyl-CoA metabolism as a critical anti-ferroptosis mechanism, which can be investigated as potential therapeutic candidate for tumor treatment.
Jianwen Chen, Bao Zhao, H. Dong et al.· Proceedings of the National...· 0 citations
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