Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

Ma, Q.; Wang, X.; Duan, M.; Su, P.-L.; Li, J.; Krull, J.; Jin, J.; Chen, H.; Sun, Y.; Wu, W.; He, K.; Carpenter, R.; Zhang, C.; Cao, S.; Xu, D.; Wang, G.; Li, L.; Xin, G.; Carbone, D. P.; Li, Z.

doi:10.1101/2025.05.16.654579

bioRxiv · 10.1101/2025.05.16.654579

Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

Ma, Q.·Wang, X.·Duan, M.·Su, P.-L.·Li, J.·Krull, J.·Jin, J.·Chen, H.·Sun, Y.·Wu, W.·He, K.·Carpenter, R.·Zhang, C.·Cao, S.·Xu, D.·Wang, G.·Li, L.·Xin, G.·Carbone, D. P.·Li, Z.

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Abstract

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.

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Ma, Q., Wang, X., Duan, M., Su, P.-L., Li, J., Krull, J., Jin, J., Chen, H., Sun, Y., Wu, W., He, K., Carpenter, R., Zhang, C., Cao, S., Xu, D., Wang, G., Li, L., Xin, G., Carbone, D. P., Li, Z.. 2025-05-22. Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer. https://doi.org/10.1101/2025.05.16.654579

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