Deep Learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures
Zheng, W.; Pu, M.; Li, X.; Jin, S.; Li, X.; Zhou, J.; zhang, y.
Show abstract
Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the therapeutic treatment of cancers. Even though somatic mutations have been directly linked to tumorigenesis and metastasis, it is less explored whether the metastatic events can be identified through genomic mutation signatures, a concise representation of the mutational processes. Here, applying mutation signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts, we developed MetaWise, a Deep Neural Network (DNN) model. This model accurately classified metastatic tumors from primary tumors. Signatures of non-coding mutations also have a major impact on the model performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analysis into the MetaWise model identified several mutational signatures directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures and DNA damage response deficiency signatures.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Novel cancer subtyping method based on patient-specific gene regulatory network 95%
- Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia 94%
- Uncovering Hidden Cancer Self-Dependencies through Analysis of shRNA-Level Dependency Scores 94%
Similar papers in this journal
- Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer 95%
- Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer 93%
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma 92%
Similar papers in this journal
- Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning：esophageal cancer as an example 95%
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer 94%
- DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements 93%
Similar papers in this journal
Similar papers in this journal
- M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis 93%
- Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients. 93%
- Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.