ArticleNature communications2021
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
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- 3D "Emboli" Culture Models Epithelial Breast Cancer Cell Oxidative Mitochondrial Metabolism with Relevance for Lung Metastasis.Cancer research communications · 2026Article
- The evolving landscape of brain metastasis: volume II.Trends in cancer · 2026Review
- Current Role and Future Frontiers of Spatial Transcriptomics in Genitourinary Cancers.Cancers · 2025Review
- Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine.The British journal of radiology · 2025Review
- Cytogenetic signatures favoring metastatic organotropism in colorectal cancer.Nature communications · 2025Article
- Regulation of metastatic organotropism.Trends in cancer · 2025Review
- Deciphering organotropism reveals therapeutic targets in metastasis.Frontiers in cell and developmental biology · 2025Review
- Article
- Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning.World journal of gastrointestinal oncology · 2024Article
- Tumor-to-Tumor Metastasis of Lung Cancer to Kidney Cancer: A Review of the Literature and Our Experience.Diagnostics (Basel, Switzerland) · 2024Review
- Cusp bifurcation in a metastatic regulatory network.Journal of theoretical biology · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Estimating Metastatic Risk of Pancreatic Ductal Adenocarcinoma at Single-Cell Resolution.International journal of molecular sciences · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Article
- Screening of early-staged colorectal neoplasia by clonal hematopoiesis-based liquid biopsy and machine-learning.American journal of cancer research · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risks of primary tumors. MetaNet achieves high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identify Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis and demonstrate their higher metastatic risk and shorter disease-free survival. In addition, we identify genomic alterations associated with organ-specific metastases and employ them to stratify patients into various risk groups with propensities toward different metastatic organs. This organotropic stratification method achieves better prognostic value than the standard histological grading system in prostate cancer, especially in the identification of Bone-MFP and Liver-MFP subtypes, with potential in informing organ-specific examinations in follow-ups.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.