Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
Metastasis leads to most cancer deaths, but its spatiotemporal behavior remains unpredictable at early stage. Here, we developed 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 achieved high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identified Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis, and demonstrated their high metastatic risks with significantly shorter disease-free survivals and higher migratory potential. In addition, we identified genomic alterations associated with organ-specific metastases, and employed them to stratify patients into the risk groups with propensities toward different metastatic organs. Remarkably, this organotropic stratification achieved better prognostic value than standard histological grading system in prostate cancer, especially between Bone-MFP and Liver-MFP subtypes, with organotropic insights to inform organ-specific examinations in follow-ups.
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This is a list of supplementary files associated with this preprint. Click to download.
Supplementary Figures
Supplementary Table 1 Mapping of raw tissue sites to general anatomic organs for primary tumor site.
Supplementary Table 2 Mapping of raw tissue sites to general anatomic organs for metastatic site.
Supplementary Table 3 89 genomic variants enriched in organotropic metastases (FDR < 0.1 in chi-square test and variant fraction in bone, brain, liver and lung metastases larger than 1%).
Posted 18 Sep, 2020
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
Posted 18 Sep, 2020
Metastasis leads to most cancer deaths, but its spatiotemporal behavior remains unpredictable at early stage. Here, we developed 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 achieved high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identified Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis, and demonstrated their high metastatic risks with significantly shorter disease-free survivals and higher migratory potential. In addition, we identified genomic alterations associated with organ-specific metastases, and employed them to stratify patients into the risk groups with propensities toward different metastatic organs. Remarkably, this organotropic stratification achieved better prognostic value than standard histological grading system in prostate cancer, especially between Bone-MFP and Liver-MFP subtypes, with organotropic insights to inform organ-specific examinations in follow-ups.
Figure 1
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Figure 7