This preprint is under consideration at a Nature Research Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
Jiguang Wang
The Hong Kong University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6923-4097
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Keywords
Metastasis, Gene Regulatory Networks, Data Integration, Machine Learning
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryFigures.pdf
Supplementary Figures
- SupplementaryTable1.xlsx
Supplementary Table 1 Mapping of raw tissue sites to general anatomic organs for primary tumor site.
- SupplementaryTable5.xlsx
- SupplementaryTable2.xlsx
Supplementary Table 2 Mapping of raw tissue sites to general anatomic organs for metastatic site.
- SupplementaryTable4.xlsx
- SupplementaryTable3.xlsx
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%).
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 18 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This preprint is under consideration at a Nature Research Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors
Jiguang Wang
The Hong Kong University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6923-4097
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 18 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7