January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

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  <identifier identifierType="DOI">10.5281/zenodo.5782816</identifier>
  <creators>
    <creator>
      <creatorName>Alexander Chervov</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Andrei Zinovyev</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>clinical trajectories,</subject>
    <subject>breast cancer</subject>
    <subject>transcriptome</subject>
    <subject>principal tree</subject>
    <subject>survival analysis</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-01-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5782816</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5782815</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Clinical trajectory is a clinically relevant sequence of ordered patient phenotypes representing consecutive states of a developing disease and leading to some final state. Extracting trajectories from large scale&amp;nbsp;medical data is of great interest for dynamical phenotyping of various diseases but remains a challenge for&amp;nbsp;machine learning methods, especially in the case of synchronic (with short follow up) observations. Here&amp;nbsp;we describe an approach for trajectory-based analysis of cancer data using elastic principal trees and test&amp;nbsp;it on a large collection of molecular tumoral profiles for breast cancer. We show that the disease progress&amp;nbsp;quantified with pseudotime (the geodesic distance from the root) along a particular trajectory can serve&amp;nbsp;as a significant prognostic factor, not redundant with gene expression-based predictors. We conclude that&amp;nbsp;application of the elastic principal trees to transcriptomic data can be of interest for clinical applications.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
      <awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>Agence Nationale de la Recherche</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001665</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/ANR//ANR-19-P3IA-0001/">ANR-19-P3IA-0001</awardNumber>
      <awardTitle>PaRis Artificial Intelligence Research InstitutE</awardTitle>
    </fundingReference>
  </fundingReferences>
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