January 27, 2021 Conference paper Open Access

Alexander Chervov; Andrei Zinovyev

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    <subfield code="a">Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees</subfield>
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    <subfield code="a">&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;</subfield>
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