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Related Experiment Video

Updated: Jan 10, 2026

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Transfer Learning for Survival-based Clustering of Predictors with an Application to TP53 Mutation Annotation.

Xiaoqian Liu1, Hao Yan2, Haoming Shi3

  • 1Department of Statistics, University of California at Riverside.

Biorxiv : the Preprint Server for Biology
|November 24, 2025
PubMed
Summary

A new method, Transfer Learning-Survival-based Clustering of Predictors (TL-SCP), accurately annotates TP53 mutations for Li-Fraumeni syndrome (LFS) patients. This approach improves understanding of TP53 mutation effects on survival, aiding clinical management.

Keywords:
ClusteringCox modelHomogeneityTP53 variantsTransfer learning

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Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Cancer Research

Background:

  • TP53 mutations are frequent in cancers and cause Li-Fraumeni syndrome (LFS), a hereditary cancer predisposition.
  • Accurate TP53 mutation annotation is crucial for managing LFS patients.

Purpose of the Study:

  • To develop a novel computational approach for clustering TP53 mutations based on their survival effects.
  • To enhance mutation annotation for improved clinical interpretability in LFS.

Main Methods:

  • Developed Survival-based Clustering of Predictors (SCP) using fusion-penalized Cox regression.
  • Introduced TL-SCP, a transfer learning extension of SCP, to leverage external data for improved performance.
  • Utilized weighted rank averaging to integrate ranking information from source to target datasets.

Main Results:

  • TL-SCP demonstrated superior performance over SCP in simulations for clustering recovery and coefficient estimation.
  • TL-SCP successfully identified biologically meaningful TP53 mutation clusters in LFS patients.
  • The method provided improved clinical interpretability compared to existing annotations.

Conclusions:

  • TL-SCP offers a robust and interpretable method for TP53 mutation annotation in LFS.
  • This approach can significantly aid in the clinical management and understanding of hereditary cancer syndromes.