Related Experiment Video

Updated: Aug 15, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

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A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers

David Wang1,2, Mathieu Quesnel-Vallieres1,3, San Jewell1

  • 1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Nature Communications
|January 4, 2023
PubMed

Insights

We developed CHESSBOARD, a new algorithm for analyzing RNA splicing data to identify cancer subtypes. This method improves personalized cancer treatment by revealing novel splicing profiles linked to drug response.

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Personalized cancer treatment relies on accurate cancer sub-typing.
  • RNA splicing alterations are increasingly recognized as important in cancer development and progression.
  • Existing methods may not fully capture the complexity of splicing data.

Purpose of the Study:

  • To introduce CHESSBOARD, an unsupervised algorithm for sub-typing cancer based on RNA splicing patterns.
  • To evaluate CHESSBOARD's performance on synthetic and real-world leukemia datasets.
  • To explore the clinical utility of RNA splicing profiles in cancer diagnostics and treatment selection.

Main Methods:

  • Developed CHESSBOARD, an unsupervised algorithm for RNA splicing data analysis.
  • CHESSBOARD identifies 'tiles' defined by unique splicing changes in patient subsets.
  • The algorithm handles flexible numbers of tiles, quantifies splicing uncertainty, and models missing data.

Main Results:

  • CHESSBOARD successfully identified reproducible splicing tiles in leukemia datasets.
  • Detected tiles showed potential regulatory drivers and correlations with known Acute Myeloid Leukemia (AML) mutations.
  • Integrating splicing profiles with mutation data improved drug response correlation.

Conclusions:

  • CHESSBOARD is an effective tool for unsupervised sub-typing of cancer using RNA splicing data.
  • Identified splicing profiles have potential as biomarkers for cancer diagnostics and predicting drug response.
  • This approach offers a novel way to enhance personalized cancer therapy.

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