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RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data

  • 0Graduate Group of Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
Communications Biology +

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September 24, 2025

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September 24, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

RCANE, a deep-learning tool, predicts genome-wide somatic copy-number aberrations (SCNAs) from RNA sequencing data. This approach offers a cost-effective method for cancer research and diagnostics.

Area Of Science

  • Genomics
  • Bioinformatics
  • Cancer Research

Background

  • Transcriptome sequencing (RNA-seq) is crucial for cancer research, analyzing gene expression.
  • Somatic copy-number aberrations (SCNAs) are key drivers of cancer development.
  • Inferring SCNAs from RNA-seq offers a cost-effective alternative to DNA assays.

Purpose Of The Study

  • To introduce RCANE, a deep-learning framework for predicting genome-wide SCNAs using only RNA-seq data.
  • To demonstrate RCANE's effectiveness across diverse cancer types.
  • To provide a scalable and robust solution for SCNA profiling.

Main Methods

  • Developed a deep-learning framework named RCANE.
  • Trained RCANE on The Cancer Genome Atlas (TCGA) and DepMap cell-line cohorts.
  • Evaluated RCANE's performance against existing SCNA inference methods.

Main Results

  • RCANE accurately predicts genome-wide SCNAs from RNA-seq data.
  • The framework demonstrates superior performance compared to current approaches.
  • RCANE provides a scalable and robust solution for SCNA profiling.

Conclusions

  • RCANE enhances SCNA profiling using only RNA-seq data.
  • This method improves cancer diagnostics and therapeutic decision-making.
  • RCANE offers a valuable tool for cancer research and clinical applications.

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