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Related Concept Videos

Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...

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Related Experiment Video

Updated: Jun 24, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling.

Shaya Akbarinejad1,2, Sarah Doppler1,3, Jos de Graaf1,2

  • 1TRON - Translational Oncology, University Medical Center of Johannes Gutenberg University Mainz gGmbH, Freiligrathstraße 12, Mainz, 55131, Germany.

Genome Medicine
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

A new computational pipeline, DEEPctMUT, enhances tumor-naïve circulating tumor DNA (ctDNA) detection. This method achieves high sensitivity for cancer monitoring, comparable to more complex tumor-informed approaches.

Keywords:
Deep learningLiquid biopsyMachine learningSomatic variant callingTumor-naïve ctDNA assayctDNA mutation detection

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Related Experiment Videos

Last Updated: Jun 24, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Area of Science:

  • Biotechnology
  • Genomics
  • Computational Biology

Background:

  • Circulating tumor DNA (ctDNA) detection enables minimally invasive cancer monitoring via blood samples.
  • Tumor-informed ctDNA assays are sensitive but face limitations in cost, tissue availability, and turnaround time.
  • Tumor-naïve assays offer faster results and broader use but typically lack sensitivity.

Purpose of the Study:

  • To develop DEEPctMUT, a computational pipeline for highly sensitive tumor-naïve ctDNA detection.
  • To integrate unique molecular identifiers (UMIs) with advanced error-polishing strategies.
  • To overcome the sensitivity limitations of existing tumor-naïve ctDNA methods.

Main Methods:

  • Developed DEEPctMUT, a computational pipeline integrating UMIs with machine learning for error suppression, deep learning (DeepES) for noise filtering, and patient-matched PBMC for variant removal.
  • Designed an efficient sequencing panel and validated the pipeline using cell lines, spike-in samples, healthy, and colorectal cancer (CRC) plasma samples.
  • Created a panel-independent version of DEEPctMUT for broader applicability.

Main Results:

  • DEEPctMUT achieved high sensitivity, detecting mutations down to 0.03% variant allele frequency (VAF), surpassing other tumor-naïve methods.
  • In head-to-head comparisons, DEEPctMUT identified pre-surgical CRC cases with 100% sensitivity, compared to 50% for the Roche Avenio Surveillance Kit.
  • The panel-independent DEEPctMUT improved performance on existing assay data without re-training.

Conclusions:

  • Advanced computational error-polishing techniques in DEEPctMUT significantly reduce technical artifacts.
  • The developed tumor-naïve method achieves sensitivity comparable to traditional tumor-informed approaches.
  • DEEPctMUT offers a sensitive, efficient, and broadly applicable solution for ctDNA analysis.