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Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

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Related Experiment Video

Updated: Oct 7, 2025

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

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

Published on: October 18, 2013

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer

Sayed Mohammad Ebrahim Sahraeian¹, Li Tai Fang¹, Konstantinos Karagiannis²

¹Roche Sequencing Solutions, Santa Clara, CA, 95050, USA.

|January 8, 2022

Summary

This study identifies optimal deep learning strategies for accurate somatic mutation detection using reference cancer data. A model trained on combined real and spike-in mutations showed the highest performance for cancer mutation analysis.

Keywords:

Convolutional neural networks Deep learning Model training strategies Somatic mutation Well-characterized somatic reference samples

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Published on: March 21, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Related Experiment Videos

Last Updated: Oct 7, 2025

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

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

Published on: October 18, 2013

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

Published on: March 21, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate somatic mutation detection is crucial for understanding cancer.
Deep convolutional neural networks offer a promising approach for mutation detection.
NeuSomatic was previously introduced as a deep learning method for somatic mutation detection.

Purpose of the Study:

To investigate best practices for deep learning-based cancer mutation detection.
To identify robust strategies for building deep learning models using comprehensive reference data.
To evaluate model performance across diverse real-world sample scenarios.

Main Methods:

Utilized SEQC2 consortium's somatic reference datasets for model training and validation.
Developed and tested deep learning models, including strategies combining real and spike-in mutations.
Assessed model performance across various sequencing technologies, DNA inputs, purities, and coverages.

Main Results:

A model trained on a combination of real and spike-in mutations demonstrated the highest average performance.
The identified strategy achieved high robustness across multiple experimental conditions.
Deep learning models significantly outperformed conventional approaches, especially in challenging scenarios.

Conclusions:

A specific deep learning strategy enhances the accuracy and robustness of somatic mutation detection.
This approach shows superiority over conventional methods in diverse and challenging genomic contexts.
The findings provide best practices for applying deep learning in cancer mutation analysis.