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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

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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...
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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Exon Recombination02:32

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

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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.
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Cancer-Critical Genes I: Proto-oncogenes01:33

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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.
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Related Experiment Video

Updated: Feb 24, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Mutation Clusters from Cancer Exome.

Zura Kakushadze1,2, Willie Yu3

  • 1Quantigic® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA. zura@quantigic.com.

Genes
|August 16, 2017
PubMed
Summary
This summary is machine-generated.

Our K-means algorithm reveals stable mutation clustering structures in cancer exome data, outperforming non-deterministic methods like nonnegative matrix factorization (NMF). This stability is crucial for developing rapid, cost-effective early cancer diagnostics.

Keywords:
DNAK-meanscancer signaturesclusteringcorrelationcovarianceeRankexomegenomeindustry classificationmachine learningmatrixnonnegative matrix factorizationquantitative financesamplesomatic mutationsource codestatistical risk model

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Area of Science:

  • Computational Biology
  • Genomics
  • Machine Learning

Background:

  • Cancer genomics research relies on analyzing mutation patterns in patient samples.
  • Accurate and stable identification of these patterns is essential for understanding cancer development and for diagnostic applications.
  • Current computational methods may lack the necessary stability and efficiency for large-scale genomic data analysis.

Purpose of the Study:

  • To evaluate the stability and performance of a statistically deterministic K-means clustering algorithm for identifying cancer mutation structures.
  • To compare the K-means algorithm's results with those obtained from nonnegative matrix factorization (NMF).
  • To assess the implications of stable mutation structure extraction for early cancer diagnostics.

Main Methods:

  • Application of the K-means clustering algorithm to a large dataset of 10,656 published exome samples across 32 cancer types.
  • Validation of the K-means algorithm's stability using in-sample and out-of-sample testing.
  • Comparison with nonnegative matrix factorization (NMF) applied to the same and additional datasets (1389 genome samples across 14 cancer types).

Main Results:

  • A majority of cancer types analyzed exhibited a stable mutation clustering structure when analyzed with K-means.
  • The K-means algorithm demonstrated both in-sample and out-of-sample stability.
  • Nonnegative matrix factorization (NMF) showed instabilities in extracted cancer signatures from exome samples, despite being computationally intensive.

Conclusions:

  • The statistically deterministic K-means algorithm provides stable and reliable identification of cancer mutation structures from exome data.
  • K-means offers a more stable alternative to NMF for cancer signature extraction, with significant advantages in speed and cost.
  • Stable mutation structure analysis holds promise for advancing early-stage cancer diagnostics, including novel blood-test technologies.