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Cancers Originate from Somatic Mutations in a Single Cell02:21

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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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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Somatic spinal reflexes are rapid, involuntary muscular responses to external stimuli that involve the somatic musculature and the spinal cord.
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Cancer subtype identification using somatic mutation data.

Marieke Lydia Kuijjer1,2, Joseph Nathaniel Paulson3,4,5, Peter Salzman6

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. mkuijjer@jimmy.harvard.edu.

British Journal of Cancer
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This summary is machine-generated.

We developed a new method to analyze cancer mutation data, revealing subtypes linked to targeted treatments. This approach helps classify patients for personalized cancer therapy, improving treatment outcomes.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Next-generation sequencing has advanced cancer driver mutation identification.
  • Large-scale cancer sample screening generates abundant data for analysis.
  • Patient classification by mutation profiles aids in identifying treatment subgroups, but data sparseness and heterogeneity pose challenges.

Purpose of the Study:

  • To introduce a novel method for de-sparsifying somatic mutation data.
  • To apply this method to a large dataset across multiple cancer types.
  • To identify patient subtypes and potential therapeutic associations.

Main Methods:

  • Developed a pathway-based de-sparsification method for somatic mutation data.
  • Applied the method to 5805 primary tumor samples from 23 cancer types in The Cancer Genome Atlas.
  • Integrated phenotypic data for association analysis.

Main Results:

  • De-sparsified mutation data showed associations with phenotypic data across most cancer types.
  • Identified poor prognostic subtypes in three cancer types linked to signal transduction pathways with available targeted treatments.
  • Discovered subtype-drug associations for 14 additional subtypes.
  • Uncovered nine pan-cancer subtypes associated with four overarching biological pathway sets.

Conclusions:

  • The study represents a significant advancement in understanding cancer mutational patterns.
  • The developed method facilitates the identification of clinically relevant cancer subtypes.
  • Findings support the potential for pathway-informed precision oncology.