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Related Concept Videos

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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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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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Evaluating somatic tumor mutation detection without matched normal samples.

Jamie K Teer1, Yonghong Zhang2, Lu Chen3

  • 1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA. Jamie.Teer@moffitt.org.

Human Genomics
|September 6, 2017
PubMed
Summary
This summary is machine-generated.

Detecting somatic mutations in tumors without matched normal samples is possible, offering similar sensitivity but lower precision. This tumor-only approach is useful for classifying samples based on known mutations when normal samples are unavailable.

Keywords:
Cancer genomicsNext-generation sequencingPrecision medicineSomatic mutation

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Recurrent somatic mutations in tumors are crucial for understanding cancer progression and therapeutic targets.
  • Distinguishing inherited genetic variants from somatic mutations in sequencing data is challenging.
  • Traditional methods require both tumor and normal samples from the same individual, limiting sample size.

Purpose of the Study:

  • To evaluate a method for detecting somatic mutations in tumor samples using only a subset of normal samples.
  • To develop and refine a tumor-only mutation detection strategy.
  • To compare the performance of tumor-only versus matched tumor/normal analysis.

Main Methods:

  • Developed and tuned a method for somatic mutation detection in tumor samples.
  • Utilized population variation datasets and a subset of normal samples for filtering inherited variants and technical artifacts.
  • Compared tumor-only mutation detection with matched tumor/normal approaches on targeted gene sequencing and whole exome sequencing data.

Main Results:

  • Tumor-only mutation detection demonstrated similar recall (43-60%) but lower precision (20-21%) compared to matched tumor/normal methods (recall 43-73%, precision 30-82%).
  • Incorporating a small pool of normal samples improved precision in tumor-only analysis.
  • A significant number of variants were uniquely detected in the tumor-only analysis.

Conclusions:

  • A method for somatic mutation detection without matched normal samples expands the study of tumors, especially when normal samples are unavailable.
  • Tumor-only detection is suitable for sample classification based on known mutations due to similar sensitivity, despite lower precision.
  • While matched tumor-normal analysis is preferred for higher precision, tumor-only detection is feasible for specific applications.