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Related Concept Videos

Mutations01:39

Mutations

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Overview
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Mutations01:35

Mutations

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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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Wood Panel Products01:18

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Wood panel products are essential materials used in construction for applications such as flooring, siding, and roofing, typically available in standard dimensions of 4 feet by 8 feet, with thicknesses varying from one-quarter of an inch to one and one-eighth inches. Among the most common types of wood panels is plywood, which is produced by gluing multiple layers of thin wood veneers under pressure. The grain of the outer veneers runs lengthwise, while the grains of the interior layers run...
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Viral Mutations00:36

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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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Mutation, Gene Flow, and Genetic Drift01:09

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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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Mutations in Microorganisms01:18

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Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
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Author Spotlight: Modeling Brain Tumors In Vivo Using Electroporation-Based Delivery of Plasmid DNA Representing Patient Mutation Signatures
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Optimizing panel-based tumor mutational burden (TMB) measurement.

J Budczies1, M Allgäuer2, K Litchfield3

  • 1Institute of Pathology, University Hospital Heidelberg, Heidelberg; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany.

Annals of Oncology : Official Journal of the European Society for Medical Oncology
|July 4, 2019
PubMed
Summary
This summary is machine-generated.

Panel sequencing tumor mutational burden (psTMB) estimates are replacing whole exome sequencing (WES) for predicting immune checkpoint blockade (ICB) response. A mathematical law reveals psTMB accuracy limitations, guiding panel design and a new classification scheme for improved biomarker reliability.

Keywords:
TMBimmune checkpoint blockadeimmune-oncologypanel sequencingtumor mutational burden

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Area of Science:

  • Oncology
  • Genomics
  • Biostatistics

Background:

  • Panel sequencing tumor mutational burden (psTMB) is emerging as a predictive biomarker for immune checkpoint blockade (ICB).
  • Whole exome sequencing (WES) based tumor mutational burden (TMB) is being supplanted by psTMB.
  • Understanding psTMB variability is crucial for its clinical application.

Purpose of the Study:

  • To derive a mathematical law governing psTMB variability.
  • To assess the impact of panel size and TMB level on psTMB accuracy.
  • To propose improvements in psTMB classification and panel design for reliable ICB prediction.

Main Methods:

  • Mathematical modeling of psTMB using random mutation models.
  • In silico simulations on TCGA pan-cancer and WES datasets.
  • Analysis of intratumoral heterogeneity in the TRACERx 100 cohort.

Main Results:

  • A mathematical law shows coefficient of variation (CV) of psTMB is inversely proportional to the square root of panel size and TMB level.
  • Simulations confirmed this law, with CV at 35% for 10 muts/Mbp on large panels.
  • Misclassification rates of 10-19% were observed, highlighting limitations of small panels for ICB prediction.

Conclusions:

  • A universal mathematical law governs psTMB accuracy limitations and misclassification rates.
  • Optimized panel design and a novel three-tier classification scheme can mitigate these limitations.
  • Indel burden can serve as an independent marker complementing missense mutation burden for TMB reporting.