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Related Concept Videos

The Evidence for Evolution02:55

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

Updated: Feb 1, 2026

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TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors.

Marleen M Nieboer1, Lambert C J Dorssers2, Roy Straver1

  • 1Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.

Plos One
|November 30, 2018
PubMed
Summary

TargetClone is a new computational method that reconstructs tumor subclonal evolution from targeted sequencing data. It accurately deconvolves tumor heterogeneity, aiding cancer progression and treatment response studies.

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Area of Science:

  • Oncology
  • Computational Biology
  • Genetics

Background:

  • Tumors exhibit significant subclonal heterogeneity, complicating the study of cancer progression and treatment response.
  • Accurate reconstruction of tumor evolutionary relationships is essential but challenging due to sample heterogeneity and difficulties in obtaining unbiased read depth measurements.
  • Microdissection coupled with targeted sequencing offers a way to reduce heterogeneity, but requires specialized computational tools.

Purpose of the Study:

  • To introduce TargetClone, a novel computational method for reconstructing tumor subclonal evolution trees.
  • To enable accurate inference of copy numbers, alleles, and tumor fraction from targeted sequencing data.
  • To provide a tool specifically designed for microdissected samples to overcome limitations in existing methods.

Main Methods:

  • Developed TargetClone, a Python-based computational method utilizing single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant data.
  • Designed TargetClone to reconstruct subclonal evolution trees and infer genomic features like copy number and tumor fraction.
  • Validated TargetClone on simulated data and real-world datasets from testicular germ cell and ovarian cancers.

Main Results:

  • TargetClone demonstrated low error rates on simulated data, indicating high accuracy.
  • The method successfully reconstructed expected evolutionary trees in complex cancer datasets.
  • TargetClone provides essential insights into tumor heterogeneity and subclonal architecture.

Conclusions:

  • TargetClone is an effective tool for deconvolving tumor heterogeneity from targeted sequencing data.
  • The method facilitates a deeper understanding of tumor evolution, progression, and response to therapy.
  • TargetClone is publicly available, promoting further research in cancer genomics and personalized medicine.