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Related Concept Videos

Phylogeny01:23

Phylogeny

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Phylogenetic Trees03:21

Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Related Experiment Video

Updated: Mar 18, 2026

Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues
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Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues

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FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe.

E Michael Gertz1, Salim Akhter Chowdhury2,3, Woei-Jyh Lee1

  • 1Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, United States of America.

Plos One
|July 1, 2016
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Summary
This summary is machine-generated.

FISH trees 3.0 models tumor evolution using ploidy and copy-number changes. This approach reveals insights into tumor progression and heterogeneity, aiding in understanding cancer development.

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Area of Science:

  • Oncology
  • Genetics
  • Bioinformatics

Background:

  • Intra-tumor heterogeneity is a significant challenge in cancer research.
  • Fluorescence in situ hybridization (FISH) enables detection of copy-number alterations in solid tumors.
  • Tumor evolution can be modeled using phylogenetic trees, incorporating ploidy and gene copy-number variations.

Purpose of the Study:

  • To introduce FISHtrees 3.0, a novel computational tool for modeling tumor evolution.
  • To implement a ploidy-based tree-building method using mixed integer linear programming (MILP).
  • To develop a method for constructing consensus graphs to compare tumor progression across multiple samples.

Main Methods:

  • FISH analysis to assess copy-number changes and ploidy in tumor cells.
  • Development of a MILP-based algorithm for constructing phylogenetic trees incorporating ploidy.
  • Implementation of a consensus graph method for multi-sample tumor progression analysis.
  • Validation using simulated data and real-world FISH data from cervical and breast cancer cases.

Main Results:

  • FISHtrees 3.0 accurately models tumor evolution, outperforming ploidy-less methods on simulated data.
  • Analysis of cervical and breast cancer data revealed significant tumor heterogeneity and divergence.
  • DCIS (ductal carcinoma in situ) samples exhibited less complex evolutionary trees than paired IDC (invasive ductal carcinoma) samples.
  • Low consensus between DCIS and IDC trees suggests challenges in predicting progression risk.

Conclusions:

  • FISHtrees 3.0 provides a robust framework for analyzing tumor evolution and heterogeneity.
  • Ploidy-based modeling offers novel insights into cancer progression dynamics.
  • The findings highlight the complexity of tumor evolution and may inform biomarker discovery for DCIS progression.