Evidence map›Paper›PMID 26072490›Full record

ArticleBioinformatics (Oxford, England)2015

Inferring models of multiscale copy number evolution for single-tumor phylogenetics.

Salim Akhter Chowdhury, E Michael Gertz, Darawalee Wangsa, Kerstin Heselmeyer-Haddad, Thomas Ried, Alejandro A Schäffer, Russell Schwartz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.Journal of computational biology : a journal of computational molecular cell biology · 2021
    Article
  5. Article
  6. Article
  7. Single-Cell-Derived Primary Rectal Carcinoma Cell Lines Reflect Intratumor Heterogeneity Associated with Treatment Response.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020
    Article
  8. Copy number evolution with weighted aberrations in cancer.Bioinformatics (Oxford, England) · 2020
    Article
  9. Article
  10. Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.Journal of computational biology : a journal of computational molecular cell biology · 2020
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Advances in understanding tumour evolution through single-cell sequencing.Biochimica et biophysica acta. Reviews on cancer · 2017
    Review
  16. The Cellular Origin and Evolution of Breast Cancer.Cold Spring Harbor perspectives in medicine · 2017
    Review
  17. Article
  18. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Salim Akhter ChowdhuryJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
E Michael GertzJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Darawalee WangsaJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Kerstin Heselmeyer-HaddadJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Thomas RiedJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Alejandro A SchäfferJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Russell SchwartzJoint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.

Funding

Intratumor heterogeneity and tumor evolutionZIABC010834 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI RIED, THOMAS · 2009 to 2023
$12.2M
Case Studies In GeneticsZIALM000097 · NLM · NATIONAL LIBRARY OF MEDICINE · PI SCHAFFER, ALEJANDRO A · 2009 to 2019
$4.6M
Heterogeneous Cancer Progression from Microarray DataR01CA140214 · NCI · CARNEGIE-MELLON UNIVERSITY · PI SCHWARTZ, RUSSELL S · 2009 to 2013
$1.4M
Inferring in vivo Capsid Assembly Kinetics from in vitro by Stochastic SimulationR01AI076318 · NIAID · CARNEGIE-MELLON UNIVERSITY · PI SCHWARTZ, RUSSELL S · 2009 to 2012
$1.1M
Intramural NIH HHSNCI NIH HHS 1R01CA140214NIAID NIH HHS 1R01AI076318
6 · The paper itself

Abstract

motivationPhylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models.

resultsWe propose a framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. We propose a new algorithm for identification of most parsimonious combinations of single gene and single chromosome events. We extend it via dynamic programming to include genome duplications. We implement an expectation maximization (EM)-like method to estimate mutation-specific and tumor-specific event rates concurrently with tree reconstruction. Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival. AVAILABILITY AND IMPLEMENTATION: Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

Indexed as

Evolution, MolecularGene DosageModels, GeneticAlgorithmsDisease ProgressionFemaleGenomicsHumansNeoplasmsPhylogenySoftwareUterine Cervical Neoplasms

Identifiers

PMID26072490
PMCPMC4481700

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.