Evidence map›Paper›PMID 31780749›Full record

ArticleNature communications2019

Characterization of genetic subclonal evolution in pancreatic cancer mouse models.

Noushin Niknafs, Yi Zhong, John Alec Moral, Lance Zhang, Melody Xiaoshan Shao, April Lo, Alvin Makohon-Moore, Christine A Iacobuzio-Donahue, Rachel Karchin

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.5field-weighted citation impact, top 15% of its field
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

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  6. Computational modeling of human genetic variants in mice.bioRxiv : the preprint server for biology · 2025
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  7. Loss ofTheranostics · 2025
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  13. Article
  14. Current methods in translational cancer research.Cancer metastasis reviews · 2021
    Review
  15. Review
  16. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Noushin NiknafsDepartment of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.ORCID http://orcid.org/0000-0002-4852-3674
Yi ZhongSloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.ORCID http://orcid.org/0000-0002-4730-5077
John Alec MoralDepartment of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Lance ZhangSloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Melody Xiaoshan ShaoDepartment of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.
April LoDepartment of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.
Alvin Makohon-MooreSloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Christine A Iacobuzio-DonahueHuman Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. iacobuzc@mskcc.org.ORCID http://orcid.org/0000-0002-4672-3023
Rachel KarchinDepartment of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA. karchin@jhu.edu.ORCID http://orcid.org/0000-0002-5069-1239
Memorial Sloan Kettering Cancer Center · USJohns Hopkins University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Interrogating the Evolutionary Dynamics of Cancer for Clinical Benefit and ActionabilityR35CA220508 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI IACOBUZIO-DONAHUE, CHRISTINE A · 2018 to 2024
$6.9M
Translational Research in Oncology Training ProgramT32CA160001 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI SAWYERS, CHARLES L. · 2011 to 2020
$2.0M
TGF-beta Signaling in Pancreatic Cancer ProgressionR01CA140599 · NCI · JOHNS HOPKINS UNIVERSITY · PI IACOBUZIO-DONAHUE, CHRISTINE A · 2009 to 2013
$1.7M
(PQB6) Genetics of Subclonal Evolution in Pancreatic CancerR01CA179991 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI IACOBUZIO-DONAHUE, CHRISTINE A · 2013 to 2016
$1.3M
Subclonal Evolution in Metastatic Pancreatic CancerF31CA180682 · NCI · JOHNS HOPKINS UNIVERSITY · PI MAKOHON-MOORE, ALVIN · 2013 to 2014
$74k
NCI NIH HHS F31 CA180682NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA140599NCI NIH HHS R01 CA179991NCI NIH HHS R35 CA220508NCI NIH HHS T32 CA160001
6 · The paper itself

Abstract

The KPC mouse model, driven by the Kras and Trp53 transgenes, is well regarded for faithful recapitulation of human pancreatic cancer biology. However, the extent that this model recapitulates the subclonal evolution of this tumor type is unknown. Here we report evidence of continuing subclonal evolution after tumor initiation that largely reflect copy number alterations that target cellular processes of established significance in human pancreatic cancer. The evolutionary trajectories of the mouse tumors show both linear and branching patterns as well as clonal mixing. We propose the KPC model and derivatives have unexplored utility as a functional system to model the mechanisms and modifiers of tumor evolution.

Indexed as

AdenocarcinomaAnimalsClonal EvolutionDisease Models, AnimalDNA Copy Number VariationsEvolution, MolecularMiceMice, TransgenicMutationPancreatic NeoplasmsProto-Oncogene Proteins p21(ras)Receptor, Transforming Growth Factor-beta Type IITumor Suppressor Protein p53Hras protein, mouseProto-Oncogene Proteins p21(ras)Receptor, Transforming Growth Factor-beta Type IITgfbr2 protein, mouseTrp53 protein, mouseTumor Suppressor Protein p53

Identifiers

PMID31780749
PMCPMC6882784
OpenAlexW2990050632

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.