Evidence map›Paper›PMID 31744819›Full record

ArticleCancer research2020

Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer.

Runpu Chen, Steve Goodison, Yijun Sun

Abstract read
In one paragraph

Article in Cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. The In Vivo Selection Method in Breast Cancer Metastasis.International journal of molecular sciences · 2021
    Review
  9. Article
  10. Article
  11. Cytokines in various molecular subtypes of breast cancer.International journal of immunopathology and pharmacology
    Article
  12. Article
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

3 authors.

Runpu ChenDepartment of Computer Science and Engineering, The State University of New York, Buffalo, New York.ORCID 0000-0001-5290-6064
Steve GoodisonDepartment of Health Sciences Research, Mayo Clinic, Jacksonville, Florida. yijunsun@buffalo.edu goodison.steven@mayo.edu.
Yijun SunDepartment of Computer Science and Engineering, The State University of New York, Buffalo, New York. yijunsun@buffalo.edu goodison.steven@mayo.edu.

Funding

Oral Microbiome and Periodontitis: A Prospective Study in Postmenopausal WomenR01DE024523 · NIDCR · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI WACTAWSKI-WENDE, JEAN · 2014 to 2018
$3.8M
Advanced Computational Approaches to Delineating Dynamic Cancer Progression Processes by Using Massive Static Sample DataR01CA241123 · NCI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI GOODISON, STEVE, SUN, YIJUN · 2020 to 2024
$1.8M
Developing Advanced Algorithms to Address Major Computational Challenges in Current Microbiome ResearchR01AI125982 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SUN, YIJUN · 2016 to 2018
$973k
NCI NIH HHS R01 CA241123NIAID NIH HHS R01 AI125982NIDCR NIH HHS R01 DE024523
6 · The paper itself

Abstract

The interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics, and targeted therapeutics. SIGNIFICANCE: Analysis of matched primary and metastatic tumor samples supports a unidirectional, linear cancer evolution process and sheds light on longstanding issues regarding the origins of molecular subtypes and their progression relationships.

Indexed as

Evolution, MolecularModels, BiologicalAdultAgedAged, 80 and overBreastBreast NeoplasmsCohort StudiesComputational BiologyDatasets as TopicDisease ProgressionFemaleGene Expression ProfilingHumansLinear ModelsMiddle Aged

Identifiers

PMID31744819
PMCPMC8978345

What OpenQuestion holds

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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.