Evidence map›Paper›PMID 34145270›Full record

ArticleNPJ breast cancer2021

Establishment and characterization of immortalized human breast cancer cell lines from breast cancer patient-derived xenografts (PDX).

Yongxian Zhuang, Jordan M Grainger, Peter T Vedell, Jia Yu, Ann M Moyer, Huanyao Gao, Xiao-Yang Fan, Sisi Qin, Duan Liu, Krishna R Kalari and 4 more

Open access · goldAbstract read
In one paragraph

Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Cell Immortality:Life (Basel, Switzerland) · 2024
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. 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

14 authors at 3 institutions in 1 country.

Yongxian ZhuangDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Jordan M GraingerDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Peter T VedellDivision of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-6503-7619
Jia YuDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Ann M MoyerDepartment of Lab Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0003-2590-7218
Huanyao GaoDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-9441-9579
Xiao-Yang FanDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Sisi QinDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Duan LiuDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0003-1065-246X
Krishna R KalariDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Matthew P GoetzDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-4383-270X
Judy C BougheyDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.
Richard M WeinshilboumDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
Liewei WangDivision of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA. wang.liewei@mayo.edu.ORCID http://orcid.org/0000-0003-3818-8531
Mayo Clinic · USMayo Clinic in Florida · USMayo Clinic in Arizona · US

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI AKHILESH PANDEY · 1985 to 2026
$151.3M
TRAINING GRANT IN CLINICAL PHARMACOLOGYT32GM008685 · NIGMS · MAYO CLINIC ROCHESTER · PI Liewei Wang · 1998 to 2026
$8.2M
Investigate the role of TPD52-AMPK pathway in tumorigenesis and cancer therapyR01CA196648 · NCI · MAYO CLINIC ROCHESTER · PI WANG, LIEWEI · 2016 to 2020
$1.7M
NCI NIH HHS P30 CA015083NCI NIH HHS R01 CA196648NIGMS NIH HHS T32 GM008685
6 · The paper itself

Abstract

The application of patient-derived xenografts (PDX) in drug screening and testing is a costly and time-consuming endeavor. While cell lines permit extensive mechanistic studies, many human breast cancer cell lines lack patient characteristics and clinical treatment information. Establishing cell lines that retain patient's genetic and drug response information would enable greater drug screening and mechanistic studies. Therefore, we utilized breast cancer PDX from the Mayo Breast Cancer Genome Guided Therapy Study (BEAUTY) to establish two immortalized, genomically unique breast cancer cell lines. Through extensive genetic and therapeutic testing, the cell lines were found to retain the same clinical subtype, major somatic alterations, and drug response phenotypes as their corresponding PDX and patient tumor. Our findings demonstrate PDX can be utilized to develop immortalized breast cancer cell lines and provide a valuable tool for understanding the molecular mechanism of drug resistance and exploring novel treatment strategies.

Identifiers

PMID34145270
PMCPMC8213738
OpenAlexW3166669750

What OpenQuestion holds

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Registered trials

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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.