Evidence map›Paper›PMID 29768500›Full record

ArticlePloS one2018

Clinically relevant inflammatory breast cancer patient-derived xenograft-derived ex vivo model for evaluation of tumor-specific therapies.

Bedrich L Eckhardt, Maria Gagliardi, LaKesla Iles, Kurt Evans, Cristina Ivan, Xiuping Liu, Chang-Gong Liu, Glauco Souza, Arvind Rao, Funda Meric-Bernstam and 2 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

13 citing papers in PubMed, 21 citations in OpenAlex.

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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

12 authors at 2 institutions in 1 country.

Bedrich L EckhardtDepartment of Breast Medical Oncology, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Maria GagliardiDepartment of Breast Medical Oncology, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
LaKesla IlesDepartment of Experimental Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Kurt EvansDepartment of Investigational Cancer Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Cristina IvanDepartment of Experimental Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Xiuping LiuDepartment of Experimental Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Chang-Gong LiuDepartment of Experimental Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Glauco SouzaNano3D Biosciences, Houston, Texas, United States of America.
Arvind RaoDepartment of Bioinformatics and Computational Biology, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Funda Meric-BernstamDepartment of Investigational Cancer Therapeutics, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Naoto T UenoDepartment of Breast Medical Oncology, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.
Geoffrey A BartholomeuszMorgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.ORCID 0000-0002-4622-4195
The University of Texas MD Anderson Cancer Center · USNano3D Biosciences (United States) · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672NIH HHS P30CA016672
6 · The paper itself

Abstract

Inflammatory breast cancer (IBC) is a rare and aggressive presentation of invasive breast cancer with a 62% to 68% 5-year survival rate. It is the most lethal form of breast cancer, and early recognition and treatment is important for patient survival. Like non-inflammatory breast cancer, IBC comprises multiple subtypes, with the triple-negative subtype being overrepresented. Although the current multimodality treatment regime of anthracycline- and taxane-based neoadjuvant therapy, surgery, and radiotherapy has improved the outcome of patients with triple-negative IBC, overall survival continues to be worse than in patients with non-inflammatory locally advanced breast cancer. Translation of new therapies into the clinics to successfully treat IBC has been poor, in part because of the lack of in vitro preclinical models that can accurately predict the response of the original tumor to therapy. We report the generation of a preclinical IBC patient-derived xenograft (PDX)-derived ex vivo (PDXEx) model and show that it closely replicates the tissue architecture of the original PDX tumor harvested from mice. The gene expression profile of our IBC PDXEx model had a high degree of correlation to that of the original tumor. This suggests that the process of generating the PDXEx model did not significantly alter the molecular signature of the original tumor. We demonstrate a high degree of similarity in drug response profile between a PDX mouse model and our PDXEx model generated from the same original PDX tumor tissue and treated with the same panel of drugs, indicating that our PDXEx model had high predictive value in identifying effective tumor-specific therapies. Finally, we used our PDXEx model as a platform for a robotic-based high-throughput drug screen of a 386-drug anti-cancer compound library. The top candidates identified from this drug screen all demonstrated greater therapeutic efficacy than the standard-of-care drugs used in the clinic to treat triple-negative IBC, doxorubicin and paclitaxel. Our PDXEx model is simple, and we are confident that it can be incorporated into a PDX mouse system for use as a first-pass screening platform. This will permit the identification of effective tumor-specific therapies with high predictive value in a resource-, time-, and cost-efficient manner.

Indexed as

Drug Evaluation, PreclinicalHigh-Throughput Screening AssaysXenograft Model Antitumor AssaysAnimalsAntineoplastic AgentsBiomarkers, TumorFemaleGene Expression ProfilingHumansInflammatory Breast NeoplasmsMiceMice, Inbred NODMice, SCIDTumor Cells, CulturedAntineoplastic AgentsBiomarkers, Tumor

Identifiers

PMID29768500
PMCPMC5955489
OpenAlexW2804476031

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.