Evidence map›Paper›PMID 28085924›Full record

ArticlePloS one2017

Microfluidic Biopsy Trapping Device for the Real-Time Monitoring of Tumor Microenvironment.

Angela Babetski Holton, Francy L Sinatra, Jenny Kreahling, Amy J Conway, David A Landis, Soner Altiok

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. 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
2.1field-weighted citation impact, top 14% 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, 33 citations in OpenAlex.

  1. Review
  2. Review
  3. Spheroids and organoids derived from colorectal cancer as tools forExploration of targeted anti-tumor therapy · 2024
    Review
  4. Progress and application of lung-on-a-chip for lung cancer.Frontiers in bioengineering and biotechnology · 2024
    Review
  5. Cancer 3D Models for Metallodrug Preclinical Testing.International journal of molecular sciences · 2023
    Review
  6. Review
  7. Patient-Derived In Vitro and In Vivo Models of Cancer.Advances in experimental medicine and biology · 2022
    Article
  8. Article
  9. Article
  10. Microfluidics for interrogating live intact tissues.Microsystems & nanoengineering · 2020
    Review
  11. Review
  12. Review
  13. Addressing Patient Specificity in the Engineering of Tumor Models.Frontiers in bioengineering and biotechnology · 2019
    Review
  14. Article
  15. Article
  16. 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

6 authors at 2 institutions in 1 country.

Angela Babetski HoltonDraper, Cambridge, Massachusetts, United States of America.
Francy L SinatraDraper, Cambridge, Massachusetts, United States of America.
Jenny KreahlingH. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America.
Amy J ConwayDraper, Cambridge, Massachusetts, United States of America.
David A LandisDraper, Cambridge, Massachusetts, United States of America.
Soner AltiokH. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America.
Draper Laboratory · USMoffitt Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumor microenvironment is composed of cellular and stromal components such as tumor cells, mesenchymal cells, immune cells, cancer associated fibroblasts and the supporting extracellular matrix. The tumor microenvironment provides crucial support for growth and progression of tumor cells and affects tumor response to therapeutic interventions. To better understand tumor biology and to develop effective cancer therapeutic agents it is important to develop preclinical platforms that can faithfully recapitulate the tumor microenvironment and the complex interaction between the tumor and its surrounding stromal elements. Drug studies performed in vitro with conventional two-dimensional cancer cell line models do not optimally represent clinical drug response as they lack true tumor heterogeneity and are often performed in static culture conditions lacking stromal tumor components that significantly influence the metabolic activity and proliferation of cells. Recent microfluidic approaches aim to overcome such obstacles with the use of cell lines derived in artificial three-dimensional supportive gels or micro-chambers. However, absence of a true tumor microenvironment and full interstitial flow, leads to less than optimal evaluation of tumor response to drug treatment. Here we report a continuous perfusion microfluidic device coupled with microscopy and image analysis for the assessment of drug effects on intact fresh tumor tissue. We have demonstrated that fine needle aspirate biopsies obtained from patient-derived xenograft models of adenocarcinoma of the lung can successfully be analyzed for their response to ex vivo drug treatment within this biopsy trapping microfluidic device, wherein a protein kinase C inhibitor, staurosporine, was used to assess tumor cell death as a proof of principle. This approach has the potential to study tumor tissue within its intact microenvironment to better understand tumor response to drug treatments and eventually to choose the most effective drug and drug combination for individual patients in a cost effective and timely manner.

Indexed as

AnimalsAntibiotics, AntineoplasticAntibodies, MonoclonalApoptosisBiopsyCell Culture TechniquesCell ProliferationDoxorubicinEnzyme InhibitorsFemaleHumansMiceMice, HairlessMice, SCIDMicrofluidic Analytical TechniquesNeoplasmsAntibiotics, AntineoplasticAntibodies, MonoclonalDoxorubicinEnzyme InhibitorsStaurosporine

Identifiers

PMID28085924
PMCPMC5235371
OpenAlexW2579680847

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.