Evidence map›Paper›PMID 33572297›Full record

ReviewCancers2021

Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models.

P Jonathan Li, Jeroen P Roose, David M Jablons, Johannes R Kratz

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 51% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 1 institution in 1 country.

P Jonathan LiDepartment of Surgery, University of California, San Francisco, CA 94143, USA.
Jeroen P RooseDepartment of Anatomy, University of California, San Francisco, CA 94143, USA.
David M JablonsDepartment of Surgery, University of California, San Francisco, CA 94143, USA.
Johannes R KratzDepartment of Surgery, University of California, San Francisco, CA 94143, USA.
University of California, San Francisco · US

Funding

Balanced signaling cues to guide cell transitions in the blood lineage continuumR01HL120724 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHAKRABORTY, ARUP K., NOLAN, GARRY P · 2015 to 2019
$3.7M
Loss of Intrinsic Control in Autoimmune T Helper Cells with Signaling VariantsR01AI104789 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROOSE, JEROEN · 2014 to 2018
$1.9M
NIH-NCI R01 - CA187318)NIH-NHLBI R01 - HL120724NIH-NIAID R01-AI104789, P01-AI091580
6 · The paper itself

Abstract

3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications.

Indexed as

3D culturesbioinformaticslung cancerorganoidsspheroidsxenografts

Identifiers

PMID33572297
PMCPMC7915264
OpenAlexW3126423732

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.