Evidence map›Paper›PMID 37398306›Full record

ArticlebioRxiv : the preprint server for biology2023

Cell morphology best predicts tumorigenicity and metastasis

Sydney Conner, Justinne R Guarin, Thanh T Le, Jackson Fatherree, Charlotte Kelley, Samantha Payne, Ken Salhany, Rachel McGinn, Emily Henrich, Anna Yui and 5 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 1 institution in 1 country.

Sydney ConnerDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Justinne R GuarinDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Thanh T LeDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Jackson FatherreeDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Charlotte KelleyDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Samantha PayneDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Ken SalhanyDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Rachel McGinnDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Emily HenrichDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Anna YuiDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Savannah ParkerDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Deepti SrinivasanDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Hanan BloomerDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Hannah BorgesDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Madeleine J OudinDepartment of Biomedical Engineering, 200 College Avenue, Tufts University, Medford MA 02155, USA.
Tufts University · US

Funding

Defining and Exploiting the Neuronal Identity of CancersDP2CA271387 · NCI · TUFTS UNIVERSITY MEDFORD · PI OUDIN, MADELEINE JULIE · 2021 to 2024
$2.3M
Investigating mechanisms of chemoresistance in triple-negative breast cancerR01CA255742 · NCI · TUFTS UNIVERSITY MEDFORD · PI Madeleine Julie Oudin · 2022 to 2026
$1.8M
Investigating the role of the extracellular matrix in metastasis and chemo-resistanceR00CA207866 · NCI · TUFTS UNIVERSITY MEDFORD · PI OUDIN, MADELEINE JULIE · 2018 to 2020
$696k
NCI NIH HHS DP2 CA271387NCI NIH HHS R00 CA207866NCI NIH HHS R01 CA255742
6 · The paper itself

Abstract

Background: Metastasis is the leading cause of death in breast cancer patients. For metastasis to occur, tumor cells must invade locally, intravasate, and colonize distant tissues and organs, all steps that require tumor cell migration. The majority of studies on invasion and metastasis rely on human breast cancer cell lines. While it is known that these cells have different properties and abilities for growth and metastasis, the Methods: We evaluated the liver and lung metastasis of human TNBC cell lines MDA-MB-231, MDA-MB-468, BT549, Hs578T, BT20, and SUM159 in immunocompromised mice. We characterized each cell line's cell morphology, proliferation, and motility in 2D and 3D to determine the variation in these parameters between cell lines. Results: We identified MDA-MB-231, MDA-MB-468, and BT549 cells as highly tumorigenic and metastatic, Hs578T as poorly tumorigenic and metastatic, BT20 as intermediate tumorigenic with poor metastasis to the lungs but highly metastatic to the livers, and SUM159 as intermediate tumorigenic but poorly metastatic to the lungs and livers. We showed that metrics that characterize cell morphology are the most predictive of tumor growth and metastatic potential to the lungs and liver. Further, we found that no single Conclusions: Our results provide an important resource for the TNBC research community, identifying the metastatic potential of 6 commonly used cell lines. Our findings also support the use of cell morphological analysis to investigate the metastatic potential and emphasize the need for multiple

Indexed as

adhesioncell linesinvasionmetastasismigrationtriple-negative breast cancer

Identifiers

PMID37398306
PMCPMC10312673
OpenAlexW4380589947

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.