Evidence map›Paper›PMID 40093054›Full record

ArticlebioRxiv : the preprint server for biology2025

A patient-derived ovarian cancer organoid platform to study susceptibility to natural killer cells.

Marisa Mercadante, Armin Scheben, Jacob Estrada, Jan Savas-Carstens, William Sullivan, Nicholas Housel, Tatiana Volpari, Jax Hebner, Maria Sapar, Tom Rusielewicz and 4 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Marisa MercadanteThe New York Stem Cell Foundation Research Institute, New York, NY.
Armin SchebenThe New York Stem Cell Foundation Research Institute, New York, NY.
Jacob EstradaThe New York Stem Cell Foundation Research Institute, New York, NY.
Jan Savas-CarstensThe New York Stem Cell Foundation Research Institute, New York, NY.
William SullivanThe New York Stem Cell Foundation Research Institute, New York, NY.
Nicholas HouselThe Stevens Institute of Technology, Hoboken, NJ.
Tatiana VolpariThe New York Stem Cell Foundation Research Institute, New York, NY.
Jax HebnerThe New York Stem Cell Foundation Research Institute, New York, NY.
Maria SaparThe New York Stem Cell Foundation Research Institute, New York, NY.
Tom RusielewiczThe New York Stem Cell Foundation Research Institute, New York, NY.
Frederick J MonsmaThe New York Stem Cell Foundation Research Institute, New York, NY.
Stefan SemrauThe New York Stem Cell Foundation Research Institute, New York, NY.
Yinan WangThe New York Stem Cell Foundation Research Institute, New York, NY.
Laura A MartinThe New York Stem Cell Foundation Research Institute, New York, NY.ORCID 0000-0003-2677-1025

Funding

Establishing efficient technologies for ovarian cancer organoid derivation from fresh tumor resectionsR21CA240219 · NCI · NEW YORK STEM CELL FOUNDATION · PI ANDRES-MARTIN, LAURA · 2019 to 2021
$626k
NCI NIH HHS R21 CA240219
6 · The paper itself

Abstract

Intratumoral heterogeneity drives therapy resistance and relapses in advanced stage cancers, such as ovarian cancer. Here, we present a live cell imaging assay using patient-derived ovarian cancer organoids for real time capture and quantification of natural killer cell-mediated apoptotic events in >500 organoids simultaneously. Our assay revealed significant inter- and intratumor response heterogeneity and identified a rare resistant organoid population, opening avenues to test immunomodulatory strategies that overcome resistance.

Identifiers

PMID40093054
PMCPMC11908259

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.