Evidence map›Paper›PMID 38191672›Full record

ReviewOncogene2024

Genetically-engineered mouse models of small cell lung cancer: the next generation.

Matthew G Oser, David MacPherson, Trudy G Oliver, Julien Sage, Kwon-Sik Park

Open access · greenAbstract readReview
In one paragraph

Review in Oncogene, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
10.7field-weighted citation impact, top 1% 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

32 citing papers in PubMed, 38 citations in OpenAlex.

  1. Article
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  14. MST1/2 DKO abates salvianolic acid B's therapeutic effect on CClNaunyn-Schmiedeberg's archives of pharmacology · 2025
    Article
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  16. Review
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  19. Dopamine DCell death & disease · 2025
    Article
  20. 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

5 authors at 5 institutions in 1 country.

Matthew G OserDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA. matthew_oser@dfci.harvard.edu.ORCID 0000-0003-2047-0969
David MacPhersonDivision of Human Biology, Fred Hutch Cancer Center, Seattle, WA, 98109, USA.ORCID 0000-0003-3729-907X
Trudy G OliverDepartment of Pharmacology & Cancer Biology, Duke University, Durham, NC, 27708, USA.
Julien SageDepartment of Pediatrics, Stanford University, Stanford, CA, 94305, USA.ORCID 0000-0002-8928-9968
Kwon-Sik ParkDepartment of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, 22903, USA. kp5an@virginia.edu.ORCID 0000-0003-3346-9086
Brigham and Women's Hospital · USDuke University · USFred Hutch Cancer Center · USStanford University · USUniversity of Virginia · US

Funding

Project 4: Risk stratification for pulmonary nodules detected by CT imaging using plasma and imaging biomarkersP50CA228944 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI David MacPherson · 2019 to 2026
$19.6M
Coordinating center for the NCI small cell lung cancer research consortiumU24CA213274 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Lauren Averett Byers, JOHN D. MINNA · 2017 to 2026
$12.9M
Investigating molecular and cellular mechanisms of SCLC development to identify novel therapeutic strategiesR35CA231997 · NCI · STANFORD UNIVERSITY · PI SAGE, JULIEN · 2019 to 2025
$6.2M
Employing CRISPR inactivation screening and in vivo models towards improving treatments for SCLCU01CA235625 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MACPHERSON, DAVID · 2019 to 2023
$2.7M
Targeting BCAT1 and branched-chain amino acid metabolism for the detection and prevention of SCLCU01CA224293 · NCI · UNIVERSITY OF VIRGINIA · PI PARK, KWON-SIK · 2018 to 2022
$2.2M
Mechanisms by which LSD1 Promotes Neuroendocrine Differentiation and Small Cell Lung CancerR37CA269990 · NCI · DANA-FARBER CANCER INST · PI Matthew Gilbert Oser · 2023 to 2026
$1.8M
NCI NIH HHS P50 CA228944NCI NIH HHS R35 CA231997NCI NIH HHS R37 CA269990NCI NIH HHS U01 CA224293NCI NIH HHS U01 CA235625NCI NIH HHS U24 CA213274
6 · The paper itself

Abstract

Small cell lung cancer (SCLC) remains the most fatal form of lung cancer, with patients in dire need of new and effective therapeutic approaches. Modeling SCLC in an immunocompetent host is essential for understanding SCLC pathogenesis and ultimately discovering and testing new experimental therapeutic strategies. Human SCLC is characterized by near universal genetic loss of the RB1 and TP53 tumor suppressor genes. Twenty years ago, the first genetically-engineered mouse model (GEMM) of SCLC was generated using conditional deletion of both Rb1 and Trp53 in the lungs of adult mice. Since then, several other GEMMs of SCLC have been developed coupling genomic alterations found in human SCLC with Rb1 and Trp53 deletion. Here we summarize how GEMMs of SCLC have contributed significantly to our understanding of the disease in the past two decades. We also review recent advances in modeling SCLC in mice that allow investigators to bypass limitations of the previous generation of GEMMs while studying new genes of interest in SCLC. In particular, CRISPR/Cas9-mediated somatic gene editing can accelerate how new genes of interest are functionally interrogated in SCLC tumorigenesis. Notably, the development of allograft models and precancerous precursor models from SCLC GEMMs provides complementary approaches to GEMMs to study tumor cell-immune microenvironment interactions and test new therapeutic strategies to enhance response to immunotherapy. Ultimately, the new generation of SCLC models can accelerate research and help develop new therapeutic strategies for SCLC.

Indexed as

Lung NeoplasmsSmall Cell Lung CarcinomaAnimalsCell Transformation, NeoplasticGene EditingHumansLungMiceTumor Microenvironment

Identifiers

PMID38191672
PMCPMC11180418
OpenAlexW4390669840

What OpenQuestion holds

Textmetadata
LicenceTDM
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.