Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,254

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data

Open access
Rudin, Charles M., Poirier, John T., Byers, Lauren Averett, Dive, Caroline, Dowlati, Afshin, George, Julie, Heymach, John V., Johnson, Jane E., Lehman, Jonathan M., MacPherson, David, Massion, Pierre P., Minna, John D., Oliver, Trudy G., Quaranta, Vito, Sage, Julien, Thomas, Roman K., Vakoc, Christopher R., Gazdar, Adi F.·2019·Nature Reviews Cancer

Abstract

Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors. Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease. This Opinion, written by many leading experts in small cell lung cancer (SCLC) research, proposes a new model of SCLC subtypes defined by differential expression of four key transcription regulators. Such classification should help to focus and accelerate therapeutic research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41568-019-0133-9

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 64 references

Cited by

See all 1,254

Metadata

Authors
Rudin, C. M., Poirier, J. T., Byers, L. A., Dive, C., Dowlati, A., George, J., Heymach, J. V., Johnson, J. E., Lehman, J. M., MacPherson, D., Massion, P. P., Minna, J. D., Oliver, T. G., Quaranta, V., Sage, J., Thomas, R. K., Vakoc, C. R., & Gazdar, A. F.
Venue
Nature Reviews Cancer
Published
2019-03-29
Volume
19
Issue
5
Pages
289-297
ISSN
  • 1474-175X (ISSN-L)
  • 1474-1768
OpenAlex
W2928432651
Cited by
1,254
References
64
FWCI
14.64
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. https://app.cochat.ai/papers/10.1038/s41568-019-0133-9
The paper
Rudin, C. M., Poirier, J. T., Byers, L. A., Dive, C., Dowlati, A., George, J., Heymach, J. V., Johnson, J. E., Lehman, J. M., MacPherson, D., Massion, P. P., Minna, J. D., Oliver, T. G., Quaranta, V., Sage, J., Thomas, R. K., Vakoc, C. R., & Gazdar, A. F. (2019). Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nature Reviews Cancer. https://doi.org/10.1038/s41568-019-0133-9

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.