The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer
Open accessAbstract
Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.
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.
Integrity signals
Registry
Cites
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.
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
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335
Self-citationSCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool
Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480
Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer
Brahmer, J., Reckamp, K. L., Baas, P., Crinò, L., Eberhardt, W. E. E., Poddubskaya, E., Antonia, S., Pluzanski, A., Vokes, E. E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504627 · Cited by 8,447
Self-citationMutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802
Self-citationPembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer
Gandhi, L., Rodríguez-Abreu, D., Gadgeel, S., Esteban, E., Felip, E., De Angelis, F., Domine, M., Clingan, P., Hochmair, M. J., Powell, S. F., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1801005 · Cited by 6,607
Herbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449
Self-citation
Cited by
Enfield, K. S. S., Colliver, E., Lee, C., Magness, A., Moore, D. A., Sivakumar, M., Grigoriadis, K., Pich, O., Karasaki, T., Hobson, P. S., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1380 · Cited by 151
Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34025-x · Cited by 147
Metadata
- Authors
- Ravi, A., Hellmann, M. D., Arniella, M. B., Holton, M., Freeman, S. S., Naranbhai, V., Stewart, C., Leshchiner, I., Kim, J., Akiyama, Y., Griffin, A. T., Vokes, N. I., Sakhi, M., Kamesan, V., Rizvi, H., Ricciuti, B., Forde, P. M., Anagnostou, V., Riess, J. W., Gibbons, D. L., Pennell, N. A., Velcheti, V., Digumarthy, S. R., Mino-Kenudson, M., Califano, A., Heymach, J. V., Herbst, R. S., Brahmer, J. R., Schalper, K. A., Velculescu, V. E., Henick, B. S., Rizvi, N., Jänne, P. A., Awad, M. M., Chow, A., Greenbaum, B. D., Luksza, M., Shaw, A. T., Wolchok, J., Hacohen, N., Getz, G., & Gainor, J. F.
- Venue
- Nature Genetics
- Published
- 2023-04-06
- Volume
- 55
- Issue
- 5
- Pages
- 807-819
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W4362673605
- Cited by
- 188
- References
- 33
- FWCI
- 19.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. https://app.cochat.ai/papers/10.1038/s41588-023-01355-5
- The paper
- Ravi, A., Hellmann, M. D., Arniella, M. B., Holton, M., Freeman, S. S., Naranbhai, V., Stewart, C., Leshchiner, I., Kim, J., Akiyama, Y., Griffin, A. T., Vokes, N. I., Sakhi, M., Kamesan, V., Rizvi, H., Ricciuti, B., Forde, P. M., Anagnostou, V., Riess, J. W., . . . Gainor, J. F. (2023). Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nature Genetics. https://doi.org/10.1038/s41588-023-01355-5
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.
