The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma
Open accessAbstract
SNF'ing out antitumor immunity: Immune checkpoint inhibitors induce durable tumor regressions in some, but not all, cancer patients. Understanding the mechanisms that determine tumor sensitivity to these drugs could potentially expand the number of patients who benefit (see the Perspective by Ghorani and Quezada). Pan et al. discovered that tumor cells in which a specific SWI/SNF chromatin remodeling complex had been experimentally inactivated were more sensitive to T cell–mediated killing. The cells were more responsive to interferon-γ, leading to increased secretion of cytokines that promote antitumor immunity. Miao et al. examined the genomic features of tumors from patients with metastatic renal cell carcinoma who had been treated with immune checkpoint inhibitors. Tumors harboring inactivating mutations in PBRM1 , which encodes a subunit of the same SWI/SNF complex, were more likely to respond to the drugs. Science , this issue p. 770 , p. 801 ; see also p. 745
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Hypoxia can directly upregulate the expression of various cellular immune checkpoints (such as programmed cell death‐ligand 1, PD‐L1) in the TME.”
From Hypoxia signaling in cancer: Implications for therapeutic interventions (2023) · cites this paper as [107]
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
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
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
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Self-citationPD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration
Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs017 · Cited by 9,049
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Cited by
Sorted by citations
Items 1,001–1,001 of 1,001
Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis
Ding, X., Zhang, L., Fan, M., & Li, L. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00486-7 · Cited by 2
Metadata
- Authors
- Miao, D., Margolis, C. A., Gao, W., Voss, M. H., Li, W., Martini, D. J., Norton, C., Bossé, D., Wankowicz, S. M., Cullen, D., Horak, C., Wind-Rotolo, M., Tracy, A., Giannakis, M., Hodi, F. S., Drake, C. G., Ball, M. W., Allaf, M. E., Snyder, A., Hellmann, M. D., Ho, T., Motzer, R. J., Signoretti, S., Kaelin, W. G., Choueiri, T. K., & Van Allen, E. M.
- Affiliations
- Broad Institute (US)
- Dana-Farber Cancer Institute (US)
- Memorial Sloan Kettering Cancer Center (US)
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
- Bristol-Myers Squibb (United States) (US)
- Bristol-Myers Squibb, New York, NY 10154, USA.
- Columbia University Irving Medical Center (US)
- Johns Hopkins University (US)
- Mayo Clinic in Arizona (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Choueiri, Toni K.
- ORCID
- Miao, Diana
- Margolis, Claire A.
- Gao, Wenhua
- Voss, Martin H.
- Li, Wei
- Martini, Dylan J.
- Bossé, Dominick
- Wind-Rotolo, Megan
- Tracy, Adam
- Giannakis, Marios
- Hodi, Frank Stephen
- Drake, Charles G.
- Ball, Mark W.
- Allaf, Mohamad E.
- Snyder, Alexandra
- Hellmann, Matthew D.
- Ho, Thai
- Motzer, Robert J.
- Signoretti, Sabina
- Kaelin, William G.
- Choueiri, Toni K.
- Van Allen, Eliezer M.
- Venue
- Science
- Published
- 2018-02-16
- Volume
- 359
- Issue
- 6377
- Pages
- 801-806
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6035749
- OpenAlex
- W2782183686
- Cited by
- 1,249
- References
- 57
- Citations by year
- 2026: 73 · 2025: 93 · 2024: 153 · 2023: 164 · 2022: 186 · 2021: 195 · 2020: 179 · 2019: 113 · 2018: 89
- FWCI
- 54.48
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (K08 CA188615, U24CA224316)
- Howard Hughes Medical Institute (Medical Research Fellowship)
- American Association for Cancer Research (KureIt Grant for Kidney Research)
- NIH Office of the Director (P30 CA008748)
- Bristol-Myers Squibb (II-ON Consortium)
- Dana-Farber Cancer Institute (Loker Pinard Funds for Kidney Cancer Research)
- Dana-Farber/Harvard Cancer Center (Kidney SPORE)
- BroadNext10
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Health Sciences › Medicine › Oncology › Renal cell carcinoma treatment
- Keywords
- clear cell renal cell carcinoma, immune checkpoint inhibitors, SWI/SNF complex, PBRM1 mutation, tumor response prediction, antitumor immunity, interferon-gamma, T cell-mediated killing, metastatic renal cell carcinoma, cancer genomics
- MeSH terms
- Carcinoma, Renal Cell, Chromosomal Proteins, Non-Histone, Humans, Immunotherapy, Kidney Neoplasms, Mutation, Transcription Factors, Cohort Studies, Gene Expression Profiling, Genomics, Exome, B7-H1 Antigen, CTLA-4 Antigen, Programmed Cell Death 1 Receptor
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
