The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade
Open accessAbstract
Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.
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
“Clinically relevant mechanisms of include mutations in beta-2-microglobulin (B2M) resulting in loss of MHC Class I presentation, downregulation of the antigen presentation machinery, and mutations in the IFNγ-JAK/STAT pathway that impair response to T cell mediated anti-tumor activity7–9.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [9]
In the citing paper
“perturbations of B2M, HLA-A, JAK1, JAK2, STAT1, IFNGR1 and IFNGR2 (Fig. 2d,e, Extended Data Fig. 3e,f, Supplementary Table 2) conferred immune evasion, consistent with clinical mechanisms of resistance to immunotherapy7–9.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [9]
In the citing paper
“We previously identified an ICR signature, which is predictive of intrinsic resistance to ICI, in a subset of malignant cells in patients with metastatic melanoma using scRNA-seq9.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [9]
In the citing paper
“consistent with our previously reported patient and pre-clinical data, where we demonstrate that upregulation of MYC and CDK4/6 (and their transcriptional targets) was associated with increased resistance to immunotherapy, and CDK4/6 inhibitors reduced resistance in human and pre-clinical melanoma models9.”
From Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion (2021) · cites this paper as [9]
In the citing paper
“This occurs either upon the derepression of endogenous retroviruses and consequent nucleic acid sensing (Goel et al., 2017; Vanpouille-Box et al., 2019) or following the establishment of a permanent cell cycle blockage (i.e., cellular senescence) characterized by an abundant secretory component, the so-called senescence-associated secretory phenotype (SASP) (Jerby-Arnon et al., 2018).”
From Immunomodulation by targeted anticancer agents (2021) · cites this paper as Jerby-Arnon et al., 2018
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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
(2008) · Journal of Machine Learning Research · Cited by 33,051
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
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157
Self-citationPrimary, Adaptive, and Acquired Resistance to Cancer Immunotherapy
Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017) · Cell · doi:10.1016/j.cell.2017.01.017 · Cited by 5,588
The immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Self-citationThe future of immune checkpoint therapy
Sharma, P., & Allison, J. P. (2015) · Science · doi:10.1126/science.aaa8172 · Cited by 4,878
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448
Genomic classification of cutaneous melanoma
Akbani, R., Akdemir, K. C., Aksoy, B. A., Albert, M., Ally, A., Amin, S. B., Arachchi, H., Arora, A., Auman, J. T., Ayala, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.044 · Cited by 3,151
Self-citationGenomic correlates of response to CTLA-4 blockade in metastatic melanoma
Van Allen, E. M., Miao, D., Schilling, B., Shukla, S. A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Geukes Foppen, M. H., Goldinger, S. M., et al. (2015) · Science · doi:10.1126/science.aad0095 · Cited by 2,839
Self-citationSystematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab
Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401
Self-citationAbsolute quantification of somatic DNA alterations in human cancer
Carter, S. L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T. I., Laird, P. W., Onofrio, R. C., Winckler, W., Weir, B. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2203 · Cited by 2,031
Curran, M. A., Montalvo, W., Yagita, H., & Allison, J. P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0915174107 · Cited by 1,874
Loss of PTEN Promotes Resistance to T Cell–Mediated Immunotherapy
Peng, W., Chen, J. Q., Liu, C., Malu, S., Creasy, C., Tetzlaff, M. T., Xu, C., McKenzie, J. A., Zhang, C., Liang, X., et al. (2016) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0283 · Cited by 1,539
Self-citationCDK4/6 inhibition triggers anti-tumour immunity
Goel, S., DeCristo, M. J., Watt, A. C., BrinJones, H., Sceneay, J., Li, B. B., Khan, N., Ubellacker, J. M., Xie, S., Metzger-Filho, O., et al. (2017) · Nature · doi:10.1038/nature23465 · Cited by 1,513
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348
In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target
Manguso, R. T., Pope, H. W., Zimmer, M. D., Brown, F. D., Yates, K. B., Miller, B. C., Collins, N. B., Bi, K., LaFleur, M. W., Juneja, V. R., et al. (2017) · Nature · doi:10.1038/nature23270 · Cited by 1,147
Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996) · Knowledge Discovery and Data Mining · Cited by 1,131
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
Simoudis, E., Han, J., & Fayyad, U. M. (1996) · Cited by 1,121
A smart local moving algorithm for large-scale modularity-based community detection
Waltman, L., & van Eck, N. J. (2013) · The European Physical Journal B · doi:10.1140/epjb/e2013-40829-0 · Cited by 1,088
A draft network of ligand–receptor-mediated multicellular signalling in human
Ramilowski, J. A., Goldberg, T., Harshbarger, J., Kloppmann, E., Lizio, M., Satagopam, V. P., Itoh, M., Kawaji, H., Carninci, P., Rost, B., et al. (2015) · Nature Communications · doi:10.1038/ncomms8866 · Cited by 887
Identification of essential genes for cancer immunotherapy
Patel, S. J., Sanjana, N. E., Kishton, R. J., Eidizadeh, A., Vodnala, S. K., Cam, M., Gartner, J. J., Jia, L., Steinberg, S. M., Yamamoto, T. N., et al. (2017) · Nature · doi:10.1038/nature23477 · Cited by 870
A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Pan, D., Kobayashi, A., Jiang, P., Ferrari de Andrade, L., Tay, R. E., Luoma, A. M., Tsoucas, D., Qiu, X., Lim, K., Rao, P., et al. (2018) · Science · doi:10.1126/science.aao1710 · Cited by 852
Self-citationCDK4/6 Inhibition Augments Antitumor Immunity by Enhancing T-cell Activation
Deng, J., Wang, E. S., Jenkins, R. W., Li, S., Dries, R., Yates, K., Chhabra, S., Huang, W., Liu, H., Aref, A. R., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0915 · Cited by 770 · cited 10×
“CDK4/6 Inhibitors Repress the Resistance Program in Melanoma Cells We therefore tested if CDK4/6i could shift the malignant cell population to a less immune resistant state.”
InfluentialSelf-citationLin, J., Izar, B., Wang, S., Yapp, C., Mei, S., Shah, P., Santagata, S., & Sorger, P. K. (2018) · eLife · doi:10.7554/elife.31657 · Cited by 685
Self-citationNon-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance
Hugo, W., Shi, H., Sun, L., Piva, M., Song, C., Kong, X., Moriceau, G., Hong, A., Dahlman, K. B., Johnson, D. B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.061 · Cited by 618
MYC: Connecting selective transcriptional control to global RNA production
Kress, T. R., Sabò, A., & Amati, B. (2015) · Nature reviews. Cancer · doi:10.1038/nrc3984 · Cited by 521
Schaer, D. A., Beckmann, R. P., Dempsey, J. A., Huber, L., Forest, A., Amaladas, N., Li, Y., Wang, Y. C., Rasmussen, E. R., Chin, D., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.053 · Cited by 469
Clinical outcomes in metastatic uveal melanoma treated with PD‐1 and PD‐L1 antibodies
Algazi, A. P., Tsai, K. K., Shoushtari, A. N., Munhoz, R. R., Eroglu, Z., Piulats, J. M., Ott, P. A., Johnson, D. B., Hwang, J. J., DAUD, A. I., et al. (2016) · Cancer · doi:10.1002/cncr.30258 · Cited by 399
Self-citationCharacterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Fan, J., Salathia, N. S., Liu, R., Kaeser, G. E., Yung, Y. C., Herman, J. L., Kaper, F., Fan, J., Zhang, K., Chun, J. J. M., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3734 · Cited by 376
Preparation of Single‐Cell RNA‐Seq Libraries for Next Generation Sequencing
Trombetta, J. J., Gennert, D. G., Lu, D. Y., Satija, R., Shalek, A. K., & Regev, A. (2014) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb0422s107 · Cited by 279
Self-citationGong, X., Litchfield, L. M., Webster, Y., Chio, L.-C., Wong, S. S., Stewart, T. R., Dowless, M. S., Dempsey, J. A., Zeng, Y. A., Torres, R., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.11.006 · Cited by 246
Lin, J., Izar, B., Wang, S., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/151738 · Cited by 104
Self-citationLesterhuis, W. J., Rinaldi, C., Jones, A. C., Rozali, E. N., Dick, I. M., Khong, A., Boon, L., Robinson, B. W. S., Nowak, A. K., Bosco, A., et al. (2015) · Scientific Reports · doi:10.1038/srep12298 · Cited by 69
Gordy, J. T., Luo, K., Zhang, H., Biragyn, A., & Markham, R. B. (2016) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-016-0189-y · Cited by 37
Oki, S., Ohta, T., Shioi, G., Hatanaka, H., Ogasawara, O., Okuda, Y., Kawaji, H., Nakaki, R., Sese, J., & Meno, C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/262899 · Cited by 17
Cited by
Sorted by citations
Inflammation and cancer: triggers, mechanisms, and consequences
Greten, F. R., & Grivennikov, S. I. (2019) · Immunity · doi:10.1016/j.immuni.2019.06.025 · Cited by 3,806
Zhang, Y., & Zhang, Z. (2020) · Cellular and Molecular Immunology · doi:10.1038/s41423-020-0488-6 · Cited by 2,906
Tertiary lymphoid structures improve immunotherapy and survival in melanoma
Cabrita, R., Lauss, M., Sanna, A., Donia, M., Skaarup Larsen, M., Mitra, S., Johansson, I., Phung, B., Harbst, K., Vallon-Christersson, J., et al. (2020) · Nature · doi:10.1038/s41586-019-1914-8 · Cited by 2,374
Self-citationThe evolving landscape of biomarkers for checkpoint inhibitor immunotherapy
Havel, J. J., Chowell, D., & Chan, T. A. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0116-x · Cited by 2,286
Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer
Zhang, L., Li, Z., Skrzypczynska, K. M., Fang, Q., Zhang, W., O’Brien, S. A., He, Y., Wang, L., Zhang, Q., Kim, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.048 · Cited by 1,620
Self-citation
Metadata
- Authors
- Jerby-Arnon, L., Shah, P., Cuoco, M. S., Rodman, C., Su, M.-J., Melms, J. C., Leeson, R., Kanodia, A., Mei, S., Lin, J.-R., Wang, S., Rabasha, B., Liu, D., Zhang, G., Margolais, C., Ashenberg, O., Ott, P. A., Buchbinder, E. I., Haq, R., Hodi, F. S., Boland, G. M., Sullivan, R. J., Frederick, D. T., Miao, B., Moll, T., Flaherty, K. T., Herlyn, M., Jenkins, R. W., Thummalapalli, R., Kowalczyk, M. S., Cañadas, I., Schilling, B., Cartwright, A. N. R., Luoma, A. M., Malu, S., Hwu, P., Bernatchez, C., Forget, M.-A., Barbie, D. A., Shalek, A. K., Tirosh, I., Sorger, P. K., Wucherpfennig, K., Van Allen, E. M., Schadendorf, D., Johnson, B. E., Rotem, A., Rozenblatt-Rosen, O., Garraway, L. A., Yoon, C. H., Izar, B., & Regev, A.
- Affiliations
- Dana-Farber Cancer Institute (US)
- Broad Institute (US)
- Harvard University (US)
- The Wistar Institute (US)
- Massachusetts General Hospital (US)
- Essen University Hospital (DE)
- The University of Texas MD Anderson Cancer Center (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Izar, Benjamin
- ORCID
- Shah, Parin
- Cuoco, Michael S.
- Rodman, Christopher
- Melms, Johannes C.
- Kanodia, Abhay
- Mei, Shaolin
- Lin, Jia-Ren
- Wang, Shu
- Liu, David
- Zhang, Gao
- Ashenberg, Orr
- Ott, Patrick A.
- Buchbinder, Elizabeth I.
- Haq, Rizwan
- Hodi, F. Stephen
- Boland, Genevieve M.
- Sullivan, Ryan J.
- Frederick, Dennie T.
- Moll, Tabea
- Flaherty, Keith T.
- Herlyn, Meenhard
- Jenkins, Russell W.
- Thummalapalli, Rohit
- Kowalczyk, Monika S.
- Cañadas, Israel
- Schilling, Bastian
- Cartwright, Adam N.R.
- Luoma, Adrienne M.
- Hwu, Patrick
- Bernatchez, Chantale
- Forget, Marie-Andrée
- Barbie, David A.
- Shalek, Alex K.
- Tirosh, Itay
- Sorger, Peter K.
- Wucherpfennig, Kai
- Schadendorf, Dirk
- Johnson, Bruce E.
- Rotem, Asaf
- Garraway, Levi A.
- Yoon, Charles H.
- Izar, Benjamin
- Regev, Aviv
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 175
- Issue
- 4
- Pages
- 984-997.e24
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6410377
- OpenAlex
- W2898830231
- Cited by
- 1,429
- References
- 47
- Citations by year
- 2026: 166 · 2025: 187 · 2024: 195 · 2023: 207 · 2022: 203 · 2021: 202 · 2020: 161 · 2019: 102 · 2018: 3
- FWCI
- 44.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- CRI
- NCI (K08-CA222663, 1U24CA180922, R33-CA202820)
- DFCI
- SITC
- Klarman Cell Observatory
- STARR Cancer Consortium
- Koch Institute (P30-CA14051)
- Harvard
- MIT
- AMRF
- Broad Institute
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- melanoma, immune checkpoint inhibitors, immunotherapy resistance, T-cell exclusion, immune evasion, cold tumors, anti-PD-1 therapy, CDK4/6 inhibition, cellular senescence, cancer cell states, predictive biomarkers, combination therapy
- MeSH terms
- Aged, Aged, 80 and over, Animals, Antineoplastic Agents, Female, Humans, Immunotherapy, Male, Melanoma, Mice, Inbred C57BL, Middle Aged, T-Lymphocytes, Tumor Escape, Cell Line, Tumor, Protein Kinase Inhibitors, Cyclin-Dependent Kinase 4, Cyclin-Dependent Kinase 6, Mice, 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
