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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer
Open accessAbstract
We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.
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“The nature of these mutations differs for each cancer [90] and in each individual [90,91,92].”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [92]
Experimental — accuracy not yet measuredIn the citing paper
“First, although tumors are readily recognized through gene expression signatures, they retain transcriptomic features of the parental tissue so that tumors arising in the same organ are more similar to each other than tumors arising in different organs.”
From Metabolic reprogramming and cancer progression (2020) · cites this paper as [3]
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“A study that classified thousands of human tumors found that tissue of origin was a major factor defining DNA methylation and gene expression patterns (3).”
From Metabolic reprogramming and cancer progression (2020) · cites this paper as [3]
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Self-citationDNA-methylation for the detection and distinction of 19 human malignancies
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Self-citationTGFβ-mediated MMP13 secretion drives myoepithelial cell dependent breast cancer progression
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Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profilingPreprint
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Self-citationHexokinase 2 is a transcriptional target and a positive modulator of AHR signalling
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Self-citationDistinct EpCAM-Positive Stem Cell Niches Are Engaged in Chronic and Neoplastic Liver Diseases
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“The gene expression profiles for another 32 types of cancer were also downloaded from the TCGA project (21) and processed in the same way as the HCC data.”
MethodLin, P., Lin, Y.-Q., Gao, R., Wen, R., Qin, H., He, Y., & Yang, H. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101078 · Cited by 16
Self-citationKarlow, J. A., Miao, B., Xing, X., Wang, T., & Zhang, B. (2021) · Communications Biology · doi:10.1038/s42003-021-02094-1 · Cited by 17
Self-citationHu, T., Chen, Y., Liu, Y., Zhang, D., Pan, J., & Long, M. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01297-8 · Cited by 17
Self-citationBalikov, D. A., Hu, K., Liu, C.-J., Betz, B. L., Chinnaiyan, A. M., Devisetty, L. V., Venneti, S., Tomlins, S. A., Cani, A. K., & Rao, R. C. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189992 · Cited by 16
Wu, W., Zhang, W., Zhang, W., Liu, K., Chen, X., Zhou, Z., Liu, J., Zhu, T., & Hu, J.-K. (2021) · BMC Cancer · doi:10.1186/s12885-021-08946-7 · Cited by 16
“heterogeneity and molecular characteristics are important explanations for the differential treatment outcomes of cancer patients [28, 29].”
BackgroundSelf-citationLuo, G., Chen, G., Chen, P., & Zhou, J. (2022) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207325666220221092318 · Cited by 17
Identifying novel SMYD3 interactors on the trail of cancer hallmarks
Fasano, C., Signorile, M. L., Marco, K. D., Forte, G., Sanese, P., Grossi, V., & Simone, C. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.03.037 · Cited by 17
“…has been detected in a uterine corpus endometrial carcinoma (UCEC) sample [92], and eleven truncating mutations F90Lfs*3 have been found in various cancer types, including colorectal adenocarcinoma [92–97], esophageal adenocarcinoma (one mutation) [98], and UCEC (four mutations) [92] (Appendix Fig.”
BackgroundAnalysis of extracellular matrix network dynamics in cancer using the MatriNet database
Kontio, J., Rollé, V., Pesola, V., Lamba, R., Dittmann, A., Díaz‐Navarro, A., Koivunen, J. T., Pihlajaniemi, T. A., & Izzi, V. (2022) · Matrix Biology · doi:10.1016/j.matbio.2022.05.006 · Cited by 16
Oku, Y., Madia, F., Lau, P., Paparella, M., McGovern, T. J., Luijten, M., & Jacobs, M. N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232112718 · Cited by 18
Mutation profiles of oral squamous cell carcinoma cells
Chen, X., Zhao, W., Chen, S., & Yu, D. (2021) · Advances in Oral and Maxillofacial Surgery · doi:10.1016/j.adoms.2021.100026 · Cited by 16
Reducing MYC's transcriptional footprint unveils a good prognostic gene signature in melanomaPreprint
Fluck, M. F. Z., Massó-Vallés, D., Giuntini, F., González-Larreategui, Í., Kaur, J., Casacuberta‐Serra, S., Jauset, T., Martínez-Martín, S., Martín-Fernández, G., del Pozo, E. S., et al. (2023) · Genes & Development · doi:10.1101/gad.350078.122 · Cited by 16
The Hallmarks of Predictive Oncology
Singhal, A., Zhao, X., Wall, P., So, E., Calderini, G., Partin, A., Koussa, N. C., Vasanthakumari, P., Narykov, O., Zhu, Y., et al. (2025) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0760 · Cited by 19
Self-citationBiomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics
Dragani, T. A., Matarese, V., & Colombo, F. (2020) · BioEssays · doi:10.1002/bies.201900122 · Cited by 15
Wilde, J. D., Paemel, R. V., Koker, A. D., Roelandt, S., de Velde, S. V., Callewaert, N., Dorpe, J. V., Creytens, D., Wilde, B. D., & Preter, K. D. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102091 · Cited by 15
Systemic Therapy for Hepatocellular Carcinoma: Advances and Hopes
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Muzzi, J. C. D., Magno, J. M., Cardoso, M. A., de Moura, J. F., Castro, M. A. A., & de Figueiredo, B. C. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.672319 · Cited by 15
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“The optimal cutoff values of ERBB2 CN for 12-month PFS and 24-month PFS on the basis of the ROC curve analyses were 15.2 copies (area under the curve: 0.64) and 16.6 copies (area under the curve: 0.68), respectively (Data Supplement).”
BackgroundInfluentialCyclin genes as potential novel prognostic biomarkers and therapeutic targets in breast cancer
Liu, N.-Q., Cao, W., Wang, X., Chen, J., & Nie, J. (2022) · Oncology Letters · doi:10.3892/ol.2022.13494 · Cited by 18
“cBioportal (63) was further used to analyze the genomic profiles of cyclin genes in TCGA, and analyze the relationship amongst genes through protein‐protein interaction (PPI) analysis.”
MethodRerouting the drug response: Overcoming metabolic adaptation in KRAS-mutant cancers
Moss, D., McCann, C. K., & Kerr, E. M. (2022) · Science Signaling · doi:10.1126/scisignal.abj3490 · Cited by 16
Germline Rare Deleterious Variant Load Alters Cancer Risk, Age of Onset and Tumor Characteristics
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Quintanilha, J. C. F., Graf, R. P., & Oxnard, G. R. (2023) · The Oncologist · doi:10.1093/oncolo/oyac265 · Cited by 16
Peng, L., Li, Y., Yao, S., Gaedcke, J., Baart, V. M., Sier, C. F. M., Neeße, A., Ellenrieder, V., Bohnenberger, H., Fuchs, F., et al. (2023) · Cancers · doi:10.3390/cancers15051587 · Cited by 15
Swapna, L. S., Huang, M., & Li, Y. (2023) · Genome biology · doi:10.1186/s13059-023-03034-4 · Cited by 14
“Several databases host tremendous amounts of bulk RNA-seq data such as Gene Expression Omnibus (GEO) [11, 12], Genotype-Tissue Expression (GTEx) [13], and The Cancer Genome Atlas (TCGA), a repository of bulk RNA-seq data for more than 11,000 primary cancer samples [14].”
BackgroundModulation of Notch Signaling by Small-Molecular Compounds and Its Potential in Anticancer Studies
Czerwonka, A., Kałafut, J., & Nees, M. (2023) · Cancers · doi:10.3390/cancers15184563 · Cited by 15
Mutant p53 gain-of-function stimulates canonical Wnt signaling via PI3K/AKT pathway in colon cancer
Alvarado-Ortiz, E., Ortíz-Sánchez, E., Sarabia-Sánchez, M. Á., de la Cruz-López, K. G., García-Carrancá, A. M., & Robles‐Flores, M. (2023) · Journal of Cell Communication and Signaling · doi:10.1007/s12079-023-00793-4 · Cited by 18
“In the case of genotyped patients with altered p53 (mutp53) and unaltered p53 (wtp53), they were subclassified starting from data obtained from TCGA using colon adenocarcinoma samples (Pancancer, n = 594) (Hoadley et al. 2018).”
BackgroundHe, F., Bandyopadhyay, A. M., Klesse, L. J., Rogojina, A. T., Chun, S. H., Butler, E. B., Hartshorne, T., Holland, T., Garcia, D. K., Weldon, K. S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43373-1 · Cited by 16
“This tissue-of-origin dominated pattern was previously observed in adult cancers 46 .”
BackgroundBeyond BRCA: Diagnosis and management of homologous recombination repair deficient pancreatic cancer
LaRose, M., Manji, G. A., & Bates, S. E. (2023) · Seminars in Oncology · doi:10.1053/j.seminoncol.2023.11.001 · Cited by 16
Sun, M., Xu, B., Chen, C., Zhu, Y., Li, X., & Chen, K. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01638-6 · Cited by 15
“To extend methylation-based cancer classification beyond single tissue-of-origin, several groups developed multi-cancer classifiers with large methylation data-sets and machine learning, but challenges remained [6, 44–46].”
BackgroundMethodInfluentialSelf-citationDash, S., Ueda, T., Komuro, A., Honda, M., Sugisawa, R., & Okada, H. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06531-x · Cited by 15
Wang, X., Yuan, Z., Li, Z., He, X., Zhang, Y., Wang, X., Su, J., Wu, X., Li, M., Du, F., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1354313 · Cited by 15
Rajapakse, V. N., Herrada, S. P., & Lavi, O. (2020) · Science Advances · doi:10.1126/sciadv.aaz4125 · Cited by 15
Tumor Profiling at the Service of Cancer Therapy
Fernández–Rozadilla, C., Simões, A. R., LLeonart, M. E., Carnero, A., & Carracedo, Á. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.595613 · Cited by 17
Shao, Z., Wang, T., Zhang, M., Jiang, Z., Huang, S., & Zeng, P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009250 · Cited by 13
“With datasets available from The Cancer Genome Atlas (TCGA) project as an illustrative example [23], in this study we aim to utilize appropriate mediation models to investigate the mechanism of gene expression on the signaling pathway from DNA methylation to the survival risk of cancers.”
MethodInfluentialSelf-citationKumar, N. A. N., Jose, A., Usman, N., Rajan, K., Munisamy, M., Shetty, P. S., & Rao, M. (2021) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-021-02314-6 · Cited by 15
Metadata
- Authors
- Hoadley, K. A., Yau, C., Hinoue, T., Wolf, D. M., Lazar, A. J., Drill, E., Shen, R., Taylor, A. M., Cherniack, A. D., Thorsson, V., Akbani, R., Bowlby, R., Wong, C. K., Wiznerowicz, M., Sanchez-Vega, F., Robertson, A. G., Schneider, B. G., Lawrence, M. S., Noushmehr, H., Malta, T. M., Caesar-Johnson, S. J., Demchok, J. A., Felau, I., Kasapi, M., Ferguson, M. L., Hutter, C. M., Sofia, H. J., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J. C., Zhang, J. (., Chudamani, S., Liu, J., Lolla, L., Naresh, R., Pihl, T., Sun, Q., Wan, Y., Wu, Y., Cho, J., DeFreitas, T., Frazer, S., Gehlenborg, N., Getz, G., Heiman, D. I., Kim, J., Lawrence, M. S., Lin, P., Meier, S., Noble, M. S., Saksena, G., Voet, D., Zhang, H., Bernard, B., Chambwe, N., Dhankani, V., Knijnenburg, T., Kramer, R., Leinonen, K., Liu, Y., Miller, M., Reynolds, S., Shmulevich, I., Thorsson, V., Zhang, W., Akbani, R., Broom, B. M., Hegde, A. M., Ju, Z., Kanchi, R. S., Korkut, A., Li, J., Liang, H., Ling, S., Liu, W., Lu, Y., Mills, G. B., Ng, K.-S., Rao, A., Ryan, M., Wang, J., Weinstein, J. N., Zhang, J., Abeshouse, A., Armenia, J., Chakravarty, D., Chatila, W. K., de Bruijn, I., Gao, J., Gross, B. E., Heins, Z. J., Kundra, R., La, K., Ladanyi, M., Luna, A., Nissan, M. G., Ochoa, A., Phillips, S. M., Reznik, E., Sanchez-Vega, F., Sander, C., Schultz, N., Sheridan, R., Sumer, S. O., Sun, Y., Taylor, B. S., Wang, J., Zhang, H., Anur, P., Peto, M., Spellman, P., Benz, C., Stuart, J. M., Wong, C. K., Yau, C., Hayes, D. N., Parker, J. S., Wilkerson, M. D., Ally, A., Balasundaram, M., Bowlby, R., Brooks, D., Carlsen, R., Chuah, E., Dhalla, N., Holt, R., Jones, S. J. M., Kasaian, K., Lee, D., Ma, Y., Marra, M. A., Mayo, M., Moore, R. A., Mungall, A. J., Mungall, K., Robertson, A. G., Sadeghi, S., Schein, J. E., Sipahimalani, P., Tam, A., Thiessen, N., Tse, K., Wong, T., Berger, A. C., Beroukhim, R., Cherniack, A. D., Cibulskis, C., Gabriel, S. B., Gao, G. F., Ha, G., Meyerson, M., Schumacher, S. E., Shih, J., Kucherlapati, M. H., Kucherlapati, R. S., Baylin, S., Cope, L., Danilova, L., Bootwalla, M. S., Lai, P. H., Maglinte, D. T., Van Den Berg, D. J., Weisenberger, D. J., Auman, J. T., Balu, S., Bodenheimer, T., Fan, C., Hoadley, K. A., Hoyle, A. P., Jefferys, S. R., Jones, C. D., Meng, S., Mieczkowski, P. A., Mose, L. E., Perou, A. H., Perou, C. M., Roach, J., Shi, Y., Simons, J. V., Skelly, T., Soloway, M. G., Tan, D., Veluvolu, U., Fan, H., Hinoue, T., Laird, P. W., Shen, H., Zhou, W., Bellair, M., Chang, K., Covington, K., Creighton, C. J., Dinh, H., Doddapaneni, H., Donehower, L. A., Drummond, J., Gibbs, R. A., Glenn, R., Hale, W., Han, Y., Hu, J., Korchina, V., Lee, S., Lewis, L., Li, W., Liu, X., Morgan, M., Morton, D., Muzny, D., Santibanez, J., Sheth, M., Shinbrot, E., Wang, L., Wang, M., Wheeler, D. A., Xi, L., Zhao, F., Hess, J., Appelbaum, E. L., Bailey, M., Cordes, M. G., Ding, L., Fronick, C. C., Fulton, L. 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- Affiliations
- University of California, San Francisco (US)
- Memorial Sloan Kettering Cancer Center (US)
- Broad Institute (US)
- Vanderbilt University Medical Center (US)
- Henry Ford Health (US)
- Buck Institute for Research on Aging (US)
- Van Andel Institute (US)
- BC Cancer Agency (CA)
- Institute for Systems Biology (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of California, Santa Cruz (US)
- University of North Carolina at Chapel Hill (US)
- Poznan University of Medical Sciences (PL)
- Corresponding author
- Felau, Ina
- Nissan, Moriah G.
- Weisenberger, Daniel J.
- Jones, Corbin D.
- Bellair, Michelle
- Mallery, David
- Oosterhuis, Wolter
- Kefford, Richard
- Vocke, Cathy D.
- Zuna, Rosemary
- Rubin, Mark A.
- Aredes, Natália D.
- ORCID
- Wolf, Denise M.
- Drill, Esther
- Shen, Ronglai
- Taylor, Alison M.
- Schneider, Barbara G.
- Malta, Tathiane M.
- Felau, Ina
- Kasapi, Melpomeni
- Ferguson, Martin L.
- Sofia, Heidi J.
- Tarnuzzer, Roy
- Yang, Liming
- Zenklusen, Jean C.
- Zhang, Jiashan (Julia)
- Liu, Jia
- Pihl, Todd
- Sun, Qiang
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- Prados, Michael
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- Roggin, Kevin
- Kendler, Ady
- Steele, Ruth
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- Forgie, Ian
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- Carney, Michael
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- Unterberg, Andreas
- Bossler, Aaron
- Ma, Deqin
- Milhem, Mohammed
- Godwin, Andrew K.
- Madan, Rashna
- Adebamowo, Clement
- Adebamowo, Sally N.
- Boussioutas, Alex
- Beer, David
- Giordano, Thomas
- Bocklage, Therese
- Landrum, Lisa
- Moore, Kathleen
- Moxley, Katherine
- Zuna, Rosemary
- Feldman, Michael
- Luketich, James
- Carlotti, Carlos Gilberto
- Kemp, Rafael
- Tirapelli, Daniela
- Catto, James
- Swisher, Elizabeth
- Creaney, Jenette
- Robinson, Bruce
- Shelley, Carl Simon
- Bradford, Carol
- Carey, Thomas
- Prince, Mark
- Rozek, Laura
- Wolf, Gregory
- Fong, Kwun M.
- Yang, Ian
- Rathmell, W. Kimryn
- Kovatich, Albert J.
- Shriver, Craig D.
- DiPersio, John
- Drake, Bettina
- Govindan, Ramaswamy
- Heath, Sharon
- Ley, Timothy
- Rubin, Mark A.
- Lee, Jung Il
- Aredes, Natália D.
- Mariamidze, Armaz
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 173
- Issue
- 2
- Pages
- 291-304.e6
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5957518
- OpenAlex
- W2796153844
- Cited by
- 2,546
- References
- 58
- Citations by year
- 2026: 284 · 2025: 294 · 2024: 296 · 2023: 355 · 2022: 375 · 2021: 374 · 2020: 330 · 2019: 178 · 2018: 56
- FWCI
- 71.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-nd)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc-nd)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by-nc-nd)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- National Human Genome Research Institute
- National Cancer Institute
- National Institutes of Health (U24 CA143866, U24 CA143843, P30 CA016672, U24 CA143867, U24 CA143840, U24 CA143882, U24 CA143858, U24 CA143835, U54 HG003273, U24 CA143883, U24 CA143848, U24 CA144025, U24 CA143845, U54 HG003079, U24 CA143799, U54 HG003067)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- cell of origin, molecular classification of cancer, pan-cancer analysis, TCGA, aneuploidy, DNA methylation, mRNA expression, microRNA expression, reverse phase protein array, histological subtypes, tissue type, stemness, molecular clustering
- MeSH terms
- Aneuploidy, Chromosomes, Humans, Mutation, Neoplasm Proteins, Neoplasms, RNA, Messenger, Cluster Analysis, Databases, Factual, CpG Islands, DNA Methylation, MicroRNAs
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.
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