CoChat
Sign Up
Cited by 94Acute Lymphoblastic Leukemia researchAcute Myeloid Leukemia ResearchCancer Genomics and Diagnostics

Relapse-Fated Latent Diagnosis Subclones in Acute B Lineage Leukemia Are Drug Tolerant and Possess Distinct Metabolic Programs

Open access
Dobson, Stephanie M., García-Prat, Laura, Vanner, Robert J., Wintersinger, Jeffrey, Waanders, Esmé, Gu, Zhaohui, McLeod, Jessica, Gan, Olga I. +25 more·2020·Cancer Discovery
DOI 10.1158/2159-8290.cd-19-1059PMC PMC7122013

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Disease recurrence causes significant mortality in B-progenitor acute lymphoblastic leukemia (B-ALL). Genomic analysis of matched diagnosis and relapse samples shows relapse often arising from minor diagnosis subclones. However, why therapy eradicates some subclones while others survive and progress to relapse remains obscure. Elucidation of mechanisms underlying these differing fates requires functional analysis of isolated subclones. Here, large-scale limiting dilution xenografting of diagnosis and relapse samples, combined with targeted sequencing, identified and isolated minor diagnosis subclones that initiate an evolutionary trajectory toward relapse [termed diagnosis Relapse Initiating clones (dRI)]. Compared with other diagnosis subclones, dRIs were drug-tolerant with distinct engraftment and metabolic properties. Transcriptionally, dRIs displayed enrichment for chromatin remodeling, mitochondrial metabolism, proteostasis programs, and an increase in stemness pathways. The isolation and characterization of dRI subclones reveals new avenues for eradicating dRI cells by targeting their distinct metabolic and transcriptional pathways before further evolution renders them fully therapy-resistant. Significance: Isolation and characterization of subclones from diagnosis samples of patients with B-ALL who relapsed showed that relapse-fated subclones had increased drug tolerance and distinct metabolic and survival transcriptional programs compared with other diagnosis subclones. This study provides strategies to identify and target clinically relevant subclones before further evolution toward relapse. See related video: https://vimeo.com/442838617 See related article by E. Waanders et al .

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 · 10.1158/2159-8290.cd-19-1059

Cites

Statistics

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

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?

  • Short-term targeted inhibitor treatment can lead to a small subset of drug-insensitive cells in both patient tumors and laboratory models within 1-14 days.

    “Short-term targeted inhibitor treatment can lead to a small subset of drug-insensitive cells in both patient tumors and laboratory models within 1–14 days, a timeframe less likely to induce permanent genetic alterations [114,115,116].”

    From KRAS: Biology, Inhibition, and Mechanisms of Inhibitor Resistance (2024) · cites this paper as [116]

    The Results section ('dRI subclones display differential response to chemotherapeutic agents'; page unavailable) reports reduced drug sensitivity in patient-derived leukemia subclones tested in xenografts. But the experiments used chemotherapy agents over four weeks, not targeted inhibitors over 1–14 days, and the paper describes the relapse-fated subclones as already present at diagnosis rather than arising during short-term treatment. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

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

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

The citation lists haven’t been fetched yet

References 65 · Cited by 94

Research works printed but not linked: 57

  1. [1]Forman SJ, Rowe JM. The myth of the second remission of acute leukemia in the adult. Blood 2013; 121(7): 1077– 82 doi 10.1182/blood-2012-08-234492. 23243288 PMC3575753 · DOI
  2. [2]Liew E, Atenafu EG, Schimmer AD, Yee KW, Schuh AC, Minden MD, Outcomes of adult patients with relapsed acute lymphoblastic leukemia following frontline treatment with a pediatric regimen. Leuk Res 2012; 36(12): 1517– 20 doi 10.1016/j.leukres.2012.08.019. 22940259 · DOI
  3. [3]Hunger SP, Mullighan CG. Acute Lymphoblastic Leukemia in Children. N Engl J Med 2015; 373(16): 1541– 52 doi 10.1056/NEJMra1400972. 26465987 · DOI
  4. [4]Mullighan CG, Phillips LA, Su X, Ma J, Miller CB, Shurtleff SA, Genomic analysis of the clonal origins of relapsed acute lymphoblastic leukemia. Science 2008; 322(5906): 1377– 80 doi 10.1126/science.1164266. 19039135 PMC2746051 · DOI
  5. [5]Ma X, Edmonson M, Yergeau D, Muzny DM, Hampton OA, Rusch M, Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat Commun 2015; 6: 6604 doi 10.1038/ncomms7604. 25790293 PMC4377644 · DOI
  6. [6]Oshima K, Khiabanian H, da Silva-Almeida AC, Tzoneva G, Abate F, Ambesi-Impiombato A, Mutational landscape, clonal evolution patterns, and role of RAS mutations in relapsed acute lymphoblastic leukemia. Proc Natl Acad Sci U S A 2016; 113(40): 11306– 11 doi 10.1073/pnas.1608420113. 27655895 PMC5056035 · DOI
  7. [7]Anderson K, Lutz C, van Delft FW, Bateman CM, Guo Y, Colman SM, Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 2011; 469(7330): 356– 61 doi 10.1038/nature09650. 21160474 · DOI
  8. [8]Notta F, Mullighan CG, Wang JC, Poeppl A, Doulatov S, Phillips LA, Evolution of human BCR-ABL1 lymphoblastic leukaemia-initiating cells. Nature 2011; 469(7330): 362– 7 doi 10.1038/nature09733. 21248843 · DOI
  9. [9]Kreso A, Dick JE. Evolution of the cancer stem cell model. Cell Stem Cell 2014; 14(3): 275– 91 doi 10.1016/j.stem.2014.02.006. 24607403 · DOI
  10. [10]Foo J, Michor F. Evolution of acquired resistance to anti-cancer therapy. J Theor Biol 2014; 355: 10– 20 doi 10.1016/j.jtbi.2014.02.025. 24681298 PMC4058397 · DOI
  11. [11]Kim C, Gao R, Sei E, Brandt R, Hartman J, Hatschek T, Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing. Cell 2018; 173(4): 879– 93 e13 doi 10.1016/j.cell.2018.03.041. 29681456 PMC6132060 · DOI
  12. [12]Iqbal Z, Aleem A, Iqbal M, Naqvi MI, Gill A, Taj AS, Sensitive detection of pre-existing BCR-ABL kinase domain mutations in CD34+ cells of newly diagnosed chronic-phase chronic myeloid leukemia patients is associated with imatinib resistance: implications in the post-imatinib era. PLoS One 2013; 8(2): e55717 doi 10.1371/journal.pone.0055717. 23409026 PMC3568121 · DOI
  13. [13]Tzoneva G, Dieck CL, Oshima K, Ambesi-Impiombato A, Sanchez-Martin M, Madubata CJ, Clonal evolution mechanisms in NT5C2 mutant-relapsed acute lymphoblastic leukaemia. Nature 2018; 553(7689): 511– 4 doi 10.1038/nature25186. 29342136 PMC5931372 · DOI
  14. [14]Hunter C, Smith R, Cahill DP, Stephens P, Stevens C, Teague J, A hypermutation phenotype and somatic MSH6 mutations in recurrent human malignant gliomas after alkylator chemotherapy. Cancer Res 2006; 66(8): 3987– 91 doi 10.1158/0008-5472.CAN-06-0127. 16618716 PMC7212022 · DOI
  15. [15]Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Signatures of mutational processes in human cancer. Nature 2013; 500(7463): 415– 21 doi 10.1038/nature12477. 23945592 PMC3776390 · DOI · Paper page
  16. [16]Churchman ML, Low J, Qu C, Paietta EM, Kasper LH, Chang Y, Efficacy of Retinoids in IKZF1-Mutated BCR-ABL1 Acute Lymphoblastic Leukemia. Cancer Cell 2015; 28(3): 343– 56 doi 10.1016/j.ccell.2015.07.016. 26321221 PMC4573904 · DOI
  17. [17]Ebinger S, Ozdemir EZ, Ziegenhain C, Tiedt S, Castro Alves C, Grunert M, Characterization of Rare, Dormant, and Therapy-Resistant Cells in Acute Lymphoblastic Leukemia. Cancer Cell 2016; 30(6): 849– 62 doi 10.1016/j.ccell.2016.11.002. 27916615 PMC5156313 · DOI
  18. [18]Polak R, de Rooij B, Pieters R, den Boer ML. B-cell precursor acute lymphoblastic leukemia cells use tunneling nanotubes to orchestrate their microenvironment. Blood 2015; 126(21): 2404– 14 doi 10.1182/blood-2015-03-634238. 26297738 · DOI
  19. [19]Iwamoto S, Mihara K, Downing JR, Pui CH, Campana D. Mesenchymal cells regulate the response of acute lymphoblastic leukemia cells to asparaginase. J Clin Invest 2007; 117(4): 1049– 57 doi 10.1172/JCI30235. 17380207 PMC1821067 · DOI
  20. [20]Mullighan CG, Zhang J, Kasper LH, Lerach S, Payne-Turner D, Phillips LA, CREBBP mutations in relapsed acute lymphoblastic leukaemia. Nature 2011; 471(7337): 235– 9 doi 10.1038/nature09727. 21390130 PMC3076610 · DOI
  21. [21]Kamel-Reid S, Letarte M, Sirard C, Doedens M, Grunberger T, Fulop G, A model of human acute lymphoblastic leukemia in immune-deficient SCID mice. Science 1989; 246(4937): 1597– 600. 2595371
  22. [22]Lock RB, Liem N, Farnsworth ML, Milross CG, Xue C, Tajbakhsh M, The nonobese diabetic/severe combined immunodeficient (NOD/SCID) mouse model of childhood acute lymphoblastic leukemia reveals intrinsic differences in biologic characteristics at diagnosis and relapse. Blood 2002; 99(11): 4100– 8. 12010813
  23. [23]Ng SW, Mitchell A, Kennedy JA, Chen WC, McLeod J, Ibrahimova N, A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 2016; 540(7633): 433– 7 doi 10.1038/nature20598. 27926740 · DOI · Paper page
  24. [24]Liem NL, Papa RA, Milross CG, Schmid MA, Tajbakhsh M, Choi S, Characterization of childhood acute lymphoblastic leukemia xenograft models for the preclinical evaluation of new therapies. Blood 2004; 103(10): 3905– 14 doi 10.1182/blood-2003-08-2911. 14764536 · DOI
  25. [25]Meyer LH, Eckhoff SM, Queudeville M, Kraus JM, Giordan M, Stursberg J, Early relapse in ALL is identified by time to leukemia in NOD/SCID mice and is characterized by a gene signature involving survival pathways. Cancer Cell 2011; 19(2): 206– 17 doi 10.1016/j.ccr.2010.11.014. 21295523 · DOI
  26. [26]Shlush LI, Mitchell A, Heisler L, Abelson S, Ng SWK, Trotman-Grant A, Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature 2017; 547(7661): 104– 8 doi 10.1038/nature22993. 28658204 · DOI
  27. [27]Clappier E, Gerby B, Sigaux F, Delord M, Touzri F, Hernandez L, Clonal selection in xenografted human T cell acute lymphoblastic leukemia recapitulates gain of malignancy at relapse. J Exp Med 2011; 208(4): 653– 61 doi 10.1084/jem.20110105. 21464223 PMC3135355 · DOI
  28. [28]Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O’Laughlin M, Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 2014; 25(3): 379– 92 doi 10.1016/j.ccr.2014.01.031. 24613412 PMC3983786 · DOI
  29. [29]Richter-Pechanska P, Kunz JB, Bornhauser B, von Knebel Doeberitz C, Rausch T, Erarslan-Uysal B, PDX models recapitulate the genetic and epigenetic landscape of pediatric T-cell leukemia. EMBO Mol Med 2018; 10(12) doi 10.15252/emmm.201809443. PMC6284381 30389682 · DOI
  30. [30]Waanders EGZ, Dobsom SM, Antic Z, Crawford JC, Ma X, Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia. Blood Cancer Discovery 2020; 1: 1– 16 doi 10.1158/0008-5472.BCD-19-0041. PMC7418874 32793890 · DOI
  31. [31]Rehe K, Wilson K, Bomken S, Williamson D, Irving J, den Boer ML, Acute B lymphoblastic leukaemia-propagating cells are present at high frequency in diverse lymphoblast populations. EMBO Mol Med 2013; 5(1): 38– 51 doi 10.1002/emmm.201201703. 23229821 PMC3569652 · DOI
  32. [32]Schinnerl D, Mejstrikova E, Schumich A, Zaliova M, Fortschegger K, Nebral K, CD371 cell surface expression: a unique feature of DUX4-rearranged acute lymphoblastic leukemia. Haematologica 2019; 104(8): e352– e5 doi 10.3324/haematol.2018.214353. 30705095 PMC6669149 · DOI
  33. [33]Chiaretti S, Li X, Gentleman R, Vitale A, Vignetti M, Mandelli F, Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 2004; 103(7): 2771– 8 doi 10.1182/blood-2003-09-3243. 14684422 · DOI
  34. [34]Slamova L, Starkova J, Fronkova E, Zaliova M, Reznickova L, van Delft FW, CD2-positive B-cell precursor acute lymphoblastic leukemia with an early switch to the monocytic lineage. Leukemia 2014; 28(3): 609– 20 doi 10.1038/leu.2013.354. 24270736 · DOI
  35. [35]O’Connell MP, Marchbank K, Webster MR, Valiga AA, Kaur A, Vultur A, Hypoxia induces phenotypic plasticity and therapy resistance in melanoma via the tyrosine kinase receptors ROR1 and ROR2. Cancer Discov 2013; 3(12): 1378– 93 doi 10.1158/2159-8290.CD-13-0005. 24104062 PMC3918498 · DOI
  36. [36]Kemper K, de Goeje PL, Peeper DS, van Amerongen R. Phenotype switching: tumor cell plasticity as a resistance mechanism and target for therapy. Cancer Res 2014; 74(21): 5937– 41 doi 10.1158/0008-5472.CAN-14-1174. 25320006 · DOI · Paper page
  37. [37]Chaidos A, Barnes CP, Cowan G, May PC, Melo V, Hatjiharissi E, Clinical drug resistance linked to interconvertible phenotypic and functional states of tumor-propagating cells in multiple myeloma. Blood 2013; 121(2): 318– 28 doi 10.1182/blood-2012-06-436220. 23169779 · DOI
  38. [38]Pisco AO, Huang S. Non-genetic cancer cell plasticity and therapy-induced stemness in tumour relapse: ‘What does not kill me strengthens me’. Br J Cancer 2015; 112(11): 1725– 32 doi 10.1038/bjc.2015.146. 25965164 PMC4647245 · DOI
  39. [39]Belviso S, Iuliano R, Amato R, Perrotti N, Menniti M. The human asparaginase enzyme (ASPG) inhibits growth in leukemic cells. PLoS One 2017; 12(5): e0178174 doi 10.1371/journal.pone.0178174. 28542249 PMC5443537 · DOI
  40. [42]van Galen P, Mbong N, Kreso A, Schoof EM, Wagenblast E, Ng SWK, Integrated Stress Response Activity Marks Stem Cells in Normal Hematopoiesis and Leukemia. Cell Rep 2018; 25(5): 1109– 17 e5 doi 10.1016/j.celrep.2018.10.021. 30380403 · DOI · Paper page
  41. [44]Luria SE, Delbruck M. Mutations of Bacteria from Virus Sensitivity to Virus Resistance. Genetics 1943; 28(6): 491– 511. 17247100 PMC1209226
  42. [45]Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen P, Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med 2011; 17(9): 1086– 93 doi 10.1038/nm.2415. 21873988 · DOI
  43. [46]Jin L, Hope KJ, Zhai Q, Smadja-Joffe F, Dick JE. Targeting of CD44 eradicates human acute myeloid leukemic stem cells. Nat Med 2006; 12(10): 1167– 74 doi 10.1038/nm1483. 16998484 · DOI
  44. [47]Chen WC, Yuan JS, Xing Y, Mitchell A, Mbong N, Popescu AC, An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers. Cancer Res 2016; 76(5): 1214– 24 doi 10.1158/0008-5472.CAN-15-2743. 26833125 · DOI
  45. [48]Mazurier F, Doedens M, Gan OI, Dick JE. Rapid myeloerythroid repopulation after intrafemoral transplantation of NOD-SCID mice reveals a new class of human stem cells. Nat Med 2003; 9(7): 959– 63 doi 10.1038/nm886. 12796774 · DOI
  46. [49]Hu Y, Smyth GK. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J Immunol Methods 2009; 347(1–2): 70– 8 doi 10.1016/j.jim.2009.06.008. 19567251 · DOI
  47. [50]Edmonson MN, Zhang J, Yan C, Finney RP, Meerzaman DM, Buetow KH. Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format. Bioinformatics 2011; 27(6): 865– 6 doi 10.1093/bioinformatics/btr032. 21278191 PMC3051333 · DOI
  48. [51]Mullighan CG. Single nucleotide polymorphism microarray analysis of genetic alterations in cancer. Methods Mol Biol 2011; 730: 235– 58 doi 10.1007/978-1-61779-074-4_17. 21431646 · DOI
  49. [52]Pounds S, Cheng C, Mullighan C, Raimondi SC, Shurtleff S, Downing JR. Reference alignment of SNP microarray signals for copy number analysis of tumors. Bioinformatics 2009; 25(3): 315– 21 doi 10.1093/bioinformatics/btn624. 19052058 PMC2639073 · DOI
  50. [53]Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004; 5(4): 557– 72 doi 10.1093/biostatistics/kxh008. 15475419 · DOI
  51. [54]Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 2007; 23(6): 657– 63 doi 10.1093/bioinformatics/btl646. 17234643 · DOI
  52. [55]Lin M, Wei LJ, Sellers WR, Lieberfarb M, Wong WH, Li C. dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data. Bioinformatics 2004; 20(8): 1233– 40 doi 10.1093/bioinformatics/bth069. 14871870 · DOI
  53. [56]Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29(1): 15– 21 doi 10.1093/bioinformatics/bts635. 23104886 PMC3530905 · DOI · Paper page
  54. [57]Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 2015; 31(2): 166– 9 doi 10.1093/bioinformatics/btu638. 25260700 PMC4287950 · DOI · Paper page
  55. [58]Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550 doi 10.1186/s13059-014-0550-8. 25516281 PMC4302049 · DOI · Paper page
  56. [59]Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc 2019; 14(2): 482– 517 doi 10.1038/s41596-018-0103-9. 30664679 PMC6607905 · DOI · Paper page
  57. [60]Notta F, Doulatov S, Laurenti E, Poeppl A, Jurisica I, Dick JE. Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment. Science 2011; 333(6039): 218– 21 doi 10.1126/science.1201219. 21737740 · DOI · Paper page

Authors
Dobson, S. M., García-Prat, L., Vanner, R. J., Wintersinger, J., Waanders, E., Gu, Z., McLeod, J., Gan, O. I., Grandal, I., Payne-Turner, D., Edmonson, M. N., Ma, X., Fan, Y., Voisin, V., Chan-Seng-Yue, M., Xie, S. Z., Hosseini, M., Abelson, S., Gupta, P., Rusch, M., Shao, Y., Olsen, S. R., Neale, G., Chan, S. M., Bader, G., Easton, J., Guidos, C. J., Danska, J. S., Zhang, J., Minden, M. D., Morris, Q., Mullighan, C. G., & Dick, J. E.
Affiliations
  • University Health Network (CA)
  • 2Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
  • University of Toronto (CA)
  • St. Jude Children's Research Hospital (US)
  • Hospital for Sick Children (CA)
  • 1Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Corresponding author
Dick, John E.
ORCID
Venue
Cancer Discovery
Publisher
American Association for Cancer Research (AACR)
Published
2020-04-01
Volume
10
Issue
4
Pages
568-587
ISSN
  • 2159-8274 (ISSN-L)
  • 2159-8290
DOI
10.1158/2159-8290.cd-19-1059
PMC
PMC7122013
OpenAlex
W3008763686
Cited by
94
References
65
Citations by year
2026: 7 · 2025: 7 · 2024: 10 · 2023: 13 · 2022: 16 · 2021: 23 · 2020: 18
FWCI
13.12
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Cancer Discovery (publishedVersion)
  • PubMed (publishedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
Funders
  • Princess Margaret Cancer Centre Foundation Ontario Institute for Cancer Research
  • NCI (P30 CA021765, R35 CA197695)
  • Natural Sciences and Engineering Research Council
  • Dutch Cancer Society (KUN2012-5366)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Acute Lymphoblastic Leukemia research
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
relapse, drug tolerance, chromatin remodeling, mitochondrial metabolism, proteostasis, stemness, clonal evolution, xenotransplantation, limiting dilution, treatment resistance
MeSH terms
Clone Cells, Female, Humans, Male, Recurrence, Leukemia, Myeloid, Acute

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