The DOI matches its Crossref record; 134 of the 134 references it cites were found in a registry or an index.
IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression
Open accessAbstract
Aryl hydrocarbon receptor (AHR) activation by tryptophan (Trp) catabolites enhances tumor malignancy and suppresses anti-tumor immunity. The context specificity of AHR target genes has so far impeded systematic investigation of AHR activity and its upstream enzymes across human cancers. A pan-tissue AHR signature, derived by natural language processing, revealed that across 32 tumor entities, interleukin-4-induced-1 (IL4I1) associates more frequently with AHR activity than IDO1 or TDO2, hitherto recognized as the main Trp-catabolic enzymes. IL4I1 activates the AHR through the generation of indole metabolites and kynurenic acid. It associates with reduced survival in glioma patients, promotes cancer cell motility, and suppresses adaptive immunity, thereby enhancing the progression of chronic lymphocytic leukemia (CLL) in mice. Immune checkpoint blockade (ICB) induces IDO1 and IL4I1. As IDO1 inhibitors do not block IL4I1, IL4I1 may explain the failure of clinical studies combining ICB with IDO1 inhibition. Taken together, IL4I1 blockade opens new avenues for cancer therapy.
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
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
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
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
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Cited by
Tryptophan metabolism in health and disease
Xue, C., Li, G., Zheng, Q., Gu, X., Shi, Q., Su, Y., Chu, Q., Yuan, X., Bao, Z., Lu, J., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.06.004 · Cited by 961
Macrophage diversity in cancer revisited in the era of single-cell omics
Ma, R.-Y., Black, A., & Qian, B.-Z. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.04.008 · Cited by 698
Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease
Mulder, K., Patel, A. A., Kong, W. T., Piot, C., Halitzki, E., Dunsmore, G., Khalilnezhad, S., Irac, S. E., Dubuisson, A., Chevrier, M., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.07.007 · Cited by 678
Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends
Sun, Q., Hong, Z., Zhang, C., Wang, L., Han, Z., & Ma, D. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01522-4 · Cited by 598
Indoleamine 2,3-dioxygenase (IDO) inhibitors and cancer immunotherapy
Fujiwara, Y., Kato, S., Nesline, M. K., Conroy, J. M., DePietro, P., Pabla, S., & Kurzrock, R. (2022) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2022.102461 · Cited by 304
Metadata
- Authors
- Sadik, A., Somarribas Patterson, L. F., Öztürk, S., Mohapatra, S. R., Panitz, V., Secker, P. F., Pfänder, P., Loth, S., Salem, H., Prentzell, M. T., Berdel, B., Iskar, M., Faessler, E., Reuter, F., Kirst, I., Kalter, V., Foerster, K. I., Jäger, E., Guevara, C. R., Sobeh, M., Hielscher, T., Poschet, G., Reinhardt, A., Hassel, J. C., Zapatka, M., Hahn, U., von Deimling, A., Hopf, C., Schlichting, R., Escher, B. I., Burhenne, J., Haefeli, W. E., Ishaque, N., Böhme, A., Schäuble, S., Thedieck, K., Trump, S., Seiffert, M., & Opitz, C. A.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 182
- Issue
- 5
- Pages
- 1252-1270.e34
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3049896770
- Cited by
- 559
- References
- 134
- FWCI
- 20.69
- 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: IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression. https://app.cochat.ai/papers/10.1016/j.cell.2020.07.038
- The paper
- Sadik, A., Somarribas Patterson, L. F., Öztürk, S., Mohapatra, S. R., Panitz, V., Secker, P. F., Pfänder, P., Loth, S., Salem, H., Prentzell, M. T., Berdel, B., Iskar, M., Faessler, E., Reuter, F., Kirst, I., Kalter, V., Foerster, K. I., Jäger, E., Guevara, C. R., . . . Opitz, C. A. (2020). IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression. Cell. https://doi.org/10.1016/j.cell.2020.07.038
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.