Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
02 10 2020
02 10 2020
Historique:
received:
13
03
2020
accepted:
08
09
2020
entrez:
3
10
2020
pubmed:
4
10
2020
medline:
21
10
2020
Statut:
epublish
Résumé
Immune-related adverse events (irAEs), caused by anti-PD-1/PD-L1 antibodies, can lead to fulminant and even fatal consequences and thus require early detection and aggressive management. However, a comprehensive approach to identify biomarkers of irAE is lacking. Here, we utilize a strategy that combines pharmacovigilance data and omics data, and evaluate associations between multi-omics factors and irAE reporting odds ratio across different cancer types. We identify a bivariate regression model of LCP1 and ADPGK that can accurately predict irAE. We further validate LCP1 and ADPGK as biomarkers in an independent patient-level cohort. Our approach provides a method for identifying potential biomarkers of irAE in cancer immunotherapy using both pharmacovigilance data and multi-omics data.
Identifiants
pubmed: 33009409
doi: 10.1038/s41467-020-18742-9
pii: 10.1038/s41467-020-18742-9
pmc: PMC7532211
doi:
Substances chimiques
Biomarkers, Tumor
0
LCP1 protein, human
0
Microfilament Proteins
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
4946Références
Postow, M. A., Sidlow, R. & Hellmann, M. D. Immune-related adverse events associated with immune checkpoint blockade. N. Engl. J. Med. 378, 158–168 (2018).
doi: 10.1056/NEJMra1703481
Wang, D. Y. et al. Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. 4, 1721–1728 (2018).
doi: 10.1001/jamaoncol.2018.3923
Salem, J. E. et al. Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study. Lancet Oncol. 19, 1579–1589 (2018).
doi: 10.1016/S1470-2045(18)30608-9
Johnson, D. B. et al. Fulminant myocarditis with combination immune checkpoint blockade. N. Engl. J. Med. 375, 1749–1755 (2016).
doi: 10.1056/NEJMoa1609214
Subudhi, S. K. et al. Clonal expansion of CD8 T cells in the systemic circulation precedes development of ipilimumab-induced toxicities. Proc. Natl Acad. Sci. USA 113, 11919–11924 (2016).
doi: 10.1073/pnas.1611421113
Bomze, D., Hasan Ali, O., Bate, A. & Flatz, L. Association between immune-related adverse events during anti-PD-1 therapy and tumor mutational burden. JAMA Oncol. 5, 1633–1635 (2019).
doi: 10.1001/jamaoncol.2019.3221
Bate, A. & BA, S. J. E. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol. Drug Saf. 18, 427–436 (2009).
doi: 10.1002/pds.1742
Johnson, D. B. et al. A case report of clonal EBV-like memory CD4+ T cell activation in fatal checkpoint inhibitor-induced encephalitis. Nat. Med. 25, 1243–1250 (2019).
doi: 10.1038/s41591-019-0523-2
Head, L. et al. Biomarkers to predict immune-related adverse events with checkpoint inhibitors. J. Clin. Oncol. 37, 131–131 (2019).
doi: 10.1200/JCO.2019.37.8_suppl.131
Diehl, A., Yarchoan, M., Hopkins, A., Jaffee, E. & Grossman, S. A. Relationships between lymphocyte counts and treatmentrelated toxicities and clinical responses in patients with solid tumors treated with PD-1 checkpoint inhibitors. Oncotarget 8, 114268–114280 (2017).
doi: 10.18632/oncotarget.23217
Fujisawa, Y. et al. Fluctuations in routine blood count might signal severe immune-related adverse events in melanoma patients treated with nivolumab. J. Dermatol. Sci. 88, 225–231 (2017).
doi: 10.1016/j.jdermsci.2017.07.007
Sanlorenzo, M. et al. Pembrolizumab cutaneous adverse events and their association with disease progression. JAMA Dermatol. 151, 1206–1212 (2015).
doi: 10.1001/jamadermatol.2015.1916
Haratani, K. et al. Association of immune-related adverse events with nivolumab efficacy in non-small cell lung cancer. JAMA Oncol. 4, 374–378 (2018).
doi: 10.1001/jamaoncol.2017.2925
Yarchoan, M., Hopkins, A. & Jaffee, E. M. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med. 377, 2500–2501 (2017).
doi: 10.1056/NEJMc1713444
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
doi: 10.1038/s41568-019-0116-x
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
doi: 10.1016/j.cell.2014.12.033
Lee, J. S. & Ruppin, E. Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1. JAMA Oncol. 5, 1614–1618 (2019).
doi: 10.1001/jamaoncol.2019.2311
Oshima, Y., Tanimoto, T., Yuji, K. & Tojo, A. EGFR-TKI-associated interstitial pneumonitis in nivolumab-treated patients with non-small cell lung cancer. JAMA Oncol. 4, 1112–1115 (2018).
doi: 10.1001/jamaoncol.2017.4526
Hayashi, T. et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann. Intern. Med. 140, 992–1000 (2004).
Wabnitz, G. H. et al. Costimulation induced phosphorylation of L-plastin facilitates surface transport of the T cell activation molecules CD69 and CD25. Eur. J. Immunol. 37, 649–662 (2007).
doi: 10.1002/eji.200636320
Kamiński, M. M. et al. T cell activation is driven by an ADP-dependent glucokinase linking enhanced glycolysis with mitochondrial reactive oxygen species generation. Cell Rep. 2, 1300–1315 (2012).
doi: 10.1016/j.celrep.2012.10.009
Kennedy, L. B. & Salama, A. K. S. A review of cancer immunotherapy toxicity. CA Cancer J. Clin. https://doi.org/10.3322/caac.21596 (2020).
doi: 10.3322/caac.21596
pubmed: 31944278
Martins, F. et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat. Rev. Clin. Oncol. 16, 563–580 (2019).
doi: 10.1038/s41571-019-0218-0
Yarchoan, M. et al. PD-L1 expression and tumor mutational burden are independent biomarkers in most cancers. JCI Insight 4, e126908 (2019).
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).
doi: 10.1016/j.immuni.2018.03.023
Hellmann, M. D. et al. Tumor mutational burden and efficacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer Cell 33, 853–861.e4 (2018).
doi: 10.1016/j.ccell.2018.04.001
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).
Ye, Y. et al. Sex-associated molecular differences for cancer immunotherapy. Nat. Commun. 11, 1779 (2020).
doi: 10.1038/s41467-020-15679-x
Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade find the latest version: IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Investig. 127, 2930–2940 (2017).
doi: 10.1172/JCI91190
Kwon, S., Oh, S. & Lee, Y. The use of random-effect models for high-dimensional variable selection problems. Comput. Stat. Data Anal. 103, 401–412 (2016).
doi: 10.1016/j.csda.2016.05.016
Kirpich, A. et al. Variable selection in omics data: a practical evaluation of small sample sizes. PLoS ONE 13, 1–19 (2018).
doi: 10.1371/journal.pone.0197910
Vasquez, M. M. et al. Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC Med. Res. Methodol. 16, 1–19 (2016).
doi: 10.1186/s12874-016-0254-8
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
doi: 10.1111/j.1467-9868.2005.00503.x
Zhang, C. H. & Huang, J. The sparsity and bias of the lasso selection in high-dimensional linear regression. Ann. Stat. 36, 1567–1594 (2008).
doi: 10.1214/07-AOS520
Wu, C. & Ma, S. A selective review of robust variable selection with applications in bioinformatics. Brief. Bioinform. 16, 873–883 (2014).
doi: 10.1093/bib/bbu046
Maag, J. L. V. gganatogram: an R package for modular visualisation of anatograms and tissues based on ggplot2. F1000Research 7, 1576 (2018).
doi: 10.12688/f1000research.16409.1
Petryszak, R. et al. Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44, D746–D752 (2016).
doi: 10.1093/nar/gkv1045
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: an R package for comparing biological themes among gene clusters. Omi. A J. Integr. Biol. 16, 284–287 (2012).
doi: 10.1089/omi.2011.0118