A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy.
Journal
NPJ precision oncology
ISSN: 2397-768X
Titre abrégé: NPJ Precis Oncol
Pays: England
ID NLM: 101708166
Informations de publication
Date de publication:
31 Mar 2022
31 Mar 2022
Historique:
received:
14
10
2021
accepted:
11
02
2022
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
2
4
2022
Statut:
epublish
Résumé
Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ~30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction. We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well-characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signaling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining.
Identifiants
pubmed: 35361874
doi: 10.1038/s41698-022-00262-y
pii: 10.1038/s41698-022-00262-y
pmc: PMC8971493
doi:
Types de publication
Journal Article
Langues
eng
Pagination
19Informations de copyright
© 2022. The Author(s).
Références
Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer. New Engl. J. Med. 375, 1823–1833 (2016).
pubmed: 27718847
doi: 10.1056/NEJMoa1606774
Garon, E. B. et al. Five-year overall survival for patients with advanced non‒small-cell lung cancer treated with pembrolizumab: results from the phase I KEYNOTE-001 study. J. Clin. Oncol. 37, 2518–2527 (2019).
pubmed: 31154919
pmcid: 6768611
doi: 10.1200/JCO.19.00934
Brahmer, J. R. et al. The Society for Immunotherapy of Cancer consensus statement on immunotherapy for the treatment of non-small cell lung cancer (NSCLC). J. Immunother. Cancer 6, 75 (2018).
pubmed: 30012210
pmcid: 6048854
doi: 10.1186/s40425-018-0382-2
Daniello, L. et al. Therapeutic and prognostic implications of immune-related adverse events in advanced non-small-cell lung cancer. Front. Oncol. 11, 703893 (2021).
pubmed: 34268127
pmcid: 8277237
doi: 10.3389/fonc.2021.703893
Champiat, S. et al. Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin. Cancer Res. 23, 1920–1928 (2017).
pubmed: 27827313
doi: 10.1158/1078-0432.CCR-16-1741
Doroshow, D. B. et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat. Rev. Clin. Oncol. 18, 345–362 (2021).
pubmed: 33580222
doi: 10.1038/s41571-021-00473-5
Planchard, D. et al. Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up † † Footnotes Approved by the ESMO Guidelines Committee: February 2002, last update September 2018. This publication supersedes the previously published version—Ann Oncol 2016; 27 (Suppl 5): v1–v27. Ann. Oncol. 29, iv192–iv237 (2018).
pubmed: 30285222
doi: 10.1093/annonc/mdy275
ESMO. Clinical Practice Living Guidelines – Metastatic Non-Small-Cell Lung Cancer. https://www.esmo.org/guidelines/lung-and-chest-tumours/clinical-practice-living-guidelines-metastatic-non-small-cell-lung-cancer .
Hanna, N. H. et al. Therapy for stage IV non–small-cell lung cancer with driver alterations: ASCO and OH (CCO) joint guideline update. J. Clin. Oncol. 39, 1040–1091 (2021).
pubmed: 33591844
doi: 10.1200/JCO.20.03570
Carbone, D. P. et al. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. New Engl. J. Med. 376, 2415–2426 (2017).
pubmed: 28636851
doi: 10.1056/NEJMoa1613493
Schoenfeld, A. J. et al. Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas ✰. Ann. Oncol. 31, 599–608 (2020).
pubmed: 32178965
doi: 10.1016/j.annonc.2020.01.065
Tanizaki, J. et al. Peripheral blood biomarkers associated with clinical outcome in non-small cell lung cancer patients treated with nivolumab. J. Thorac. Oncol. 13, 97–105 (2018).
pubmed: 29170120
doi: 10.1016/j.jtho.2017.10.030
Wu, T. D. et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579, 274–278 (2020).
pubmed: 32103181
doi: 10.1038/s41586-020-2056-8
Krieg, C. et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24, 144–153 (2018).
pubmed: 29309059
doi: 10.1038/nm.4466
Bartel, D. P. Metazoan microRNAs. Cell 173, 20–51 (2018).
pubmed: 29570994
pmcid: 6091663
doi: 10.1016/j.cell.2018.03.006
Mehta, A. & Baltimore, D. MicroRNAs as regulatory elements in immune system logic. Nat. Rev. Immunol. 16, 279–294 (2016).
pubmed: 27121651
doi: 10.1038/nri.2016.40
Rainen, L. et al. Stabilization of mRNA expression in whole blood samples. Clin. Chem. 48, 1883–1890 (2002).
pubmed: 12406972
doi: 10.1093/clinchem/48.11.1883
Shukla, S. et al. Development of a RNA-seq based prognostic signature in lung adenocarcinoma. J. Natl Cancer Inst. 109, djw200 (2016).
pmcid: 5051943
doi: 10.1093/jnci/djw200
Pölsterl, S. scikit-survival: a library for time-to-event analysis built on top of scikit-learn. J. Mach. Learn. Res. 21, 1–6 (2020).
Gandhi, L. et al. Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer. New Engl. J. Med. 378, 2078–2092 (2018).
pubmed: 29658856
doi: 10.1056/NEJMoa1801005
Mountzios, G. et al. Association of the advanced lung cancer inflammation index (ALI) with immune checkpoint inhibitor efficacy in patients with advanced non-small-cell lung cancer. Esmo Open 6, 100254 (2021).
pubmed: 34481329
pmcid: 8417333
doi: 10.1016/j.esmoop.2021.100254
Paz-Ares, L. et al. LBA80Pembrolizumab (pembro) plus platinum-based chemotherapy (chemo) for metastatic NSCLC: Tissue TMB (tTMB) and outcomes in KEYNOTE-021, 189, and 407. Ann. Oncol. 30, v917–v918 (2019).
doi: 10.1093/annonc/mdz394.078
Davis, A. A. & Patel, V. G. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J. Immunother. Cancer 7, 278 (2019).
pubmed: 31655605
pmcid: 6815032
doi: 10.1186/s40425-019-0768-9
Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife https://doi.org/10.7554/elife.05005 (2015).
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
pubmed: 27899662
doi: 10.1093/nar/gkw1092
Stutvoet, T. S. et al. MAPK pathway activity plays a key role in PD‐L1 expression of lung adenocarcinoma cells. J. Pathol. 249, 52–64 (2019).
pubmed: 30972766
pmcid: 6767771
doi: 10.1002/path.5280
Hwang, S. et al. Immune gene signatures for predicting durable clinical benefit of anti-PD-1 immunotherapy in patients with non-small cell lung cancer. Sci. Rep.-UK 10, 643 (2020).
doi: 10.1038/s41598-019-57218-9
Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).
pubmed: 30127393
pmcid: 6487502
doi: 10.1038/s41591-018-0136-1
Auslander, N. et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. 24, 1545–1549 (2018).
pubmed: 30127394
pmcid: 6693632
doi: 10.1038/s41591-018-0157-9
Ayers, M. et al. IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
pubmed: 28650338
pmcid: 5531419
doi: 10.1172/JCI91190
Christopoulos, P. et al. Large cell neuroendocrine lung carcinoma induces peripheral T-cell repertoire alterations with predictive and prognostic significance. Lung Cancer 119, 48–55 (2018).
pubmed: 29656752
doi: 10.1016/j.lungcan.2018.03.002
Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016).
pubmed: 26901407
pmcid: 7446107
doi: 10.1038/nm.4051
Halvorsen, A. R. et al. Circulating microRNAs associated with prolonged overall survival in lung cancer patients treated with nivolumab. Acta Oncol. 57, 1–7 (2018).
doi: 10.1080/0284186X.2018.1465585
Fan, J. et al. Circulating microRNAs predict the response to anti-PD-1 therapy in non-small cell lung cancer. Genomics 112, 2063–2071 (2020).
pubmed: 31786291
doi: 10.1016/j.ygeno.2019.11.019
Boeri, M. et al. Circulating miRNAs and PD-L1 tumor expression are associated with survival in advanced NSCLC patients treated with immunotherapy: a prospective study. Clin. Cancer Res. 25, 2166–2173 (2019).
pubmed: 30617131
pmcid: 6445748
doi: 10.1158/1078-0432.CCR-18-1981
Peng, X.-X. et al. Correlation of plasma exosomal microRNAs with the efficacy of immunotherapy in EGFR/ALK wild-type advanced non-small cell lung cancer. J. Immunother. Cancer 8, e000376 (2020).
pubmed: 31959728
pmcid: 7057418
doi: 10.1136/jitc-2019-000376
Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334.e10 (2019).
pubmed: 30979687
pmcid: 6620049
doi: 10.1016/j.immuni.2019.03.009
Engblom, C., Pfirschke, C. & Pittet, M. J. The role of myeloid cells in cancer therapies. Nat. Rev. Cancer 16, 447–462 (2016).
pubmed: 27339708
doi: 10.1038/nrc.2016.54
He, G. et al. Peritumoural neutrophils negatively regulate adaptive immunity via the PD-L1/PD-1 signalling pathway in hepatocellular carcinoma. J. Exp. Clin. Cancer Res. 34, 141–11 (2015).
pubmed: 26581194
pmcid: 4652417
doi: 10.1186/s13046-015-0256-0
Ballbach, M. et al. Expression of checkpoint molecules on myeloid-derived suppressor cells. Immunol. Lett. 192, 1–6 (2017).
pubmed: 28987474
doi: 10.1016/j.imlet.2017.10.001
Huber, V. et al. Tumor-derived microRNAs induce myeloid suppressor cells and predict immunotherapy resistance in melanoma. J. Clin. Invest. 128, 5505–5516 (2018).
pubmed: 30260323
pmcid: 6264733
doi: 10.1172/JCI98060
Best, M. G. et al. Swarm intelligence-enhanced detection of non- small-cell lung cancer using tumor-educated platelets. Cancer Cell 32, 238–252.e9 (2017).
pubmed: 28810146
pmcid: 6381325
doi: 10.1016/j.ccell.2017.07.004
Best, M. G. et al. RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics. Cancer Cell 28, 666–676 (2015).
pubmed: 26525104
pmcid: 4644263
doi: 10.1016/j.ccell.2015.09.018
Nilsson, R. J. A. et al. Blood platelets contain tumor-derived RNA biomarkers. Blood 118, 3680–3683 (2011).
pubmed: 21832279
pmcid: 7224637
doi: 10.1182/blood-2011-03-344408
Wessels, S. et al. Comprehensive serial biobanking in advanced NSCLC: feasibility, challenges and perspectives. Transl. Lung Cancer Res. 9, 1000–1014 (2020).
pubmed: 32953480
pmcid: 7481602
doi: 10.21037/tlcr-20-137
Travis, W. D. et al. The 2015 World Health Organization classification of lung tumors impact of genetic, clinical and radiologic advances since the 2004 classification. J. Thorac. Oncol. 10, 1243–1260 (2015).
pubmed: 26291008
doi: 10.1097/JTO.0000000000000630
Volckmar, A. et al. Combined targeted DNA and RNA sequencing of advanced NSCLC in routine molecular diagnostics: Analysis of the first 3,000 Heidelberg cases. Int. J. Cancer 145, 649–661 (2019).
pubmed: 30653256
doi: 10.1002/ijc.32133
Hanna, N. H. et al. Therapy for stage IV non–small-cell lung cancer without driver alterations: ASCO and OH (CCO) joint guideline update. J. Clin. Oncol. 38, 1608–1632 (2020).
pubmed: 31990617
doi: 10.1200/JCO.19.03022
Davidson-Pilon, C. et al. CamDavidsonPilon/lifelines: 0.26.0. https://doi.org/10.5281/zenodo.4816284 (2021).
Witten, D. M. & Tibshirani, R. Survival analysis with high-dimensional covariates. Stat. Methods Med. Res. 19, 29–51 (2010).
pubmed: 19654171
doi: 10.1177/0962280209105024
Shukla, S. et al. Development of a RNA-seq based prognostic signature in lung adenocarcinoma. J. Natl Cancer Inst. 109, djw200 (2016).
pmcid: 5051943
doi: 10.1093/jnci/djw200
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 12, 2825–2830 (2011).
Hu, Z. et al. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non–small-cell lung cancer. J. Clin. Oncol. 28, 1721–1726 (2010).
pubmed: 20194856
doi: 10.1200/JCO.2009.24.9342
Cho, J. Y. et al. Gene expression signature–based prognostic risk score in gastric cancer. Clin. Cancer Res. 17, 1850–1857 (2011).
pubmed: 21447720
pmcid: 3078023
doi: 10.1158/1078-0432.CCR-10-2180
Yu, S.-L. et al. MicroRNA signature predicts survival and relapse in lung cancer. Cancer Cell 13, 48–57 (2008).
pubmed: 18167339
doi: 10.1016/j.ccr.2007.12.008
Baron, U. et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci. Transl. Med. 10, eaan3508 (2018).
pubmed: 30068569
doi: 10.1126/scitranslmed.aan3508
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
pubmed: 19261174
pmcid: 2690996
doi: 10.1186/gb-2009-10-3-r25
Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 47, gky1141 (2018).
Waskom, M. et al. mwaskom/seaborn: v0.8.1 (September 2017). https://doi.org/10.5281/zenodo.883859 (2017).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J. Integr. Biol. 16, 284–287 (2012).
doi: 10.1089/omi.2011.0118