Identified lung adenocarcinoma metabolic phenotypes and their association with tumor immune microenvironment.
Immune infiltration
Immunotherapy
Lung adenocarcinoma
Metabolism
PD-1
Journal
Cancer immunology, immunotherapy : CII
ISSN: 1432-0851
Titre abrégé: Cancer Immunol Immunother
Pays: Germany
ID NLM: 8605732
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
received:
01
08
2020
accepted:
18
02
2021
pubmed:
5
3
2021
medline:
30
9
2021
entrez:
4
3
2021
Statut:
ppublish
Résumé
Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown. Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients. And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system. This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.
Sections du résumé
BACKGROUND
BACKGROUND
Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), causes high mortality around the world. Previous studies have suggested that the metabolic pattern of tumor is associated with tumor response to immunotherapy and patient's survival outcome. Yet, this relationship in LUAD is still unknown.
METHODS
METHODS
Therefore, in this study, we identified the immune landscape in different tumor subtypes classified by metabolism-related genes expression with a large-scale dataset (tumor samples, n = 2181; normal samples, n = 419). We comprehensively correlated metabolism-related phenotypes with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in LUAD patients.
RESULTS
RESULTS
And we confirmed tumors with activated lipid metabolism tend to have higher immunocytes infiltration and better response to checkpoint immunotherapy. This work highlights the connection between the metabolic pattern of tumor and tumor immune infiltration in LUAD. A scoring system based on metabolism-related gene expression is not only able to predict prognosis of patient with LUAD but also applied to pan-cancer. LUAD response to checkpoint immunotherapy can also be predicted by this scoring system.
CONCLUSIONS
CONCLUSIONS
This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients' response to immunotherapy.
Identifiants
pubmed: 33659999
doi: 10.1007/s00262-021-02896-6
pii: 10.1007/s00262-021-02896-6
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2835-2850Subventions
Organisme : Anhui Provincial Natural Science Foundation
ID : 1808085QH270
Organisme : Anhui Provincial Natural Science Foundation
ID : 2008085QH428
Organisme : Fundamental Research Funds for the Central Universities
ID : WK9110000121
Organisme : National Natural Science Foundation of China
ID : 81703622
Organisme : National Natural Science Foundation of China
ID : 82073893
Organisme : Postdoctoral Research Foundation of China
ID : 2018M633002
Organisme : Hunan Provincial Natural Science Foundation of China
ID : 2018JJ3838
Organisme : Hunan Provincial Health Committee Foundation of China
ID : C2019186
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
Références
Bade BC, Dela Cruz CS, Cancer L (2020) Epidemiology, etiology, and prevention. Clin Chest Med 41(2020):1–24
pubmed: 32008623
doi: 10.1016/j.ccm.2019.10.001
Myers DJ, Wallen JM (2020) Lung Adenocarcinoma. StatPearls Publishing, Treasure Island, FL. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519578/
Herbst RS, Baas P, Kim DW, Felip E, Perez-Gracia JL, Han JY, Molina J, Kim JH, Arvis CD, Ahn MJ, Majem M, Fidler MJ, de Castro G, Jr., Garrido M, Lubiniecki GM, Shentu Y, Im E, Dolled-Filhart M, Garon EG (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387:1540–1550
Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E, Barlesi F, Kohlhaufl M, Arrieta O, Burgio MA, Fayette J, Lena H, Poddubskaya E, Gerber DE, Gettinger SN, Rudin CM, Rizvi N, Crino L, Blumenschein GR Jr, Antonia SJ, Dorange C, Harbison CT, Graf Finckenstein F, Brahmer JR (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med 373:1627–1639
pubmed: 26412456
pmcid: 5705936
doi: 10.1056/NEJMoa1507643
Li J, He Y, Tan Z, Lu J, Li L, Song X, Shi F, Xie L, You S, Luo X, Li N, Li Y, Liu X, Tang M, Weng X, Yi W, Fan J, Zhou J, Qiang G, Qiu S, Wu W, Bode AM, Cao Y (2018) Wild-type IDH2 promotes the Warburg effect and tumor growth through HIF1alpha in lung cancer. Theranostics 8:4050–4061
pubmed: 30128035
pmcid: 6096397
doi: 10.7150/thno.21524
Chang CH, Qiu J, O’Sullivan D, Buck MD, Noguchi T, Curtis JD, Chen Q, Gindin M, Gubin MM, van der Windt GJ, Tonc E, Schreiber RD, Pearce EJ, Pearce EL (2015) Metabolic competition in the tumor microenvironment is a driver of cancer Progression. Cell 162:1229–1241
pubmed: 26321679
pmcid: 4864363
doi: 10.1016/j.cell.2015.08.016
Ho PC, Bihuniak JD, Macintyre AN, Staron M, Liu X, Amezquita R, Tsui YC, Cui G, Micevic G, Perales JC, Kleinstein SH, Abel ED, Insogna KL, Feske S, Locasale JW, Bosenberg MW, Rathmell JC, Kaech SM (2015) Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162:1217–1228
pubmed: 4567953
pmcid: 4567953
doi: 10.1016/j.cell.2015.08.012
Ramapriyan R, Caetano MS, Barsoumian HB, Mafra ACP, Zambalde EP, Menon H, Tsouko E, Welsh JW, Cortez MA (2019) Altered cancer metabolism in mechanisms of immunotherapy resistance. Pharmacol Ther 195:162–171
pubmed: 30439456
doi: 10.1016/j.pharmthera.2018.11.004
Li X, Wenes M, Romero P, Huang SC, Fendt SM, Ho PC (2019) Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat Rev Clin Oncol 16:425–441
pubmed: 30914826
doi: 10.1038/s41571-019-0203-7
Lim SM, Hong MH, Kim HR (2020) Immunotherapy for non-small cell lung cancer: current landscape and future perspectives. Immune Netw 20:10
doi: 10.4110/in.2020.20.e10
Raju S, Joseph R, Sehgal S (2018) Review of checkpoint immunotherapy for the management of non-small cell lung cancer. Immunotargets Ther 7:63–75
pubmed: 30105218
pmcid: 6074780
doi: 10.2147/ITT.S125070
Bradbury PA, Shepherd FA (2008) Immunotherapy for lung cancer. J Thorac Oncol 3:S164–S170
pubmed: 18520304
doi: 10.1097/JTO.0b013e318174e9a7
Hsu ML, Naidoo J (2020) Principles of immunotherapy in non-small cell lung cancer. Thorac Surg Clin 30:187–198
pubmed: 32327177
doi: 10.1016/j.thorsurg.2020.01.009
Barrueto L, Caminero F, Cash L, Makris C, Lamichhane P, Deshmukh RR (2020) Resistance to checkpoint inhibition in cancer immunotherapy. Transl Oncol 13:100738
pubmed: 32114384
pmcid: 7047187
doi: 10.1016/j.tranon.2019.12.010
Faruki H, Mayhew GM, Serody JS, Hayes DN, Perou CM, Lai-Goldman M (2017) Lung adenocarcinoma and squamous cell carcinoma gene expression subtypes demonstrate significant differences in tumor immune landscape. J Thorac Oncol 12:943–953
pubmed: 28341226
pmcid: 6557266
doi: 10.1016/j.jtho.2017.03.010
Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, Sethumadhavan S, Woo HK, Jang HG, Jha AK, Chen WW, Barrett FG, Stransky N, Tsun ZY, Cowley GS, Barretina J, Kalaany NY, Hsu PP, Ottina K, Chan AM, Yuan B, Garraway LA, Root DE, Mino-Kenudson M, Brachtel EF, Driggers EM, Sabatini DM (2011) Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476:346–350
pubmed: 21760589
pmcid: 3353325
doi: 10.1038/nature10350
Ball GH, Hall DJ (1967) A clustering technique for summarizing multivariate data. Behav Sci 12:153–155
pubmed: 6030099
doi: 10.1002/bs.3830120210
Monti S, Tamayo P, Mesirov J, Golub T (2003) Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52(1):91–118
doi: 10.1023/A:1023949509487
Hanzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7
pubmed: 23323831
pmcid: 3618321
doi: 10.1186/1471-2105-14-7
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z (2017) Pan-Cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262
pubmed: 28052254
doi: 10.1016/j.celrep.2016.12.019
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:47
doi: 10.1093/nar/gkv007
Hartigan JA, Wong MA (1979) Algorithm as 136 A K-means clustering algorithm. J R Stat Soc Ser C Appl Stat 28(1):100–108
Yu G, Wang LG, Han Y, He QY (2012) Clusterprofiler: an R package for comparing biological themes among gene clusters. Omics J Integr Bio 16:284–287
doi: 10.1089/omi.2011.0118
Goeman JJ (2010) L1 penalized estimation in the Cox proportional hazards model. Biom J 52:70–84
pubmed: 19937997
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Ghasemi A, Zahediasl S (2012) Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab 10:486–489
pubmed: 23843808
pmcid: 3693611
doi: 10.5812/ijem.3505
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, Liu J, Freeman GJ, Brown MA, Wucherpfennig KW, Liu XS (2018) Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24:1550–1558
pubmed: 30127393
pmcid: 6487502
doi: 10.1038/s41591-018-0136-1
Lu X, Jiang L, Zhang L, Zhu Y, Hu W, Wang J, Ruan X, Xu Z, Meng X, Gao J, Su X, Yan F (2019) Immune signature-based subtypes of cervical squamous cell carcinoma tightly associated with human papillomavirus type 16 expression. Mol Features Clin Outcome, Neoplasia 21:591–601
Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, Piha-Paul SA, Yearley J, Seiwert TY, Ribas A, McClanahan TK (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127:2930–2940
pubmed: 28650338
pmcid: 5531419
doi: 10.1172/JCI91190
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) Proc: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinfor 12:77
doi: 10.1186/1471-2105-12-77
Matthias S (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847
doi: 10.1093/bioinformatics/btw313
Schreiber RD, Old LJ, Smyth MJ (2011) Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science 331:1565–1570
pubmed: 21436444
doi: 10.1126/science.1203486
Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L (2020) Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer. Brief Bioinform. https://doi.org/10.1093/bib/bbaa026
doi: 10.1093/bib/bbaa026
pubmed: 32866969
pmcid: 8382976
Davoli T, Uno H, Wooten EC, Elledge SJ (2017) Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. https://doi.org/10.1126/science.aaf8399
doi: 10.1126/science.aaf8399
pubmed: 28104840
pmcid: 5592794
Datta M, Coussens LM, Nishikawa H, Hodi FS, Jain RK (2019) Reprogramming the tumor microenvironment to improve immunotherapy: emerging strategies and combination therapies. Am Soc Clin Oncol Educ Book 39:165–174
pubmed: 31099649
pmcid: 6596289
doi: 10.1200/EDBK_237987
Havel JJ, Chowell D, Chan TA (2019) The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19:133–150
pubmed: 30755690
pmcid: 6705396
doi: 10.1038/s41568-019-0116-x
Sokratous G, Polyzoidis S, Ashkan K (2017) Immune infiltration of tumor microenvironment following immunotherapy for glioblastoma multiforme. Hum Vaccin Immunother 13:2575–2582
pubmed: 28362548
pmcid: 5703406
doi: 10.1080/21645515.2017.1303582
Altorki NK, Markowitz GJ, Gao D, Port JL, Saxena A, Stiles B, McGraw T, Mittal V (2019) The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer 19:9–31
pubmed: 30532012
pmcid: 6749995
doi: 10.1038/s41568-018-0081-9
Nazareth MR, Broderick L, Simpson-Abelson MR, Kelleher RJ Jr, Yokota SJ, Bankert RB (2007) Characterization of human lung tumor-associated fibroblasts and their ability to modulate the activation of tumor-associated T cells. J Immunol 178:5552–5562
pubmed: 17442937
doi: 10.4049/jimmunol.178.9.5552
Salmon H, Franciszkiewicz K, Damotte D, Dieu-Nosjean MC, Validire P, Trautmann A, Mami-Chouaib F, Donnadieu E (2012) Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors. J Clin Invest 122:899–910
pubmed: 22293174
pmcid: 3287213
doi: 10.1172/JCI45817
Liu F, Qin L, Liao Z, Song J, Yuan C, Liu Y, Wang Y, Xu H, Zhang Q, Pei Y, Zhang H, Pan Y, Chen X, Zhang Z, Zhang W, Zhang B (2020) Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma. Exp Hematol Oncol 9:10
pubmed: 32509418
pmcid: 7249423
doi: 10.1186/s40164-020-00165-3
Galon J, Bruni D (2019) Approaches to treat immune hot altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov 18:197–218
pubmed: 30610226
doi: 10.1038/s41573-018-0007-y
Rodallec A, Sicard G, Fanciullino R, Benzekry S, Lacarelle B, Milano G, Ciccolini J (2018) Turning cold tumors into hot tumors: harnessing the potential of tumor immunity using nanoparticles. Expert Opin Drug Metab Toxicol 14:1139–1147
pubmed: 30354685
Zhang J, Shi Z, Xu X, Yu Z, Mi J (2019) The influence of microenvironment on tumor immunotherapy. FEBS J 286:4160–4175
pubmed: 31365790
pmcid: 6899673
doi: 10.1111/febs.15028
Cascone T, McKenzie JA, Mbofung RM, Punt S, Wang Z, Xu C, Williams LJ, Wang Z, Bristow CA, Carugo A, Peoples MD, Li L, Karpinets T, Huang L, Malu S, Creasy C, Leahey SE, Chen J, Chen Y, Pelicano H, Bernatchez C, Gopal YNV, Heffernan TP, Hu J, Wang J, Amaria RN, Garraway LA, Huang P, Yang P, Wistuba SE II, Woodman J, Roszik RE, Davis MA, Davies JV, Heymach P, Hwu W. Peng (2018) Increased tumor glycolysis characterizes immune resistance to adoptive T cell therapy. Cell Metab 27(5):977–987
pubmed: 5932208
pmcid: 5932208
doi: 10.1016/j.cmet.2018.02.024
Afonso J, Santos LL, Longatto-Filho A, Baltazar F (2020) Competitive glucose metabolism as a target to boost bladder cancer immunotherapy. Nat Rev Urol 17:77–106
pubmed: 31953517
doi: 10.1038/s41585-019-0263-6
Amobi A, Qian F, Lugade AA, Odunsi K (2017) Tryptophan catabolism and cancer immunotherapy targeting IDO mediated immune suppression. Adv Exp Med Biol 1036:129–144
pubmed: 29275469
doi: 10.1007/978-3-319-67577-0_9
pmcid: 29275469
Harel M, Ortenberg R, Varanasi SK, Mangalhara KC, Mardamshina M, Markovits E, Baruch EN, Tripple V, Arama-Chayoth M, Greenberg E, Shenoy A, Ayasun R, Knafo N, Xu S, Anafi L, Yanovich-Arad G, Barnabas GD, Ashkenazi S, Besser MJ, Schachter J, Bosenberg M, Shadel GS, Barshack I, Kaech SM, Markel G, Geiger T (2019) Proteomics of melanoma response to immunotherapy reveals mitochondrial dependence. Cell 179(1):236–250
pubmed: 31495571
pmcid: 7993352
doi: 10.1016/j.cell.2019.08.012
Li S, Xuan Y, Gao B, Sun X, Miao S, Lu T, Wang Y, Jiao W (2018) Identification of an eight-gene prognostic signature for lung adenocarcinoma. Cancer Manag Res 10:3383–3392
pubmed: 30237740
pmcid: 6138967
doi: 10.2147/CMAR.S173941
Sun S, Guo W, Wang Z, Wang X, Zhang G, Zhang H, Li R, Gao Y, Qiu B, Tan F, Gao Y, Xue Q, Gao S, He J (2020) Development and validation of an immune-related prognostic signature in lung adenocarcinoma. Cancer Med 9:5960–5975
pubmed: 32592319
pmcid: 7433810
doi: 10.1002/cam4.3240