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18.03.2020 | Original Article

Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis

verfasst von: Guoshu Bi, Zhencong Chen, Xiaodong Yang, Jiaqi Liang, Zhengyang Hu, Yunyi Bian, Qihai Sui, Runmei Li, Cheng Zhan, Hong Fan

Erschienen in: Cancer Immunology, Immunotherapy | Ausgabe 7/2020

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Abstract

Purpose

To comprehensively elucidate the landscape of the tumor environment (TME) of lung adenocarcinoma (LUAD), which has a profound impact on prognosis and response to immunotherapy.

Methods and materials

Using a large dataset of LUAD patients from The Cancer Genome Atlas, Gene Expression Omnibus database (GEO), and our institution (n = 1411), we estimated the infiltration pattern of 24 immune cell populations in each sample and systematically correlated the TME phenotypes with genomic traits and clinicopathologic characteristics.

Results

The LUAD microenvironment was classified into two distinct TME clusters (A and B), and a random forest classifier model was constructed. TMEcluster A was characterized by sparse distribution of immune cell infiltration, relatively low levels of immunomodulators and slightly higher mutation load. By contrast, enrichment of both cytotoxic T cells and immunosuppressor cells was observed in TMEcluster B. Moreover, several immune-related cytokines or markers including IFN-γ, TNF-β, and several immune checkpoint molecules such as PD-L1 were also upregulated in TMEcluster B. Multivariable Cox analysis revealed that the TMEcluster was an independent prognostic factor (TMEcluster B vs. A, hazard ratio = 0.68, 95% confidence interval = 0.50–0.91, p = 0.010). These findings were all externally validated in the data from the GEO database and our institution.

Conclusions

Our findings describe a comprehensive landscape of LUAD immune infiltration pattern and integrate several previously proposed biomarkers associated with distinct immunophenotypes, thus shedding light on how tumors interact with immune microenvironment. Our results may guide a more precise immune therapeutic strategy for LUAD patients.

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Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424PubMed

Barta JA, Powell CA, Wisnivesky JP (2019) Global epidemiology of lung cancer. Ann Glob Health. 85(1):8. https://doi.org/10.5334/aogh.2419 CrossRefPubMedPubMedCentral

Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Csoszi T, Fulop A et al (2016) Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med 375(19):1823–1833PubMedCrossRef

Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J et al (2016) Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. The Lancet 387(10030):1837–1846CrossRef

Gentzler R, Hall R, Kunk PR, Gaughan E, Dillon P, Slingluff CL et al (2016) Beyond melanoma: inhibiting the PD-1/PD-L1 pathway in solid tumors. Immunotherapy 8(5):583–600PubMedCrossRef

Jia Q, Wu W, Wang Y, Alexander PB, Sun C, Gong Z et al (2018) Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun 9(1):5361PubMedPubMedCentralCrossRef

Herbst RS, Baas P, Kim D-W, Felip E, Pérez-Gracia JL, Han J-Y et al (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. The Lancet 387(10027):1540–1550CrossRef

Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230):124–128PubMedPubMedCentralCrossRef

Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515(7528):568–571PubMedPubMedCentralCrossRef

10.

Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674CrossRefPubMed

11.

Spranger S (2016) Mechanisms of tumor escape in the context of the T-cell-inflamed and the non-T-cell-inflamed tumor microenvironment. Int Immunol 28(8):383–391PubMedPubMedCentralCrossRef

12.

Zeng D, Li M, Zhou R, Zhang J, Sun H, Shi M et al (2019) Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunol Res 7(5):737–750PubMedCrossRef

13.

Tamborero D, Rubio-Perez C, Muinos F, Sabarinathan R, Piulats JM, Muntasell A et al (2018) A pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations. Clin Cancer Res 24(15):3717–3728PubMedCrossRef

14.

Xiao Y, Ma D, Zhao S, Suo C, Shi J, Xue MZ et al (2019) Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer. Clin Cancer Res 25(16):5002–5014PubMedCrossRef

15.

Cai Y, Chang Y, Liu Y (2019) Multi-omics profiling reveals distinct microenvironment characterization of endometrial cancer. Biomed Pharmacother 118:109244PubMedCrossRef

16.

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12(5):453–457PubMedPubMedCentralCrossRef

17.

Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF et al (2009) Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462(7269):108–112PubMedPubMedCentralCrossRef

18.

Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7PubMedPubMedCentralCrossRef

19.

Li Y, Ge D, Gu J, Xu F, Zhu Q, Lu C (2019) A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies. BMC Cancer 19(1):886PubMedPubMedCentralCrossRef

20.

Yang X, Shi Y, Li M, Lu T, Xi J, Lin Z et al (2019) Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma. J Transl Med 17(1):217PubMedPubMedCentralCrossRef

21.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl Acids Res 43(7):e47PubMedCrossRefPubMedCentral

22.

Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC et al (2013) Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39(4):782–795PubMedCrossRef

23.

Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P (2015) The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1(6):417–425PubMedPubMedCentralCrossRef

24.

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

25.

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545–15550PubMedCrossRefPubMedCentral

26.

Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P et al (2012) Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med 366(26):2455–2465PubMedPubMedCentralCrossRef

27.

Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S et al (2016) Genomic and transcriptomic features of response to Anti-PD-1 therapy in metastatic melanoma. Cell 165(1):35–44PubMedPubMedCentralCrossRef

28.

Wilke CM, Wei S, Wang L, Kryczek I, Kao J, Zou W (2011) Dual biological effects of the cytokines interleukin-10 and interferon-γ. Cancer Immunol Immunother 60(11):1529–1541PubMedCrossRef

29.

Bronte V, Murray PJ (2015) Understanding local macrophage phenotypes in disease: modulating macrophage function to treat cancer. Nat Med 21(2):117–119PubMedCrossRef

30.

Rooney Michael S, Shukla Sachet A, Wu Catherine J, Getz G, Hacohen N (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160(1–2):48–61PubMedPubMedCentralCrossRef

31.

DuPage M, Cheung AF, Mazumdar C, Winslow MM, Bronson R, Schmidt LM et al (2011) Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression. Cancer Cell 19(1):72–85PubMedPubMedCentralCrossRef

32.

Schreiber RD, Old LJ, Smyth MJ (2011) Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science 331(6024):1565–1570PubMedCrossRef

33.

Viganò S, Perreau M, Pantaleo G, Harari A (2012) Positive and negative regulation of cellular immune responses in physiologic conditions and diseases. Clin Dev Immunol 2012:485781PubMedPubMedCentral

34.

Spranger S, Spaapen RM, Zha Y, Williams J, Meng Y, Ha TT et al (2013) Up-regulation of PD-L1, IDO, and T(regs) in the melanoma tumor microenvironment is driven by CD8(+) T cells. Sci Transl Med. 5(200):200ra116PubMedPubMedCentralCrossRef

35.

Bald T, Landsberg J, Lopez-Ramos D, Renn M, Glodde N, Jansen P et al (2014) Immune cell-poor melanomas benefit from PD-1 blockade after targeted type I IFN activation. Cancer Discov 4(6):674–687PubMedCrossRef

36.

Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH et al (2014) Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 20(19):5064–5074PubMedPubMedCentralCrossRef

37.

Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M et al (2017) First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. N Engl J Med 376(25):2415–2426PubMedPubMedCentralCrossRef

38.

Spranger S, Luke JJ, Bao R, Zha Y, Hernandez KM, Li Y et al (2016) Density of immunogenic antigens does not explain the presence or absence of the T-cell-inflamed tumor microenvironment in melanoma. Proc Natl Acad Sci USA 113(48):E7759–E7768PubMedCrossRefPubMedCentral

39.

Woo SR, Fuertes MB, Corrales L, Spranger S, Furdyna MJ, Leung MY et al (2014) STING-dependent cytosolic DNA sensing mediates innate immune recognition of immunogenic tumors. Immunity 41(5):830–842PubMedPubMedCentralCrossRef

40.

Luksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD, Solovyov A et al (2017) A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551(7681):517–520PubMedPubMedCentralCrossRef

41.

Porta-Pardo E, Godzik A (2016) Mutation drivers of immunological responses to cancer. Cancer Immunol Res 4(9):789–798PubMedPubMedCentralCrossRef

42.

Wellenstein MD, de Visser KE (2018) Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity 48(3):399–416PubMedCrossRef

43.

Koyama S, Akbay EA, Li YY, Aref AR, Skoulidis F, Herter-Sprie GS et al (2016) STK11/LKB1 deficiency promotes neutrophil recruitment and proinflammatory cytokine production to suppress T-cell activity in the lung tumor microenvironment. Cancer Res 76(5):999–1008PubMedPubMedCentralCrossRef

44.

Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L et al (2015) Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science (New York, NY) 350(6257):207–211CrossRef

45.

Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD et al (2017) Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med 14(5):e1002309PubMedPubMedCentralCrossRef

Titel: Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis
verfasst von: Guoshu Bi
Zhencong Chen
Xiaodong Yang
Jiaqi Liang
Zhengyang Hu
Yunyi Bian
Qihai Sui
Runmei Li
Cheng Zhan
Hong Fan
Publikationsdatum: 18.03.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Cancer Immunology, Immunotherapy / Ausgabe 7/2020
Print ISSN: 0340-7004
Elektronische ISSN: 1432-0851
DOI: https://doi.org/10.1007/s00262-020-02546-3

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Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis

Abstract

Purpose

Methods and materials

Results

Conclusions

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Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Purpose

Methods and materials

Results

Conclusions

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