Skip to main content

Advertisement

SpringerLink
Log in

Identified lung adenocarcinoma metabolic phenotypes and their association with tumor immune microenvironment

  • Original Article
  • Published:
Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

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

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

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

This work revealed the significant connection between metabolic pattern of tumor and tumor immune infiltration, regulating LUAD patients’ response to immunotherapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

All data used in this work can be acquired from the Gene Expression Omni-bus (GEO; https://www.ncbi.nlm.nih.gov/geo/), the Cancer Genome Atlas (TCGA) datasets (https://xenabrowser.net/).

Abbreviations

AUC:

Area under the curve

CNAs:

Copy number alternations

DEGs:

Differentially expressed genes

FDR:

False discovery rate

FPKM:

Fragments per kilobase million

GEO:

Gene expression omnibus

GEP:

Gene expression profile

GO:

Gene ontology

GSVA:

Gene set variation analysis

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

NSCLC:

Non-small cell lung cancer

ROC:

Receiver operating characteristic

TIDE:

Tumor immune dysfunction and exclusion

TME:

Tumor microenvironment

TPM:

Transcripts per kilobase million

t-SNE:

T-distributed stochastic neighbor embedding

References

  1. Bade BC, Dela Cruz CS, Cancer L (2020) Epidemiology, etiology, and prevention. Clin Chest Med 41(2020):1–24

    Article  PubMed  Google Scholar 

  2. Myers DJ, Wallen JM (2020) Lung Adenocarcinoma. StatPearls Publishing, Treasure Island, FL. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519578/

  3. 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

  4. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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

    Article  CAS  PubMed  Google Scholar 

  9. 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

    Article  CAS  PubMed  Google Scholar 

  10. Lim SM, Hong MH, Kim HR (2020) Immunotherapy for non-small cell lung cancer: current landscape and future perspectives. Immune Netw 20:10

    Article  Google Scholar 

  11. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bradbury PA, Shepherd FA (2008) Immunotherapy for lung cancer. J Thorac Oncol 3:S164–S170

    Article  PubMed  Google Scholar 

  13. Hsu ML, Naidoo J (2020) Principles of immunotherapy in non-small cell lung cancer. Thorac Surg Clin 30:187–198

    Article  PubMed  Google Scholar 

  14. Barrueto L, Caminero F, Cash L, Makris C, Lamichhane P, Deshmukh RR (2020) Resistance to checkpoint inhibition in cancer immunotherapy. Transl Oncol 13:100738

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ball GH, Hall DJ (1967) A clustering technique for summarizing multivariate data. Behav Sci 12:153–155

    Article  CAS  PubMed  Google Scholar 

  18. 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

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  20. 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

    Article  CAS  PubMed  Google Scholar 

  21. 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

    Article  CAS  Google Scholar 

  22. 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

    Google Scholar 

  23. 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

    Article  CAS  Google Scholar 

  24. Goeman JJ (2010) L1 penalized estimation in the Cox proportional hazards model. Biom J 52:70–84

    PubMed  Google Scholar 

  25. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ghasemi A, Zahediasl S (2012) Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab 10:486–489

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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

    CAS  Google Scholar 

  29. 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

    Article  PubMed  PubMed Central  Google Scholar 

  30. 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

    Google Scholar 

  31. 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

    Article  Google Scholar 

  32. Matthias S (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  34. 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

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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

    Article  PubMed  PubMed Central  Google Scholar 

  36. 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

    Article  PubMed  PubMed Central  Google Scholar 

  37. Havel JJ, Chowell D, Chan TA (2019) The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19:133–150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sokratous G, Polyzoidis S, Ashkan K (2017) Immune infiltration of tumor microenvironment following immunotherapy for glioblastoma multiforme. Hum Vaccin Immunother 13:2575–2582

    Article  PubMed  PubMed Central  Google Scholar 

  39. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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

    Article  CAS  PubMed  Google Scholar 

  41. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Galon J, Bruni D (2019) Approaches to treat immune hot altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov 18:197–218

    Article  CAS  PubMed  Google Scholar 

  44. 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

    CAS  PubMed  Google Scholar 

  45. Zhang J, Shi Z, Xu X, Yu Z, Mi J (2019) The influence of microenvironment on tumor immunotherapy. FEBS J 286:4160–4175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 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

    Article  CAS  PubMed  Google Scholar 

  48. 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

    Article  CAS  PubMed  Google Scholar 

  49. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by Anhui Provincial Natural Science Foundation (No. 1808085QH270; 2008085QH428) and ‘the Fundamental Research Funds for the Central Universities’ (No. WK9110000121).This work was supported by the National Natural Science Foundation of China (Nos. 82073893; 81703622), China, Postdoctoral Science Foundation (No. 2018M633002), Hunan Provincial Natural Science Foundation of China (No.2018JJ3838), Hunan Provincial Health Committee Foundation of China (No. C2019186) and Xiangya Hospital Central South University postdoctoral foundation.

Author information

Authors and Affiliations

  1. Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, People’s Republic of China

    Xian-Ning Wu & Mei-Qing Xu

  2. School of Nursing, Anhui Medical University, Hefei, People’s Republic of China

    Dan Su

  3. School of Life Sciences, University of Science and Technology of China (USTC), Hefei, 230027, Anhui, People’s Republic of China

    Yi-De Mei

  4. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China

    Hao Zhang, Ze-Yu Wang & Quan Cheng

  5. One-Third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Hei Longjiang, People’s Republic of China

    Nan Zhang

  6. Department of Pathology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Li-Ling Li

  7. Department of Pathology, Xiangya Medical School, Central South University, Changsha, People’s Republic of China

    Li-Ling Li

  8. Department of Ophthalmology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, People’s Republic of China

    Li Peng

  9. Aier School of Ophthalmology, Central South University, Changsha, People’s Republic of China

    Jun-Yi Jiang

  10. Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Jia-Yi Yang

  11. Department of Clinical Pharmacology, and Geriatric Urology, Xiangya International Medical Center, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Dong-Jie Li

  12. National Clinical Research Center for Geriatric Disorders, Changsha, People’s Republic of China

    Dong-Jie Li

  13. Department of Psychiatry, The Second People’s Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, People’s Republic of China

    Hui Cao

  14. Department of Neurology, Hunan Aerospace Hospital, Changsha, People’s Republic of China

    Zhi-Wei Xia

  15. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Quan Cheng

  16. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Wen-Jing Zeng & Quan Cheng

Authors
  1. Xian-Ning Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi-De Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mei-Qing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ze-Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Li-Ling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jun-Yi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jia-Yi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Dong-Jie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhi-Wei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Wen-Jing Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Quan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Nan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XW, DS, YM, MX, HZ, ZW and ZN designed and drafted the manuscript; LL, PL, JJ, JY and DL wrote figure legends and revised the article; HC, ZX and QC conducted the data analysis; All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Quan Cheng or Nan Zhang.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, XN., Su, D., Mei, YD. et al. Identified lung adenocarcinoma metabolic phenotypes and their association with tumor immune microenvironment. Cancer Immunol Immunother 70, 2835–2850 (2021). https://doi.org/10.1007/s00262-021-02896-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00262-021-02896-6

Keywords

Search

Navigation