Advertisement

New insight on the correlation of metabolic status on 18F-FDG PET/CT with immune marker expression in patients with non-small cell lung cancer

  • Yang Wang
  • Ning Zhao
  • Zhanbo Wu
  • Na Pan
  • Xuejie Shen
  • Ting Liu
  • Feng Wei
  • Jian YouEmail author
  • Wengui XuEmail author
  • Xiubao RenEmail author
Original Article
  • 118 Downloads
Part of the following topical collections:
  1. Oncology – Chest

Abstract

Background

Metabolic information obtained through 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used to evaluate malignancy by calculating the glucose uptake rate, and these parameters play important roles in determining the prognosis of non-small cell lung cancer (NSCLC). The expression of immune-related markers in tumor tissue reflects the immune status in the tumor microenvironment. However, there is lack of reports on the association between metabolic variables and intra-tumor immune markers. Herein, we investigate the correlation between metabolic status on 18F-FDG PET/CT and intra-tumor immunomarkers’ expression in NSCLC patients.

Methods

From April 2008 to August 2014, 763 patients were enrolled in the analysis to investigate the role of maximum standardized uptake value (SUVmax) in lung cancer. One hundred twenty-two tumor specimens were analyzed by immunohistochemistry (IHC) to intra-tumor immune cells and programmed death protein ligand 1(PD-L1) expression on tumor cells. The correlation between metabolic variables and the expression of tissue immune markers were analyzed.

Results

SUVmax values have significant variations in different epidermal growth factor receptor (EGFR) statuses (wild type vs mutant type), high/low neutrophil-to-lymphocyte ratio (NLR) groups, and high/low platelets-to-lymphocyte ratio (PLR) groups (p < 0.001, p < 0.001, p = 0.003, respectively). SUVmax was an independent prognostic factor in lung cancer patients (p = 0.013). IHC demonstrated a statistically significant correlation between SUVmax and the expression of CD8 tumor-infiltrating lymphocytes (p = 0.015), CD163 tumor-associated macrophages (TAMs) (p = 0.003), and Foxp3-regulatory T cells (Tregs) (p = 0.004), as well as PD-1 and PD-L1 (p = 0.003 and p = 0.012, respectively). With respect to patient outcomes, disease stage, BMI, SUVmax, metabolic tumor volume (MTV), TLG (tumor lesion glycolysis), CD163-TAMs, CD11c-dendritic cells (DCs), PD-L1, and Tregs showed a statistically significant correlation with progression-free survival (PFS) (p < 0.001, 0.023, < 0.001, 0.007, 0.005, 0.004, 0.008, 0.048, and 0.014, respectively), and disease stage, SUVmax, MTV, TLG, CD163-TAMs, CD11c-DCs, and PD-L1 showed a statistically significant correlation with overall survival (OS) (p < 0.001, < 0.001, 0.014, 0.012, < 0.001, 0.001, and < 0.001, respectively).

Conclusion

This study revealed an association between metabolic variable and immune cell expression in the tumor microenvironment and suggests that SUVmax on 18F-FDG PET/CT could be a potential predictor for selecting candidates for immunotherapy.

Keywords

Non-small cell lung cancer 18F-FDG PET/CT Standardized uptake value Immunomarkers Prognosis 

Abbreviations

NSCLC

Non-small cell lung cancer

18F-FDG PET/CT

18F-Flurodeoxyglucose positron emission tomography/computed tomography

TILs

Tumor-infiltrating lymphocytes

TAMs

Tumor-associated macrophages

Tregs

Regulatory T cells

DCs

Dendritic cells

PD-1

Programmed death protein 1

IHC

Immunohistochemistry

SUV

Standardized uptake value

PFS

Progression-free survival

OS

Overall survival

SCC

Squamous cell carcinoma

ADC

Adenocarcinoma

NLR

Neutrophil-to-lymphocyte ratio

PLR

Platelets-to-lymphocyte ratio

BMI

Body mass index

Notes

Funding information

This work was supported by National Natural Science Funds of China (Nos. 81401887 and 81401888).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent and ethics statement

Informed consent was obtained from all individual participants included in the study. Written informed obtained from each subject complies with the Declaration of Helsinki. The study was approved by the Ethical Committee of TMUCHI.

Confirmation statement

My study’s involvement with human subjects complies with the Declaration of Helsinki.

Supplementary material

259_2019_4500_Fig4_ESM.png (33 kb)
Supplementary Figure 1

(PNG 33 kb)

259_2019_4500_MOESM1_ESM.tif (645 kb)
High resolution image (TIF 644 kb)
259_2019_4500_Fig5_ESM.png (25 kb)
Supplementary Figure 2

(PNG 25 kb)

259_2019_4500_MOESM2_ESM.tif (421 kb)
High resolution image (TIF 421 kb)
259_2019_4500_Fig6_ESM.png (85 kb)
Supplementary Figure 3

(PNG 84 kb)

259_2019_4500_MOESM3_ESM.tif (142 kb)
High resolution image (TIF 141 kb)
259_2019_4500_Fig7_ESM.png (55 kb)
Supplementary Figure 4

(PNG 54 kb)

259_2019_4500_MOESM4_ESM.tif (112 kb)
High resolution image (TIF 112 kb)
259_2019_4500_Fig8_ESM.png (58 kb)
Supplementary Figure 5

(PNG 58 kb)

259_2019_4500_MOESM5_ESM.tif (118 kb)
High resolution image (TIF 117 kb)
259_2019_4500_Fig9_ESM.png (75 kb)
Supplementary Figure 6

(PNG 74 kb)

259_2019_4500_MOESM6_ESM.tif (125 kb)
High resolution image (TIF 124 kb)
259_2019_4500_MOESM7_ESM.doc (52 kb)
ESM 1 (DOC 52 kb)

References

  1. 1.
    Saintigny P, Burger JA. Recent advances in non-small cell lung cancer biology and clinical management. Discov Med. 2012;13:287–97.Google Scholar
  2. 2.
    Ashamalla H, Rafla S, Parikh K, Mokhtar B, Goswami G, Kambam S, et al. The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer. Int J Radiat Oncol Biol Phys. 2005;63:1016–23.  https://doi.org/10.1016/j.ijrobp.2005.04.021.CrossRefGoogle Scholar
  3. 3.
    Mac MM, Hicks RJ. The role of positron emission tomography/computed tomography in radiation therapy planning for patients with lung cancer. Semin Nucl Med. 2012;42:308–19.  https://doi.org/10.1053/j.semnuclmed.2012.04.003.CrossRefGoogle Scholar
  4. 4.
    Opoka L, Szolkowska M, Podgajny Z, Kunikowska J, Baranska I, Blasinska-Przerwa K, et al. Assessment of recurrence of non-small cell lung cancer after therapy using CT and Integrated PET/CT. Pneumonol Alergol Pol. 2013;81:214–20.Google Scholar
  5. 5.
    Palsson-McDermott EM, O’Neill LA. The Warburg effect then and now: from cancer to inflammatory diseases. Bioessays. 2013;35:965–73.  https://doi.org/10.1002/bies.201300084.CrossRefGoogle Scholar
  6. 6.
    Appelberg R, Moreira D, Barreira-Silva P, Borges M, Silva L, Dinis-Oliveira RJ, et al. The Warburg effect in mycobacterial granulomas is dependent on the recruitment and activation of macrophages by interferon-gamma. Immunology. 2015;145:498–507.  https://doi.org/10.1111/imm.12464.CrossRefGoogle Scholar
  7. 7.
    Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14:1014–22.  https://doi.org/10.1038/ni.2703.CrossRefGoogle Scholar
  8. 8.
    Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21:309–22.  https://doi.org/10.1016/j.ccr.2012.02.022.CrossRefGoogle Scholar
  9. 9.
    Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12:298–306.  https://doi.org/10.1038/nrc3245.CrossRefGoogle Scholar
  10. 10.
    Dunn GP, Old LJ, Schreiber RD. The three Es of cancer immunoediting. Annu Rev Immunol. 2004;22:329–60.  https://doi.org/10.1146/annurev.immunol.22.012703.104803.CrossRefGoogle Scholar
  11. 11.
    Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373:1627–39.  https://doi.org/10.1056/NEJMoa1507643.CrossRefGoogle Scholar
  12. 12.
    Herbst RS, Baas P, Kim DW, Felip E, Perez-Gracia JL, Han JY, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet. 2016;387:1540–50.  https://doi.org/10.1016/S0140-6736(15)01281-7.CrossRefGoogle Scholar
  13. 13.
    Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387:1837–46.  https://doi.org/10.1016/S0140-6736(16)00587-0.CrossRefGoogle Scholar
  14. 14.
    Chang CH, Qiu J, O’Sullivan D, Buck MD, Noguchi T, Curtis JD, et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell. 2015;162:1229–41.  https://doi.org/10.1016/j.cell.2015.08.016.CrossRefGoogle Scholar
  15. 15.
    Pearce EL, Pearce EJ. Metabolic pathways in immune cell activation and quiescence. Immunity. 2013;38:633–43.  https://doi.org/10.1016/j.immuni.2013.04.005.CrossRefGoogle Scholar
  16. 16.
    Na F, Wang J, Li C, Deng L, Xue J, Lu Y. Primary tumor standardized uptake value measured on F18-Fluorodeoxyglucose positron emission tomography is of prediction value for survival and local control in non-small-cell lung cancer receiving radiotherapy: meta-analysis. J Thorac Oncol. 2014;9:834–42.  https://doi.org/10.1097/JTO.0000000000000185.CrossRefGoogle Scholar
  17. 17.
    Nair VS, Krupitskaya Y, Gould MK. Positron emission tomography 18F-fluorodeoxyglucose uptake and prognosis in patients with surgically treated, stage I non-small cell lung cancer: a systematic review. J Thorac Oncol. 2009;4:1473–9.  https://doi.org/10.1097/JTO.0b013e3181bccbc6.CrossRefGoogle Scholar
  18. 18.
    Paesmans M, Berghmans T, Dusart M, Garcia C, Hossein-Foucher C, Lafitte JJ, et al. Primary tumor standardized uptake value measured on fluorodeoxyglucose positron emission tomography is of prognostic value for survival in non-small cell lung cancer: update of a systematic review and meta-analysis by the European Lung Cancer Working Party for the International Association for the Study of Lung Cancer Staging Project. J Thorac Oncol. 2010;5:612–9.  https://doi.org/10.1097/JTO.0b013e3181d0a4f5.CrossRefGoogle Scholar
  19. 19.
    Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454:436–44.  https://doi.org/10.1038/nature07205.CrossRefGoogle Scholar
  20. 20.
    Templeton AJ, McNamara MG, Seruga B, Vera-Badillo FE, Aneja P, Ocana A, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106:u124.  https://doi.org/10.1093/jnci/dju124.CrossRefGoogle Scholar
  21. 21.
    Liu H, Wu Y, Wang Z, Yao Y, Chen F, Zhang H, et al. Pretreatment platelet-to-lymphocyte ratio (PLR) as a predictor of response to first-line platinum-based chemotherapy and prognosis for patients with non-small cell lung cancer. J Thorac Dis. 2013;5:783–9.  https://doi.org/10.3978/j.issn.2072-1439.2013.12.34.Google Scholar
  22. 22.
    Kaya V, Yildirim M, Demirpence O, Yildiz M, Yalcin AY. Prognostic significance of basic laboratory methods in non- small-cell-lung cancer. Asian Pac J Cancer Prev. 2013;14:5473–6.CrossRefGoogle Scholar
  23. 23.
    Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30:1073–81.  https://doi.org/10.1093/carcin/bgp127.CrossRefGoogle Scholar
  24. 24.
    Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357:539–45.  https://doi.org/10.1016/S0140-6736(00)04046-0.CrossRefGoogle Scholar
  25. 25.
    Elkord E, Alcantar-Orozco EM, Dovedi SJ, Tran DQ, Hawkins RE, Gilham DE. T regulatory cells in cancer: recent advances and therapeutic potential. Expert Opin Biol Ther. 2010;10:1573–86.  https://doi.org/10.1517/14712598.2010.529126.CrossRefGoogle Scholar
  26. 26.
    Na II, Byun BH, Kim KM, Cheon GJ, Choe DH, Koh JS, et al. 18F-FDG uptake and EGFR mutations in patients with non-small cell lung cancer: a single-institution retrospective analysis. Lung Cancer. 2010;67:76–80.  https://doi.org/10.1016/j.lungcan.2009.03.010.CrossRefGoogle Scholar
  27. 27.
    Mak RH, Digumarthy SR, Muzikansky A, Engelman JA, Shepard JA, Choi NC, et al. Role of 18F-fluorodeoxyglucose positron emission tomography in predicting epidermal growth factor receptor mutations in non-small cell lung cancer. Oncologist. 2011;16:319–26.  https://doi.org/10.1634/theoncologist.2010-0300.CrossRefGoogle Scholar
  28. 28.
    Higashi K, Ueda Y, Sakurai A, Mingwang X, Xu L, Murakami M, et al. Correlation of Glut-1 glucose transporter expression with [18F]FDG uptake in non-small cell lung cancer. Eur J Nucl Med. 2000;27(12):1778–85.  https://doi.org/10.1007/s002590000367.CrossRefGoogle Scholar
  29. 29.
    Wei T, Zhang J, Qin Y, Wu Y, Zhu L, Lu L, et al. Increased expression of immunosuppressive molecules on intratumoral and circulating regulatory T cells in non-small-cell lung cancer patients. Am J Cancer Res. 2015;5:2190–201.Google Scholar
  30. 30.
    Jayaraman P, Jacques MK, Zhu C, Steblenko KM, Stowell BL, Madi A, et al. TIM3 mediates t cell exhaustion during Mycobacterium tuberculosis infection. PLoS Pathog. 2016;12:e1005490.  https://doi.org/10.1371/journal.ppat.1005490.CrossRefGoogle Scholar
  31. 31.
    Liang B, Workman C, Lee J, Chew C, Dale BM, Colonna L, et al. Regulatory T cells inhibit dendritic cells by lymphocyte activation gene-3 engagement of MHC class II. J Immunol. 2008;180:5916–26.CrossRefGoogle Scholar
  32. 32.
    Rabe H, Nordstrom I, Andersson K, Lundell AC, Rudin A. Staphylococcus aureus convert neonatal conventional CD4(+) T cells into FOXP3(+) CD25(+) CD127(low) T cells via the PD-1/PD-L1 axis. Immunology. 2014;141:467–81.  https://doi.org/10.1111/imm.12209.CrossRefGoogle Scholar
  33. 33.
    Hasegawa T, Suzuki H, Yamaura T, Muto S, Okabe N, Osugi J, et al. Prognostic value of peripheral and local forkhead box P3+ regulatory T cells in patients with non-small-cell lung cancer. Mol Clin Oncol. 2014;2:685–94.  https://doi.org/10.3892/mco.2014.299.CrossRefGoogle Scholar
  34. 34.
    Bille A, Okiror L, Skanjeti A, Errico L, Arena V, Penna D, et al. The prognostic significance of maximum standardized uptake value of primary tumor in surgically treated non-small-cell lung cancer patients: analysis of 413 cases. Clin Lung Cancer. 2013;14:149–56.  https://doi.org/10.1016/j.cllc.2012.04.007.CrossRefGoogle Scholar
  35. 35.
    Lopci E, Toschi L, Grizzi F, Rahal D, Olivari L, Castino GF, et al. Correlation of metabolic information on FDG-PET with tissue expression of immune markers in patients with non-small cell lung cancer (NSCLC) who are candidates for upfront surgery. Eur J Nucl Med Mol Imaging. 2016;43:1954–61.  https://doi.org/10.1007/s00259-016-3425-2.CrossRefGoogle Scholar
  36. 36.
    Fox CJ, Hammerman PS, Thompson CB. Fuel feeds function: energy metabolism and the T-cell response. Nat Rev Immunol. 2005;5:844–52.  https://doi.org/10.1038/nri1710.CrossRefGoogle Scholar
  37. 37.
    Jones RG, Thompson CB. Revving the engine: signal transduction fuels T cell activation. Immunity. 2007;27:173–8.  https://doi.org/10.1016/j.immuni.2007.07.008.CrossRefGoogle Scholar
  38. 38.
    van Bruggen R, Koker MY, Jansen M, van Houdt M, Roos D, Kuijpers TW, et al. Human NLRP3 inflammasome activation is Nox1–4 independent. Blood. 2010;115:5398–400.  https://doi.org/10.1182/blood-2009-10-250803.CrossRefGoogle Scholar
  39. 39.
    Zou W, Chen L. Inhibitory B7-family molecules in the tumour microenvironment. Nat Rev Immunol. 2008;8:467–77.  https://doi.org/10.1038/nri2326.CrossRefGoogle Scholar
  40. 40.
    Curiel TJ, Wei S, Dong H, Alvarez X, Cheng P, Mottram P, et al. Blockade of B7-H1 improves myeloid dendritic cell-mediated antitumor immunity. Nat Med. 2003;9:562–7.  https://doi.org/10.1038/nm863.CrossRefGoogle Scholar
  41. 41.
    Freeman GJ, Long AJ, Iwai Y, Bourque K, Chernova T, Nishimura H, et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J Exp Med. 2000;192:1027–34.CrossRefGoogle Scholar
  42. 42.
    Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012;149:274–93.  https://doi.org/10.1016/j.cell.2012.03.017.CrossRefGoogle Scholar
  43. 43.
    Spranger S, Koblish HK, Horton B, Scherle PA, Newton R, Gajewski TF. Mechanism of tumor rejection with doublets of CTLA-4, PD-1/PD-L1, or IDO blockade involves restored IL-2 production and proliferation of CD8(+) T cells directly within the tumor microenvironment. J Immunother Cancer. 2014;2:3.  https://doi.org/10.1186/2051-1426-2-3.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiotherapyTianjin Medical University Cancer Institute and HospitalTianjinChina
  2. 2.National Clinical Research Center of CancerTianjinChina
  3. 3.Key Laboratory of Cancer Prevention and TherapyTianjinChina
  4. 4.Tianjin’s Clinical Research Center for CancerTianjinChina
  5. 5.Key Laboratory of Cancer Immunology and BiotherapyTianjinChina
  6. 6.Department of ImmunologyTianjin Medical University Cancer Institute and HospitalTianjinChina
  7. 7.Department of Thoracic surgeryTianjin Medical University Cancer Institute and HospitalTianjinChina
  8. 8.Department of Molecular Imaging and Nuclear MedicineTianjin Medical University Cancer Institute and HospitalTianjinChina

Personalised recommendations