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Cancer Immunology, Immunotherapy

, Volume 67, Issue 7, pp 1135–1146 | Cite as

Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profiles

  • Yochai Adir
  • Shoval Tirman
  • Shirley Abramovitch
  • Cynthia Botbol
  • Aviv Lutaty
  • Tali Scheinmann
  • Eyal Davidovits
  • Irit Arbel
  • Giora Davidovits
  • Sonia Schneer
  • Michal Shteinberg
  • Hagit Peretz Soroka
  • Ruven Tirosh
  • Fernando Patolsky
Original Article

Abstract

Lung cancer is the leading cause of cancer death worldwide. Survival is largely dependent on the stage of diagnosis: the localized disease has a 5-year survival greater than 55%, whereas, for spread tumors, this rate is only 4%. Therefore, the early detection of lung cancer is key for improving prognosis. In this study, we present an innovative, non-invasive, cancer detection approach based on measurements of the metabolic activity profiles of immune system cells. For each Liquid ImmunoBiopsy test, a 384 multi-well plate is loaded with freshly separated PBMCs, and each well contains 1 of the 16 selected stimulants in several increasing concentrations. The extracellular acidity is measured in both air-open and hermetically-sealed states, using a commercial fluorescence plate reader, for approximately 1.5 h. Both states enable the measurement of real-time accumulation of ‘soluble’ versus ‘volatile’ metabolic products, thereby differentiating between oxidative phosphorylation and aerobic glycolysis. The metabolic activity profiles are analyzed for cancer diagnosis by machine-learning tools. We present a diagnostic accuracy study, using a multivariable prediction model to differentiate between lung cancer and control blood samples. The model was developed and tested using a cohort of 200 subjects (100 lung cancer and 100 control subjects), yielding 91% sensitivity and 80% specificity in a 20-fold cross-validation. Our results clearly indicate that the proposed clinical model is suitable for non-invasive early lung cancer diagnosis, and is indifferent to lung cancer stage and histological type.

Keywords

Liquid biopsy Metabolic profile Lung cancer Early detection Cancer diagnosis 

Abbreviations

AUC

Area under the curve

COPD

Chronic obstructive pulmonary disease

CV

Cross-validation

LDCT

Low-dose computed tomography

MAP

Metabolic activity profile

OXPHOS

Oxidative phosphorylation

ROC

Receiver-operating characteristic

SCLC

Small cell lung cancer

SVM

Support vector machine

USPSTF

US Preventive Services Task Force

Notes

Acknowledgements

The authors wish to thank the principal investigators, Prof. Fernando Patolsky, Prof. Nadir Arber, Dr. Mirjana Wollner, and Dr. Ran Kremer; the coordinators of this study, including multi-site coordinator Janna Bronstein, for patient recruitment, collection of specimens, and clinical data; Dr. Sara Bar Yehuda for regulatory support; Dr. Uri Itay and Chaim Singal for their help in the statistical analysis of the data; and furthermore, all the donor patients for their contribution to this clinical study.

Author contributions

FP conceived and designed the experiments and assisted in manuscript preparation. HPS and RT designed and performed preliminary pre-clinical studies and helped analyzing preliminary data. ST, ED, GD, and IA synchronized the study, helped analyzing data, and assisted in manuscript preparation. SA, CB, TS, and AL performed clinical testing, assisted in data analysis, and assisted in manuscript preparation. ST and ED developed classification model and performed statistical analysis. YA, SS, and MS assisted in clinical sample collection, clinical study coordination, and manuscript preparation.

Funding

This work was supported by Savicell Diagnostics and by Grants from the Israel Science Foundation (ISF) through the Legacy Program.

Compliance with ethical standards

Conflict of interest

Fernando Patolsky received consulting fees from Savicell Diagnostics. Shoval Tirman, Aviv Lutaty, Tali Scheinmann, Eyal Davidovits, Irit Arbel, and Giora Davidovits are employed by Savicell Diagnostics. Shirley Abramovitch and Cynthia Botbol were employed by Savicell Diagnostics. Eyal Davidovits, Irit Arbel, and Giora Davidovits are officers in Savicell Diagnostics. Eyal Davidovits and Giora Davidovits are on the board of directors of Savicell Diagnostics. Yochai Adir, Shoval Tirman, Shirley Abramovitch, Cynthia Botbol, Aviv Lutaty, Tali Scheinmann, Eyal Davidovits, Irit Arbel, Giora Davidovits, and Fernando Patolsky own stock and/or options in Savicell Diagnostics’ parent company. The other authors declare that they have no conflict of interest.

Ethical approval and ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committees and with the 1964 Helsinki declaration and its later amendments. Institutional review board approval numbers are: 0105-13-CMC for Carmel Medical Center, Haifa; 0274-15-RMB for Rambam Medical Center, Haifa; and 0009-13-TLV for Sourasky Medical Center, Tel Aviv.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

262_2018_2173_MOESM1_ESM.pdf (665 kb)
Supplementary material 1 (PDF 665 KB)

References

  1. 1.
    Dela Cruz CS, Tanoue LT, Matthay RA (2011) Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med 32:605–644.  https://doi.org/10.1016/j.ccm.2011.09.001 CrossRefPubMedGoogle Scholar
  2. 2.
    Howlader N, Noone A, Krapcho M et al (2016) SEER Cancer Statistics Review 1975–2013. National Cancer Institute; Bethesda, MD. https://seer.cancer.gov/csr/1975_2013/. Accessed 4 May 2017
  3. 3.
    Smith RA, Cokkinides V, Brooks D et al (2011) Cancer screening in the United States, 2011. CA Cancer J Clin 61:8–30.  https://doi.org/10.3322/caac.20096 CrossRefPubMedGoogle Scholar
  4. 4.
    Moyer VA (2014) Screening for lung cancer: U.S. preventive services task force recommendation statement. Ann Intern Med 160:330–338.  https://doi.org/10.7326/M13-2771 PubMedCrossRefGoogle Scholar
  5. 5.
    Rampinelli C, De Marco P, Origgi D et al (2017) Exposure to low dose computed tomography for lung cancer screening and risk of cancer: secondary analysis of trial data and risk-benefit analysis. BMJ 356:j347.  https://doi.org/10.1136/bmj.j347 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Marshall HM, Bowman RV, Yang IA et al (2013) Screening for lung cancer with low-dose computed tomography: a review of current status. J Thorac Dis 5:S524–S539.  https://doi.org/10.3978/j.issn.2072-1439.2013.09.06 PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Pinsky PF, Kramer BS (2015) Lung cancer risk and demographic characteristics of current 20–29 pack-year smokers: implications for screening. J Natl Cancer Inst 107:djv226.  https://doi.org/10.1093/jnci/djv226 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Rodriguez-Roisin R, Soriano JB (2008) Chronic obstructive pulmonary disease with lung cancer and/or cardiovascular disease. Proc Am Thorac Soc 5:842–847.  https://doi.org/10.1513/pats.200807-075TH CrossRefPubMedGoogle Scholar
  9. 9.
    Siravegna G, Marsoni S, Siena S, Bardelli A (2017) Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 14:531–548.  https://doi.org/10.1038/nrclinonc.2017.14 CrossRefPubMedGoogle Scholar
  10. 10.
    Aravanis AM, Lee M, Klausner RD (2017) Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell 168:571–574.  https://doi.org/10.1016/j.cell.2017.01.030 CrossRefPubMedGoogle Scholar
  11. 11.
    Fujii T, Barzi A, Sartore-Bianchi A et al (2017) Mutation-enrichment next-generation sequencing for quantitative detection of KRAS mutations in urine cell-free DNA from patients with advanced cancers. Clin Cancer Res 23:3657–3666.  https://doi.org/10.1158/1078-0432.CCR-16-2592 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Li X, Hayward C, Fong P-Y et al (2013) A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med 5:207ra142.  https://doi.org/10.1126/scitranslmed.3007013 PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Lanman RB, Mortimer SA, Zill OA et al (2015) Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free circulating tumor DNA. PLoS One 10:e0140712.  https://doi.org/10.1371/journal.pone.0140712 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Brock G, Castellanos-Rizaldos E, Hu L et al (2015) Liquid biopsy for cancer screening, patient stratification and monitoring. Transl Cancer Res 4:280–290.  https://doi.org/10.3978/j.issn.2218-676X.2015.06.05 CrossRefGoogle Scholar
  15. 15.
    Vachani A, Hammoud Z, Springmeyer S et al (2015) Clinical utility of a plasma protein classifier for indeterminate lung nodules. Lung 193:1023–1027.  https://doi.org/10.1007/s00408-015-9800-0 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Whitney DH, Elashoff MR, Porta-Smith K et al (2015) Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genom 8:18.  https://doi.org/10.1186/s12920-015-0091-3 CrossRefGoogle Scholar
  17. 17.
    Anderson D, Najafzadeh M, Gopalan R et al (2014) Sensitivity and specificity of the empirical lymphocyte genome sensitivity (LGS) assay: implications for improving cancer diagnostics. FASEB J 28:4563–4570.  https://doi.org/10.1096/fj.14-254748 CrossRefPubMedGoogle Scholar
  18. 18.
    Pantel K, Alix-Panabières C (2013) Real-time liquid biopsy in cancer patients: Fact or fiction? Cancer Res 73:6384–6388.  https://doi.org/10.1158/0008-5472.CAN-13-2030 CrossRefPubMedGoogle Scholar
  19. 19.
    Hiley CT, Le Quesne J, Santis G et al (2016) Challenges in molecular testing in non-small-cell lung cancer patients with advanced disease. Lancet 388:1002–1011.  https://doi.org/10.1016/S0140-6736(16)31340-X CrossRefPubMedGoogle Scholar
  20. 20.
    Bettegowda C, Sausen M, Leary R (2014) Detection of circulating tumor DNA in early-and late-stage human malignancies. Sci Transl 6:224ra24.  https://doi.org/10.1126/scitranslmed.3007094 CrossRefGoogle Scholar
  21. 21.
    Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029–1033.  https://doi.org/10.1126/science.1160809 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Kaelin WG Jr, Thompson CB (2010) Q&A: cancer: clues from cell metabolism. Nature 465:562–564.  https://doi.org/10.1038/465562a CrossRefPubMedGoogle Scholar
  23. 23.
    DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB (2008) The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab 7:11–20.  https://doi.org/10.1016/j.cmet.2007.10.002 CrossRefPubMedGoogle Scholar
  24. 24.
    MacIver NJ, Jacobs SR, Wieman HL et al (2008) Glucose metabolism in lymphocytes is a regulated process with significant effects on immune cell function and survival. J Leukoc Biol 84:949–957.  https://doi.org/10.1189/jlb.0108024 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Fox C, Hammerman P, Thompson C (2005) Fuel feeds function: energy metabolism and the T-cell response. Nat Rev Immunol 5:844–852.  https://doi.org/10.1038/nri1710 CrossRefPubMedGoogle Scholar
  26. 26.
    Michalek RD, Rathmell JC (2010) The metabolic life and times of a T-cell. Immunol Rev 236:190–202.  https://doi.org/10.1111/j.1600-065X.2010.00911.x CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Pearce E (2010) Metabolism in T cell activation and differentiation. Curr Opin Immunol 22:314–320.  https://doi.org/10.1016/j.coi.2010.01.018 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Chang CH, Curtis JD, Maggi LB Jr et al (2013) Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153:1239–1251.  https://doi.org/10.1016/j.cell.2013.05.016 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Dietl K, Renner K, Dettmer K et al (2010) Lactic acid and acidification inhibit TNF secretion and glycolysis of human monocytes. J Immunol 184:1200–1209.  https://doi.org/10.4049/jimmunol.0902584 CrossRefPubMedGoogle Scholar
  30. 30.
    Jellusova J, Cato MH, Apgar JR et al (2017) Gsk3 is a metabolic checkpoint regulator in B cells. Nat Immunol 18:303–312.  https://doi.org/10.1038/ni.3664 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Patsoukis N, Bardhan K, Chatterjee P et al (2015) PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat Commun 6:6692.  https://doi.org/10.1038/ncomms7692 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Yang Z, Matteson EL, Goronzy JJ, Weyand CM (2015) T-cell metabolism in autoimmune disease. Arthritis Res Ther 17:29.  https://doi.org/10.1186/s13075-015-0542-4 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Beezhold K, Byersdorfer CA (2018) Targeting immuno-metabolism to improve anti-cancer therapies. Cancer Lett 414:127–135.  https://doi.org/10.1016/J.CANLET.2017.11.005 CrossRefPubMedGoogle Scholar
  34. 34.
    Chimenti MS, Triggianese P, Conigliaro P et al (2015) The interplay between inflammation and metabolism in rheumatoid arthritis. Cell Death Dis 6:e1887.  https://doi.org/10.1038/cddis.2015.246 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Dunn GP, Bruce AT, Ikeda H et al (2002) Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 3:991–998.  https://doi.org/10.1038/ni1102-991 CrossRefPubMedGoogle Scholar
  36. 36.
    Swann JB, Smyth MJ (2007) Immune surveillance of tumors. J Clin Invest 117:1137–1146.  https://doi.org/10.1172/JCI31405 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Dunn GP, Old LJ, Schreiber RD (2004) The three Es of cancer immunoediting. Annu Rev Immunol 22:329–360.  https://doi.org/10.1146/annurev.immunol.22.012703.104803 CrossRefPubMedGoogle Scholar
  38. 38.
    Michalek RD, Gerriets VA, Jacobs SR et al (2011) Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J Immunol 186:3299–3303.  https://doi.org/10.4049/jimmunol.1003613 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Pearce E, Poffenberger M, Chang C (2013) Fueling immunity: insights into metabolism and lymphocyte function. Science 342:1242454.  https://doi.org/10.1126/science.1242454 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  41. 41.
    Weissferdt A, Moran CA (2014) Reclassification of early stage pulmonary adenocarcinoma and its consequences. J Thorac Dis 6:S581–S588.  https://doi.org/10.3978/j.issn.2072-1439.2014.07.41 PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Goldstraw P, Ball D, Jett JR et al (2011) Non-small-cell lung cancer. Lancet 378:1727–1740.  https://doi.org/10.1016/S0140-6736(10)62101-0 CrossRefPubMedGoogle Scholar
  43. 43.
    Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA Cancer J Clin 65:5–29.  https://doi.org/10.3322/caac.21254 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Sekine Y, Katsura H, Koh E et al (2012) Early detection of COPD is important for lung cancer surveillance. Eur Respir J 39:1230–1240.  https://doi.org/10.1183/09031936.00126011 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Chemistry, Faculty of Exact SciencesTel Aviv UniversityTel AvivIsrael
  2. 2.Savicell Diagnostics Ltd.HaifaIsrael
  3. 3.Pulmonary Division, Faculty of Medicine, Lady Davis Carmel Medical CenterThe Technion, Institute of TechnologyHaifaIsrael

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