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Baseline metabolic tumor burden on FDG PET/CT scans predicts outcome in advanced NSCLC patients treated with immune checkpoint inhibitors

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

We aimed to evaluate if imaging biomarkers on FDG PET are associated with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs).

Methods

In this retrospective monocentric study, we included 109 patients with advanced NSCLC who underwent baseline FDG PET/CT before ICI initiation between July 2013 and September 2018. Clinical, biological (including dNLR = neutrophils/[leukocytes minus neutrophils]), pathological and PET parameters (tumor SUVmax, total metabolic tumor volume [TMTV]) were evaluated. A multivariate prediction model was developed using Cox models for progression-free survival (PFS) and overall survival (OS). The association between biomarkers on FDG PET/CT and disease clinical benefit (DCB) was tested using logistic regression.

Results

Eighty patients were eligible. Median follow-up was 11.6 months (95%CI 7.7–15.5). Sixty-four and 52 patients experienced progression and death, respectively. DCB was 40%. In multivariate analyses, TMTV > 75 cm3 and dNLR > 3 were associated with shorter OS (HR 2.5, 95%CI 1.3–4.7 and HR 3.3, 95%CI 1.6–6.4) and absence of DCB (OR 0.3, 95%CI 0.1–0.9 and OR 0.4, 95%CI 0.2–0.9). Unlike TMTV, dNLR was a significant prognostic factor for PFS (HR 1.9, 95%CI 1.1–3.3) along with anemia (HR 1.9, 95%CI 1.2–3.8). No association was observed between tumor SUVmax and PFS or OS.

Conclusion

Baseline tumor burden (TMTV) on FDG PET/CT scans and inflammatory status (dNLR) were associated with poor OS and absence of DCB for ICI treatment in advanced NSCLC patients, unlike tumor SUVmax, and may be used together to improve the selection of appropriate candidates.

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References

  1. Giroux Leprieur E, Dumenil C, Julie C, Giraud V, Dumoulin J, Labrune S, et al. Immunotherapy revolutionises non-small-cell lung cancer therapy: results, perspectives and new challenges. Eur J Cancer. 2017;78:16–23.

    Article  CAS  PubMed  Google Scholar 

  2. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WEE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med. 2015;373:123–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372:2018–28.

    Article  PubMed  Google Scholar 

  5. 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.

    Article  CAS  PubMed  Google Scholar 

  6. Gulley JL, Rajan A, Spigel DR, Iannotti N, Chandler J, Wong DJL, et al. Avelumab for patients with previously treated metastatic or recurrent non-small-cell lung cancer (JAVELIN Solid Tumor): dose-expansion cohort of a multicentre, open-label, phase 1b trial. Lancet Oncol. 2017;18:599–610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Prelaj A, Tay R, Ferrara R, Chaput N, Besse B, Califano R. Predictive biomarkers of response for immune checkpoint inhibitors in non-small-cell lung cancer. Eur J Cancer. 2018;106:144–59.

    Article  PubMed  CAS  Google Scholar 

  8. Hendriks LE, Rouleau E, Besse B. Clinical utility of tumor mutational burden in patients with non-small cell lung cancer treated with immunotherapy. Transl Lung Cancer Res. 2018;7:647–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol. 2018;4:351–7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tanizaki J, Haratani K, Hayashi H, Chiba Y, Nakamura Y, Yonesaka K, et al. Peripheral blood biomarkers associated with clinical outcome in non-small cell lung cancer patients treated with nivolumab. J Thorac Oncol. 2018;13:97–105.

    Article  CAS  PubMed  Google Scholar 

  11. Cao D, Xu H, Xu X, Guo T, Ge W. A reliable and feasible way to predict the benefits of Nivolumab in patients with non-small cell lung cancer: a pooled analysis of 14 retrospective studies. Oncoimmunology. 2018;7:e1507262.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of neutrophil to lymphocyte ratio and absolute neutrophil count with outcomes with PD-1 axis inhibitors in patients with advanced non-small-cell lung cancer. Clin Lung Cancer. 2018;19:426–434.e1.

    Article  CAS  PubMed  Google Scholar 

  13. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018;233:6337–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dercle L, Ammari S, Champiat S, Massard C, Ferté C, Taihi L, et al. Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy. Eur J Cancer. 2016;65:33–42.

    Article  PubMed  Google Scholar 

  15. Seban R-D, Nemer JS, Marabelle A, Yeh R, Deutsch E, Ammari S, et al. Prognostic and theranostic 18F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: association with outcome and transcriptomics. Eur J Nucl Med Mol Imaging. 2019;46(11):2298–310.

    Article  CAS  PubMed  Google Scholar 

  16. Qin B-D, Jiao X-D, Zang Y-S. Tumor mutation burden to tumor burden ratio and prediction of clinical benefit of anti-PD-1/PD-L1 immunotherapy. Med Hypotheses. 2018;116:111–3.

    Article  CAS  PubMed  Google Scholar 

  17. Dercle L, Seban R-D, Lazarovici J, Schwartz LH, Houot R, Ammari S, et al. 18F-FDG PET and CT scans detect new imaging patterns of response and progression in patients with Hodgkin lymphoma treated by anti-programmed death 1 immune checkpoint inhibitor. J Nucl Med. 2018;59:15–24.

    Article  CAS  PubMed  Google Scholar 

  18. Zhang H, Wroblewski K, Appelbaum D, Pu Y. Independent prognostic value of whole-body metabolic tumor burden from FDG-PET in non-small cell lung cancer. Int J Comput Assist Radiol Surg. 2013;8:181–91.

    Article  PubMed  Google Scholar 

  19. Liao S, Penney BC, Wroblewski K, Zhang H, Simon CA, Kampalath R, et al. Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2012;39:27–38.

    Article  CAS  PubMed  Google Scholar 

  20. Chin AL, Kumar KA, Guo HH, Maxim PG, Wakelee H, Neal JW, et al. Prognostic value of pretreatment FDG-PET parameters in high-dose image-guided radiotherapy for oligometastatic non-small-cell lung cancer. Clin Lung Cancer. 2018;19:e581–8.

    Article  PubMed  Google Scholar 

  21. Sharma A, Mohan A, Bhalla AS, Sharma MC, Vishnubhatla S, Das CJ, et al. Role of various metabolic parameters derived from baseline 18F-FDG PET/CT as prognostic markers in non-small cell lung cancer patients undergoing platinum-based chemotherapy. Clin Nucl Med. 2018;43:e8–17.

    Article  PubMed  Google Scholar 

  22. Winther-Larsen A, Fledelius J, Sorensen BS, Meldgaard P. Metabolic tumor burden as marker of outcome in advanced EGFR wild-type NSCLC patients treated with erlotinib. Lung Cancer. 2016;94:81–7.

    Article  PubMed  Google Scholar 

  23. Kaira K, Higuchi T, Naruse I, Arisaka Y, Tokue A, Altan B, et al. Metabolic activity by 18F-FDG-PET/CT is predictive of early response after nivolumab in previously treated NSCLC. Eur J Nucl Med Mol Imaging. 2018;45:56–66.

    Article  CAS  PubMed  Google Scholar 

  24. Sacher AG, Gandhi L. Biomarkers for the Clinical Use of PD-1/PD-L1 Inhibitors in non-small-cell lung cancer: A Review. JAMA Oncol. 2016;2:1217–22.

    Article  PubMed  Google Scholar 

  25. Meignan M, Sasanelli M, Casasnovas RO, Luminari S, Fioroni F, Coriani C, et al. Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients. Eur J Nucl Med Mol Imaging. 2014;41:1113–22.

    Article  CAS  PubMed  Google Scholar 

  26. Ferrucci PF, Ascierto PA, Pigozzo J, Del Vecchio M, Maio M, Antonini Cappellini GC, et al. Baseline neutrophils and derived neutrophil-to-lymphocyte ratio: prognostic relevance in metastatic melanoma patients receiving ipilimumab. Ann Oncol. 2016;27:732–8.

    Article  CAS  PubMed  Google Scholar 

  27. Tumeh PC, Hellmann MD, Hamid O, Tsai KK, Loo KL, Gubens MA, et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol Res. 2017;5:417–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liao S, Penney BC, Zhang H, Suzuki K, Pu Y. Prognostic value of the quantitative metabolic volumetric measurement on 18F-FDG PET/CT in Stage IV nonsurgical small-cell lung cancer. Acad Radiol. 2012;19:69–77.

    Article  PubMed  Google Scholar 

  29. Aide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging. 2017;44:17–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Houdu B, Lasnon C, Licaj I, Thomas G, Do P, Guizard A-V, et al. Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. Eur J Nucl Med Mol Imaging. 2018;46(2):421–8.

    Article  PubMed  Google Scholar 

  31. Ilyas H, Mikhaeel NG, Dunn JT, Rahman F, Möller H, Smith D, et al. Is there an optimal method for measuring baseline metabolic tumor volume in diffuse large B cell lymphoma? Eur J Nucl Med Mol Imaging. 2019;46:520–1.

    Article  PubMed  Google Scholar 

  32. Ceriani L, Milan L, Johnson PWM, Martelli M, Presilla S, Giovanella L, et al. Baseline PET features to predict prognosis in primary mediastinal B cell lymphoma: a comparative analysis of different methods for measuring baseline metabolic tumour volume. Eur J Nucl Med Mol Imaging. 2019;46:1334–44.

    Article  PubMed  Google Scholar 

  33. Young H, Baum R, Cremerius U, Herholz K, Hoekstra O, Lammertsma AA, et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. Eur J Cancer. 1999;35:1773–82.

    Article  CAS  PubMed  Google Scholar 

  34. de Geus-Oei L-F, van der Heijden HFM, Visser EP, Hermsen R, van Hoorn BA, Timmer-Bonte JNH, et al. Chemotherapy response evaluation with 18F-FDG PET in patients with non-small cell lung cancer. J Nucl Med. 2007;48:1592–8.

    Article  PubMed  Google Scholar 

  35. Shang J, Ling X, Zhang L, Tang Y, Xiao Z, Cheng Y, et al. Comparison of RECIST, EORTC criteria and PERCIST for evaluation of early response to chemotherapy in patients with non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2016;43:1945–53.

    Article  CAS  PubMed  Google Scholar 

  36. Dooms C, van Baardwijk A, Verbeken E, van Suylen RJ, Stroobants S, De Ruysscher D, et al. Association between 18F-fluoro-2-deoxy-D-glucose uptake values and tumor vitality: prognostic value of positron emission tomography in early-stage non-small cell lung cancer. J Thorac Oncol. 2009;4:822–8.

    Article  PubMed  Google Scholar 

  37. Machtay M, Duan F, Siegel BA, Snyder BS, Gorelick JJ, Reddin JS, et al. Prediction of survival by [18F]fluorodeoxyglucose positron emission tomography in patients with locally advanced non-small-cell lung cancer undergoing definitive chemoradiation therapy: results of the ACRIN 6668/RTOG 0235 trial. J Clin Oncol. 2013;31:3823–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Seban R-D, Robert C, Dercle L, Yeh R, Dunant A, Reuze S, et al. Increased bone marrow SUVmax on 18F-FDG PET is associated with higher pelvic treatment failure in patients with cervical cancer treated by chemoradiotherapy and brachytherapy. Oncoimmunology. 2019;8:e1574197.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Grizzi F, Castello A, Lopci E. Is it time to change our vision of tumor metabolism prior to immunotherapy? Eur J Nucl Med Mol Imaging. 2018;45:1072–5.

    Article  PubMed  Google Scholar 

  40. Yan Y, Kumar AB, Finnes H, Markovic SN, Park S, Dronca RS, et al. Combining immune checkpoint inhibitors with conventional cancer therapy. Front Immunol. 2018;9:1739.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Kaira K, Shimizu K, Kitahara S, Yajima T, Atsumi J, Kosaka T, et al. 2-Deoxy-2-[fluorine-18] fluoro-d-glucose uptake on positron emission tomography is associated with programmed death ligand-1 expression in patients with pulmonary adenocarcinoma. Eur J Cancer. 2018;101:181–90.

    Article  CAS  PubMed  Google Scholar 

  42. Takada K, Toyokawa G, Tagawa T, Kohashi K, Akamine T, Takamori S, et al. Association between PD-L1 expression and metabolic activity on 18F-FDG PET/CT in patients with small-sized lung cancer. Anticancer Res. 2017;37:7073–82.

    CAS  PubMed  Google Scholar 

  43. Zhang M, Wang D, Sun Q, Pu H, Wang Y, Zhao S, et al. Prognostic significance of PD-L1 expression and 18F-FDG PET/CT in surgical pulmonary squamous cell carcinoma. Oncotarget. 2017;8:51630–40.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Li X, Fu Q, Zhu Y, Wang J, Liu J, Yu X, et al. CD147-mediated glucose metabolic regulation contributes to the predictive role of 18 F-FDG PET/CT imaging for EGFR-TKI treatment sensitivity in NSCLC. Mol Carcinog. 2019;58:247–57.

    Article  CAS  PubMed  Google Scholar 

  45. 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.

    Article  CAS  PubMed  Google Scholar 

  46. Arbour KC, Mezquita L, Long N, Rizvi H, Auclin E, Ni A, et al. Impact of baseline steroids on efficacy of programmed cell death-1 and programmed death-ligand 1 blockade in patients with non-small-cell lung cancer. J Clin Oncol. 2018;36:2872–8.

    Article  CAS  PubMed  Google Scholar 

  47. Ricciuti B, Dahlberg SE, Adeni A, Sholl LM, Nishino M, Awad MM. Immune checkpoint inhibitor outcomes for patients with non-small-cell lung cancer receiving baseline corticosteroids for palliative versus nonpalliative indications. J Clin Oncol. 2019;37:1927–34.

    Article  CAS  PubMed  Google Scholar 

  48. Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M, Kulangara K, et al. PD-L1 Immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC ssay comparison project. J Thorac Oncol. 2017;12:208–22.

    Article  PubMed  Google Scholar 

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Gustave Roussy, Department of Nuclear Medicine and Endocrine Oncology, Université Paris-Saclay, Villejuif, France

    Romain-David Seban, Arnaud Berenbaum, Serena Grimaldi & Sophie Leboulleux

  2. Gustave Roussy, Department of Medical Oncology, Thoracic Unit, Villejuif, France

    Laura Mezquita, David Planchard & Benjamin Besse

  3. Gustave Roussy Cancer Campus, UMR1015, Université Paris Saclay, Villejuif, France

    Laurent Dercle

  4. Gustave Roussy, Department of Radiation Oncology, Villejuif, France

    Angela Botticella, Cécile Le Pechoux & Eric Deutsch

  5. Gustave Roussy, Department of Radiology, Université Paris-Saclay, Villejuif, France

    Caroline Caramella & Samy Ammari

  6. Gustave Roussy Cancer Campus INSERM U1030Radiomics team, 94800, Villejuif, France

    Eric Deutsch

  7. Gustave Roussy Drug Development Department (DITEP), Villejuif, France

    Eric Deutsch & Benjamin Besse

  8. Gustave Roussy, Department of Pathology, Université Paris-Saclay, Villejuif, France

    Julien Adam

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  1. Romain-David Seban
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  2. Laura Mezquita
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  4. Laurent Dercle
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  11. Samy Ammari
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Contributions

1) All authors made substantial contributions to the design of the work; or the acquisition, analysis or interpretation of data.

2) All authors revised it critically for important intellectual content.

3) All authors approved the version to be published.

4) All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Material preparation, data collection and analysis were performed by R-D. Seban, L. Mezquita, A. Berenbaum and B. Besse.

The first draft of the manuscript was written by R-D Seban, L. Mezquita and B. Besse.

All authors commented on previous versions of the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Benjamin Besse.

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Conflict of interest

The following competing interests have been declared.

  • R-D S, A.B, L.D, A.B, C.C, E.D, S.G, J.A, S.A, S.L: The authors declare that they have no conflict of interest.

  • L.Mezquita:

    • Consulting, advisory role: Roche Diagnostics

    • Lectures and educational activities: Bristol-Myers Squibb, Tecnofarma, Roche, AstraZeneca

    • Travel, Accommodations, Expenses: Chugai

  • C.Le Pechoux:

    • Consulting advisory role: Amgen, Astra Zeneca, Nanobiotix, Roche

    • Lectures and educational activities: Astra Zeneca, Lilly

  • D. Planchard:

    • Consulting, advisory role or lectures: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Daiichi Sankyo, Eli Lilly, Merck, MedImmune, Novartis, Pfizer, prIME Oncology, Peer CME, Roche. Honoraria: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Merck, Novartis, Pfizer, prIME Oncology, Peer CME, Roche

    • Clinical trials research as principal or co-investigator (Institutional financial interests): AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Eli Lilly, Merck, Novartis, Pfizer, Roche, Medimmun, Sanofi-Aventis, Taiho Pharma, Novocure, Daiichi Sankyo

    • Travel, Accommodations, Expenses: AstraZeneca, Roche, Novartis, prIME Oncology, Pfizer

  • B. Besse:

    • Sponsored Research at Gustave Roussy Cancer Center

      Abbvie, Amgen, AstraZeneca, Biogen, Blueprint Medicines, BMS, Celgene, Eli Lilly, GSK, Ignyta, IPSEN, Merck KGaA, MSD, Nektar, Onxeo, Pfizer, Pharma Mar, Sanofi, Spectrum Pharmaceuticals, Takeda, Tiziana Pharm

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All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration.

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Seban, RD., Mezquita, L., Berenbaum, A. et al. Baseline metabolic tumor burden on FDG PET/CT scans predicts outcome in advanced NSCLC patients treated with immune checkpoint inhibitors. Eur J Nucl Med Mol Imaging 47, 1147–1157 (2020). https://doi.org/10.1007/s00259-019-04615-x

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