Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers

Abstract

Checkpoint inhibitors (CPI) have significantly changed the therapeutic landscape of oncology. We adopted a non-invasive metabolomic approach to understand immunotherapy response and failure in 28 urological cancer patients. In total, 134 metabolites were quantified in patient sera before the first, second, and third CPI doses. Modeling the association between metabolites and CPI response and patient characteristics revealed that one predictive metabolite class  (n = 9/10) were very long-chain fatty acid-containing lipids (VLCFA-containing lipids). The best predictive performance was achieved through a multivariate model, including age and a centroid of VLCFA-containing lipids prior to first immunotherapy (sensitivity: 0.850, specificity: 0.825, ROC: 0.935). We hypothesize that the association of VLCFA-containing lipids with CPI response is based on enhanced peroxisome signaling in T cells, which results in a switch to fatty acid catabolism. Beyond use as a novel predictive non-invasive biomarker, we envision that nutritional supplementation with VLCFA-containing lipids might serve as an immuno sensitizer.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Abbreviations

CI:

Confidence interval

CPI:

Checkpoint inhibitor

CR:

Complete remission

FAO:

Fatty acid oxidation

FDG-PET:

Fluorodeoxyglucose positron emission tomography

GLC:

Glucose

GPL:

Glycerophospholipid

HR:

Hazard ratio

LCFA:

Long-chain fatty acid

LMM:

Linear mixed effects model

LOD:

Limit of detection

MS:

Mass spectroscopy

NMR:

Nuclear magnetic resonance

PC:

Phosphatidylcholine

PD:

Progressive disease

PPAR-α:

Peroxisome proliferation-activated receptor α

PR:

Partial remission

RCC:

Renal cell carcinoma

ROC:

Receiver operating characteristic

SD:

Stable disease

SLC27A2:

Solute carrier family 27 member 2 gene

sGPL:

Saturated GPL

SM:

Sphingomyelin

SVM:

Support vector machine

TCGA:

The Cancer Genome Atlas

TMB:

Tumor mutational burden

tSNE:

T-distributed stochastic neighbor embedding

UC:

Urothelial carcinoma

uGPL:

Unsaturated GPL

VLCFA:

Very long-chain fatty acid

References

  1. 1.

    Astarita G, Langridge J (2013) An emerging role for metabolomics in nutrition science. J Nutr Nutr 6(4–5):181–200

    CAS  Google Scholar 

  2. 2.

    Pavlova NN, Thompson CB (2016) The Emerging Hallmarks of Cancer Metabolism. Cell Metab 23(1):27–47

    CAS  Article  Google Scholar 

  3. 3.

    Zhu A, Lee D, Shim H (2011) Metabolic positron emission tomography imaging in cancer detection and therapy response. Semin Oncol. https://doi.org/10.1053/j.seminoncol.2010.11.012

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Hakimi AA, Reznik E, Lee CH et al (2016) An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell. https://doi.org/10.1016/j.ccell.2015.12.004

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Li B, Qiu B, Lee DSM et al (2014) Fructose-1,6-bisphosphatase opposes renal carcinoma progression. Nature. https://doi.org/10.1038/nature13557

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Sahu D, Lotan Y, Wittmann B et al (2017) Metabolomics analysis reveals distinct profiles of nonmuscle-invasive and muscle-invasive bladder cancer. Cancer Med. https://doi.org/10.1002/cam4.1109

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Nizioł J, Bonifay V, Ossoliński K et al (2018) Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA. Anal Bioanal Chem. https://doi.org/10.1007/s00216-018-1059-x

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Fritsche KL (2015) The science of fatty acids and inflammation. Adv Nutr. https://doi.org/10.3945/an.114.006940

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Parisi LR, Li N, Atilla-Gokcumen GE (2017) Very long chain fatty acids are functionally involved in necroptosis. Cell Chem Biol. https://doi.org/10.1016/j.chembiol.2017.08.026

    Article  PubMed  Google Scholar 

  10. 10.

    Sonoda J, Pei L, Evans RM (2008) Nuclear receptors: decoding metabolic disease. FEBS Lett 582(1):2–9

    CAS  Article  Google Scholar 

  11. 11.

    Prado-García H, Sánchez-García J (2017) Editoral: immuno-metabolism in tumor microenvironment. Front Immunol 8:374

    Article  Google Scholar 

  12. 12.

    Haas R, Smith J, Rocher-Ros V et al (2015) Lactate regulates metabolic and proinflammatory circuits in control of T cell migration and effector functions. PLoS Biol. https://doi.org/10.1371/journal.pbio.1002202

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lyssiotis CA, Kimmelman AC (2017) Metabolic interactions in the tumor microenvironment. Trends Cell Biol 27(11):863–875

    CAS  Article  Google Scholar 

  14. 14.

    Balar AV, Castellano D, O’Donnell PH et al (2017) First-line pembrolizumab in cisplatin-ineligible patients with locally advanced and unresectable or metastatic urothelial cancer (KEYNOTE-052): a multicentre, single-arm, phase 2 study. Lancet Oncol. https://doi.org/10.1016/S1470-2045(17)30616-2

    Article  PubMed  Google Scholar 

  15. 15.

    Teng F, Meng X, Kong L, Yu J (2018) Progress and challenges of predictive biomarkers of anti PD-1/PD-L1 immunotherapy: a systematic review. Cancer Lett 414:166–173

    CAS  Article  Google Scholar 

  16. 16.

    Yarchoan M, Hopkins A, Jaffee EM (2017) Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 377(25):2500–2501

    Article  Google Scholar 

  17. 17.

    Galon J, Costes A, Sanchez-Cabo F et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. https://doi.org/10.1126/science.1129139

    Article  PubMed  Google Scholar 

  18. 18.

    Giannakis M, Li H, Jin C et al (2017) Metabolomic correlates of response in nivolumab-treated renal cell carcinoma and melanoma patients. J Clin Oncol 35:3036

    Article  Google Scholar 

  19. 19.

    Johnson CH, Spilker ME, Goetz L et al (2016) Metabolite and microbiome interplay in cancer immunotherapy. Cancer Res 76(21):6146–6152

    CAS  Article  Google Scholar 

  20. 20.

    Schmerler D, Neugebauer S, Ludewig K et al (2012) Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients. J Lipid Res. https://doi.org/10.1194/jlr.P023309

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Tomas L, Edsfeldt A, Mollet IG et al (2018) Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques. Eur Heart J. https://doi.org/10.1093/eurheartj/ehy124

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Ramos M (2019) CuratedTCGAData: curated data from the Cancer Genome Atlas (TCGA) as multiassayexperiment objects. R package version 1.6.0

  23. 23.

    Thorsson V, Gibbs DL, Brown SD et al (2018) The immune landscape of cancer. Immunity. https://doi.org/10.1016/j.immuni.2018.03.023

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Pinheiro J, Bates D, DebRoy S (2019) Nlme: linear and nonlinear mixed effects models. In: R package version 3.1-141

  25. 25.

    Betof AS, Nipp RD, Giobbie-Hurder A et al (2017) Impact of age on outcomes with immunotherapy for patients with melanoma. Oncologist. https://doi.org/10.1634/theoncologist.2016-0450

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Maia MC, Almeida L, Bergerot PG et al (2018) Relationship of tumor mutational burden (TMB) to immunotherapy response in metastatic renal cell carcinoma (mRCC). J Clin Oncol. https://doi.org/10.1200/jco.2018.36.6_suppl.662

    Article  Google Scholar 

  27. 27.

    Halczy-Kowalik L, Drozd A, Stachowska E et al (2019) Fatty acids distribution and content in oral squamous cell carcinoma tissue and its adjacent microenvironment. PLoS One. https://doi.org/10.1371/journal.pone.0218246

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Paul A, Kumar S, Raj A et al (2018) Alteration in lipid composition differentiates breast cancer tissues: a 1 H HRMAS NMR metabolomic study. Metabolomics. https://doi.org/10.1007/s11306-018-1411-3

    Article  PubMed  Google Scholar 

  29. 29.

    Pakiet A, Kobiela J, Stepnowski P et al (2019) Changes in lipids composition and metabolism in colorectal cancer: a review. Lipids Health Dis 18(1):29

    Article  Google Scholar 

  30. 30.

    Zhang Y, Kurupati R, Liu L et al (2017) Enhancing CD8 + T cell fatty acid catabolism within a metabolically challenging tumor microenvironment increases the efficacy of melanoma immunotherapy. Cancer Cell. https://doi.org/10.1016/j.ccell.2017.08.004

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Yan D, Adeshakin AO, Xu M et al (2019) Lipid metabolic pathways confer the immunosuppressive function of myeloid-derived suppressor cells in tumor. Front Immunol. https://doi.org/10.3389/fimmu.2019.01399

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Namgaladze D (1861) Brüne B (2016) Macrophage fatty acid oxidation and its roles in macrophage polarization and fatty acid-induced inflammation. Biochim Biophys Acta 1861(11):1796–1807

    Article  Google Scholar 

  33. 33.

    Zhang Q, Wang H, Mao C et al (2018) Fatty acid oxidation contributes to IL-1β secretion in M2 macrophages and promotes macrophage-mediated tumor cell migration. Mol Immunol. https://doi.org/10.1016/j.molimm.2017.12.011

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The National Center for Tumor Diseases (NCT) is supported by the German Cancer Research Center (DKFZ), the University Hospital Heidelberg in cooperation with the Medical Faculty Heidelberg, and by the German Cancer Aid (Deutsche Krebshilfe). This project was supported by the NCT Elevator Pitch 2015.

Author information

Affiliations

Authors

Contributions

Conception of the work: SZ, CG; funding acquisition: CG, DJ; sample collection and processing: RK, EC, MJ, AM; metabolomic analysis: MS, BW, AM; data collection and data analysis: AM, SZ, CG, CH; manuscript writing/editing: AM, SZ, GC; critical revision of the manuscript: DJ, MS, BW, RK, RK, EB, MJ; final approval: all the authors.

Corresponding author

Correspondence to Carsten Grüllich.

Ethics declarations

Conflict of interest

Matthias Scheffler and Barbara Wolf are employed by BIOCRATES Life Sciences AG. The other authors have declared no competing interests.

Ethical approval and ethical standards

The ethics committee of the University of Heidelberg, Germany approved the study design (S023/2016). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

All patients provided written informed consent. Informed consent included the permission to take blood samples for research purposes and the use of their anonymized clinical data. Any patient identifiable information was excluded from further sample handling and data processing.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 89 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mock, A., Zschäbitz, S., Kirsten, R. et al. Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers. Cancer Immunol Immunother 68, 2005–2014 (2019). https://doi.org/10.1007/s00262-019-02428-3

Download citation

Keywords

  • Cancer immunotherapy
  • Cancer metabolomics
  • Renal cell carcinoma
  • Urothelial cancer